Systems and methods for quantum-ai-based energy management in power systems

Abstract

A method for managing a power grid is disclosed. The method may include: receiving, at one or more quantum-artificial intelligence (Al) processors, datasets related to the power grid; optimizing at least one Security-Constrained Unit Commitment (SCUC) module based on the datasets; forecasting grid performance based on the optimization; and generating a report of the forecasted predictions. The method may improve grid reliability, support clean energy integration, and enhance climate resilience.

Description

WSGR Docket No.66368-702.601 SYSTEMS AND METHODS FOR QUANTUM-AI-BASED ENERGY MANAGEMENT IN POWER SYSTEMS CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No.63 / 640,834, filed April 30, 2024, which application is incorporated herein by reference for all purposes. BACKGROUND

[0002] Security-Constrained Unit Commitment (SCUC) is an optimization problem in power system operations, solved by grid operators to determine efficient schedules for generation units while maintaining system security and accounting for contingencies. SCUC directly supports operational planning, resource allocation, and grid reliability across both transmission and distribution networks. In the context of smart grids, where the energy management system functions as the control center, SCUC plays an important role in balancing key attributes such as reliability, resilience, sustainability, affordability, and equity. These factors are especially important as the grid integrates more clean energy, adapts to climate-related risks, and manages increasing data complexity. A robust energy management system that incorporates SCUC is important for building a secure, intelligent grid. A secure intelligent grid contributes to sustainable development, affordable and clean energy, and climate action. SUMMARY

[0003] Solving SCUC poses challenges due to grid complexity and the nonlinear nature of the problem. As a complex, large-scale, mixed-integer, nonlinear, NP-hard problem, SCUC incorporates numerous variables and equations, making it a computational challenge to produce an efficient and reliable energy management system. Classical computers are challenged by the complexity and scale of modern grids. Traditional methods are challenged by the complexity and scale of modern grids, necessitating newer innovative solutions that ensure both operational efficiency and adaptability in the dynamic energy sector. Further, the surge in renewable energy adoption, alongside demands for higher grid security and customer expectations for reliable power, emphasizes the need for advanced energy management systems to solve SCUC problems effectively. The systems and methods described herein may address at least some of these problems by solving SCUC problems more effectively and more efficiently than classical computers.

[0004] The present disclosure provides energy management systems for transmission and distribution networks within power systems. In some examples, the present disclosure providesWSGR Docket No.66368-702.601 quantum-energy management systems (Q-EMSs). In some examples, the Q-EMS may employ quantum and artificial intelligence to enhance the operation, reliability, resiliency, security, and efficiency of power system networks. In some examples, an energy management system (EMS) may be employed. In some examples, the EMS may be a suite of computer-aided tools utilized by operators to enhance the transmission system’s reliability. In some examples, an EMS may collect and process real-time data from various sources, aiding operators, and applications in conducting essential operations, modeling, and analysis to promote optimal performance and efficiency of the power grid.

[0005] In an aspect, the present disclosure provides a method for managing a distributed energy management system. The method may comprise: (a) receiving, at one or more non-classical- artificial intelligence (AI) processors, one or more datasets relating to the distributed energy management system; (b) optimizing, at the one or more non-classical-AI processors, at least one module of a security constrained unit commitment (SCUC) problem based at least in part on the one or more datasets relating to the distributed energy management system; (c) forecasting predictions for the distributed energy management system based at least in part on the optimization of the at least one module of the SCUC; and (d) generating, at the one or more non- classical-AI processors, a report of the predictions.

[0006] In some embodiments, the distributed energy management system comprises both transmission and distribution. In some embodiments, the distributed management system comprises a power grid. In some embodiments, the report comprises an indication to perform one or more recovery operations. In some embodiments, the non-classical-AI processor is a quantum-AI processor. In some embodiments, the at least one module comprises a mixed- integer linear optimization problem. In some embodiments, the one or more non-classical-AI processors is configured to implement a support vector machine. In some embodiments, the one or more datasets relating to the power grid comprises one or more of: a dataset on power generator specifications, a dataset on system load forecast, a dataset on topological data, a dataset of regulatory compliance, a dataset on contingency analysis, a dataset on availability of electricity, or a dataset of historical operational data.

[0007] In some embodiments, (b) comprises executing at least one quantum algorithm and at least one machine learning algorithm. In some embodiments, the at least one quantum algorithm comprises a Quantum Approximate Optimization Algorithm (QAOA), a Variational Quantum Eigensolver (VQE), or both. In some embodiments, the at least one machine learning algorithm comprises a neural network, a support vector machine, or reinforcement learning.WSGR Docket No.66368-702.601

[0008] In some embodiments, the at least one module comprises one or more of: a SCUC, a power flow module, a unit commitment module, an economic dispatch module, an optimal power flow module, a contingency analysis module, a transmission-constrained unit commitment module, a security constrained optimal power flow module, or a security constrained unit commitment module. In some embodiments, at least two of the SCUC, the power flow module, the unit commitment module, the economic dispatch module, the optimal power flow module, the contingency analysis module, the transmission-constrained unit commitment module, the security constrained optimal power flow module, and the security constrained unit commitment module are solved together. In some embodiments, each of the SCUC, the power flow module, the unit commitment module, the economic dispatch module, the optimal power flow module, the contingency analysis module, the transmission-constrained unit commitment module, the security constrained optimal power flow module, and the security constrained unit commitment module are solved together.

[0009] In some embodiments, the report comprises a list or description of contingency events, equipment or component damage, power system failures, large-scale power outages, or any combination thereof. In some embodiments, the report details the number of overloads observed. In some embodiments, the report provides a power grid operator with an alert of future contingency events. In some embodiments, the future contingency events comprise branch overloads and voltage violations. In some embodiments, the processor assesses potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not within the subset. In some embodiments, the assessment comprises providing a recommendation to a power grid operator to avoid the subset of scenarios. In some embodiments, the prediction comprises demand, generation capacity, and potential system disturbances.

[0010] In another aspect, the present disclosure provides a system for managing a power grid. The system may comprise: a computing system communicatively coupled to one or more non- classical -artificial intelligence (AI) processors, and wherein the one or more non-classical- artificial intelligence (AI) processors is configured to at least: (a) receive one or more datasets relating to the power grid; (b) optimize at least one module of a SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecast predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generate a report of the predictions.

[0011] In some embodiments, the non-classical-AI processor comprises at least one machine learning algorithm and at least one quantum algorithm. In some embodiments, the at least oneWSGR Docket No.66368-702.601 machine learning algorithm is a neural network, a support vector machine, or a reinforcement learning algorithm. In some embodiments, the at least one quantum algorithm comprises a Quantum Approximate Optimization Algorithm (QAOA) and / or a Variational Quantum Eigensolver (VQE). In some embodiments, the one or more datasets comprise one or more of: a dataset on power generator specifications, a dataset on system load forecast, a dataset on topological data, a dataset of regulatory compliance, a dataset on contingency analysis, a dataset on market price of electricity, or a dataset of historical operational data.

[0012] In some embodiments, the at least one module comprises one or more of: a SCUC, a power flow module, a unit commitment module, an economic dispatch module, an optimal power flow module, a contingency analysis module, a transmission-constrained unit commitment module, a security constrained optimal power flow module, or a security constrained unit commitment module. In some embodiments, the prediction comprises demand, generation capacity, and potential system disturbances. In some embodiments, the report comprises a list or description of contingency events, equipment or component damage, power system failures, or large-scale power outages. In some embodiments, the report is configured to provide a power grid operator with an alert of future contingency events.

[0013] In some embodiments, the quantum-AI processor is configured to assess potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not within the subset. In some embodiments, the assessment comprises a recommendation to a power grid operator to avoid the subset of scenarios.

[0014] In some embodiments, the computing system comprises at least one peripheral device. In some embodiments, the at least one peripheral device is coupled to the computing system via at least one interface. In some embodiments, the at least one peripheral device is at least one output device, at least one input device, at least one storage device, or a combination thereof. In some embodiments, the at least one output device is a display. In some embodiments, the at least one input device is a keypad, mouse, touch screen, stylus, or a combination thereof. In some embodiments, the at least one storage device comprises additional instructions for the computing system to execute. In any aspect or embodiment, the computing system may comprise a bus system.

[0015] In another aspect, the present disclosure provides a method for managing a distributed energy management system. The method may comprise: (a) receiving, at one or more artificial intelligence (AI) processor, one or more datasets relating to the power grid, wherein the one or more data sets comprises both transmission and distribution data; (b) optimizing, at the one orWSGR Docket No.66368-702.601 more AI processors, at least one module of an SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecasting predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generating, at the one or more AI processors, a report of the predictions.

[0016] In some embodiments, the predictions comprise both transmission and distribution information. In some embodiments, the report comprises an indication to perform one or more recovery operations.

[0017] In another aspect, the present disclosure provides a system for managing a power grid. The system may comprise: a computing system communicatively coupled to one or more artificial intelligence (AI) processors, and wherein the one or more artificial intelligence (AI) processors is configured to at least: (a) receive one or more datasets relating to the power grid, wherein the one or more data sets comprises both transmission and distribution data; (b) optimize at least one module of a SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecast predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generate a report of the predictions.

[0018] In some embodiments, the predictions comprise both transmission and distribution information. In some embodiments, the report comprises an indication to perform one or more recovery operations.

[0019] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

[0020] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. INCORPORATION BY REFERENCE

[0021] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.WSGR Docket No.66368-702.601 To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:

[0023] FIG 1 shows a general schematic illustration of a computer system.

[0024] FIG.2A shows a schematic illustration of a WSCC 9-bus test case featuring nine buses and three generators.

[0025] FIG.2B shows an IEEE 33-Bus radial distribution system consisting of 33 buses and 32 lines.

[0026] FIG.3A illustrates an EMS for the WSCC 9-Bus Test Case.

[0027] FIG.3B illustrates an ADMS within the context of the IEEE 33-Bus radial distribution system.

[0028] FIG.3C shows the integration of Quantum-AI in the EMS / ADMS.

[0029] FIG.4 illustrates potential operational disruptions across different scenarios and outlines the interplay between EMS and ADMS.

[0030] FIG.5 depicts a process flow for a Quantum AI-based approach to EMS / ADMS.

[0031] FIG.6 shows a table comparing the performance of classical and quantum-AI approaches for handling N-1, N-2, N-3, N-4, and N-5 contingency situations in EMS / ADMS.

[0032] FIG.7 shows a block diagram of a computing system tailored for EMS / ADMS, including quantum-AI capabilities.

[0033] FIG.8 illustrates a method for distributed energy system management in a non-classical AI processor.

[0034] FIG.9 illustrates an alternative method for distributed energy system management in an AI processor.

[0035] FIG.10 illustrates a block diagram representing various examples of modules to address specific aspects of power grid management.WSGR Docket No.66368-702.601 DETAILED DESCRIPTION

[0036] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0037] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0038] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0039] Certain inventive embodiments herein contemplate numerical ranges. When ranges are present, the ranges include the range endpoints. Additionally, every sub range and value within the range is present as if explicitly written out.

[0040] The term “about” or “approximately” may mean within an acceptable error range for the particular value, which will depend in part on how the value is measured or determined, e.g., the limitations of the measurement system. For example, “about” may mean within 1 or more than 1 standard deviation, per the practice in the art. Alternatively, “about” may mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Where particular values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value may be assumed.

[0041] The present disclosure relates to providing Quantum and Artificial Intelligence (AI) solutions for Energy Management Systems (EMS) within the power grid. The present disclosure further relates to systems and methods of the present disclosure propose an Advanced Energy Management System (AEMS) model called the Quantum-Energy Management System (Q- EMS), capable of providing solutions to both transmission and distribution management systems and offer power grid operators faster and more accurate decisions-making solution in grid planning and operation.WSGR Docket No.66368-702.601

[0042] In some examples, energy management may require management of factors. In some examples, the ideal smart grid may ensure many of these factors. In an ideal example, the fundamental pillar is ensuring safety and security. In some examples, there are three pairs of attributes surrounding this fundamental pillar: the grid must be clean and sustainable, affordable and equitable, and reliable and resilient. In an ideal example, grid operators maintain a balance among these attributes, around the fundamental pillar. In some examples, this may be achieved through energy management systems, which serve as the brain behind the smart grid. In some examples, at least one factor may require management. In some examples, at least two factors may require management. In some examples, at least three factors may require management. In some examples, at least four factors may require management. In some examples, at least five factors may require management. In some examples, a factor may be climate change and extreme weather events. In some examples, a factor may be clean energy and distributed energy resources. In some examples, a factor may be data.

[0043] In some examples, a power grid may be configured to improve safety and security. In some examples, there may be at least one other pair of attributes for power grid safety and security. In some examples, there may be at least two other pairs of attributes for power grid safety and security. In some examples, there may be at least three other pairs of attributes for power grid safety and security. In some examples, there may be at least four other pairs of attributes for power grid safety and security. In some examples, there may be at least five other pairs of attributes for power grid safety and security.

[0044] In some examples, the power grid may further comprise attributes for maintenance. In some examples, the attributes may be directed to keeping the power grid clean and sustainable. In some examples, the attributes may be directed to keeping the power grid affordable and equitable. In some examples, the attributes may be directed to keeping the power grid reliable and resilient. In some examples, the attributes may be balanced for power grid safety and security. In some examples, one attribute may be unbalanced for power grid safety and security. In some examples, two attributes may be unbalanced for power grid safety and security. In some examples, three or more attributes may be unbalanced for power grid safety and security. In some examples, all available attributes may be unbalanced in reference to each other for power grid safety and security. Computer System

[0045] In some examples, a computer system may be programmed as part of the EMS, which is illustrated in FIG.1. In some examples, the computer system may be a classical computer. In some examples, the computer system may be a binary computer. In some examples, theWSGR Docket No.66368-702.601 computer system may be a supercomputer. In some examples, the computer system may be a mainframe computer. In some examples, the computer system may be a workstation computer. In some examples, the computer system may be a personal computer. The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG.1 shows a computer which may be used with variations, examples, or embodiments of the present disclosure herein. In some examples, the computer system may be an analog computer. In some examples, the computer system may be a digital computer. In some examples, the computer system may be a hybrid computer. In some examples, the computer system may be a tablet. In some examples, the computer system may be smartphone.

[0046] In some examples, the computer system 100 can regulate various aspects of pointwise perimetry mapping, such as, for example, point selection, path selection and mapping, sensitivity measurements, rate change analysis, and indication generation. In some examples, the computer system 100 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. In some examples, the electronic device can be an optical perimetry machine such as visual field perimetry testing equipment or visual field analyzers.

[0047] In some examples, the computer system may be integrated into a network of computers to implement the EMS. In some examples, the computer system may be connected to other computer systems. In some examples, the computer system may be connected to outside components to provide data to the central local computer system. In some examples, the provided data may be applied to make EMS decisions. In some examples, the computer system 100 may include one or more processors 101, a memory 103, and a storage 108 that communicate with each other, and with other components, via a bus 140. The bus 140 may also link a display 132, one or more input devices 133 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 134, one or more storage devices 135, and various tangible storage media 136. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 140. For instance, the various tangible storage media 136 can interface with the bus 140 via storage medium interface 126. Computer system 100 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[0048] In some examples, a processor may be present within the computer system. In some examples, the processor assesses potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not withinWSGR Docket No.66368-702.601 the subset. In some examples, the computer system 100 includes one or more processor(s) 101 (e.g., central processing units (CPUs) or general-purpose graphics processing units (GPGPUs)) that carry out functions. Processor(s) 101 optionally contains a cache memory unit 102 for temporary local storage of instructions, data, or computer addresses. Processor(s) 101 may be configured to assist in execution of computer readable instructions. Computer system 100 may provide functionality for the components depicted in FIG.1 as a result of the processor(s) 101 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 103, storage 108, storage devices 135, and / or storage medium 136. The computer-readable media may store software that implements particular embodiments, and processor(s) 101 may execute the software. Memory 103 may read the software from one or more other computer-readable media (such as mass storage device(s) 135, 136) or from one or more other sources through a suitable interface, such as network interface 120. The software may cause processor(s) 101 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. In some examples, carrying out such processes or steps may include defining data structures stored in memory 103 and modifying the data structures as directed by the software.

[0049] In some examples, the EMS may use memory to execute the various necessary steps to operate within the EMS. In some examples, memory may be a component within the classical computer system. In some examples, the memory may be random access memory component (e.g., RAM 104) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random- access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 105), and any combinations thereof. In some examples, ROM 105 may act to communicate data and instructions unidirectionally to processor(s) 101, and RAM 104 may act to communicate data and instructions bidirectionally with processor(s) 101. In some examples, ROM 105 and RAM 104 may include any suitable tangible computer- readable media described below. In one example, a basic input / output system 106 (BIOS), including basic routines that help to transfer information between elements within computer system 100, such as during start-up, may be stored in the memory 103.

[0050] In some examples, the computer system may comprise fixed storage components. In some examples, the fixed storage component provides long term data storage that allow EMS systems to maintain and save data. In some examples, fixed storage 108 may connect bidirectionally to processor(s) 101, optionally through storage control unit 107. In some examples, fixed storage 108 may provide additional data storage capacity and may also include any suitable tangible computer-readable media described herein. In some examples, storage 108WSGR Docket No.66368-702.601 may be used to store operating system 109, executable(s) 110, data 111, applications 112 (application programs), and the like. In some examples, storage 108 can also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. In some examples, information in storage 108 may, in appropriate cases, may be incorporated as virtual memory in memory 103. In some examples, storage device(s) 135 may be removably interfaced with computer system 100 (e.g., via an external port connector (not shown)) via a storage device interface 125. Particularly, storage device(s) 135 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 100. In some examples, software may reside, completely or partially, within a machine-readable medium on storage device(s) 135. In another example, software may reside, completely or partially, within processor(s) 101.

[0051] In some examples, the computer system 100 may also include an input device 133, allowing a user to control potential EMS input. In one example, a user of computer system 100 may enter commands and / or other information into computer system 100 via input device(s) 133. In some examples, the input device 133 may include, but are not limited to, an alpha- numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some examples, the input device may be a Kinect, Leap Motion, or the like. In some examples, input device(s) 133 may be interfaced to a bus via any of a variety of input interfaces 123 (e.g., input interface 123) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0052] In some examples, a computer system may connect a user to the EMS, allowing for remote system access, maintenance, and operations. In some examples, when a computer system 100 is connected to a network 130, the computer system 100 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 130. In some examples, communications to and from computer system 100 may be sent through network interface 120. In some examples, network interface 120 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 130, and computer system 100 may store the incoming communications in memory 103 for processing. In some examples, aWSGR Docket No.66368-702.601 computer system 100 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 103 and communicated to network 130 from network interface 120. In some examples, a processor 101 may access these communication packets stored in memory 103 for processing.

[0053] In some examples, the EMS may allow for connection between more than one computer system. In some examples, the EMS may allow for connection between the computer systems and one or more users. In some examples, the connection between user and computer systems may be a network. In some examples, the network interface 120 may include, but is not limited to, a network interface card, a modem, and any combination thereof. In some examples, a network 130 or network segment 130 may include, but is not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. In some examples, a network, such as network 130, may employ a wired and / or a wireless mode of communication. In some examples, any network topology may be used.

[0054] In some examples, the user may access the EMS from a local location. In some examples, the user may access the EMS from a remote location. In some examples, the user may visualize the EMS information and data through a display 132. In some examples, the user may further analyze EMS information and data through peripheral output devices. In some examples, a display 132 may include, but is not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. In some examples, the display 132 can interface to the processor(s) 101, memory 103, and fixed storage 108, as well as other devices, such as input device(s) 133, via the bus 140. In some examples, the display 132 may be linked to the bus via a video interface 122, and transport of data between the display 132 and the bus can be controlled via the graphics control 121. In some examples, the display may be a video projector. In some examples, the display may be a head-mounted display (HMD) such as a VR headset. In some examples, suitable VR headsets may include, by way of non- limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In some examples, the display may be a combination of devices such as those disclosed herein.WSGR Docket No.66368-702.601

[0055] In some examples, a computer system 100 may include one or more other peripheral output devices 134 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. In some examples, a peripheral output device may be connected to the bus via an output interface 124. In some examples, an output interface 124 may include, but is not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0056] In some examples, a computer system may utilize software in order to allow EMS functionality and access. In some examples, a computer system 100 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. In some examples, software may encompass logic, and reference to logic may encompass software. In some examples, a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. In some examples, a suitable combination of hardware, software, or both may be utilized.

[0057] In some examples, the EMS system connection may be maintained by electronic hardware and computer software. In some examples, various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. In some examples, various illustrative logical blocks, modules, and circuits described may be implemented or performed with a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In some examples, a general-purpose processor may be a microprocessor, any conventional processor, controller, microcontroller, or state machine. In some examples, a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0058] In some examples, the EMS system may employ algorithms to aid in system management and operation. In some examples, the steps of a method or algorithm may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. In some examples, a software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, aWSGR Docket No.66368-702.601 removable disk, a CD-ROM, or any other form of storage medium known in the art. In some examples, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In some examples, the storage medium may be integral to the processor. In some examples, the processor and the storage medium may reside in an ASIC. In some examples, the ASIC may reside in a user terminal. In some examples, the processor and the storage medium may reside as discrete components in a user terminal.

[0059] In some examples, suitable computing devices may include, by way of non-limiting examples, cloud computing platforms, distributed computing platforms, server clusters, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, and netpad computers. In some examples, the EMS system may comprise a combination thereof.

[0060] In some examples, the computing device includes an operating system configured to perform executable instructions. In some examples, the operating software, including programs and data, may manage the device’s hardware and provides services for execution of applications. In some examples, suitable server operating systems may include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. In some examples, suitable personal computer operating systems may include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some examples, the operating system may provide cloud computing. In some examples, suitable mobile smartphone operating systems may include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research in Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®. Non-Transitory Computer Readable Storage Medium

[0061] In some examples, an EMS may include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In some examples, a computer readable storage medium may be a tangible component of a computing device. In some examples, a computer readable storage medium may be optionally removable from a computing device. In some examples, a computer readable storage medium may include, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computingWSGR Docket No.66368-702.601 systems and services, and the like. In some examples, the program and instructions may be permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media. Computer Programs

[0062] In some examples, an EMS may include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. In some examples, computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, which perform particular tasks or implement particular abstract data types. In some examples, a computer program may be written in various versions of various languages.

[0063] In some examples, the functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some examples, a computer program may comprise one sequence of instructions. In some examples, a computer program may comprise a plurality of sequences of instructions. In some examples, a computer program may be provided from one location. In other examples, a computer program may be provided from a plurality of locations. In some examples, a computer program may include one or more software modules. In some examples, a computer program may include, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof. Software Modules

[0064] In some examples, an EMS may include software, server, and / or database modules, or use of the same. In some examples, software modules may be created by using established machines, software, and languages. In some examples, software may be implemented. In some examples, a software module may comprise a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In some examples, a software module may comprise a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In some examples, the one or more software modules may comprise, by way of non-limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some examples, software modules may be in one computer program or application. In some examples, softwareWSGR Docket No.66368-702.601 modules may be in more than one computer program or application. In some examples, software modules may be hosted on one machine. In some examples, software modules may be hosted on more than one machine. In some examples, software modules may be hosted on a distributed computing platform such as a cloud computing platform. In some examples, software modules may be hosted on one or more machines in one location. In some examples, software modules may be hosted on one or more machines in more than one location. Databases

[0065] In some examples, an EMS may include one or more databases, or use of the same. In some examples, databases may be suitable for storage and retrieval of information, for example customer incident data. In some examples, suitable databases may include, by way of non- limiting examples, relational databases, non-relational databases, object-oriented databases, object databases, entity-relationship model databases, associative databases, XML databases, document-oriented databases, and graph databases. In some examples, suitable databases may include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some examples, a database may be Internet-based. In some examples, a database may be web-based. In some examples, a database may be cloud computing based. In some examples, a database may be a distributed database. In some examples, a database may be based on one or more local computer storage devices. Quantum Computers

[0066] In some examples, an EMS may employ a combination of one or more computers. In some examples, the computers may be classical binary computers. In some examples, the computers may be quantum computers. In some examples, the quantum computer may be designed to operate within the energy grid system. In some examples, the quantum computer design may include a quantum processor. In some examples, the quantum processor may be capable of executing quantum algorithms. In some examples, one quantum algorithm may be executed. In some examples, two quantum algorithms may be executed. In some examples, three quantum algorithms may be executed. In some examples, more than three quantum algorithms may be executed. In some examples, the quantum computer design may include quantum memory. In some examples, the quantum computer design may include a storage unit. In some examples, the quantum memory and storage unit may operate separately. In some examples, the quantum memory and storage unit may be in communication with each other. In some examples, the quantum memory and storage unit may be in communication with other components in a bus system. In some examples, the bus may be wireless. In some examples, the bus may be wired. InWSGR Docket No.66368-702.601 some examples, the bus may serve as the main data conduit. In some examples, the bus may serve as the command transfer within the system.

[0067] In some examples, a quantum processing unit (QPU), or quantum processor, may be used. In some examples, the processor assesses potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not within the subset. In some examples, the QPU may function as the core computational engine of the quantum computer. In some examples, the QPU may leverage unique particle behaviors unlike classical binary computers. In some examples, the particles may be electrons. In some examples, the particles may be photons. In some examples, the unique particle behavior may perform certain types of calculation much more rapidly than classical binary computers. In some examples, the QPU may employ quantum mechanics principles to produce this advanced capacity. In some examples, the quantum mechanics principle may be superposition, which is the phenomenon where a particle can exist in multiple states simultaneously.

[0068] In some examples, the quantum processor may allow for the functionality of the quantum computer system. In some examples, the functionality may allow the EMS to operate more efficiently and accurately. In some examples, one quantum processor may be present. In some examples, more than one quantum processor may be present. In some examples, the quantum processor may execute software elements. In some examples, the software elements may include different software types. In some examples, the software type may be components. In some examples, the software type may be programs. In some examples, the software type may be applications. In some examples, the software type may be operating systems. In some examples, the software type may be middleware. In some examples, the software type may be firmware. In some examples, the software type may be routines. In some examples, the software type may be sub-routines. In some examples, the software type may be APIs. In some examples, the software type may be instruction sets. In some examples, the software elements may storage media. In some examples, one storage media may be present. In some examples, more than one storage media may be present. In some examples, the storage media may be transitory. In some examples, the storage media may be non-transitory. In some examples, the storage media may be tangible. In some examples, the storage media may be cloud-based. In some examples, the storage media may be computer readable. In some examples, the storage media may be human- readable. In some examples, the storage media may be memory units. In some examples, the storage media may be storage units. In some examples, the storage media may be storage devices. In some examples, the storage media may be specific storage mediums. In some examples, the specific storage medium may be Read Only Memory (ROM).WSGR Docket No.66368-702.601

[0069] In some examples, memory may allow the EMS to retain data to aid in decision making and operation. In some examples, quantum computer system memory may be composed of various components. In some examples, the components may be non-transitory. In some examples, the components may be tangible. In some examples, the components may be computer readable. In some examples, the component may be storage media. In some examples, the component may be random-access memory (RAM). In some examples, RAM may be static RAM (SRAM). In some examples, RAM may be dynamic RAM (DRAM). In some examples, RAM may be double-data-rate DRAM (DDRAM). In some examples, RAM may be synchronous DRAM (SDRAM). In some examples, RAM may be quantum RAM (QRAM). In some examples, the system may include read-only memory components (ROM). In some examples, the system may be a combination of components thereof.

[0070] In some examples, the quantum computer system may feature fixed storages to allows EMS to store large amounts of data and process data efficiently. In some examples, the quantum computer system may be bi-directional. In some examples, the quantum computer system may be bi-directionally connected to the quantum processor. In some examples, the connection may be through a storage control unit. In some examples, the storage control unit may be fixed storage. In some examples, the fixed storage component may enhance the overall data storage capacity of the system. In some examples, the fixed storage component may include a variety of suitable media. In some examples, the media may be non-transitory. In some examples, the media may be tangible. In some examples, the media may be computer readable. In some examples, the storage media may house elements. In some examples, the element may be an operating system. In some examples, the element may be executable files (EXECs). In some examples, the elements may be various data sets. In some examples, the elements may be application programs (API applications). In some examples, the fixed storage may be a secondary storage medium. In some examples, the secondary storage medium may be a hard disk. In some examples, the secondary storage medium may operate at a slower speed compared to primary storage.

[0071] In some examples, the quantum computer system has a bus, which may interconnect the EMS. In some examples, the quantum computer system may connect a wide array of subsystems. In some examples, the bus may be digital signal lines that serve a common function. In some examples, the bus may be digital signal lines that do not serve a common function. In some examples, one digital signal line may be present. In some examples, more than one digital signal line may be present. In some examples, the quantum computer may have a bus structure. In some examples, the bus structure may be a memory bus. In some examples, the bus structureWSGR Docket No.66368-702.601 may be a memory controller. In some examples, the bus structure may be a peripheral bus. In some examples, the bus structure may be a local bus. In some examples, the bus structure may be a combination thereof. In some examples, the quantum computer may utilize a variety of bus architectures.

[0072] In some examples, the quantum computer system may be connected to an EMS network. In some examples, the quantum computer system may communicate with other devices. In some examples, the quantum computer system may be a general-purpose digital computer. In some examples, the general-purpose digital computers may be connected to the same network. In some examples, the general-purpose digital computers may be connected to different networks. In some examples, the quantum computer network may be a hybrid network. In some examples, the hybrid network may combine classical binary computing and quantum computing. In some examples, the hybrid network may allow classical binary computing and quantum computing to work in tandem, which enhances computational capabilities.

[0073] In some examples, the quantum computer system may include a display to allow user visualization of the EMS. In some examples, the display may visually represent the quantum computer information and data. In some examples, a display 132 may include, but is not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. In some examples, the display may be an alternate display. In some examples, the display may connect to a QPU. In some examples, the display may connect to a Central Processing Unit (CPU) of a classical binary computer. In some examples, the connection to a classical binary computer CPU may take place in hybrid classical-quantum computer systems. In some examples, the display may connect to memory. In some examples, the display may connect to fixed storage. In some examples, the display may connect to input devices through the bus system. In some examples, the connection between the display and input devices may be through the bus system. In some examples, the connection between the display and bus may be facilitated by a video interface. In some examples, the connection between the display and bus may transport data through graphic control.

[0074] In some examples, the quantum computer system may include peripheral output devices for further EMS access. In some examples, one peripheral output device may be present. In some examples, more than one peripheral output device may be present. In some examples, the peripheral output device may be audio speakers. In some examples, the peripheral output device may be a printer. In some examples, the peripheral output device may be a check printer. InWSGR Docket No.66368-702.601 some examples, the peripheral output device may be a receipt printer. In some examples, the peripheral output device may be a combination thereof. In some examples, the peripheral output device and bus may be connected by an output interface. In some examples, the output interface may be serial ports. In some examples, the output interface may be parallel connections.

[0075] In some examples, quantum computer system may provide EMS functionality. In some examples, quantum computer system may provide functionality through logic. In some examples, the logic functionality may be hardwired. In some examples, the logic functionality may be otherwise embodied in a circuit. In some examples, the circuitry may operate independently to execute various processes or steps of processes as described or illustrated in relevant documentation. In some examples, the circuitry may operate in conjunction with software to execute various processes or steps of processes as described or illustrated in relevant documentation. In some examples, references to software within this may also imply logic, and vice versa.

[0076] In some examples, the EMS may utilize a plurality of processors. In some examples, a general-purpose processor may operate in tandem with a quantum processor. In some examples, a general-purpose processor may operate in tandem with a digital signal processor (DSP). In some examples, a general-purpose processor may operate in tandem with an application-specific integrated circuit (ASIC). In some examples, a general-purpose processor may operate in tandem with a field-programmable gate array (FPGA). In some examples, a general-purpose processor may operate in tandem with other programmable logic devices. In some examples, a general-purpose processor may operate in tandem with discrete gate logic. In some examples, a general-purpose processor may operate in tandem with transistor logic. In some examples, a general-purpose processor may operate in tandem with a discrete hardware component. In some examples, the general-purpose processor may be a microprocessor. In some examples, the general-purpose processor may be any conventional processor. In some examples, the general- purpose processor may be a controller. In some examples, the general-purpose processor may be a microcontroller. In some examples, the general-purpose processor may be a state machine. In some examples, the general-purpose processor may be a combination thereof.

[0077] In some examples, an EMS may utilize an algorithm to aid in function efficiency. In some examples, the method or algorithm steps may be represented in various ways. In some examples, the steps may be represented directly through hardware. In some examples, the steps may be represented within a software module executed by a processor. In some examples, the steps may be represented as a software module implemented using digital logic devices. In some examples, the steps may be represented in a combination thereof. In some examples, theWSGR Docket No.66368-702.601 software module may be housed in different types of memory. In some examples, the memory may be RAM memory. In some examples, the memory may be flash memory. In some examples, the memory may be ROM. In some examples, the memory may be magnetic hard drives. In some examples, the memory may be solid-state hard drives. In some examples, the memory may be any other kind of non-transitory, tangible computer-readable storage medium that may be recognized in the field.

[0078] In some examples, quantum computer systems may include components. In some examples, quantum computer systems may include subcomponents. In some examples, one component or subcomponent may be present. In some examples, more than one component or subcomponent may be present. In some examples, the components or subcomponents may encompass a cloud-based quantum technology system. In some examples, the components or subcomponents may be a network. In some examples, the components or subcomponents may be a QPU. In some examples, the components or subcomponents may be memory. In some examples, the components or subcomponents may be any prior listed component. In some examples, the cloud-based quantum technology system may be a cloud-based computing system. In some examples, the cloud-based computing system may further comprise a front-end system. In some examples, the front-end system may be an input device. In some examples, the front- end system may provide information to the back-end platform. In some examples, the back-end platform may be a server. In some examples, the back-end platform may be a storage system. In some examples, the front-end system and back-end platform may run parallel. In some examples, the front-end system and back-end platform may interact. In some examples, the interaction may be facilitated by software. In some examples, the software may be middleware. In some examples, the middleware may enable back-end systems to offer services and online network storage capabilities to front-end clients. In some examples, one front-end client may be present. In some examples, more than one front-end client may be present. Energy Management Systems (EMS)

[0079] In some examples, power grid safety and security may be adjusted through the Energy Management Systems (EMS). In some examples, the EMS may be the operational base behind the power grid. In some examples, the EMS may comprise a suite of computer-aided tools. In some examples, the computer-aided tools may represent the capability of the grid to maintain the balance between energy grid priorities. In some examples, the priority may be energy supply and demand. In some examples, the priority may be power grid security. In some examples, the priority may be power grid sustainability. In some examples, the priority may be power gridWSGR Docket No.66368-702.601 resiliency. In some examples, the priority may be efficient electricity costs for customers. In some examples, the priority may be efficient electricity costs for grid operators.

[0080] The grid operators are under difficult challenges. In some examples, the grid operators may need to increase the capacity and generation of renewable energy resources. In some examples, the grid operators may consider the high demands for higher grid security. In some examples, the grid operators may consider customer expectations for reliable power. In some examples, grid operators may balance customer expectations with challenges from the complexity and scale of modern grids. In some examples, grid operators may develop solutions for both operational efficiency and adaptability in the dynamic energy sector.

[0081] An Energy Management System (EMS) involves dispatching generation units while emphasizing system security and accounting for potential failures. EMS operability may also consider the fluctuating nature of clean energy resources and the impact of extreme weather events. As a complex, large-scale, mixed-integer, nonlinear, NP-hard problem, EMS incorporates numerous variables and equations, posing significant challenges for efficient and reliable energy management.

[0082] While EMS is an important component of the power grid, it further has impact in U.S. national security and paves the way for emission reduction. In some examples, EMS determines the optimal scheduling of generation units. In some examples, EMS may consider security constraints for reliable grid operation. In some examples, EMS enables operators to better manage, optimize, and analyze operations while minimizing risk and increasing flexibility. In some examples, EMS operability allows for operational planning. In some examples, EMS operability allows for enhancing grid stability and security. Despite its importance, solving EMS poses challenges due to its complexity and need for high computational efficiency to accommodate the nonlinear nature of the problem.

[0083] In some examples, EMS may comprise solving a set of optimization problems on a daily basis. In some examples, EMS may comprise solving a set of optimization problems on a weekly basis. In some examples, EMS may comprise solving a set of optimization problems on a biweekly basis. In some examples, EMS may comprise solving a set of optimization problems on a monthly basis. In some examples, EMS may solve a set of optimization problems around every 10 seconds. In some examples, EMS may solve a set of optimization problems around every 30 seconds. In some examples, EMS may solve a set of optimization problems around every 60 seconds. In some examples, EMS may solve a set of optimization problems around every 2 minutes. In some examples, EMS may solve a set of optimization problems around every 3 minutes. In some examples, EMS may solve a set of optimization problems aroundWSGR Docket No.66368-702.601 every 4 minutes. In some examples, EMS may solve a set of optimization problems around every 5 minutes. In some examples, EMS may solve a set of optimization problems around every 6 minutes. In some examples, EMS may solve a set of optimization problems around every 7 minutes. In some examples, EMS may solve a set of optimization problems around every 8 minutes. In some examples, EMS may solve a set of optimization problems around every 9 minutes. In some examples, EMS may solve a set of optimization problems around every 10 minutes. In some examples, EMS may solve a set of optimization problems around every 30 minutes. In some examples, EMS may solve a set of optimization problems around every 60 minutes. In some examples, the set of problems may be large scale. In some examples, the set of problems may be small scale. In some examples, the set of problems may be integer programming. In some examples, the set of problems may be mixed-integer programming. In some examples, the set of problems may be linear programming. In some examples, the set of problems may be nonlinear programming. In some examples, the set of problems may be NP- hard. In some examples, the set of problems may be NP-complete. In some examples, the set of problems may further include variables. In some examples, these variables may be extensive. In some examples, the set of problems may further include equations. In some examples, these equations may be extensive. In some examples, the set of problems may be a combination thereof.

[0084] In some examples, the Energy Management System (EMS) may be essential for maintaining power grid balance. In some examples, the EMS may consider delivering electricity to customers. In some examples, the electricity to customers may be clean. In some examples, the electricity to customers may be reliable. In some examples, the electricity to customers may be affordable. In some examples, the EMS may consider reliability. In some examples, the EMS may consider security. In some examples, the EMS may consider efficiency. In some examples, the EMS may guide operators in electric power grid operation. In some examples, the EMS may determine the optimal power-generating unit scheduling. In some examples, the EMS may predict potential outages. In some examples, the EMS may provide outage solutions in normal conditions. In some examples, the EMS may provide outage solutions in contingency conditions.

[0085] In some examples, the EMS may be complex. In some examples, the EMS complexity may increase depending on the evolution of the power system. In some examples, the EMS complexity may be impacted by the integration of renewable energy sources. In some examples, the renewable energy source may be solar energy. In some examples, the renewable energy source may be wind energy. In some examples, the renewable energy source may be geothermal energy. In some examples, the renewable energy source may be hydropower. In some examples,WSGR Docket No.66368-702.601 the renewable energy source may be ocean energy. In some examples, the renewable energy source may be bioenergy. In some examples, the integration of renewable energy may introduce variability into the power supply. In some examples, the integration of renewable energy may introduce unpredictability into the power supply. In some examples, the renewable energy may be affected by weather conditions, whereas fossil fuel-based power plants are not. In some examples, weather conditions may affect energy output prediction accuracy. In some examples, the EMS may be managed to balance fluctuations in supply with demand.

[0086] In some examples, the EMS complexity may be affected by greenhouse gas emission reduction. In some examples, the EMS complexity may be affected by compliance with regulations. In some examples, the regulations may be environmental. In some examples, the regulation may be federal. In some examples, the regulations may be state. In some examples, the regulations may be local. In some examples, the EMS complexity may be affected by carbon footprint considerations of energy sources. In some examples, the EMS complexity may be affected by environmental impact of energy sources. In some examples, the EMS complexity may be affected by an operator preference of clean options. In some examples, the EMS complexity may be affected by an operator preference of renewable options.

[0087] In some examples, the EMS complexity may be affected by demand variability. In some examples, demand variability may be affected by electricity usage shifts. In some examples, electricity usage shifts may be predictable. In some examples, electricity usage shifts may be unpredictable. In some examples, electricity usage shifts may be affected by consumer behavior changes. In some examples, electricity usage shifts may be affected by new emergent technology. In some examples, the EMS system may maintain power system balance when demand variability increases complexity. In some examples, the EMS system may maintain power system reliability when demand variability increases complexity. In some examples, the power system reliability and balance may be maintained by swift power generation scheduling. In some examples, the power system reliability and balance may be maintained by adaptive power generation scheduling. In some examples, the power system reliability and balance may prevent under supply of power. In some examples, the power system reliability and balance may prevent over supply of power.

[0088] In some examples, EMS may fail to effectively manage. In some examples, the failure to manage may lead to inefficiencies. In some examples, the failure to manage may lead to higher operational costs. In some examples, the failure to manage may lead to increased emissions. In some examples, the failure to manage may lead to power outages. In some examples, the failure to manage may lead to grid instability. In some examples, EMS may be efficiently managedWSGR Docket No.66368-702.601 when considering changing energy landscapes and regulatory environments. In some examples, EMS efficient management may aim for power supply reliability. In some examples, EMS efficient management may aim for power supply security. In some examples, EMS efficient management may aim for power supply resiliency.

[0089] In some examples, quantum-AI based EMS solution may be computed using different types of quantum computers known and / or contemplated in the art and should not be limited to a particular quantum computer or optimization process. In some examples, the quantum computer may be silicon-based technology. In some examples, the quantum computer may be ionic-based technology. In some examples, the quantum computer may be carbon-based technology. In some examples, the quantum computer may be photonic-based technology. In some examples, the quantum computer may be superconducting-based technology. In some examples, the quantum computer may be hybrid-based technology.

[0090] In some examples, quantum-AI may hold a significant role in changing complex EMS problems. In some examples, quantum-AI may sufficiently handle increasingly complex electric grids. In some examples, quantum-AI may sufficiently handle increased demand of electric grids. In some examples, quantum-AI may aid in maintaining electric grid balance. In some examples, power grid safety may be considered in balancing. In some examples, power grid security may be considered in balancing. In some examples, power grid sustainability may be considered in balancing. In some examples, power grid affordability may be considered in balancing. In some examples, power grid reliability is considered in balancing. In some examples, power grid resilience may be considered in balancing.

[0091] In some examples, quantum-AI may have functionality that allows for efficient power grid management. In some examples, quantum-AI may have functionality to manage complex problems. In some examples, quantum-AI may excel at processing EMS problems. In some examples, the EMS problems may be large-scale. In some examples, the EMS problems may be small-scale. In some examples, the EMS problems may be mixed integer. In some examples, the EMS problems may be integer. In some examples, the EMS problems may be linear. In some examples, the EMS problems may be nonlinear. In some examples, the EMS problems may be NP-hard. In some examples, the EMS problems may be NP-complete. In some examples, quantum-AI may handle variables. In some examples, quantum-AI may handle a vast number of variables. In some examples, quantum-AI may handle a small number of variables. In some examples, quantum-AI may handle constraints. In some examples, quantum-AI may handle a vast number of constraints. In some examples, quantum-AI may handle a small number of constraints. In some examples, quantum-AI may analyze constraints and variableWSGR Docket No.66368-702.601 simultaneously. In some examples, simultaneous analysis may allow for analysis of power grid intricate dynamics.

[0092] In some examples, quantum-AI may have functionality to manage problems at high speed and efficiency. In some examples, quantum-AI may solve optimization problems. In some examples, the speed of optimization problem solving may be faster than that of computers. In some examples, the faster optimization may impact power grid decisions. In some examples, the power grid decisions are made in real time. In some examples, the power grid decisions may balance supply and demand. In some examples, the power grid decisions may consider safety. In some examples, the power grid decisions may consider security.

[0093] In some examples, quantum-AI may have functionality to enhance predictive analytics. In some examples, AI may analyze datasets. In some examples, machine learning may analyze datasets. In some examples, quantum-AI dataset analysis may predict demand. In some examples, quantum-AI dataset analysis may predict generation capacity. In some examples, quantum-AI dataset analysis may predict potential system disturbances. In some examples, quantum-AI dataset analysis may accurately assist proactive system management. In some examples, quantum-AI dataset analysis may rapidly assist proactive system management.

[0094] In some examples, quantum-AI may have functionality to integrate renewable energy. In some examples, power grid management may be affected by renewable energy variability. In some examples, quantum-AI may optimize integration of renewable energy sources. In some examples, quantum-AI may aid in providing clean energy access to the power grid. In some examples, quantum-AI may aid in providing sustainable energy access to the power grid. In some examples, quantum-AI may aid in maintaining power grid reliability. In some examples, quantum-AI may aid in maintaining power grid resilience.

[0095] In some examples, quantum-AI may have functionality to reduce electricity cost. In some examples, quantum-AI may have functionality to increase energy equitability. In some examples, quantum-AI may reduce cost and increase equitability by optimizing generation. In some examples, quantum-AI may reduce cost and increase equitability by optimizing distribution. In some examples, cost reduction of energy may increase energy equitability.

[0096] In some examples, quantum-AI may have functionality to enhance security and resilience. In some examples, power system security and resilience may be affected by natural disasters. In some examples, power system security and resilience may be affected by system failures. In some examples, quantum-AI may allow power system adaptability to maintain stability. In some examples, the adaptability may be quick. In some examples, the adaptability may be constant.WSGR Docket No.66368-702.601

[0097] In some examples, quantum-AI may have functionality to aid in environmental sustainability. In some examples, quantum-AI may optimize routes of environmental sustainability. In some examples, quantum-AI may optimize methods of environmental sustainability. In some examples, environmental sustainability may be emission reduction. In some examples, environmental sustainability may be resource consumption reduction. In some examples, quantum-Ai may operate to align with environmental sustainability goals.

[0098] As in FIG.2A, the Western System Coordinating Council (WSCC) 9-bus test case is illustrated, which simplifies the WSCC network into an equivalent system featuring nine buses and three generators. The components are interconnected through a series of buses and lines, indicating power flow, and are managed through an Energy Management System (EMS). FIG. 2A serves as a visual tool for comprehending the structural and operational relationships of the components within an EMS system. The representative framework is provided for analyzing and optimizing the grid’s performance, taking into account the various security constraints inherent in power system operations. In this figure, the three generators are central to the network. These generators are pivotal in the EMS model, representing the primary sources of power generation. Their strategic placement and operational characteristics are vital for simulating the power generation process and for formulating effective unit commitment strategies. Additionally, the system includes three transformers. These transformers are integral to the model, playing a significant role in managing voltage levels and ensuring efficient distribution of power within the network. The interconnectedness of the system components is established through a comprehensive array of transmission lines between 9 buses. These lines are represented in the schematic, delineating the electrical connections and power flow routes between the generators, transformers, and buses.

[0099] As in FIG.2B, the Institute of Electrical and Electronics Engineers (IEEE) 33-Bus radial distribution system is illustrated, which is used test and compare various types of distribution systems. This system is managed through Distributed Energy Management System (DEMS), also known Advanced Distribution Management Systems (ADMS). The IEEE 33-Bus radial distribution system of FIG.2B serves as a standard benchmark for testing and comparing different types of power system analyses and algorithms. The IEEE 33-Bus radial distribution system comprises 33 buses and 32 lines, this system operates at a voltage of 12.66 kV. The IEEE 33-Bus radial distribution system comprises features a load size of 3.715 MW and 2.3 MVar, indicative of its scale and significance in power system studies. The system's components encompass a diverse range, including buses, lines, generators, loads, transformers, switches, andWSGR Docket No.66368-702.601 protection devices. These components collaborate harmoniously to facilitate the efficient and reliable distribution of electrical power within the system,

[0100] As in FIG.3A, the current energy management system for the WSCC 9-Bus test case is illustrated, capturing the essential components, the need for automation, challenges, system stability factors, and the detailed functionalities of an Energy Management System (EMS). FIG. 3A encompasses essential components crucial for effective energy management, highlights the imperative for automation in the system, sheds light on prevalent challenges, factors influencing system stability, and offers insights into the intricate functionalities of EMS. The comprehensive overview in FIG.3A offers a holistic understanding of the EMS landscape and the complexities involved in managing energy within the WSCC 9-Bus test case scenario.

[0101] As in FIG.3B, the current distribution energy management systems within the context of the IEEE 33-Bus standard radial distribution system is illustrated. This visualization offers a structured overview of Distribution Energy Management Systems (DEMS). This visual representation provides a structured overview of DEMS, delineating their interplay with various components, elucidating common goals shared among these systems, highlighting their distinct focuses, and outlining key functionalities essential for effective distribution energy management. Additionally, the illustration sheds light on the challenges faced by DEMS and proposes strategies aimed at overcoming these challenges. Moreover, it provides insights into future directions for technology integration and modernization efforts in grid energy management, paving the way for enhanced efficiency and sustainability in distribution energy systems.

[0102] As in FIG.4, potential operational disruptions are illustrated across different scenarios, outlining the interplay between EMS and ADMS to achieve an Advanced Energy Management System (AEMS) capable of managing both transmission and distribution systems. It depicts the phases of disruption, vulnerability, mitigation efforts, and resilience, wherein the systems recover and return to normal operations. FIG.4 illustrates a conceptual graph that visualizes the probability analysis of various N-1 scenarios in current EMS problem-solving. This graph is part of the broader discussion on the use of Quantum technology in enhancing EMS strategy formulation. In certain implementations of this approach, the quantum computer is specifically configured to compute simultaneously the probabilities for each component involved in multiple N-1 scenarios, ultimately displaying the results to the user, such as a grid operator, via a display device. Employing the principle of superposition of states, the quantum computer is adept at generating all possible feasible and optimal solutions for these EMS scenarios.WSGR Docket No.66368-702.601 Event-Driven EMS

[0103] In some instances, event-driven EM may be utilized. In some examples, event-driven EMS may allow for strategic planning. In some examples, event-driven EMS may allow for deployment of resources. In some examples, deployed resources may be flexible. In some examples, the flexible resources may be mobile units. In some examples, the flexible resources may be fuel reserves. In some examples, event-driven EMS may be employed in response to power supply interruptions. In some examples, event-driven EMS may be employed in response to power supply outages. In some examples, event-driven EMS may be employed in response to electrical distribution interruptions. In some examples, event-driven EMS may be employed in response to electrical distribution outages. In some examples, event-driven EMS may be employed in response to transmission system interruptions. In some examples, event-driven EMS may be employed in response to transmission system outages. In some examples, extreme weather may cause such outages and interruptions. In some examples, event-driven EMS may enhance power grid resilience in response to outages and interruptions.

[0104] In some examples, event-driven EMS may solve optimization problems. In some examples, the optimization problems may be basic. In some examples, the optimization problems may be complex. In some examples, the optimization problems may be combinatorial. In some examples, event-driven EMS may account for scenarios. In some examples, event- driven EMS may account for a multitude of scenarios. In some examples, event-driven EMS may account for 1-100 scenarios. In some examples, event-driven EMS may account for 100- 1000 scenarios. In some examples, event-driven EMS may account for 1000-10000 scenarios. In some examples, event-driven EMS may account for 10000-100000 scenarios. In some examples, event-driven EMS may account for 100000-1000000 scenarios. In some examples, event-driven EMS may account for 1000000-10000000 scenarios. In some examples, event-driven EMS may account for probabilities. In some examples, event-driven EMS may account for a multitude of probabilities. In some examples, event-driven EMS may account for 1-100 probabilities. In some examples, event-driven EMS may account for 100-1000 probabilities. In some examples, event-driven EMS may account for 1000-10000 probabilities. In some examples, event-driven EMS may account for 10000-100000 probabilities. In some examples, event-driven EMS may account for 100000-1000000 probabilities. In some examples, event-driven EMS may account for 1000000-10000000 probabilities. In some examples, event-driven EMS may be applied to complex power grids. In some examples, the complexity may be its vastness. In some examples, the complexity may be interconnected. In some examples, quantum computing may be used for event-driven EMS. In some examples, quantum-AI may be used for event-driven EMS. In someWSGR Docket No.66368-702.601 examples, quantum-AI may be used for event-driven EMS that may be intractable for binary computing.

[0105] In some examples, event-driven EMS may employ quantum computing. In some examples, event-driven AI by quantum computing may be used due to its superiority in handling complex optimization problems. In some examples, event-driven AI by quantum computing may simulate resource scheduling. In some examples, event-driven AI by quantum computing may plan for potential interruptions and outages. In some examples, binary computing and quantum computing may be used in tandem for event-driven EMS, creating a hybrid model. In some examples, the hybrid model may allow for some problems to be solved by binary computing. In some examples, the hybrid model may allow for some problems to be solved by quantum computing. In some examples, binary computing may be more efficient for some problems. In some examples, quantum computing may be more efficient for some problems. In some examples, quantum computing may be used for high dimensional calculations. In some examples, quantum computing may be used for complex calculations. In some examples, hybrid event-driven EMS may allow for sufficient responses based on suitable computational tools.

[0106] In some examples, event-driven EMS integrated with quantum-AI may allow higher resilience as compared to other event-driven EMS methods. In some examples, event-driven EMS integrated with quantum-AI may allow higher adaptability as compared to other event- driven EMS methods. In some examples, the integration may allow more robust planning in anticipation for extreme weather events. In some examples, the integration may allow more deployment of resources in anticipation for extreme weather events. In some examples, the integration may enhance overall power system operation efficiency even with increasing unpredictability and complexity. In some examples, the integration may enhance overall power system operation reliability even with increasing unpredictability and complexity.

[0107] In some examples, EMS may be applied to calculate the likelihood of component failure in a power system. In some examples, EMS may apply a mixed-integer linear programming. In some examples, component failure likelihood may be calculated based on multiple scenarios. In some examples, each scenario may be analyzed individually. In some examples, scenarios may be analyzed together. In some examples, the component may be a generator. In some examples, the component may be buses. In some examples, the component may be transmission lines. In some examples, the component may be a transformer. In some examples, total system failure may vary based on factors. In some examples, the factor may be which component has failed. In some examples, the factor may be the location within the power system. In some examples, the factor may be the connection to other components.WSGR Docket No.66368-702.601

[0108] In some examples, the mixed-integer linear programming (MILP) may determine failure probability when each individual component fails. In some examples, the component may be a generator. In some examples, the component may be buses. In some examples, the component may be transmission lines. In some examples, the component may be a transformer. In some examples, MILP may maintain reliability in the event of one system outage. In some examples, MILP may provide no load interruptions for customers in the event of one system outage. In some examples, both reliability and interruption maintenance studies may be N-1 reliability studies. In some examples, cases of extreme weather or catastrophic outages may be N-k reliability studies, which MILP may not efficiently handle. In some examples, N may represent the total number of components. In some examples, k may represent the number of component failures. Quantum-Energy Management System (Q-EMS)

[0109] In some examples, power grid safety and security may be adjusted through the Advanced Energy Management System (AEMS) model. In some examples, power grid safety and security may be adjusted through the Quantum-Energy Management System (Q-EMS) model.

[0110] In some examples, Q-EMS may provide improved performance and efficiency of the power grid. In some examples, Q-EMS may provide improved performance and efficiency under normal conditions. In some examples, Q-EMS may provide improved performance and efficiency under contingency conditions. In some examples, Q-EMS may provide improved performance and efficiency under both normal and contingency conditions. In some examples, the improved performance and efficiency may be employed to reach clean energy integration. In some examples, the improved performance and efficiency may be employed to build a climate- resilient grid.

[0111] In some examples, a quantum processor is a quantum mechanical system of a plurality of qubits, measurements over which will result samples from the Boltzmann distribution defined by the global energy of the system. In some examples, quantum technology may utilize qubits, as opposed to binary bits utilized in classic computing. In some examples, quantum technology may allow for the representation of multiple states simultaneously. In some examples, quantum technology may allow the qubits to remain interconnected despite distances. In some examples, quantum technology may complete numerous operations concurrently. In some examples, quantum technology may allow a machine to handle complex calculations at high speeds. In some examples, quantum technology may allow a machine to tackle problem classes intractable for classical supercomputers.WSGR Docket No.66368-702.601

[0112] Qubits are physical implementation of a quantum mechanical system represented on a Hilbert space and realizing at least two distinct and distinguishable eigenstates that represent two states of a quantum bit. A quantum bit is the analogue of the digital bit, where the ambient storing device may store two states of a two-state quantum information, but also in superpositions of the two states. In some examples, such systems may have more than two eigenstates in which case the In some examples, implementations of qubits have been proposed, the list comprising solid-state nuclear spins, measured and controlled electronically or with nuclear magnetic resonance, trapped ions, atoms in optical cavities (cavity quantum- electrodynamics), liquid state nuclear spins, electronic charge or spin degrees of freedom in quantum dots, superconducting quantum circuits based on Josephson junctions [Barone and Paterno, 1982, Physics and Applications of the Josephson Effect, John Wiley and Sons, New York; Martinis et al., 2002, Physical Review Letters 89, 117901] and electrons on Helium.

[0113] In some examples, quantum technology may be used individually. In some examples, artificial intelligence (AI) may be used individual. In some examples, quantum technology and AI may be used in combination (quantum-AI). In some examples, quantum-AI may, in part, maintain a balanced power grid. In some examples, quantum-AI may, in part, maintain a secure power grid. In some examples, quantum-AI may, in part, maintain a sustainable power grid. In some examples, quantum-AI may, in part, minimize operational costs. In some examples, quantum-AI may address optimization problems inherent to energy management system. In some examples, the optimization problems may be large scale. In some examples, the optimization problems may be small scale. In some examples, the optimization problems may be integer programming. In some examples, the optimization problems may be mixed-integer programming. In some examples, the optimization problems may be linear programming. In some examples, the optimization problems may be nonlinear programming. In some examples, the optimization problems may be NP-hard. In some examples, the optimization problems may be NP-complete. In some examples, the optimization problems may further include variables. In some examples, these variables may be extensive. In some examples, the optimization problems may further include equations. In some examples, these equations may be extensive. In some examples, the optimization problems may be a combination thereof.

[0114] In some examples, this disclosure may utilize quantum and AI technology to model, simulate, and solve the EMS problem. IN some examples, the immense processing capabilities of quantum computing, and quantum sensing which perform multiple operations simultaneously, offers a fast and accurate solution for solving EMS challenges. In some examples, the quantumWSGR Docket No.66368-702.601 approach provides a promising avenue for enhancing the efficiency, security, and resiliency of power system operations.

[0115] In some examples, Quantum-AI technology solutions may be applied to EMS. In some examples, Quantum-AI technology may utilize quantum algorithms. In some examples, Quantum Approximate Optimization Algorithm (QAOA) may be implemented. In some examples, Variational Quantum Eigensolver (VQE) may be implemented. In some examples, AI's Neural Network (NN) approach may be implemented. In some examples, QAOA and NN may be utilized in combination. In some examples, the quantum-AI approach may be used to address complex EMS problems. In some examples, the quantum-AI approach may be used to address noncomplex EMS problems. In some examples, the quantum-AI approach may be used to address large-scale EMS problems. In some examples, the quantum-AI approach may be used to address small-scale EMS problems. In some examples, the quantum-AI approach may be used to address linear EMS problems. In some examples, the quantum-AI approach may be used to address nonlinear EMS problems. In some examples, the quantum-AI approach may be used to address NP-hard EMS problems. In some examples, the quantum-AI approach may be used to address NP-complete EMS problems. In some examples, the quantum-AI approach may be used to address any combinations of EMS problems, thereof. In some examples, the quantum-AI approach may offer predictive models. In some examples, the predictive models may be efficient. In some examples, the predictive models may increase decision-making capacity. In some examples, the quantum-AI technology may implement algorithms. In some examples, the algorithms may emulate quantum mechanics principles. In some examples, the algorithms may emulate superposition. In some examples, the algorithms may emulate entanglement. In some examples, the algorithms may be computationally intensive. In some examples, the algorithms may be run in real-time. In some examples, the quantum-AI technology may involve quantum cryptography. In some examples, the quantum-AI technology may involve quantum communication. In some examples, the quantum-AI technology may involve quantum simulations.

[0116] In some examples, the quantum-AI technology approach may be applied to networks. In some examples, the quantum-AI technology approach may be applied to transmission networks. In some examples, transmission networks may allow large-scale electricity movement. In some examples, transmission networks may allow extra-high voltage electricity movement. In some examples, transmission networks may allow electricity movement from the point of generation to a substation. In some examples, transmission networks may allow electricity movement from a substation to a substation. In some examples, transmission networks may allow electricityWSGR Docket No.66368-702.601 movement from the point of generation to an energy storage unit. In some examples, transmission networks may allow electricity movement from the substation to an energy storage unit. In some examples, transmission networks may allow electricity movement from the substation to a user. In some examples, transmission networks may further comprise interconnectors, wherein the interconnectors allow power flow in and out of the network. In some examples, transmission networks may have a connection voltage. In some examples, the connection voltage may be more than about 132 kV. In some examples, the connection voltage may be more than about 200 kV. In some examples, the connection voltage may be more than about 300 kV. In some examples, the connection voltage may be up to about 400 kV. In some examples, the connection voltage may be up to about 300 kV. In some examples, the connection voltage may be up to about 200 kV. In some examples, the point of generation may be onshore wind farms. In some examples, the point of generation may be offshore wind farms. In some examples, the point of generation may be solar farms. In some examples, the point of generation may be battery storage. In some examples, the point of generation may be tidal power. In some examples, the point of generation may be nuclear-powered generators. In some examples, the point of generation may be gas-powered generators. In some examples, the energy storage unit may take excess power from the networks. In some examples, the energy storage unit may direct power to the networks when demand is high.

[0117] In some examples, the quantum-AI technology approach may be applied to distribution networks. In some examples, the distribution networks may allow electricity movement from substations to customers. In some examples, the customer may be private. In some examples, the customer may be public. In some examples, the customer may be industrial. In some examples, the substations may convert high voltage electricity to lower voltages. In some examples, the high voltage electricity may be more than about 132 kV. In some examples, the low voltage electricity may be less than about 132 kV. In some examples, the low voltage electricity may be less than about 100 kV. In some examples, the low voltage electricity may be less than about 50 kV.

[0118] In some examples, the quantum-AI technology may manage the complexities and dynamic nature of the modern power grid by optimizing decision-making processes. In some examples, the quantum-AI technology may manage the complexities and dynamic nature of the modern power grid by predicting optimal generation dispatch. In some examples, the quantum- AI technology may manage the complexities and dynamic nature of the modern power grid by adapting to real-time changes.WSGR Docket No.66368-702.601

[0119] In some examples, Q-EMS may provide improved functionality from EMS. In some examples, Q-EMS may offer an improvement in speed for conducting contingency analysis. In some examples, Q-EMS may offer an improvement in accuracy for conducting contingency analysis. In some examples, Q-EMS may offer an improvement in managing intermittent resources. In some examples, Q-EMS may impact the field of energy management systems. In some examples, Q-EMS may help achieve the building of a climate resilient grid. In some examples, Q-EMS may allow for renewable energy integration. In some examples, Q-EMS may allow for 80% renewable energy integration. In some examples, Q-EMS may allow for 85% renewable energy integration. In some examples, Q-EMS may allow for 90% renewable energy integration. In some examples, Q-EMS may allow for 95% renewable energy integration. In some examples, Q-EMS may allow for 100% renewable energy integration.

[0120] In some examples, quantum-AI may be configured to extend N-k studies. In some examples, quantum-AI may determine which amount various N-k scenarios may cause a system- wide outage. In some examples, the scenario may have k as 1, wherein 1 component has failed. In some examples, the scenario may have k as 2, wherein 2 components have failed. In some examples, the scenario may have k as 3, wherein 3 components have failed. In some examples, the scenario may have k as 4, wherein 4 components have failed. In some examples, the scenario may have k as 5 or more, wherein 5 or more components have failed. In some examples, quantum-AI may allow enhanced power grid assessment and resilience during multiple simultaneous component failures.

[0121] In some examples, quantum-AI may enhance analysis enabling simultaneous computation of multiple outputs across various scenarios. In some examples, the enhanced analysis may allow operators to assess system failure probabilities. In some examples, the enhanced analysis may allow operators to assess power outage probabilities. In some examples, the enhanced analysis may allow operators to assess overload conditions probabilities. In some examples, the enhanced analysis may be completed under different scenarios. In some examples, the scenario may be N-1, wherein one component fails. In some examples, the scenario may be N-2, wherein two components fail. In some examples, the scenario may be N-k, wherein multiple components fail. In some examples, the enhanced analysis may be important for U.S. national security. In some examples, the enhanced analysis may be important for emission reduction.

[0122] In some examples, quantum-AI can process a broad range of scenarios efficiently and accurately. In some examples, quantum-AI can process a broader range of scenarios more efficiently and accurately than binary computing methods. In some examples, the broader rangeWSGR Docket No.66368-702.601 may be possible due to quantum computing’s ability to handle problems. In some examples, the problems may be complex. In some examples, the problems may multi-variable. In some examples, the problems may be single-variable. In some examples, the broader range may be possible due to AI’s predictive analytics. In some examples, quantum-AI may enhance operator capability to predict potential failures and disruptions. In some examples, quantum-AI may enhance operator capability to plan for potential failures and disruptions. In some examples, quantum-AI may enhance operator capability to respond to potential failures and disruptions. In some examples, quantum-AI may contribute to the increased resilience of power infrastructure. In some examples, quantum-AI may contribute to the increased reliability of power infrastructure. In some examples, quantum-AI may contribute to the increased overall efficiency of power infrastructure. In some examples, quantum-AI may help mitigate risks associated with component failures.

[0123] In some examples, quantum-AI may use modeling to provide solutions to electric power grid EMS problems. In some examples, quantum-AI may use simulating to provide solutions to electric power grid EMS problems. In some examples, quantum-AI may use experimentation to provide solutions to electric power grid EMS problems. In some examples, quantum-AI may efficiently manage EMS’s interconnected problems. In some examples, quantum-AI may optimize EMS’s interconnected problems. In some examples, each interconnected problem may have an important power grid role. In some examples, the important power grid role may affect overall functionality. In some examples, the important power grid role may affect overall efficiency.

[0124] In some examples, quantum computing may significantly enhance computational power compared to binary computing techniques. In some examples, quantum computing may allow multiple functions related to the modules to be executed simultaneously using possible permutations. In some examples, parallel processing may be implements. In some examples, parallel processing capability of quantum computers may be beneficial for handling the complexity of EMS problems. In some examples, parallel processing capability of quantum computers may be beneficial for handling the scale of EMS problems. In some examples, the EMS problems may involve variables. In some examples, EMS problems may account for 1-100 variables. In some examples, EMS problems may account for 100-1000 variables. In some examples, EMS problems may account for 1000-10000 variables. In some examples, EMS problems may account for 10000-100000 variables. In some examples, EMS problems may account for 100000-1000000 variables. In some examples, EMS problems may account for 1000000-10000000 variables. In some examples, the EMS problems may involve constraints. InWSGR Docket No.66368-702.601 some examples, EMS problems may account for 1-100 constraints. In some examples, EMS problems may account for 100-1000 constraints. In some examples, EMS problems may account for 1000-10000 constraints. In some examples, EMS problems may account for 10000-100000 constraints. In some examples, EMS problems may account for 100000-1000000 constraints. In some examples, EMS problems may account for 1000000-10000000 constraints.

[0125] In some examples, AI algorithms may provide predictive insights of the power system. In some examples, the AI algorithms predictive insights may be into load demands. In some examples, the AI algorithms predictive insights may be into market trends. In some examples, the AI algorithms predictive insights may be into renewable outputs. In some examples, the AI algorithms predictive insights may be processed. In some examples, the AI algorithms predictive insights may be rapidly processed. In some examples, the AI algorithms predictive insights may be efficiently processed. In some examples, the AI algorithms predictive insights may be processed by quantum computing. In some examples, the AI algorithms predictive insights may be faster than EMS with binary computing. In some examples, the AI algorithms predictive insights may be more data-driven than EMS with binary computing. In some examples, the AI algorithms predictive insights may be more adaptive to changing conditions than EMS with binary computing.

[0126] In some examples, energy management may be maintained across various disciplines. In some examples, energy management may be maintained by solving NP-hard optimization problems. In some examples, energy management may be maintained by NP-complete optimization problems. In some examples, energy management may be maintained by predictive models. In some examples, quantum computing may provide speedups in solving complex problems, as compared to binary computing. In some examples, quantum computing speedups may be substantial. In some examples, quantum-AI EMS may optimally schedule generation units in a power grid. In some examples, quantum-AI EMS may optimally schedule generation units to meet the forecasted demand. In some examples, quantum-AI EMS may optimally schedule generation units to meet forecasted demand over a specified period. In some examples, quantum-AI EMS may optimally schedule generation units in a power grid while adhering to grid security constraints. In some examples, quantum-AI EMS may optimally schedule generation units in a power grid while adhering to operational limits. In some examples, quantum-AI EMS may optimally schedule generation units in a power grid in real time. In some examples, quantum-AI EMS may determine the start-up of generation units. In some examples, quantum-AI EMS may determine the shut-down of generation units. In some examples, the start-up shutdown of generation units may be temporary. In some examples, the start-upWSGR Docket No.66368-702.601 shutdown of generation units may be permanent. In some examples, quantum-AI EMS may determine the output levels of generation units. In some examples, quantum-AI EMS may provide so the system can withstand potential contingencies. In some examples, the contingency may be an unexpected outage. In some examples, the contingency may be a change in power grid condition.

[0127] As in FIG 3C, the Q-EMS model which represents the integration of Quantum-AI in the EMS / DEMS in FIG.3A and FIG.3B is illustrated, focusing on their applications within the WSCC 9-Bus and IEEE 33-Bus systems. This visualization outlines their interplay, common goals, distinct focuses, key functionalities, and the challenges they face, alongside strategies for overcoming these challenges and future directions for technology integration and grid energy management modernization. The common goals include enhanced grid reliability and efficiency, advanced operational efficiency, and secure and sustainable energy distribution and transmission. The distinct focuses for ESCC 9-bus includes large-scale power transmission optimization and high voltage network stability. The distinct focuses for IEEE 33-bus includes efficient power distribution management and integration of renewable energy sources. The key functionalities include real-time monitoring and control using quantum computing, advanced fault detection and isolation, quantum-enhanced load forecasting and management, and voltage and VAR optimization. The challenges include renewable energy integration and aging infrastructure and cybersecurity threats. The strategies to overcome challenges include quantum sensors for accurate data collection, quantum cryptography for secure communication, and quantum algorithms for grid optimization. The future directions include smart grid and microgrid development, integration of EV charging infrastructure, IoT and smart devices for advanced grid management, and exploring new quantum algorithms for enhanced grid resilience and efficiency. As shown in FIG.3C, quantum computing's ability lies in its capability to create multidimensional spaces, facilitating the emergence of patterns that link individual components of the power grid, such as nodes and edges. This capability allows for a more nuanced and profound understanding of the power grid, enabling more efficient and effective EMS analysis and decision-making. The adoption of quantum technology in EMS represents a significant leap forward in optimizing power grid operations and addressing the complex challenges inherent in modern energy management.

[0128] As in FIG.5, a process flow for the proposed Q-EMS is depicted, illustrating how Quantum-AI can enhance grid performance and address current challenges. This includes its application in data analysis, system optimization, security, and advanced integrations, ultimatelyWSGR Docket No.66368-702.601 providing solutions for an Advanced Energy Management System (AEMS). The Q-EMS leverages quantum and AI technologies to build an efficient energy management system.

[0129] FIG.5 delineates a comprehensive process flow for conducting proposed quantum-based analysis in EMS scenarios, encompassing various aspects of the disclosed methodologies. Generally, the process begins with the identification of the problem. This step involves determining the EMS analysis to be performed. In the power grid, for instance, the system model might be represented through a set of linear equations (SLEs). The process entails transforming the classical model, often represented by SLEs, into an optimization model suitable for EMS analysis. This approach redefines the power flow problem for each contingency scenario into an optimization problem. The goal of this optimization model is to maximize the total overload in the system, determined by the various binary (0 / 1) states of system components. This maximization helps in identifying all potential contingencies within the system. The method then progresses to executing the quantum simulation of the EMS analysis. This step may involve leveraging the principles of superposition inherent in quantum computing. Finally, the last step involves performing post-processing on the results obtained from the quantum simulation.

[0130] In one example, the first step is identifying the problem. Once the problem is identified, the system goals and constraints may then be defined. Next, using quantum problem modeling, the quantum algorithms necessary for analysis are developed. Once the algorithms are developed, either quantum optimization, quantum simulation, and quantum machine learning are then pursued. If quantum machine learning is the path chosen, the prediction may be enhanced and forecasted. Next, using quantum neural networks, adaptive demand response modeling, energy consumption pattern analysis, customized energy distribution strategies may be completed. Finally, future system expansions will be strategized.

[0131] As in FIG.6, a comparison table showcasing the comparison between classical and quantum-AI approaches for handling N-1, N-2, N-3, N-4, and N-5 contingency situations is illustrated. This table emphasizes the substantial efficiency gains achievable with a quantum-AI approach in resolving complex contingency problems by greatly reducing the solution times from minutes, hours, and days to seconds and minute. Contingencies refer to unexpected events or failures in the power system, such as equipment failures or extreme weather conditions, which can potentially disrupt the normal operation of the grid. In some examples, a hybrid Quantum-AI Security-Constrained Unit Commitment (SCUC) workflow may replace a classical optimization core with a quantum neural-network layer and, in so doing, may reduce end-to-end solution time while maintaining low optimality gaps. In some examples, an N-1 contingencyWSGR Docket No.66368-702.601 case that requires approximately sixty-three seconds on a classical solver may be completed in about forty seconds by the hybrid workflow. In some examples, an N-2 contingency case that requires approximately seventy-eight minutes classically may be solved in about fifty seconds with the hybrid workflow. In some examples, N-3 and N-4 contingency cases that require on the order of twenty-two hours and five days, respectively, on classical hardware may be completed in approximately one minute by the hybrid workflow. In some examples, an N-5 contingency case that fails to converge after a week on a classical solver may be solved in roughly one hundred and twenty seconds by the hybrid workflow. In some examples, all hybrid runs may maintain an optimality gap below about 0.5 percent. In some examples, data-pre-processing times may remain substantially identical for the classical and quantum paths, ranging from approximately twenty-one seconds to forty-three seconds—thereby indicating that the observed performance gains may be attributable to the substitution of the classical optimization step with the quantum neural-network-based step.

[0132] As in FIG.7, a block diagram of a computing and analytic system for Q-EMS is illustrated, incorporating quantum and AI capabilities, as described in various aspects of the disclosure. FIG.7 further illustrates technical specification of input data for quantum-AI EMS. The input data specifications for a quantum-based EMS model are engineered to optimize power grid operations. The quantum technology framework of FIG.7 is tailored to process multifaceted data, enabling enhanced grid security, efficiency, and economic viability. Further, detailed characteristics of power generators, encompassing operational parameters such as maximum and minimum output levels, ramp-up and ramp-down rates, start-up and shut-down times, and fuel cost algorithms may be determined. These parameters are important for modelling generator performance within quantum computational constraints. Forecasted power demand data, structured for the designated temporal horizon may also be produced by the flow of FIG.7. The resultant dataset provides granular load predictions at each node within the electrical grid, essential for optimizing generation and distribution schedules.

[0133] In some examples, a comprehensive dataset in the power grid infrastructure may include data from the grid components. In some examples, the grid components may be buses. In some examples, the grid components may be transmission lines. In some examples, the grid components may be transformers. In some examples, the grid components may include capacity ratings. In some examples, the grid components may include impedance values. In some examples, this data may be pivotal in simulating grid dynamics under various operational scenarios. In some examples, the dataset may encompass the spectrum of regulatory mandates pertinent to grid reliability. In some examples, the dataset may encompass the spectrum ofWSGR Docket No.66368-702.601 regulatory mandates pertinent to safety standards. In some examples, the data may provide information on potential grid failure scenarios. In some examples, the potential grid failure scenario may be generator outages. In some examples, the potential grid failure scenario may be transmission line disruptions. In some examples, the potential grid failure scenario may be aided by probabilistic assessments. In some examples, the potential grid failure scenario aided by probabilistic assessments may develop robust contingency plans within the quantum framework.

[0134] In some examples, the dataset may provide real-time and forecasted electricity market prices. In some examples, the market prices may be spot prices. In some examples, the market prices may be ancillary service costs. In some examples, the market prices may be imbalance penalty rates. In some examples, the market prices may be essential for economic dispatch. In some examples, the market prices may be essential for market interaction simulations. In some examples, archival datasets of past grid operations may serve as a foundational element for training AI algorithms. In some examples, the AI algorithms may be integrated within the quantum system. In some examples, the archival dataset may provide information to aid in pattern recognition. In some examples, the archival dataset may provide information to aid in predictive analytics in grid management.

[0135] In some examples, the quantum technology system may leverage these datasets to execute advanced algorithms. In some examples, the quantum technology system and datasets may aid in power generation scheduling. In some examples, the quantum technology system and datasets may aid in ensuring operation reliability while navigating complex constraints. In some examples, processing extensive data arrays may affect grid management under the EMS paradigm. In some examples, simulating multifarious scenarios may affect grid management under the EMS paradigm. In some examples, processing extensive data arrays and simulating multifarious scenarios may be simultaneous. In some examples, the grid management under the EMS paradigm may be augmented. In some examples, the augmentation may be in the operational efficiency. In some examples, the augmentation may be in the reliability. In some examples, the augmentation may be in the cost-effectiveness.

[0136] In some examples, the EMS problem for a 9-bus power system may present inoperable component scenarios. In some examples, one component may be inoperable. In some examples, two components may be inoperable. In some examples, three components are inoperable. In some examples, more than three components are inoperable. In some examples, component inoperability may be the result of component failure. In some examples, component inoperability may be the result of overload conditions. In some examples, the inoperable component scenarios may require analysis. In some examples, the analysis may be thorough. InWSGR Docket No.66368-702.601 some examples, the analysis may require advanced computational strategies. In some examples, classical binary computing may require 46 minutes to compute scenario likelihood. In some examples, classical binary computing may identify scenarios most likely to cause significant power system issues.

[0137] In some examples, implementing the quantum approach may solve exhibits. In some examples, the exhibit solutions may be efficient. In some examples, the quantum method may not experience an increase in computation time. In some examples, the quantum method may not experience an increase in computation time, even with a significant surge in the number of considered scenarios. In some examples, various complexity and scenario volume in multi- component outages may have a consistent computation time. In some examples, the consistent computation time in two-component outages may be around 50 seconds. In some examples, the consistent computation time may be around 55 seconds.

[0138] In some examples, by comparison, the quantum computational approach to an EMS scenario may demonstrate an efficient application. In some examples, the quantum computational approach to an EMS scenario may demonstrate a scalable application. In some examples, the quantum method may result in a consistent computation time, regardless of scenario complexity. In some examples, N-1 situations may result in a computation time of around 55 seconds. In some examples, N-2 situations may result in a computation time of around 55 seconds. In some examples, N-3 situations may result in a computation time of around 55 seconds.

[0139] In some examples, quantum-based energy management may involve proactively dispatching notifications. In some examples, the notifications may be warning messages. In some examples, the notifications may be audible alarms. In some examples, the notifications may be sent to power grid operators. In some examples, the notifications may be sent to customers. In some examples, the notifications may be designed to forewarn about potential overloads. In some examples, the notifications may be designed to forewarn about regulatory violations. In some examples, the regulatory violations may arise from future anticipated contingency events. In some examples, the notification may be previewing alerts. In some examples, the preview alerts may inform on potential impacts of future events on the power grid. In some examples, the notifications may preemptively address and mitigate potential issues.

[0140] In some examples, EMS may involve providing actionable recommendations for operators to preemptively address potential overloads and violations within the power system. In some examples, the recommendations can include a variety of strategic actions such as adjusting or augmenting power generation capacity, reducing load, modifying power system voltageWSGR Docket No.66368-702.601 levels, adding reactive volt-amps reactive (VAR) resources, or isolating problematic areas, among other tailored solutions.

[0141] In some examples, the EMS approach may include conducting a rapid assessment of the numerous possible equipment outages and identifying a subset of scenarios that present a high likelihood of causing significant adverse effects on the power system. In some examples, about 1 scenario may be identified. In some examples, about 2 scenarios may be identified. In some examples, about 10 scenarios may be identified. In some examples, about 20 scenarios may be identified. In some examples, about 50 scenarios may be identified. In some examples, about 100 scenarios may be identified. In some examples, the adverse effects may be scenarios where the power system is unable to meet customer demand. In some examples, the adverse effects may be widespread power outages affecting thousands of customers. In some examples, the adverse effects may be damage to additional power system components like transmission lines or transformers due to overloading. In some examples, the rapid assessment may enable operators to focus on critical scenarios and allowing the power system to remain robust and reliable.

[0142] In some examples, the quantum-based EMS may allow for the identification of the most important contingencies by conducting a thorough analysis of all or most potential contingencies. In some examples, the analysis may include the scenarios. In some examples, the scenarios may be severe. In some examples, the severe scenarios may be utilized to calculate the extent of branch or transmission line overloads. In some examples, the severe scenarios may be utilized to calculate voltage violations for individual components of the power system. In some examples, the analysis may secure operation of the power system in the event of component failures or scheduled equipment outages.

[0143] In some examples, quantum application may significantly enhance the management of linear equations. In some examples, quantum application may significantly enhance the management of differential equations. In some examples, quantum application may significantly enhance the management of optimizations. In some examples, quantum application may significantly enhance the management of machine learning challenges. In some examples, quantum computers can be employed to solve complex optimization problems related to EMS in a fraction of the time required by classical computers.

[0144] In some examples, quantum-inspired optimization methods may be integrated as subroutines in a suite of applications. In some examples, the application may be machine learning-based forecasting. In some examples, the application may be classification. In some examples, the application may be power system control. In some examples, the applications mayWSGR Docket No.66368-702.601 play a crucial role in EMS. In some examples, the role may be aiding in minimization of power grid management.

[0145] In some examples, scenarios may be beyond that which quantum-inspired algorithms may handle. In some examples, quantum-inspired algorithms may leverage concepts of quantum mechanics. In some examples, the concepts may be operable on classical binary computers. In some examples, the concepts may be operable on other computers. In some examples, the concepts may be operable on computers with quantum hardware. In some examples, quantum hardware may be used to simulate real-world challenges. In some examples, quantum hardware may be used to solve real-world challenges. In some examples, the real-world challenges may include problems in power grids. In some examples, the real-world challenges may include instances where quantum computing’s capabilities can be directly harnessed to address power generation nuances. In some examples, the real-world challenges may include instances where quantum computing’s capabilities can be directly harnessed to address power distribution nuances. In some examples, the real-world challenges may include instances where quantum computing’s capabilities can be directly harnessed to address power contingency planning nuances. In some examples, the real-world challenges may be addressed in an effective manner. In some examples, the real-world challenges may be addressed in an efficient manner.

[0146] Beyond quantum-inspired algorithms, which leverage concepts of quantum mechanics yet operate on classical computers, there are scenarios where quantum hardware itself may be used to simulate and solve real-world challenges. This includes complex problems in power grids, where quantum computing’s capabilities can be directly harnessed to address the nuances of power generation, distribution, and contingency planning in an effective and efficient manner.

[0147] Potential methods for energy system management are illustrated in FIG.8 and FIG.9. In one method for distributed energy system management, as illustrated in FIG.8, the method is a) receiving, at one or more non-classical-artificial intelligence (AI) processors, one or more datasets relating to the power grid; b) optimizing, at the one or more non-classical -AI processors, at least one module of an SCUC based at least in part on the one or more datasets relating to the power grid; c) forecasting predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and d) generating, at the one or more non-classical-AI processors, a report of the predictions. In another method for managing a distributed energy management system comprising both transmission and distribution, as illustrated in FIG.9, the method is a) receiving, at one or more artificial intelligence (AI) processor, one or more datasets relating to the power grid; b) optimizing, at the one or more AI processors, at least one module of an SCUC based at least in part on the one or more datasetsWSGR Docket No.66368-702.601 relating to the power grid; c) forecasting predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and d) generating, at the one or more AI processors, a report of the predictions. Modules

[0148] In some examples, Quantum-AI Energy Management Systems (Q-EMS) solution may implement modules. In some examples, the modules may be designed to address specific aspects of power grid management. In some examples, the modules may be designed to address specific aspects of power grid optimization. In some examples, one module may be sufficient to complete its configured goals. In some examples, more than one module may be necessary to complete its configured goals. An overall schematic of modules is illustrated in FIG.10.

[0149] In some examples, the module may be configured for data acquisition and monitoring. In some examples, the data acquisition and monitoring module may collect real-time data. In some examples, data collection may take place from one location. In some examples, data collection may take place from various locations within the power grid. In some examples, the data acquisition and monitoring module may monitor power grid status. In some examples, power grid status may indicate an outage. In some examples, power grid status may indicate optimal operations. In some examples, power grid status may indicate power grid response to specific demand. In some examples, the data acquisition and monitoring module may monitor power grid performance. In some examples, power grid performance may indicate power generation and utilization. In some examples, the data acquisition and monitoring module may provide comprehensive power grid information to operators. In some examples, the data acquisition and monitoring module may be placed at a substation. In some examples, the data acquisition and monitoring module may be placed on a power line. In some examples, the data acquisition and monitoring module may be placed at a point of generation. In some examples, a combination of data acquisition and monitoring modules may be placed throughout the power grid. In some examples, a plurality of modules may be placed at the same location.

[0150] In some examples, the module may be configured for state estimation. In some examples, the state estimation module may allow for data collection. In some examples, the state estimation module may determine the electrical network state. In some examples, the electrical network state may determine the current state of the power grid. In some examples, the electrical network state may determine the past state of the power grid. In some examples, the state estimation module may provide a consistent and reliable view of the power grid. In some examples, the state estimation module may allow for power grid optimization. In some examples, the state estimation module may allow for power grid process control.WSGR Docket No.66368-702.601

[0151] In some examples, the module may be configured for load forecasting. In some examples, the load forecasting module may predict future electricity demand. In some examples, future electricity demand may be based on historical data. In some examples, historical data may include regional customer load data. In some examples, historical data may include time series customer load profiles. In some examples, historical data may take seasonality into account. In some examples, future electricity demand may be based on real-time inputs. In some examples, the load forecasting module may consider price elasticity. In some examples, the load forecasting module may consider weather and demand response. In some examples, the load forecasting module may take into account renewable generation predictive modeling. In some examples, the load forecasting module may allow for sufficient power production without system overload.

[0152] In some examples, the module may be configured for optimal power flow. In some examples, the optimal power flow module may calculate a power grid operation method. In some examples, the optimal power flow module may calculate the most efficient power grid operation method. In some examples, the power grid operation method meets power demand. In some examples, the power grid operation method may exceed power demand. In some examples, the optimal power flow module may optimize power flow while minimizing costs and losses. In some examples, the optimal power flow module may consider constraints. In some examples, the optimal power flow module may consider operational constraints. In some examples, the constraints may include generators. In some examples, the constraints may include load. In some examples, the constraints may include transmission. In some examples, the constraints may be voltage magnitude constraints. In some examples, the constraints may be line flow thermal constraints.

[0153] In some examples, the module may be configured for demand response management. In some examples, the demand response management module may manage the demand for power. In some examples, the demand response management module may adjust the demand for power. In some examples, the demand for power may be the demand of consumers. In some examples, the demand for power may be balanced to maintain grid stability. In some examples, the demand for power may be balanced to maintain grid efficiency. In some examples, the demand response management module may incentivize reduced consumption. In some examples, incentives may correspond to peak hours. In some examples, incentives may correspond to off peak hours. In some examples, the demand response management module may temporarily shut down systems. In some examples, the demand response management module may permanently shut downWSGR Docket No.66368-702.601 systems. In some examples, the shutdown systems may be essential. In some examples, the shutdown systems may be non-essential.

[0154] In some examples, the module may be configured for contingency analysis. In some examples, the contingency analysis module may evaluate the grid ability to respond to potential failures. In some examples, the contingency analysis module may evaluate the grid ability to respond to potential failures. In some examples, the contingency analysis module may minimize load interruptions. In some examples, a contingency may be the failure or loss of a power grid component. In some examples, a power grid component may be a generator. In some examples, the power grid component may be a transformer. In some examples, the power grid component may be a transmission line. In some examples, the contingency may be a change in state of a power grid component. In some examples, the power grid may have no post-contingency problems. In some examples, the power grid may have severe post-contingency problems. In some examples, the power grid may have important post-contingency problems. In some examples, the power grid may withstand and recover from a single contingency, or failure. In some examples, the power grid may withstand and recover from a second contingencies. In some examples, the power grid may withstand and recover from more than a second contingencies, or failures.

[0155] In some examples, the module may be configured for renewable energy management. In some examples, the renewable energy management module may manage the variability of renewable energy sources. In some examples, the renewable energy management module may optimize renewable energy sources into the power grid. In some examples, the renewable energy source may be solar energy. In some examples, the renewable energy source may be wind energy. In some examples, the renewable energy source may be geothermal energy. In some examples, the renewable energy source may be hydropower. In some examples, the renewable energy source may be ocean energy. In some examples, the renewable energy source may be bioenergy.

[0156] In some examples, the module may be configured for unit commitment. In some examples, the unit commitment may determine which generation units to operate to meet demand. In some examples, the demand may be met efficiently. In some examples, the generation units may be more than 100 units. In some examples, the generation units may be more than 500 units. In some examples, the generation units may be more than 1000 units. In some examples, the generation units may be more than 2000 units. In some examples, the generation units may be more than 4000 units. In some examples, the generation units may be renewable energy source. In some examples, the generation units may be non-renewable energyWSGR Docket No.66368-702.601 sources. In some examples, the unit commitment module may consider various elements. In some examples, the element may be a time horizon. In some examples, the element may be the technical restraints corresponding with the generation unit.

[0157] In some examples, the module may be configured for economic dispatch (ED). In some examples, the ED module may optimize power generation. In some examples, the ED module may minimize costs. In some examples, the ED module may balance power generation optimization with cost minimization. In some examples, the ED module may minimize cost by determining power output of the power grid. In some examples, the ED module may minimize cost by determining power output of each generation unit. In some examples, the ED module may consider power supply flexibility to allow for sufficient output. In some examples, the ED module may maintain reserves to cover demand spikes. In some examples, the ED module may consider scheduling requirements, such as environmental restrictions, hydro conditions, or other limitations.

[0158] In some examples, the module may be configured for transmission-constrained unit commitment (TCUC). In some examples, the TCUC module may include UC transmission constraints. In some examples, transmission constraint may result from an inability to transmit power to the demand location. In some examples, transmission constraint may result from congestion at one or more network locations. In some examples, transmission constraint may result from power import problems. In some examples, power import problems may include underproduction from local grids. In some examples, power import problems may include limited circuit capacity of import circuits. In some examples, transmission constraint may result from power export problems. In some examples, power import problems may include overproduction from local grids in comparison to demand. In some examples, power import problems may include limited circuit capacity of export circuits. In some examples, the constraints may be thermal. In some examples, the constraints may be voltage. In some examples, the constraints may be stability.

[0159] In some examples, the module may be configured for security-constrained optimal power flow (SCOPF). In some examples, the SCOPF module may add additional security to the OPF. In some examples, the additional security may take into account contingencies in system security. In some examples, a contingency may be the failure or loss of a power grid component. In some examples, a power grid component may be a generator. In some examples, the power grid component may be a transformer. In some examples, the power grid component may be a transmission line. In some examples, the contingency may be a change in state of a power grid component. In some examples, the power grid may have no post-contingency problems. In someWSGR Docket No.66368-702.601 examples, the power grid may have severe post-contingency problems. In some examples, the power grid may have important post-contingency problems.

[0160] In some examples, the module may be configured for security-constrained unit commitment (SCUC). In some examples, the SCUC may add an additional security layer to the OPF. In some examples, the SCUC module may consider system security concerns. In some examples, the security concerns may consider contingency conditions resulting from power grid outages. In some examples, one power grid outage may result in contingency conditions. In some examples, two power grid outages may result in contingency conditions. In some examples, more than two power grid outages may result in contingency conditions.

[0161] In some examples, the modules may collectively provide for smooth EMS operation. In some examples, the modules may handle potential challenges. In some examples, the potential challenges may include routine monitoring. In some examples, the potential challenges may include complex decision-making. In some examples, the potential challenges may aid in maintaining the security and efficiency of the power grid. In some examples, quantum-AI may deal with the aforementioned computing and analytical challenges. In some examples, quantum optimization algorithms, may be coupled with analytical power of NN tackle challenges in EMS, enhancing energy management systems. Mathematical Modeling

[0162] In some examples, quantum-EMS may provide solutions for solving the problems in real time. In some examples, the mathematical formulation of the real-time model may include a wide range of various equations and constraints. The key components are listed below. Energy Management System (EMS):

[0163] In some cases, the energy management system is a classical energy management system. The following section illustrates components of an energy management system which may comprise a classical energy management system or a non-classical energy management system. In some examples, an objective of the Energy Management System (EMS), specifically within the Security-Constrained Unit Commitment (SCUC) model, may be to minimize the total operational cost of the power system over a predefined scheduling horizon. In some examples, this cost may include both fixed and variable components, such as startup costs incurred when bringing generating units online and generation costs based on fuel consumption. In some examples, the mathematical representation of the objective function may be expressed as:WSGR Docket No.66368-702.601 Where, ^^^^^^^^,^^^^represents the binary commitment status of generating unit ^^^^ at time ^^^^, where a value of 1 indicates that the unit is online and 0 otherwise. The variable ^^^^^^^^,^^^^denotes the power output of unit ^^^^ at time ^^^^. The term ^^^^^^^^^^^^,^^^^corresponds to the start-up cost incurred if the unit transitions from an off to an on state. The functionrepresents the generation cost, which is typically modeled as a convex or piecewise-linear function of the output power. The parameters ^^^^ and ^^^^ denote the total number of thermal generating units and the number of time periods in the planning horizon, respectively.

[0164] In some examples, the Security-Constrained Unit Commitment (SCUC) model may incorporate unit-level constraints to ensure secure and feasible operation of the power system. In some examples, these constraints may define the operational limits and requirements of individual generating units.

[0165] In some examples, a generation power limit constraint may be applied to ensure that, when a generating unit is committed, its output remains within the allowable operating range. In some examples, the generating unit may be required to produce not less than its minimum technical output and not more than its maximum rated capacity. In some examples, the combined value of active power output and spinning reserve may not exceed the unit’s maximum capacity. In some examples, this constraint may be expressed as:Wherein ^^^^^^^^,^^^^denotes the binary commitment status of generating unit ^^^^ at time ^^^^, such that a value of 1 indicates the unit is online and a value of 0 indicates it is offline; ^^^^^^^^,^^^^denotes the power output of unit ^^^^ at time ^^^^; denotes the spinning reserve allocated to unit ^^^^ at time ^^^^; ^^^^min^^^^denotes the minimum allowable generation capacity for unit ^^^^; and ^^^^^^m^^axdenotes the maximum allowable generation capacity for unit ^^^^. In some embodiments, these constraints collectively ensure that any committed unit operates within both its technical power limits and reserve capacity obligations.

[0166] Minimum On / Off Time Constraints: In some embodiments, generating units may be subject to minimum on-time and off-time requirements to prevent excessive cycling, mitigate mechanical wear, and maintain operational stability. These temporal constraints ensure that once a unit transitions to an on or off state, it remains in that state for at least a predefined minimum duration before a subsequent state change is permitted. The minimum on / off time constraints may be expressed mathematically as:WSGR Docket No.66368-702.601 Wherein ^^^^^^^o^nand ^^^^^^^o^ffrepresent the minimum required on and off times, respectively, and ^^^^^o^^^,n^^^^and ^^^^^o^^^,f^^^f^ track the duration of each state.

[0167] Ramping Rate Limits: In some embodiments, ramping constraints may be imposed on generating units to regulate the rate at which power output can be increased or decreased between consecutive time intervals. These constraints are intended to reflect the physical limitations of thermal and other generation technologies in adapting to rapid changes in system load or reserve requirements. The ramping rate limits may be expressed as:Wherein ^^^^^^^^,^^^^represent the power outputs of generating unit ^^^^ at time ^^^^ and ^^^^ − 1,respectively; ^^^^^^^^^^^^denotes the maximum ramp-up limit; and ^^^^^^^^^^^^denotes the maximum ramp- down limit for unit ^^^^. In some embodiments, these constraints ensure temporal continuity in dispatch and prevent abrupt changes that could compromise unit integrity or system stability.

[0168] Ancillary Services Provisioning: In some embodiments, generating units may be required to provide ancillary services in addition to meeting active power dispatch obligations. These services, including spinning reserves and operational reserves, are critical to grid reliability and frequency regulation. The availability of ancillary services is subject to the unit’s real-time operational status and technical characteristics.

[0169] The ancillary services constraints may be represented as:Wherein ^^^^spindenoop^^^^,^^^^tes the spinning reserve provided by unit ^^^^ at time ^^^^; ^^^^^^^^,^^^^denotes the operational reserve; ^^^^^^^^,^^^^denotes the binary on / off commitment status; ^^^^^^^^^^^^^^^^denotes the maximum sustained ramping capability of unit ^^^^; and ^^^^^^^^^^^^^^^^denotes the unit’s quick-start capacity. In some embodiments, the formulation ensures that spinning reserves are contributed by online units, while offline units may contribute quick-start operational reserves if they are capable of rapid activation.

[0170] System Constraints: In some embodiments, system-level constraints may be incorporated into the SCUC formulation to ensure that the aggregate system operates securely and reliably under both normal and contingency scenarios. These constraints encompass power balance requirements, reserve adequacy, and transmission security criteria.

[0171] Power Balance Constraint: In some embodiments, a power balance constraint may be enforced at each time interval to ensure that the total committed generation output across all units satisfies the expected system demand and compensates for energy losses occurring during transmission. This constraint may be mathematically defined as:WSGR Docket No.66368-702.601 ^^^^ � ^^^^ ⋅ ^^^^^^^^,^^^^ = ^^ loss^^^^,^^^^ ^^^^^^ + ^^^^^^^^^^^^=1 Wherein ^^^^^^^^,^^^^is the commitment status of unit ^^^^ at time ^^^^; ^^^^^^^^,^^^^is the corresponding power output; ^^^^^^^^denotes the forecasted system load at time ^^^^; anddenotes the estimated transmission losses. This equation ensures energy balance across the system in real time.

[0172] Reserve Requirements: In some embodiments, system-wide reserve requirements may be applied to maintain operational readiness and ensure system recovery during component failures or load fluctuations. These constraints ensure that sufficient spinning and operational reserves are procured across the committed fleet of units. The constraints may be represented as:Wherein ^^^^^^spin^^,^^^^and ^^^^^op^^^,^^^^denote the spinning and operational reserves, respectively, contributed by unit ^^^^ at time ^^^^; ^^^^spin op^^^^and ^^^^^^^^represent the minimum system-level reserve requirements. These constraints ensure that reserve procurement remains aligned with reliability standards and contingency planning objectives.

[0173] Network Security Constraints (N-M Contingency Analysis): In some embodiments, to ensure secure and reliable operation of the power system under contingency conditions, transmission network security constraints may be imposed to limit the flow of power through transmission lines. These constraints are designed to ensure that, under the failure of up to ^^^^ components from an ^^^^-component system (i.e., N–M contingencies), the post-contingency power flow remains within thermal and stability limits. The network security constraint may be mathematically expressed as:Wherein denotes the maximum allowable power flow through transmission line ^^^^ contingency scenario ^^^^; ^^^^^^^^,^^^^represents the power output of unit ^^^^ at time ^^^^; ^^^^^^^^^^^^^^^^^^^^,^^^^is the power transfer distribution factor that quantifies the sensitivity of line ^^^^ to changes in power injection at node ^^^^; and ^^^^^^^^,^^^^denotes the outage sensitivity factor associated with line ^^^^ under contingency ^^^^. In some embodiments, this constraint ensures that the system remains within secure operational boundaries, even when a line or component fails, by preemptively evaluating and bounding post- contingency power flows using distribution factor modeling.WSGR Docket No.66368-702.601 Extended SCUC Model:

[0174] In some examples, the Security-Constrained Unit Commitment (SCUC) model may be extended by incorporating demand-side flexibility and energy storage technologies. In some examples, these additions may improve operational flexibility and overall cost-effectiveness of the power system. Interruptible loads (IL) may be incorporated into the objective function. In some examples, the modified objective function may be represented as:Wherein ^^^^^^^^,^^^^denotes the adjusted demand of interruptible load unit ^^^^ at time ^^^^, ^^^^^^^^,^^^^denotes its participation status,represents the cost or compensation associated with curtailment.

[0175] In some examples, energy storage systems (ESS) may be incorporated into the SCUC model. The objective function may be expressed as:Wherein ^^^^chandrepresent the charging and discharging power of storage unit ^^^^, and ^^^^chand ^^^^disare the associated cost coefficients. Non-Classical Architecture for SCUC

[0176] In some cases, the architecture of the preceding sections may be extended to a non- classical architecture. In some cases, the architecture is a quantum architecture. In some cases, the architecture is a non-classical-artificial intelligence architecture. In some examples, a Quantum Neural Network (QNN) may be defined for solving Security-Constrained Unit Commitment (SCUC) problems. In some examples, the QNN may be constructed as a layered quantum circuit composed of quantum gates acting on encoded SCUC input states.

[0177] In some examples, the QNN may comprise ^^^^ hidden layers operating on an initial quantum state ℎ^^^^^^^^^^^^^^^^^^^^^^^^, yielding a final mixed state ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^that encodes the SCUC decision outputs. The transformation may be defined as: †Wherein ^^^^ = ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ⋯^^^^1 is the composite unitary operator with non-commuting gatesacross layers. In some examples, ℎ^^^^^^^^^^^^^^^^^^^^^^^^may encode generator parameters, load forecasts, reserve requirements, and network topology using amplitude or angle embedding schemes. In someWSGR Docket No.66368-702.601 examples, the final output state ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^may be measured to extract estimates of key SCUC variables, including unit commitment ^̂^^^^^^^,^^^^, dispatch power ^̂^^^^^^^,^^^^, and ancillary services ^̂^^^^^^^,^^^^.

[0178] In some examples, the propagation of information through the QNN may be represented as a series of completely positive, trace-preserving quantum channels:Each channel ℰ^^^^ acts between layer ^^^^ − 1 and ^^^^, defined as:Wherein each channel ℰ^^^^may be defined as: the modeling of temporal dependencies (such as ramping or startup constraints) and preserve causal structure across layers.

[0179] Fidelity-Based Cost Function: In some examples, a cost function based on quantum fidelity may be employed to evaluate how accurately the QNN learns SCUC mappings. In some examples, the cost function may be defined as:Wherein ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^is the desired target output state and ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^is the predicted QNN output state for training example ^^^^.

[0180] Quantum Training Procedure: In some examples, a hybrid training approach may be implemented. In some examples, the training procedure may consist of the following steps: Initialization: Randomly initialize each unitary gate ^^^^^^^^^^^^across all layers. Feedforward: Compute ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^for each input state via sequential application of unitary layers. Backpropagation: Define and ^^^^^^^^via forward and reverse propagation. Gradient Evaluation: gradient matrices ^^^^^^^^^^^^and update gates using: ^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^ → ^^^^ ^^^^^^^^^^^^^^^^Wherein ^^^^^^^^^^^^represents the fidelity gradient and ^^^^ is a learning rate. This method supports localized updates requiring only two adjacent layers.

[0181] Analytical Gradient and Cost Optimization: In some examples, the gradient of the cost function may be computed as:WSGR Docket No.66368-702.601

[0182] In some examples, this formulation may enable scalable layer-wise training without requiring full-circuit simulation and may be suitable for implementation on near-term quantum devices.

[0183] Simulation and Robustness: In some examples, QNNs may be trained using a limited dataset of quantum state pairs:wherein ^^^^ is a unitary SCUC operator. In some examples, the QNN may exhibit generalization capabilities, achieving high fidelity even with fewer training samples than the dimension of the Hilbert space.

[0184] In some examples, robustness tests may include the introduction of noise or corrupted data into training samples. In some examples, the QNN may maintain strong performance on clean validation sets, indicating resistance to noise and overfitting. In some examples, the proposed architecture may be well-suited for quantum SCUC optimization, offering advantages in handling stochastic, temporal, and topological constraints within power system operations.

[0185] Universality of QNN Architectures: In some examples, the universality of Quantum Neural Networks (QNNs) may be demonstrated by constructing a specific architecture capable of performing universal quantum computation.

[0186] In some examples, each quantum neuron may be indexed by a pair (^^^^, ^^^^), where ^^^^^^^^denotes the number of neurons in the ^^^^^^^^^^^^^^^^^^^^ℎlayer. In some examples, the connectivity structuremay restrict neuron (^^^^, ^^^^) to connect only to neurons (^^^^ − 1, ^^^^) and (^^^^ + 1, ^^^^ + (−1)^^^^ mod ^^^^^^^^),ensuring sparse and systematic inter-layer communication.

[0187] In some examples, each quantum neuron may consist of two qubits, labeled + and −, both initialized in the |00^ state. In some examples, the quantum state at layer ^^^^ may be given by:Wherein ^^^^^^^^ = ∏1^^^^=^^^^^^^^   ^^^^^^^^^^^^is the ordered product of gates acting on each neuron in layer ^^^^. In some examples, this architecture may provide sufficient expressivity and practical tractability for simulating general quantum computation, thereby confirming its universality. Algorithm for Quantum Training of the Neural Network

[0188] In some examples, the QNN-based SCUC model may be implemented on a universal quantum computing platform that supports partial trace operations, initialization of qubits in theWSGR Docket No.66368-702.601|0^state, application of universal quantum gates (e.g., CNOT, T, Hadamard), and projective measurement in the computational basis.

[0189] In some examples, the QNN training algorithm may consist of two subroutines: Subroutine 1(Fidelity estimation using the SWAP test.); Subroutine 2 (Quantum channel application and gradient computation.)

[0190] In some examples, fidelity may be estimated between a pure quantum state|^^^^^and a mixed quantum state ^^^^. In some examples, fidelity may be defined as, subroutine 1(Estimating Fidelity Using the SWAP Test) ^^^^(|^^^^^,^^^^) = ^^^^^|^^^^|^^^^^

[0191] In some examples, the SWAP test may be used to compute this fidelity using interference patterns on a control qubit. In some examples, the setup may include: a register of ^^^^ qubits in state |^^^^^; a second register of ^^^^ qubits in state ^^^^; and / or one ancillary control qubit initialized to |0^.

[0192] Step a: Initialization The initial joint state may be:

[0193] Step b: Apply Hadamard Gate: The Hadamard gate is applied to the control qubit, yielding:

[0194] Step c: Controlled-SWAP Operation: A controlled-SWAP (CSWAP) gate is applied: CSWAP ≔ |0^^0| ⊗ ^^^^ + |1^^1| ⊗ SWAPThis gate conditionally swaps the two registers, encoding overlap information.

[0195] Step d: Second Hadamard Gate: A second Hadamard gate is applied to the control qubit, creating interference that reflects the similarity between|^^^^^and ^^^^.

[0196] Step e: Measurement: The control qubit is measured. The probability of observing|0^is:

[0197] Statistical Estimation and Resource Requirements: In some examples, repeated measurements are performed ^^^^ times to estimate probabilities. The outcomes are:WSGR Docket No.66368-702.601 Wherein the statistical fluctuation ^^^^^^^^^^^^may be approximated as:

[0198] In some examples, resource requirements for Subroutine 1 include: 2^^^^ Hadamard gates. ^^^^ copies of the pure state |^^^^^. ^^^^ copies of the mixed state ^^^^. ^^^^ CSWAP operations. ^^^^ working qubits per register. For larger input dimensions, a BIGSWAP operation may be employed, requiring either ^^^^2SWAP gates for linear qubit connectivity or ^^^^ gates for fully connected qubit topologies. Algorithm for the Quantum Cost Function:

[0199] In some examples, the average fidelity cost function ^^^^ may be computed by integrating Subroutine 1 (fidelity estimation using the SWAP test) and Subroutine 2 (forward propagation through the QNN) into a unified quantum learning algorithm. In some examples, this integration allows the system to evaluate how closely the output quantum state ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^matches the target state |^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^across a defined training dataset.

[0200] The procedure may be composed of the following steps:

[0201] Step 1: Prepare two identical copies of the quantum input state |^^^^^^^^^, randomly selected from the training dataset of size ^^^^. One copy is used for forward propagation; the other is retained for fidelity testing.

[0202] Step 2: Apply Subroutine 2 to the first copy, performing the QNN transformation. The resulting quantum state ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^is generated via the unitary evolution through the QNN layers.

[0203] Step 3: Apply Subroutine 1 to estimate the fidelity between ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^and the secondcopy of |^^^^^^^^^, resulting in the fidelity score ^^^^^^^^^|ℎ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^|^^^^^^^^^.

[0204] Step 4: Repeat Steps 1–3 for the same ^^^^ a total of ^^^^ times to improve the statistical accuracy of the fidelity estimate.

[0205] Step 5: Iterate the full process for each ^^^^ = 1, … ,^^^^ in the training set. The final averagefidelity is calculated as:

[0206] Total Resource Requirements: In some examples, the quantum resource overhead for estimating the cost function across the training set may include: Quantum gates: A total of^^^^ × ^^^^ × (∑^^^^^^^^=1   ^^^^^^^^ + 3) operations, where ^^^^^^^^ denotes the number of gates in layer ^^^^, and theadditional 3 gates correspond to the Hadamard, CSWAP, and measurement operations perWSGR Docket No.66368-702.601SWAP test. Qubits: A maximum of 2^^^^ + ^^^^ + 1 qubits, where ^^^^ = max{^^^^1,^^^^2, … ,^^^^^^^^^^^^^^^^^^^^^^^^^^^^}is the width of the QNN and ^^^^ is the number of data register qubits.

[0207] Subroutine 2: Implementing the Channel ^^^^^^^^: In some examples, forward propagation through the QNN may be accomplished by applying a quantum channel ℰ^^^^at each layer ^^^^, whichmaps the output state from layer ^^^^ − 1 to the input of layer ^^^^. This process may involveappending ancilla qubits initialized to |0^ and executing a defined sequence of layer-specific unitary gates.

[0208] Subroutine 2: Implementing the Channel ^^^^^^^^: In some examples, forward propagation through the QNN may be accomplished by applying a quantum channel ℰ^^^^at each layer ^^^^, whichmaps the output state from layer ^^^^ − 1 to the input of layer ^^^^. This process may involveappending ancilla qubits initialized to |0^and executing a defined sequence of layer-specific unitary gates.

[0209] The implementation of ℰ^^^^may proceed in the following steps:

[0210] Step 2a: Initialization: In some examples, ^^^^^^^^ancillary qubits, each initialized to the state |0^, may be tensored with the input state ℎ^^^^(^^^^−1). The resulting composite state is: ^^^^(^^^^−1) ⊗ (|0^^0|)⊗^^^^^^^^This step may requirequbits in total.

[0211] Step 2b: Applying Gates: In some examples, the composite unitary operator ^^^^^^^^=∏^^^^^^^^   ^^^^ ^^^^ may be applied across the regis ^^^^^^^^=1 ^^^^ ter. Each gate ^^^^^^^^ may act on a subset of the^^^^^^^^qubits. The state evolves as: ^̃^^^(^^^^−1,^^^^) = ^^^^^^^^(^^^^(^^^^−1) ⊗ |0⋯ 0^^0⋯ 0|)(^^^^^^^^)†This operation introduces entanglement between the input and ancillary qubits, encoding inter- temporal dependencies relevant to SCUC.

[0212] Step 2c: Partial Trace: In some examples, the output state ℎ^^^^(^^^^)may be obtained bytracing out the qubits from layer ^^^^ − 1:This step may reduce the active quantum memory to ^^^^^^^^qubits without additional gate operations.

[0213] Total Resource Requirements: To propagate quantum data across all ^^^^ layers, Subroutine 2 may be applied iteratively. The total quantum resources may include: Qubits: max{^^^^1 + ^^^^2,^^^^2 + ^^^^3, … ,^^^^^^^^ + ^^^^out}WSGR Docket No.66368-702.601 Quantum Gates: ∑^^^^^^^^=1   ^^^^^^^^, where ^^^^^^^^ is the number of gates in layer ^^^^

[0214] Algorithm for Derivative: Gradient Computation: In some examples, to optimize the cost function ^^^^, it may be necessary to compute the gradient^^^^where ^^^^ is a tunable parameter of a quantum gate. Each quantum gate ^^^^ may be parameterized as:Wherein ^^^^^^^^are Pauli operators {^^^^,^^^^,^^^^,^^^^}, and ^^^^^^^^^^^^^^^^are real-valued coefficients. A single three-qubit gate may contain 43 = 64 parameters, forming:For networks with multiple gates, the parameter vector expands accordingly, such as:Resulting in 2 × 64 = 128 parameters for two 3-qubit gates.

[0215] In some examples, the gradient^^^^^^^^may be approximated using finite ^^^^^^^^^^^^(^^^^ + ^^^^^^^^) − ^^^^(^^^^)^^^^^^≈ ^^^^^^^^^^ Wherein ^^^^^^^^is a perturbation vector with zero entries except for the ^^^^-th component. This is repeated independently for each parameter.

[0216] In practice, the cost function ^^^^(^^^^ + ^^^^^^^^) may be evaluated using Subroutines 1 and 2.The update rule for QNN training is:Wherein ^^^^^^^^^^^^(^^^^)is the unitary at training step ^^^^, ^^^^ is the learning rate, and ^^^^^^^^^^^^(^^^^)is a Hermitianoperator derived from fidelity gradients.

[0217] Gradient Matrix Computation and Parameter Update Rule: In some embodiments, the Hermitian generator ^^^^^^^^^^^^(^^^^)is configured to define the direction of maximum increase for the fidelity-based cost function ^^^^, such that each training iteration results in a closer alignment between the output of the QNN and the reference output state. To compute ^^^^^^^^^^^^(^^^^), a forward and backward propagation is performed. In the forward pass, the quantum stateis through the sequential application of quantum channels as expressed by:WSGR Docket No.66368-702.601

[0218] In the backward pass, the adjoint of each layer’s quantum channel is applied to the desired output state to produce the backward-propagated stateThe gradient matrix ^^^^^^^^^^^^(^^^^)is computed using these two states and represents the commutator between the actual and ideal outputs at layer ^^^^, capturing the local derivative of the cost with respect to unitary parameters.

[0219] In some embodiments, once the partial derivatives^^^^^^^^for each trainable parameter computed, the full gradient vector is expressed as:Wherein ^^^^ = ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^64. The parameters are then updated using a gradientascent update rule:

[0220] Here, ^^^^ represents a tuning parameter that governs the step size of the update. This rule guarantees that the cost function increases monotonically:Accordingly, the training process converges toward the optimal quantum configuration corresponding to the SCUC output. Quantum Algorithm Implementation Summary:

[0221] In some embodiments, the implementation of the Quantum Neural Network (QNN)- based Security-Constrained Unit Commitment (SCUC) model may assume access to a universal quantum computing environment comprising the following primitives: (i) partial trace operations for the purpose of reducing quantum subsystems between computational layers; (ii) qubit initialization in the computational basis state |0^; (iii) application of elementary quantum logic gates, including but not limited to Hadamard, T, and controlled-NOT (CNOT) gates; and (iv) measurement of qubits in the computational basis.WSGR Docket No.66368-702.601

[0222] In some embodiments, the QNN training process may incorporate two distinct quantum subroutines. The first subroutine may be configured to estimate fidelity using the controlled- SWAP (CSWAP) test. The second subroutine may be configured to propagate quantum data through the QNN layers to evaluate the fidelity-based cost function and perform parameter optimization. In some embodiments, the quantum circuit architecture used to execute the foregoing subroutines may include: (i) a quantum register of ^^^^ qubits prepared in the pure quantum state |^^^^^; (ii) a second quantum register of ^^^^ qubits representing the QNN output as a mixed quantum state ^^^^; and (iii) a single ancillary control qubit for execution of the CSWAP gate.

[0223] In some embodiments, implementation of the described architecture resulted in cost estimates that were closely aligned with theoretical benchmarks. In further embodiments, the QNN-based model demonstrated successful generalization to power system configurations that were not included in the training data. Additionally, the QNN maintained predictive accuracy and optimization stability under conditions of noisy or partially corrupted training datasets. No evidence of barren plateau effects, commonly encountered in parameterized quantum circuits, was observed, attributable in part to the structured use of ancillary qubits at each layer of the QNN architecture.

[0224] Accordingly, in some embodiments, the proposed QNN-based SCUC model may be implemented on Noisy Intermediate-Scale Quantum (NISQ) hardware platforms. In such embodiments, the model may offer a computationally efficient and memory-optimized approach to real-time energy management and grid control operations under complex and uncertain operating conditions.

[0225] The following papers discuss the use of quantum computing in electrical grids and are incorporated herein by reference and for all practical purposes: (1) Rozhin Eskandarpour, Kumar Jang Bahadur Ghosh, Amin Khodaei, Aleksi Paaso, and Liuxi Zhang. Quantum-Enhanced Grid of the Future: A Primer.” IEEE Access 8 (2020); (2) Rozhin Eskandarpour, Amin Khodaei, “Leveraging Accuracy-Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions,” IEEE Transactions on Power Systems, 33, no.1 (2017): 1139-1141; (3) Rozhin Eskandarpour, et al. "Experimental Quantum Computing to Solve Network DC Power Flow Problem." arXiv preprint arXiv:2106.12032 (2021); (4) Rozhin Eskandarpour, Pranav Gokhale, Amin Khodaei, Fred T. Chong, Aleksi Paaso and S. Bahramirad, "Quantum Computing for Enhancing Grid Security," in IEEE Transactions onWSGR Docket No.66368-702.601 Power Systems, vol.35, no.5, pp.4135-4137, Sept.2020, doi:10.1109 / TPWRS.2020.3004073; and (5) Ullah, M.H., Rozhin Eskandarpour, Zheng, H., Khodaei, A.: Quantum computing for smart grid applications. IET Gener. Transm. Distrib.00, 1–19 (2022), https: / / doi.org / 10.1049 / gtd2.12602.

[0226] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

WSGR Docket No.66368-702.601 CLAIMS WHAT IS CLAIMED IS:

1. A method for managing a distributed energy management system, the method comprising: (a) receiving, at one or more non-classical-artificial intelligence (AI) processors, one or more datasets relating to the distributed energy management system; (b) optimizing, at the one or more non-classical-AI processors, at least one module of a security constrained unit commitment (SCUC) problem based at least in part on the one or more datasets relating to the distributed energy management system; (c) forecasting predictions for the distributed energy management system based at least in part on the optimization of the at least one module of the SCUC; and (d) generating, at the one or more non-classical-AI processors, a report of the predictions.

2. The method of claim 1, wherein the distributed energy management system comprises both transmission and distribution.

3. The method of claim 1, wherein the distributed management system comprises a power grid.

4. The method of claim 1, wherein the report comprises an indication to perform one or more recovery operations.

5. The method of claim 1, wherein the non-classical-AI processor is a quantum-AI processor.

6. The method of claim 1, wherein the at least one module comprises a mixed- integer linear optimization problem.

7. The method of claim 1, wherein the one or more non-classical-AI processors is configured to implement a support vector machine.

8. The method of claim 1, wherein the one or more datasets relating to the power grid comprises one or more of: a dataset on power generator specifications, a dataset on system load forecast, a dataset on topological data, a dataset of regulatory compliance, a dataset on contingency analysis, a dataset on availability of electricity, or a dataset of historical operational data.

9. The method of claim 1, wherein (b) comprises executing at least one quantum algorithm and at least one machine learning algorithm.WSGR Docket No.66368-702.601 10. The method of claim 9, wherein the at least one quantum algorithm comprises a Quantum Approximate Optimization Algorithm (QAOA), a Variational Quantum Eigensolver (VQE), or both.

11. The method of claim 9, wherein the at least one machine learning algorithm comprises a neural network, a support vector machine, or reinforcement learning.

12. The method of claim 1, wherein the at least one module comprises one or more of: a SCUC, a power flow module, a unit commitment module, an economic dispatch module, an optimal power flow module, a contingency analysis module, a transmission-constrained unit commitment module, a security constrained optimal power flow module, or a security constrained unit commitment module.

13. The method of claim 12, wherein at least two of the SCUC, the power flow module, the unit commitment module, the economic dispatch module, the optimal power flow module, the contingency analysis module, the transmission-constrained unit commitment module, the security constrained optimal power flow module, and the security constrained unit commitment module are solved together.

14. The method of claim 13, wherein each of the SCUC, the power flow module, the unit commitment module, the economic dispatch module, the optimal power flow module, the contingency analysis module, the transmission-constrained unit commitment module, the security constrained optimal power flow module, and the security constrained unit commitment module are solved together.

15. The method of claim 1, wherein the report comprises a list or description of contingency events, equipment or component damage, power system failures, large-scale power outages, or any combination thereof.

16. The method of claim 1, wherein the report details the number of overloads observed.

17. The method of claim 1, wherein the report provides a power grid operator with an alert of future contingency events.

18. The method of claim 17, wherein the future contingency events comprise branch overloads and voltage violations.

19. The method of claim 1, wherein the processor assesses potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not within the subset.

20. The method of claim 19, wherein the assessment comprises providing a recommendation to a power grid operator to avoid the subset of scenarios.WSGR Docket No.66368-702.601 21. The method of claim 1, wherein the prediction comprises demand, generation capacity, and potential system disturbances.

22. A system for managing a power grid, the system comprising: a computing system communicatively coupled to one or more non-classical - artificial intelligence (AI) processors, and wherein the one or more non-classical-artificial intelligence (AI) processors is configured to at least: (a) receive one or more datasets relating to the power grid; (b) optimize at least one module of a SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecast predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generate a report of the predictions.

23. The system of claim 22, wherein the non-classical-AI processor comprises at least one machine learning algorithm and at least one quantum algorithm.

24. The system of claim 23, wherein the at least one machine learning algorithm is a neural network, a support vector machine, or a reinforcement learning algorithm.

25. The system of claim 23, wherein the at least one quantum algorithm comprises a Quantum Approximate Optimization Algorithm (QAOA) and / or a Variational Quantum Eigensolver (VQE).

26. The system of claim 22, wherein the one or more datasets comprise one or more of: a dataset on power generator specifications, a dataset on system load forecast, a dataset on topological data, a dataset of regulatory compliance, a dataset on contingency analysis, a dataset on market price of electricity, or a dataset of historical operational data.

27. The system of claim 22, wherein the at least one module comprises one or more of: a SCUC, a power flow module, a unit commitment module, an economic dispatch module, an optimal power flow module, a contingency analysis module, a transmission-constrained unit commitment module, a security constrained optimal power flow module, or a security constrained unit commitment module.

28. The system of claim 22, wherein the prediction comprises demand, generation capacity, and potential system disturbances.

29. The system of claim 22, wherein the report comprises a list or description of contingency events, equipment or component damage, power system failures, or large-scale power outages.WSGR Docket No.66368-702.601 30. The system of claim 22, wherein the report is configured to provide a power grid operator with an alert of future contingency events.

31. The system of claim 22, wherein the quantum-AI processor is configured to assess potential equipment outages to identify a subset of scenarios with a higher likelihood of causing damage to a power system than that of scenarios not within the subset.

32. The system of claim 31, wherein the assessment comprises a recommendation to a power grid operator to avoid the subset of scenarios.

33. The system of claim 22, wherein the computing system comprises at least one peripheral device.

34. The system of claim 33, wherein the at least one peripheral device is coupled to the computing system via at least one interface.

35. The system of claim 33, wherein the at least one peripheral device is at least one output device, at least one input device, at least one storage device, or a combination thereof.

36. The system of claim 35, wherein the at least one output device is a display.

37. The system of claim 35, wherein the at least one input device is a keypad, mouse, touch screen, stylus, or a combination thereof.

38. The system of claim 35, wherein the at least one storage device comprises additional instructions for the computing system to execute.

39. The system of any of the preceding claims, wherein the computing system comprises a bus system.

40. A method for managing a distributed energy management system, the method comprising: (a) receiving, at one or more artificial intelligence (AI) processor, one or more datasets relating to the power grid, wherein the one or more data sets comprises both transmission and distribution data; (b) optimizing, at the one or more AI processors, at least one module of an SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecasting predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generating, at the one or more AI processors, a report of the predictions.

41. The method of claim 40, wherein the predictions comprise both transmission and distribution information.

42. The method of claim 41, wherein the report comprises an indication to perform one or more recovery operations.WSGR Docket No.66368-702.601 43. A system for managing a power grid, the system comprising: a computing system communicatively coupled to one or more artificial intelligence (AI) processors, and wherein the one or more artificial intelligence (AI) processors is configured to at least: (a) receive one or more datasets relating to the power grid, wherein the one or more data sets comprises both transmission and distribution data; (b) optimize at least one module of a SCUC based at least in part on the one or more datasets relating to the power grid; (c) forecast predictions for the power grid based at least in part on the optimization of the at least one module of the SCUC; and (d) generate a report of the predictions.

44. The system of claim 43, wherein the predictions comprise both transmission and distribution information.

45. The system of claim 44, wherein the report comprises an indication to perform one or more recovery operations.

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