Systems and methods for peak load power management for electric vehicle fleet charging and / or microgrid operation

Abstract

Utilities typically utilize techno-economic means to set peak power demand at power infrastructure sites such as power depots and microgrids. However, conventional means tend to produce suboptimal values ​​for peak power demand or are computationally too expensive to perform in real time or in a scalable manner. Accordingly, disclosed embodiments utilize a sliding time window to continuously or periodically determine peak power demand for past, present, and future portions of a current time period. These embodiments can determine optimal peak power demand for a current time period while being computationally feasible for real-time performance and scalable with the complexity of the optimization.

Description

[Technical Field]

[0001] background FIELD OF THE INVENTION FIELD OF THE INVENTION The embodiments described herein relate generally to energy management, and more particularly to managing peak power demand for an electrical infrastructure site such as a power depot or microgrid. [Background technology]

[0002] 2. Description of Related Art The power flow patterns that a power grid must withstand depend on the peak power demands of grid participants. Therefore, power consumption by large loads can affect the reliable operation of the distribution grid. For example, electric vehicle (EV) depots can have detrimental effects on the power grid when their power consumption is uncontrolled, as EV loads are significantly higher than typical residential or commercial loads and can introduce uncertainty regarding future peak power demands.

[0003] Therefore, controlling the peak power demand of an electric power infrastructure site to a predictable and consistent level can support the technical operation of the electric power infrastructure site and the grid. On the one hand, electric power infrastructure sites are encouraged to limit their peak power demand. On the other hand, the power capacity for charging devices at electric power infrastructure sites, such as power depots (e.g., EV depots), is typically oversized to handle seasonal fluctuations, maintenance outages, and unplanned events.

[0004] Large commercial or industrial peak power demands may be limited by hardware or contract. However, overall socio-technical effectiveness is improved when utility agreements encourage grid participants to regulate their own peak power. Therefore, it is generally the responsibility of the operator of the power infrastructure site to set the optimal peak power demand at the point of common coupling to the power grid, subject to future uncertainty.

[0005] Utilities typically use techno-economic instruments to set peak power demand, which may include financial mechanisms (e.g., a ratio of cost to peak kilowatt) applied over a period of time (e.g., a monthly billing period) that reduce power demand at the point of common coupling between the power infrastructure site and the power grid.

[0006] If the peak power demand of a power infrastructure site impacts the operation of the power grid during a billing period, the utility may consequently penalize the power infrastructure site over the billing period. The inventors recognized that the retroactive aspect of this result means that the relative benefit versus penalty of peak power demand changes over the billing period. Near the beginning of the billing period, there is a long, uncertain future where it is difficult to set the optimal peak power demand for the power infrastructure site. Underestimating the peak power demand for a billing period risks missing potential operational benefits such as faster charging, on-time performance, and more reserve capacity. Overestimating the peak power demand for a billing period may unnecessarily stress the power grid, resulting in a disadvantage to the utility.

[0007] Peak power demand control has traditionally been managed using estimation (e.g., based on past behavior), heuristics (e.g., based on expected utilization), ratcheting (e.g., using past peak power demand and expanding as needed), or optimization. Some non-deterministic but suboptimal solutions for controlling peak power demand include always using maximum power of equipment as soon as possible, allowing manual setting (e.g., override) of power limits based on human estimation, using heuristic estimation, using historically observed peak power demand potentially corrected for influencing factors (e.g., using linear regression), and using on-site energy storage to mitigate potential overconsumption before increasing peak power demand for a billing period.

[0008] While optimization models are a good means for determining optimal peak power demand over a billing period, the size of the optimization model can be too large to solve in a timely manner. Optimization models may also require data that is not always available, such as operating schedules (e.g., for EV fleets) and price forecasts. Furthermore, in practice, disturbances and other unexpected events occur, requiring the optimization model to be solved again. This can cause delays in scheduled operations.

[0009] Conventional methods use fixed peak power demand, do not address uncertainty, and / or are not scalable. Examples of such methods include U.S. Patent Nos. 10,647,209, 10,320,203, 11,262,718, 9,840,156, and 8,762,189. The present disclosure addresses one or more problems in these methods discovered by the inventors. Summary of the Invention [Means for solving the problem]

[0010] overview Accordingly, disclosed are systems, methods, and non-transitory computer-readable media for managing peak power demand at an electric power infrastructure site, such as a power depot (e.g., an EV depot) or a microgrid. An objective of certain embodiments is to determine optimal peak power demand for at least a partially future time period (e.g., a fixed billing period). A further objective of certain embodiments is to account for uncertainty in the future portion of the time period.

[0011] In one embodiment, the method includes using at least one hardware processor to advance a sliding time window in real time from a start time to an end time of the time period, the length of the sliding time window being less than a length of the time period; after each of multiple advancements of the sliding time window within the time period, determining past values ​​of a parameter of the power infrastructure site that occurred during a past portion of the time period extending at least from the start time of the time period to before the start of the sliding time window; determining a current value of the parameter in the sliding time window based on an operational configuration of the power infrastructure site output by a model for a current portion of the time period extending through the sliding time window; determining a future value of the parameter during a future portion of the time period extending at least from the end of the sliding time window to the end time of the time period based on a prediction of the parameter; and determining an output value of the parameter for the time period based on the past value, the present value, and the future value.

[0012] In one embodiment, the parameter is a power demand, and each of the past value, present value, future value, and output value is a value of a peak power demand. The method may further include, using at least one hardware processor, setting the determined output value after one or more of a plurality of progressions of the sliding time window within the time period as the operational peak power demand of the power infrastructure site for the remainder of the time period.

[0013] In one embodiment, the past value is an extreme value of the parameter in a past portion of the time period, the present value is an extreme value of the parameter in a current portion of the time period, and the future value is an extreme value of the parameter in a future portion of the time period, and determining the output value includes selecting the most extreme value of the parameter from the past value, the present value, and the future value.

[0014] In one embodiment, the time period is a fixed billing period during which a utility supplying power to the power infrastructure site bills the operator of the power infrastructure site for the power used by the power infrastructure site.

[0015] In one embodiment, the start of the sliding time window corresponds to the current time and the end of the sliding time window corresponds to a future time within the time period.

[0016] In one embodiment, the prediction of the parameter includes a probability distribution of a value of the parameter in a future portion of the time period. Determining the future value may include selecting a value of the parameter from the probability distribution based on the value of the risk tolerance parameter. The method may further include receiving, using at least one hardware processor, user input indicating the value of the risk tolerance parameter.

[0017] In one embodiment, the method further includes executing, using at least one hardware processor, the predictive model to generate a prediction of the parameter.

[0018] In one embodiment, the method further includes setting, using at least one hardware processor, the determined output value as a control value for the power infrastructure site after one or more of a plurality of progressions of the sliding time window within the time period. The method may further include, using at least one hardware processor, initiating control of the power infrastructure site based on the determined output value after one or more of a plurality of progressions of the sliding time window within the time period.

[0019] It should be understood that any of the features in the above-described methods may be implemented individually or with any subset of other features in any combination. Thus, to the extent that the appended claims suggest particular dependencies between features, the disclosed embodiments are not limited to those particular dependencies. Rather, any feature described herein may be combined with any other feature described herein, or may be implemented in any combination of features without any one or more other features described herein. Furthermore, any of the methods described above and elsewhere herein may be embodied individually or in any combination in executable software modules of a processor-based system, such as a server, and / or in executable instructions stored on a non-transitory computer-readable medium.

[0020] BRIEF DESCRIPTION OF THE DRAWINGS The details of the present invention, both as to its structure and operation, can be gleaned in part by studying the accompanying drawings, in which like reference numerals refer to like parts, and in which: [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 illustrates an exemplary infrastructure upon which one or more of the processes described herein may be implemented, according to one embodiment. [Figure 2] FIG. 1 illustrates an exemplary processing system in which one or more of the processes described herein may be performed, according to one embodiment. [Figure 3] FIG. 1 illustrates an exemplary electrical grid for a power infrastructure site, according to one embodiment. [Figure 4] FIG. 2 illustrates an exemplary data flow between various components of an optimization infrastructure, according to one embodiment. [Figure 5] 1 illustrates the operation of an exemplary process for determining a value of a parameter (e.g., peak power demand) of an electrical infrastructure site, according to one embodiment. [Figure 6]FIG. 1 illustrates an exemplary process for determining a value for a parameter (e.g., peak power demand) of an electrical infrastructure site, according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0022] Detailed Description In one embodiment, a system, method, and non-transitory computer-readable medium are disclosed for managing peak power demand at an electric power infrastructure site, such as a power depot or microgrid. After reading this description, it will be apparent to one skilled in the art how to implement the invention in various alternative embodiments and applications. However, while various embodiments of the invention are described herein, it is understood that these embodiments are presented for purposes of example and illustration only, and not limitation. Therefore, this detailed description of various embodiments should not be construed as limiting the scope or breadth of the invention, which is set forth in the appended claims.

[0023] The term "electrical infrastructure site" refers to any infrastructure with a clearly defined electrical boundary. Generally, an electrical infrastructure site comprises an electrical grid with interconnected loads, which may be independently controlled. For example, an electrical infrastructure site may be a power depot and / or a microgrid comprising multiple infrastructure assets. However, an electrical infrastructure site may also consist of a single infrastructure asset. An infrastructure asset may comprise a generator, an energy storage system, a load, and / or any other component of an electrical infrastructure site.

[0024] The term "power depot" refers to any power infrastructure site comprising at least one charging station configured to be electrically connected to a flexible load to charge the battery of the flexible load and / or discharge the battery of the flexible load. It should be understood that a power depot may also comprise a non-flexible load. As an example, a power depot may be an electric vehicle (EV) depot that provides private charging for a fleet of electric vehicles for a public transportation agency, a business, a utility, or other entity, public charging for personal electric vehicles, private charging for personal electric vehicles (e.g., at the home of a personal electric vehicle owner), etc.

[0025] The term "microgrid" refers to any power infrastructure site that includes a local electrical grid that can operate independently from the rest of the electrical grid. A microgrid may include multiple infrastructure assets (e.g., generators, energy storage systems, loads, etc.) connected through a local electrical grid under the same common point of coupling. The infrastructure assets of a microgrid generally include at least some type of generator or energy storage system, but may include any combination of infrastructure assets. It should be understood that power depots and microgrids are not mutually exclusive, and in some cases, a power infrastructure site may include both a power depot and a microgrid.

[0026] The term "flexible load" refers to any load that has flexibility in at least one characteristic that can be represented by a variable in an optimization model. One such characteristic may be the location at which the flexible load is charged or discharged. A flexible load with location flexibility can be charged or discharged at any of multiple locations within one or more power infrastructure sites. Another such characteristic may be the timing at which the flexible load is charged or discharged. A flexible load with timing flexibility can be charged or discharged according to different timings, such as a start time, an end time, a time range, or a time duration. Another such characteristic may be the power flow rate at which the flexible load is charged or discharged. A flexible load with power flow flexibility can be charged at any of multiple different power flow rates (e.g., any power flow rate within a range of power flows). Another such characteristic may be the operating state of the flexible load. A flexible load with operating state flexibility (e.g., a water heater, air conditioner, compressor, etc.) can transition to different states (e.g., low power mode, power off, discharge, etc.) that reduce power consumption to increase the amount of energy available to other loads and / or reduce the total amount of energy consumed. These are merely example characteristics, and it should be understood that a flexible load may be flexible with respect to other characteristics, even if not specifically described herein. A flexible load may be flexible with respect to only one characteristic, all characteristics, or any subset of characteristics. In contrast, the term "non-flexible load" refers to any load that is not flexible in any characteristic.

[0027] In a contemplated embodiment, the flexible loads are electric vehicles and the power infrastructure sites are EV depots. The electric vehicles may be flexible at least with respect to their location. In other words, because electric vehicles are mobile, they can generally be charged at any one of multiple available charging stations within any one of multiple EV depots. In particular, the electric vehicles include on-board batteries that can be electrically connected to any one of multiple available charging stations within the EV depot. Similar types of flexible loads include, but are not limited to, drones, robotic systems, mobile machinery, mobile devices, power tools, etc.

[0028] However, the disclosed techniques are not limited to electric vehicles or other mobile loads. Rather, the disclosed techniques may be applied to any load, whether mobile or stationary, so long as the load has one or more flexible characteristics that can be defined in terms of variables in an optimization model. For example, a flexible load may simply consist of a stationary or mobile battery, or may be a power-consuming system without an on-board battery for storing power. A stationary flexible load generally does not have flexibility in location, but may have flexibility in timing, power flow, operating conditions, etc. For purposes of this disclosure, a flexible load is generally assumed to include or consist of an energy storage device. While the energy storage device is primarily described herein as a battery, it should be understood that the energy storage device may include or consist of any other mechanism or component for storing energy, such as a supercapacitor, compressed gas, etc.

[0029] The power infrastructure site may also include one or more flexible generators. The term "flexible generator" refers to any generator that can be controlled to increase and / or decrease the amount of power generated. Examples of flexible generators include, but are not limited to, diesel generators, hydrogen fuel cells, distributed energy resources that can reduce power output, etc. Optimization of a power infrastructure site can optimize both the flexible load and the flexible generator variables.

[0030] As used herein, the term "electrical grid" refers to the interconnection of electrical components within an electrical infrastructure site that distributes electrical power to flexible loads. These electrical components may comprise one or more intermittent and / or non-intermittent distributed energy resources, including renewable energy resources (e.g., solar generators, wind turbines, geothermal generators, hydroelectric generators, fuel cells, etc.) and / or non-renewable energy resources (e.g., diesel generators, natural gas generators, etc.), one or more battery energy storage systems (BESSs), etc. Additionally or alternatively, if the electrical infrastructure site comprises a power depot, these electrical components may comprise charging stations. Despite the term "charging," the term "charging station" encompasses stations that can charge a load (e.g., supply power to the load's onboard batteries) as well as discharge the load (e.g., draw power from the load's onboard batteries), as well as stations that can only charge the load and stations that can only discharge the load. The electrical components within a power infrastructure site can be thought of as "nodes" of the electrical grid, and the electrical connections between these electrical components can be thought of as "edges" connecting the nodes in the electrical grid. The electrical grid of a power infrastructure site may be connected to other electrical grids (e.g., during normal operation) or may be isolated (e.g., in the case of an independently operating microgrid).

[0031] 1. Exemplary Foundations FIG. 1 illustrates an exemplary infrastructure upon which one or more of the disclosed processes may be implemented, according to one embodiment. The infrastructure may include an energy management system (EMS) 110 (e.g., comprising one or more servers) that hosts and / or executes one or more of the various functions, processes, methods, and / or software modules described herein. The EMS 110 may comprise a dedicated server or, alternatively, may be implemented in a computing cloud in which resources of one or more servers are dynamically and elastically allocated to multiple tenants based on demand. In either case, the servers may be co-located and / or geographically distributed. The EMS 110 may also include or be communicatively connected to software 112 and / or one or more databases 114. Additionally, the EMS 110 may be communicatively connected to one or more user systems 130, power infrastructure sites 140, and / or power markets 150 via one or more networks 120.

[0032] The network 120 may include the Internet, and the EMS 110 may communicate with user systems 130, power infrastructure sites 140, and / or power markets 150 over the Internet using standard transmission protocols such as HyperText Transfer Protocol (HTTP), HTTP Secure (HTTPS), File Transfer Protocol (FTP), FTP Secure (FTPS), Secure Shell FTP (SFTP), eXtensible Messaging and Presence Protocol (XMPP), Open Field Message Bus (OpenFMB), IEEE Smart Energy Profile Application Protocol (IEEE 2030.5), as well as proprietary protocols. While the EMS 110 is shown connected to various systems via a single set of networks 120, it should be understood that the EMS 110 may be connected to various systems via a different set of one or more networks. For example, EMS 110 may be connected to a subset of user systems 130, power infrastructure sites 140, and / or power markets 150 via the Internet, but may be connected to one or more other user systems 130, power infrastructure sites 140, and / or power markets 150 via an intranet. Additionally, while only a few user systems 130 and power infrastructure sites 140, one power market 150, one instance of software 112, and one set of databases 114 are shown, it should be understood that the infrastructure may comprise any number of user systems, power infrastructure sites, power markets, software instances, and databases.

[0033] User system 130 may comprise any type of computing device capable of wired and / or wireless communication, including, but not limited to, a desktop computer, a laptop computer, a tablet computer, a smartphone or other mobile phone, a server, a game console, a television, a set-top box, an electronic kiosk, a point-of-sale terminal, an embedded controller, a programmable logic controller (PLC), etc. However, it is generally contemplated that user system 130 comprises a personal computer, a mobile device, or a workstation through which an agent of the operator of power infrastructure site 140 can interact with EMS 110 as a user. These interactions may include inputting data (e.g., parameters for configuring one or more of the processes described herein) and / or receiving data (e.g., output of one or more processes described herein) via a graphical user interface provided by EMS 110 or an intervening system between EMS 110 and user system 130. A graphical user interface may comprise a screen (e.g., a web page) that includes a combination of content and elements such as text, images, video, animations, references (e.g., hyperlinks), frames, inputs (e.g., text boxes, text areas, check boxes, radio buttons, drop-down menus, buttons, forms, etc.), scripts (e.g., JavaScript), etc., which elements may include or be derived from data stored in one or more databases (e.g., database 114).

[0034] The EMS 110 may execute software 112 comprising one or more software modules that implement one or more of the disclosed processes. Additionally, the EMS 110 may comprise, be communicatively coupled to, or otherwise have access to, one or more databases 114 that store data input to and / or output from one or more of the disclosed processes. Any suitable database may be utilized, including cloud-based databases, proprietary databases, and unstructured databases (e.g., MongoDB®), including, but not limited to, MySQL®, Oracle®, IBM®, Microsoft® SQL™, Access™, PostgreSQL™, etc.

[0035] The EMS 110 may communicate with the power infrastructure sites 140 and / or the power markets 150 via application programming interfaces (APIs). For example, the EMS 110 may “push” (i.e., proactively send data) data to each power infrastructure site 140 via the API of the control system 142 of the power infrastructure site 140. The control system 142 may be a supervisory control and data acquisition (SCADA) system. Alternatively or additionally, the control system 142 of the power infrastructure site 140 may “pull” (i.e., initiate data transmission via a request) data from the EMS 110 via the EMS 110’s API. Similarly, the EMS 110 may push power market 150 data to the power market interface 152 via the power market interface’s 152 API, and / or the power market interface 152 may pull data from the EMS 110 via the EMS 110’s API.

[0036] 2. Exemplary Processing System 2 is a block diagram illustrating an exemplary wired or wireless system 200 that may be used in connection with various embodiments described herein. For example, system 200 may be used as or in conjunction with one or more of the functions, processes, or methods described herein (e.g., for storing and / or executing software 112) and may represent components of EMS 110, user systems 130, power infrastructure sites 140, control systems 142, power market interfaces 152, and / or other processing devices described herein. System 200 may be a server or any conventional personal computer, or any other processor-enabled device capable of wired or wireless data communication. Other computer systems and / or architectures may also be used, as will be apparent to those skilled in the art.

[0037] System 200 preferably includes one or more processors 210. Processor 210 may comprise a central processing unit (CPU). Additional processors may be provided, such as a graphics processing unit (GPU), auxiliary processors for managing input / output, auxiliary processors for performing floating-point operations, dedicated microprocessors (e.g., digital signal processors) with architectures suitable for fast execution of signal processing algorithms, subordinate slave processors (e.g., back-end processors) of a main processing system, additional microprocessors or controllers for dual or multiprocessor systems, and / or coprocessors. Such auxiliary processors may be separate processors or may be integrated with processor 210. Examples of processors that may be used with system 200 include, but are not limited to, any of the processors available from Intel Corporation of Santa Clara, California (e.g., Pentium™, Core i7™, Xeon™, etc.), any of the processors available from Advanced Micro Devices, Incorporated of Santa Clara, California, any of the processors available from Apple Inc. of Cupertino (e.g., A-series, M-series, etc.), any of the processors available from Samsung Electronics Company of Seoul, South Korea (e.g., Exynos™), any of the processors available from NXP Semiconductors NV of Eindhoven, The Netherlands, etc.

[0038] Processor 210 is preferably connected to communication bus 205. Communication bus 205 may include a data channel for facilitating information transfer between storage and other peripheral components of system 200. Additionally, communication bus 205 may provide a set of signals used for communication with processor 210, including a data bus, an address bus, and / or a control bus (not shown). Communication bus 205 may include any standard or non-standard bus architecture, such as, for example, an industry standard architecture (ISA), an extended industry standard architecture (EISA), a Micro Channel Architecture (MCA), a peripheral component interconnect (PCI) local bus, a bus architecture conforming to standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE), including the IEEE 488 general-purpose interface bus (GPIB), IEEE 696 / S-100, etc.

[0039] System 200 preferably includes main memory 215 and may also include secondary memory 220. Main memory 215 provides storage of instructions and data for programs executing on processor 210, such as one or more of the functions and / or modules (e.g., software 112) described herein. It should be understood that the programs stored in memory and executed by processor 210 may be written and / or compiled according to any suitable language, including, but not limited to, C / C++, Java, JavaScript, Perl, Visual Basic, .NET, etc. Main memory 215 is typically semiconductor-based memory such as dynamic random access memory (DRAM) and / or static random access memory (SRAM). Other semiconductor-based memory types include, for example, synchronous dynamic random access memory (SDRAM), Rambus dynamic random access memory (RDRAM), ferroelectric random access memory (FRAM®), etc., including read only memory (ROM).

[0040] Secondary memory 220 may optionally include internal media 225 and / or removable media 230. Removable media 230 is read from and written to in any known manner. Removable storage media 230 may be, for example, a magnetic tape drive, a compact disc (CD) drive, a digital versatile disc (DVD) drive, other optical drive, a flash memory drive, etc. Secondary memory 220 is a non-transitory computer-readable medium on which computer-executable code (e.g., software 112) and / or other data is stored. Computer software or data stored in secondary memory 220 is loaded into main memory 215 for execution by processor 210.

[0041] In alternative embodiments, secondary memory 220 may include other similar means for allowing computer programs or other data or instructions to be loaded into system 200. Such means may include, for example, a communications interface 240 that allows software and data to be transferred to system 200 from an external storage medium 245. Examples of external storage medium 245 may include an external hard disk drive, an external optical drive, an external magneto-optical drive, etc. Other examples of secondary memory 220 may include semiconductor-based memory such as programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), and flash memory (a block-oriented memory similar to EEPROM).

[0042] As mentioned above, system 200 may include a communications interface 240. Communications interface 240 allows software and data to be transferred between system 200 and an external device (e.g., a printer), a network, or other information source. For example, computer software or executable code may be transferred to system 200 from a network server (e.g., platform 110) via communications interface 240. Examples of communications interface 240 include an internal network adapter, a network interface card (NIC), a Personal Computer Memory Card International Association (PCMCIA) network card, a cardbus network adapter, a wireless network adapter, a Universal Serial Bus (USB) network adapter, a modem, a wireless data card, a communications port, an infrared interface, an IEEE 1394 Firewire, and any other device capable of interfacing system 200 with a network (e.g., network 120) or another computing device.Communications interface 240 preferably implements industry published protocol standards such as Ethernet (IEEE 802 standard), Fibre Channel, digital subscriber line (DSL), asynchronous digital subscriber line (ADSL), Frame Relay, asynchronous transfer mode (ATM), integrated digital services network (ISDN), personal communications service (PCS), transmission control protocol / Internet protocol (TCP / IP), serial line Internet protocol / point to point protocol (SLIP / PPP), etc., although customized or non-standard interface protocols may also be implemented.

[0043] The software and data transferred via communications interface 240 are typically in the form of electrical communications signals 255. These signals 255 may be provided to communications interface 240 via communications channel 250. In one embodiment, communications channel 250 may be a wired or wireless network (e.g., network 120) or any of a variety of other communications links. Communications channel 250 carries signals 255 and may be implemented using a variety of wired or wireless communications means, including wire or cable, optical fiber, conventional telephone line, cellular phone link, wireless data, communications link, radio frequency (“RF”) link, or infrared link, to name just a few.

[0044] Computer-executable code (e.g., computer programs such as software 112) is stored in main memory 215 and / or secondary memory 220. Computer programs may also be received via communications interface 240 and stored in main memory 215 and / or secondary memory 220. Such computer programs, when executed, enable system 200 to perform various functions of the disclosed embodiments described elsewhere herein.

[0045] As used herein, the term "computer-readable medium" refers to any non-transitory computer-readable storage medium used to provide computer-executable code and / or other data to or within system 200. Examples of such media include main memory 215, secondary memory 220 (including internal memory 225 and / or removable media 230), external storage media 245, and any peripheral devices (including network information servers or other network appliances) communicatively coupled to communication interface 240. These non-transitory computer-readable media are means for providing executable code, programming instructions, software, and / or other data to system 200.

[0046] In embodiments implemented using software, the software may be stored on a computer-readable medium and loaded into system 200 via removable medium 230, I / O interface 235, or communication interface 240. In such embodiments, the software is loaded into system 200 in the form of electrical communication signals 255. When executed by processor 210, the software preferably causes processor 210 to perform one or more of the processes and functions described elsewhere herein.

[0047] In one embodiment, I / O interface 235 provides an interface between one or more components of system 200 and one or more input and / or output devices. Examples of input devices include, but are not limited to, sensors, keyboards, touchscreens or other touch-sensing devices, cameras, biometric sensing devices, computer mice, trackballs, pen-based pointing devices, etc. Examples of output devices include, but are not limited to, other processing devices, cathode ray tubes (CRTs), plasma displays, light-emitting diode (LED) displays, liquid crystal displays (LCDs), printers, vacuum fluorescent displays (VFDs), surface-conduction electron-emitter displays (SEDs), field emission displays (FEDs), etc. In some cases, input and output devices may be combined, such as in the case of touch-sensitive displays (e.g., smartphones, tablets, or other mobile devices).

[0048] System 200 may also include optional wireless communication components that facilitate wireless communication over a voice network and / or a data network (e.g., in the case of user system 130 being a smartphone or other mobile device). The wireless communication components include antenna system 270, radio system 265, and baseband system 260. In system 200, radio frequency (RF) signals are transmitted and received wirelessly by antenna system 270 under the control of radio system 265.

[0049] In one embodiment, antenna system 270 may include one or more antennas and one or more multiplexers (not shown) that perform switching functions to provide transmit and receive signal paths for antenna system 270. In the receive path, the received RF signal may be coupled from the multiplexer to a low noise amplifier (not shown) that amplifies the received RF signal and transmits the amplified signal to radio system 265.

[0050] In alternative embodiments, the radio system 265 may comprise one or more radios configured to communicate over various frequencies. In one embodiment, the radio system 265 may combine a demodulator (not shown) and a modulator (not shown) into a single integrated circuit (IC). The demodulator and modulator may also be separate components. In the incoming path, the demodulator removes the RF carrier signal, leaving a baseband received audio signal that is transmitted from the radio system 265 to the baseband system 260.

[0051] If the received signal contains audio information (e.g., a user system 130 including a smartphone or other mobile device), the baseband system 260 decodes the signal and converts it to an analog signal. The signal is then amplified and sent to a speaker. The baseband system 260 also receives analog audio signals from a microphone. These analog audio signals are converted to digital signals and encoded by the baseband system 260. The baseband system 260 also encodes the digital signals for transmission and generates baseband transmit audio signals that are routed to a modulator portion of the radio system 265. The modulator mixes the baseband transmit audio signal with an RF carrier signal to generate an RF transmit signal that can be routed to the antenna system 270 and passed through a power amplifier (not shown). The power amplifier amplifies the RF transmit signal and routes it to the antenna system 270, where the signal is switched to an antenna port for transmission.

[0052] The baseband system 260 is also communicatively coupled to the processor 210. The processor 210 may have access to data storage areas 215 and 220. The processor 210 is preferably configured to execute instructions (i.e., computer programs, such as the disclosed software), which may be stored in the main memory 215 or the secondary memory 220. Computer programs may also be received from the baseband processor 260 and stored in the main memory 210 or the secondary memory 220, or executed upon receipt. Such computer programs, when executed, enable the system 200 to perform various functions of the disclosed embodiments.

[0053] 3. Example Power Infrastructure Site 3 illustrates a single-line diagram of an exemplary electrical grid for an exemplary power infrastructure site 140, according to one embodiment. As a non-limiting example, the power infrastructure site 140 may be an EV depot for charging electric vehicles as flexible loads. The power infrastructure site 140 may be connected to a power grid 310 from which electricity may be purchased (e.g., from an electricity market 150). The purchase price of electricity may vary daily (e.g., higher during the day than at night) and over several days (e.g., higher on summer days than on winter days) according to a time-of-use (ToU) rate. The ToU rate may assign peak, partial-peak, and off-peak rates to various time periods (e.g., each time interval of a day) and represent the electricity price during those time periods.

[0054] The electrical grid of power infrastructure site 140 may include nodes representing one or more distributed energy resources within power infrastructure site 140, including, for example, one or more battery energy storage systems 320 and / or one or more generators 330, shown as generators 330A and 330B. Generators 330 may include renewable energy resources (e.g., solar generators, wind generators, geothermal generators, hydroelectric generators, fuel cells, etc.) and / or non-renewable energy resources (e.g., diesel generators, natural gas generators, etc.). For example, generator 330A may be a solar generator including multiple solar cells that convert sunlight into electricity, and generator 330B may be a diesel generator that burns diesel gasoline to generate electricity.

[0055] The power distribution grid of the power infrastructure site 140 may also include nodes representing one or more charging stations 340 within the power infrastructure site 140. In the illustrated example, the power infrastructure site 140 is a power depot including multiple charging stations 340A, 340B, 340C, 340D, 340E, 340F, and 340G. Each charging station 340 may include one or more chargers 342. For example, charging station 340F is shown with two chargers 342. Each charger 342 is configured to be electrically connected to the flexible load 350 to supply electricity to and / or discharge electricity from one or more energy storage units (e.g., batteries) of the flexible load 350. When supplying or charging the batteries of the flexible load 350, electricity may flow from the generator 330 and / or the grid 310 through the power distribution grid to the batteries of the flexible load 350. Conversely, when discharging the batteries of the flexible load 350, electricity may flow from the batteries of the flexible load 350 to the grid, to the battery energy storage system 320, to the grid 310, and / or to another flexible load 350. At any given time, several chargers 342 may be connected to the flexible load 350, shown as flexible loads 350A, 350B, 350C, 350E, 350F1, and 350F2, while other chargers 342 may not be connected to the flexible load 350 and may be available to accept an incoming flexible load 350.

[0056] In one embodiment, two or more power infrastructure sites 340 may be connected via a power transmission grid (not shown) or within the same power distribution grid. In this case, power can be transmitted between the two power infrastructure sites 340. For example, a first power infrastructure site 140 may transmit power over the network from one or more of its infrastructure assets (e.g., battery energy storage system 320, generator 330, and / or flexible load 350) to a second power infrastructure site 140 to charge the one or more infrastructure assets (e.g., battery energy storage system 320 and / or flexible load 350) of the second power infrastructure site 140.

[0057] Under the control of control system 142, EMS 110, and / or another controller, one or more charging stations 340 or individual chargers 342 may be configured to initiate charging of flexible load 350 (e.g., at a configurable power flow rate), terminate charging of flexible load 350, initiate discharging of flexible load 350 (e.g., at a configurable power flow rate), terminate discharging of flexible load 350, etc. For example, charging station 340 and / or charger 342 may include processing system 200 that receives commands from a controller (e.g., control system 142 and / or EMS 110) via communication interface 240, processes the commands using processor 210 and main memory 215, and controls actuators (e.g., one or more switches) to initiate or terminate charging or discharging according to the processed commands.

[0058] It should be understood that the power infrastructure site 140 may include other loads in addition to the flexible loads 350. For example, the power infrastructure site 140 may include auxiliary loads for operating various functions of the power infrastructure site 140. However, even these auxiliary loads may be considered flexible loads 350 if they are flexible in at least one characteristic (e.g., operating state). It should also be understood that each battery energy storage system 320 can function as both a power source (i.e., when discharging) and a load (i.e., when charging) and may also be considered a flexible load 350.

[0059] The parameters (e.g., voltage, current, setpoint, etc.) of each node in the electrical grid of power infrastructure site 140 may be monitored by EMS 110 and / or simulated by EMS 110 (e.g., during load flow analysis). Thus, EMS 110 may monitor and / or simulate the parameters of the nodes in the electrical grid and, based on those parameters, control one or more physical components of power infrastructure site 140, such as charging stations 340, chargers 342, generators 330, battery energy storage systems 320, etc. This control may be performed periodically or in real time, and may be performed directly or indirectly (e.g., via control system 142).

[0060] The illustrated power infrastructure site 140 includes a particular number of charging stations 340. However, it should be understood that the power infrastructure site 140 may include any number of charging stations 340, including only a single charging station 340. Furthermore, each charging station 340 may include any number of chargers 342, including only a single charger 342. In its simplest form, the power infrastructure site 140 may consist of a single charging station 340 consisting of a single charger 342, such as a charging station 340 at a residence, a public parking lot, etc. Furthermore, although all flexible loads 350 are shown connected to a charging station, one or more flexible loads 350 may be connected to the power grid without a charging station 340.

[0061] A single EMS 110 may manage a single power infrastructure site 140 or multiple power infrastructure sites 140. When the EMS 110 manages multiple power infrastructure sites 140, the managed power infrastructure sites 140 may be identical to one another, similar to one another, or different from one another. For example, a homogeneous mix of power infrastructure sites 140 may consist of multiple power depots for one or more fleets of electric vehicles. In contrast, a heterogeneous mix of power infrastructure sites 140 may include a mix of power depots, residential charging stations 340, public charging stations 340, microgrids, etc.

[0062] 4. Example Data Flow FIG. 4 illustrates an exemplary data flow between various components of the optimization infrastructure, according to one embodiment. The software 112 hosted on the platform 110 may implement an optimizer 410. The optimizer 410 may generate an optimization model that outputs a target value based on one or more variables and inputs including schedules of one or more flexible loads 350 in one or more power infrastructure sites 140. The optimization model represents a specific software-based implementation of an optimization problem. The optimizer 410 may solve the optimization model to determine values ​​for one or more variables that optimize the target value, subject to zero, one, or more constraints (e.g., inequality and / or equality constraints representing limitations on physical and / or technical capabilities, load and / or generator flexibility, etc.). This optimization may include minimizing the target value (e.g., if the target value represents a cost) or maximizing the target value (e.g., if the target value represents a performance metric). The determined values ​​of the variables may represent the operating configuration (e.g., set points, schedules, etc.) of one or more power infrastructure sites 140 over a period of time. The optimizer 410 may also initiate control of one or more physical components within one or more power infrastructure sites 140 based on the determined values ​​for the one or more variables.

[0063] The optimizer 410 may periodically update and solve the optimization model based on real-time or updated data. For example, the optimizer may update and solve the optimization model for the operation of the power infrastructure site 140 in a sliding time window representing the optimization period. The sliding time window progresses continuously or successively to encompass the next time period for which the operation of the power infrastructure site 140 is to be optimized. It should be understood that the start of the sliding time window may be the present time or some future time (e.g., 1 minute, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour, 24 hours in the future, etc.) such that the optimization is performed prior to the future time period encompassed by the sliding time window for which the optimization is being performed. In one embodiment, the sliding time window may progress according to a time step (e.g., every 15 minutes) and utilize the most recent data available within a time range (e.g., a time range extending into the past equal in size to the time step). The size of the sliding time window may be any suitable duration (eg, 15 minutes, 30 minutes, 1 hour, 24 hours, etc.).

[0064] The optimizer 410 may collect data to utilize for optimization (e.g., as input to an optimization model) from one or more scheduling systems 420, one or more telemetry systems 430, one or more control systems 142 at one or more power infrastructure sites 140, and / or one or more power market interfaces 152 at one or more power markets 150. It should be understood that these are merely examples of data flows, and the optimizer 410 may collect data from other systems (e.g., potentially including a site management system 440). For example, the optimizer 410 may receive solutions from other optimizers that can be incorporated into the input to the optimization model. In this case, the optimizer 410 can focus on one aspect of the overall optimization so that all details of the optimization do not need to be integrated into the optimization model.

[0065] In one embodiment, the optimizer 410 may merge the collected data for multiple power infrastructure sites 140 into a single virtual site model and perform a single global optimization of the virtual site model to optimize the operation of the multiple power infrastructure sites 140 as a single unit. This virtual site model may represent a multi-tenant billing system of systems aggregating multiple power infrastructure sites 140 (e.g., EV depots) for multiple tenants (e.g., EV fleet operators).

[0066] The optimizer 410 may output data to one or more scheduling systems 420, one or more control systems 142 at one or more power infrastructure sites 140, one or more site management systems 440, and / or one or more power market interfaces 152 at one or more power markets 150. It should be understood that these are merely examples of data flows, and that the optimizer 410 may output data to other systems (e.g., potentially including a telemetry system 430). For example, the optimizer 410 may output its solution or other data to one or more other optimizers, which may each incorporate this data into inputs to their own optimization models. In this case, the optimizer 410 can focus on one aspect of the overall optimization so that all details of the optimization do not need to be integrated into the optimization model.

[0067] The scheduling system 420, the telemetry system 430, and / or the site management system 440 may be hosted on the EMS 110 or may be external to the EMS 110. In either case, when collecting data, the optimizer 410 may receive data by pulling the data from the respective systems via their APIs or by the respective systems pushing the data to the optimizer 410 via their APIs. Similarly, the optimizer 410 may output data by pushing the data to the respective systems via their APIs or by the respective systems pulling the data via their APIs.

[0068] Each scheduling system 420 may manage a timetable and the routing of the flexible loads 350 (e.g., a fleet of electric vehicles). The optimizer 410 may receive schedule information from each scheduling system 420. The schedule information provided by the scheduling system 420 may include a planned schedule for the flexible loads 350. For example, the planned schedule may include, for each flexible load 350, an estimated arrival time at which the flexible load 350 is expected to arrive at the power infrastructure site 140, an estimated departure time at which the flexible load 350 is expected to depart from the power infrastructure site 140, an auxiliary task schedule defining a time period during which the flexible load 350 is expected to perform or undergo an auxiliary task (e.g., preconditioning the cabin and / or battery of an electric vehicle, performing cleaning, maintenance, or repairs, etc.), a power flow schedule defining a power flow rate or profile for charging or discharging the flexible load 350, a priority of the flexible load 350 relative to other flexible loads 350 (e.g., indicating the relative cost of a delay in charging the flexible load 350), a preferred power infrastructure site 140 assigned to the flexible load 350, etc. Additionally, if the flexible loads 350 have routes, the schedule information may include, for each flexible load 350, the route assigned to the flexible load 350.

[0069] After solving the optimization model, the optimizer 410 may output data associated with the scheduling system 420 to the scheduling system 420 or to an intervening system. For example, if the optimizer 410 performs optimization of multiple power infrastructure sites 140, the optimizer 410 may output a ranked list of the power infrastructure sites 140, ordered, for example, from the power infrastructure site 140 with the greatest capacity to the power infrastructure site 140 with the least capacity. Such information may be used by the scheduling system 420 or an intervening system to update the routing assignments for the flexible loads 350 (e.g., by rerouting one or more flexible loads 350 from a power infrastructure 140 with less capacity to a power infrastructure site 140 with more capacity). Additionally or alternatively, the optimizer 410 may output a ranked list of flexible loads 350, for example, ordered from the flexible load 350 that is charged to an acceptable state of charge (SoC) first to the flexible load 350 that is charged to an acceptable state of charge last (e.g., reflecting which electric vehicle is ready first or can be unplugged the longest). As another example, the optimizer 410 or an intervening system may provide recommendations for improving scheduling and / or provide data from which such recommendations can be derived. For example, if the flexible loads 350 are electric vehicles, such data may identify electric vehicles that are frequently late, so that the electric vehicle's route can be changed (e.g., assigned to another electric vehicle, split with another electric vehicle, etc.).More generally, the scheduling system 420 or intervening system may analyze the data provided by the optimizer 410 to identify inefficiencies (e.g., bottlenecks, electric vehicles requiring fast charging with tight schedules, etc.) and / or inaccurate parameters (e.g., actual energy consumed by preconditioning, etc.) in the schedule of the flexible load 350 and update the schedule of the flexible load 350 (e.g., reoptimize EV route assignments) to eliminate or mitigate those inefficiencies and / or reflect more accurate parameters.

[0070] Each telemetry system 430 may collect telemetry information for one or more flexible loads 350. The telemetry system 430 may be mounted on each flexible load 350 and transmit real-time telemetry information to the optimizer 410. For example, if the flexible load 350 is an electric vehicle, the telemetry system 430 may be embedded (e.g., as software or hardware) within the electric vehicle's electronic control unit (ECU) and communicate via a wireless communication network (e.g., a cellular communication network). Alternatively, the telemetry system 430 may be external to the flexible load 350. In this case, the telemetry system 430 may collect real-time telemetry information for the multiple flexible loads 350 and forward or relay the collected telemetry information to the optimizer 410 in real time. It should be understood that, as used herein, the terms "real-time" or "real time" contemplate not only events occurring simultaneously, but also events separated by delays due to normal latencies in processing, communication, etc., and / or due to the use of time steps (e.g., with respect to a sliding time window for optimization).

[0071] The optimizer 410 may receive telemetry information from each telemetry system 430. The telemetry information provided by the telemetry system 430 may include one or more parameters for each flexible load 350 for which the transmitting telemetry system 430 collects data. For each flexible load 350, these parameters may include the flexible load's 350's estimated time of arrival at the scheduled power infrastructure site 140, the current state of charge of an energy storage unit (e.g., a battery) onboard the flexible load 350, etc. It should be understood that these parameters may change over time, and updated telemetry information reflecting any changes may be periodically received by the optimizer 410 (e.g., and used as input to the optimization model for the current optimization period). The real-time telemetry information reduces uncertainty in the optimization model.

[0072] Each site management system 440 may manage infrastructure assets, including flexible loads 350, and staff within one or more power infrastructure sites 140. After solving the optimization model, the optimizer 410 may output site information to each site management system 440. For example, this site information may include a ranked list of flexible loads 350, ordered, for example, from flexible loads 350 that can be disconnected for the longest period of time to flexible loads 350 that can be disconnected for the shortest period of time. Such information may be used by the site management system 440 to determine, for example, which flexible loads 350 can be accommodated. Additionally or alternatively, this site information may include an assignment or mapping of flexible loads 350 to charging stations 340. Such mapping may be used to automatically route flexible loads 350 to their mapped charging stations 340, for example, by sending routing instructions, such as the locations of the mapped charging stations 340, to a navigation system within each flexible load 350.

[0073] Each control system 142 may provide independent control of the power infrastructure site 140. After solving the optimization model, the optimizer 410 may output control information related to the control of the power infrastructure site 140 to the control system 142 associated with that power infrastructure site 140. Such control information may be provided to the control system 142 of each power infrastructure site 140 managed by the optimizer 410. The control information may include one or more control instructions that trigger one or more control actions in the control system 142 and / or other information used during one or more control actions in the control system 142. The control instructions may include, but are not limited to, instructions to control setpoints associated with physical components of the power infrastructure site 140 (e.g., power flow rates associated with the charging stations 340), instructions to charge the flexible load 350, instructions to discharge the flexible load 350, instructions to perform preconditioning of the flexible load 350 (e.g., cooling the cabin and / or battery of an electric vehicle, heating the cabin of an electric vehicle, etc.), etc. Other information may include, but is not limited to, a ranked list of charging stations 340 (e.g., ordered from highest capacity charging station 340 to lowest capacity charging station 340), a charging schedule for flexible loads 350, an assignment or mapping of flexible loads 350 to charging stations 340, etc.

[0074] The optimizer 410 may also receive data from each control system 142. For example, the control system 142 may report detected events within the respective power infrastructure site 140 (e.g., a charger 342 of a charging station 340 plugged into a flexible load 350), a detected state of charge for the flexible load 350 being charged, the actual power flow or power flow between the charging station 340 and the connected flexible load 350, etc. This data may be periodically received by the optimizer 410 and used as input to the optimization model for the current optimization period.

[0075] The power market interface 152 may provide an interface to the power market 150, in which the utility operating the grid 310 is a participant. In particular, the power market 150 may enable operators of the power infrastructure sites 140 to purchase electricity from utilities or other energy providers (e.g., the operating grid 310), sell electricity to energy providers (e.g., the operating grid 310), obtain service commitments from energy providers, provide service commitments to energy providers, etc. The optimizer 410 may receive pricing information from the power market interface 152. For example, this pricing information may include a forecast of electricity prices from the grid 310 for one or more future time periods (e.g., including the optimization period), uncertainty parameters for the price forecast, applicable electricity rates, etc. The optimizer 410 may incorporate this pricing information into an input to an optimization model. For example, the target value output by the optimization model may include an energy cost derived from the pricing information, in which case optimizing the target value may include minimizing the target value and, therefore, the energy cost.

[0076] The optimizer 410 may receive a forecast of DER generation from the electricity market interface 152. Alternatively, the optimizer 410 may receive a weather forecast from the electricity market interface 152 or another external system (e.g., a weather forecast service) and derive its own forecast of DER generation. It should be understood that the power generated by renewable energy resources, such as solar and wind generators, can vary greatly depending on the weather. The optimizer 410 may incorporate the forecast of DER generation into an input to an optimization model to determine, for example, how much DER generation will be available to the electricity infrastructure site 140 during the current optimization period.

[0077] The optimizer 410 may obtain service commitments from the power market 150 via the power market interface 152. For example, the optimizer 410 may determine the energy demand required for an optimization period based on a solution to the optimization model (e.g., including values ​​for one or more variables in the optimization model that optimize a target value output by the optimization model). The optimizer 410 may send a request to the power market interface 152 for a service commitment for the determined energy demand for the respective optimization period from the power market 150. In response, the optimizer 410 may receive a requested service commitment for the energy demand for the respective optimization period from the energy supplier via the power market interface 152. If the energy provider in the power market 150 does not commit to service, the optimizer 410 may update and re-run the optimization model (e.g., with constraints on the energy demand) and / or initiate or take some other corrective action.

[0078] The optimizer 410 may obtain other services from the power market 150 via the power market interface 152. For example, the optimizer 410 may receive service pricing for one or more services offered by energy providers on the power market 150 from the power market interface 152. The optimizer 410 may incorporate the service pricing into inputs to the optimization model. Based on the solution of the optimization model, the optimizer 410 may determine to purchase one or more services and request service commitments for the services from the power market 150 via the power market interface 152. In response, the optimizer 410 may receive the requested service commitments for the services from the power market interface 152. If an energy provider on the power market 150 does not commit to the service, the optimizer 410 may update the inputs to the optimization model to exclude the service pricing, re-run the optimization model, and / or initiate or take some other corrective action. In one embodiment, the optimizer 410 may receive a schedule of requested service commitments via the power market interface 152 .

[0079] The optimizer 410 may provide service commitments to the power market 150 via the power market interface 152. For example, the optimizer 410 may provide flexibility information for one or more power infrastructure sites 140 that it manages to the power market 150 via the power market interface 152. The flexibility information may include a price per kilowatt (kW) curve for flexibility at the power infrastructure site 140. Energy providers in the power market 150 may purchase flexibility from the optimizer 410 via the power market interface 152. This flexibility can be utilized for contraction (e.g., demand response) as needed. In one embodiment, the optimizer 410 may receive a schedule of service commitments provided via the power market interface 152.

[0080] It should be understood that the illustrated data flow is merely an example. The optimizer 410 may interface with a fewer, more, or different set of systems than those illustrated. Each system may be used to collect data to be incorporated into inputs to the optimization model solved by the optimizer 410 and / or as a destination for data output by the optimizer 410. Additional examples of data that may be collected and used as inputs to the optimization model include, but are not limited to, weather, conditions at the power infrastructure site 140, financial figures related to the power infrastructure site 140, uncertainty estimates (e.g., for the planned schedule of the flexible load 350), etc.

[0081] For example, the optimizer 410 may interface with a weather forecast service to receive a weather forecast for the optimization period from the weather forecast service. Incorporating the weather forecast into the optimization model can improve energy demand estimation. For example, as described above, the weather forecast may be used to predict DER generation, which can be incorporated into the inputs to the optimization model. Additionally or alternatively, the optimizer 410 may utilize the weather forecast in conjunction with the preconditioning model to more accurately estimate energy demand for preconditioning the flexible load 350. For example, during periods of relatively high or low temperatures, more energy may be required to precondition (e.g., cool or heat) an electric vehicle than during milder periods.

[0082] It should be understood that the above examples are not limiting. The optimizer 410 may utilize other inputs and / or generate other outputs than those specifically described herein. The optimizer 410 may utilize a modular “building block” architecture so that new inputs can be easily incorporated into the optimization model and / or existing inputs can be easily removed from the optimization model. Some of these inputs may represent solutions output by other optimization systems. Additionally, as described in various examples, the optimizer 410 may provide additional outputs (e.g., ranked lists) that describe capacity constraints for EV supply equipment (EVSE), such as charging stations 340. These additional outputs may be used by other systems (e.g., scheduling system 420, site management system 440, etc.), such that the optimizer 410 need not integrate all details of the optimization.

[0083] 5. Optimization The optimizer 410 may solve an optimization model to implement in software the optimization problem for one or more power infrastructure sites 140. The optimization model may be generated from a model of the power infrastructure sites 140. For example, a model of each power infrastructure site 140 managed by the optimizer 410 may be stored in a data structure in memory (e.g., database 114). The optimization model may be derived from a model of a single power infrastructure site 140 to optimize operation of a single power infrastructure site 140, or a model of multiple power infrastructure sites 140 to optimize operation across multiple power infrastructure sites 140. In one embodiment, the optimization model minimizes a target value based on inputs including the schedules of generators 330 and / or flexible loads 350 at the power infrastructure sites 140 represented by the model of the power infrastructure site 140, and one or more variables.

[0084] The variables may represent controllable parameters within the power infrastructure site 140. Examples of controllable parameters include, but are not limited to, setpoints for physical components, the amount of power output by a distributed energy resource, the power flow rate for charging or discharging the flexible load 350, the state of charge for charging the flexible load 350 or the battery energy storage system 320, the time to start charging or discharging the flexible load 350, the time to end charging or discharging the flexible load 350, the duration for charging or discharging the flexible load 350, the assignment or mapping of the flexible load 350 to the charging station 340 or the power infrastructure site 140, etc. Generally, an optimization model includes multiple variables, potentially including a mix of different types of controllable parameters. In one embodiment, the variables in the optimization model include or relate to at least power consumption and / or generation at the power infrastructure site 140.

[0085] Mathematically, the optimization model may be expressed as follows:

[0086]

number

[0087] However, the following conditions apply:

[0088]

number

[0089]

number

[0090]

number

[0091]

number

[0092]

number

[0093]

number

[0094] Although the optimization model is expressed herein as a minimization problem, it should be understood that the optimization model may alternatively be expressed herein as the following maximization problem, subject to zero, one, or multiple constraints:

[0095]

number

[0096] However, for ease of explanation, we will generally assume that the optimization model is viewed as a minimization problem. Those skilled in the art will understand how to convert between minimization and maximization problems, and which problem may be more appropriate in a given situation.

[0097]

number

[0098] The optimization model may be a stochastic optimization model that is robust to uncertainty. The optimization model may be solved using any suitable technique, including, but not limited to, stochastic multi-stage optimization, probability-constrained optimization, Markowitz mean-variance optimization, etc. There are many commercial and open-source solvers available for solving optimization models.

[0099]

number

[0100] 6. Process Overview An embodiment of a process for determining peak power demand for power infrastructure site 140 will now be described in detail. It should be understood that the described process may be embodied in one or more software modules executed by one or more hardware processors, for example, as software 112 executed by processor 210 of EMS 110. The described process may be implemented as instructions expressed in source code, object code, and / or machine code. These instructions may be executed directly by hardware processor 210 or may be executed by a virtual machine or container operating between the object code and hardware processor 210. Furthermore, the disclosed software may be built on or interfaced with one or more existing systems.

[0101] Alternatively, the described processes may be implemented as hardware components (e.g., general-purpose processors, integrated circuits (ICs), application-specific integrated circuits (ASICs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, etc.), a combination of hardware components, or a combination of hardware and software components. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are described herein generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the invention. Furthermore, the grouping of functions within a component, block, module, circuit, or step is for ease of description. Particular functions or steps may be moved from one component, block, module, circuit, or step to another without departing from the invention.

[0102] 5 illustrates the operation of an exemplary process for determining a value for a parameter (e.g., peak power demand) of a power infrastructure site, according to one embodiment. While FIG. 5 illustrates only a single time period 510, the process may operate for each of multiple consecutive time periods 510. Each time period 510 may be a fixed time period or interval, such as a billing period (e.g., a monthly billing period) during which a utility supplying power to the power infrastructure site 140 bills the operator of the power infrastructure site 140 for the power used by the power infrastructure site 140.

[0103] The sliding time window 520 progresses in real time from the start time of the current time period 510 to the end time of the current time period 510. It should be understood that the current time period 510 refers to a specific time period 510 that includes the current time 512. The length of the sliding time window 520, which may start from the current time 512, is less than the overall length of the time period 510. Thus, the sliding time window 520 may extend from the current time 512 to a future time 514 defined by the length of the sliding time window 520. In other words, the start of the sliding time window 520 corresponds to the current time 512, and the end of the sliding time window 520 corresponds to a future time 514 within the current time period 510 that is defined by adding the length of the sliding time window 520 to the current time 512. The sliding time window 520 may have any suitable length, such as, for example, one or more hours (e.g., 1 hour, 6 hours, 12 hours, etc.), one or more days (e.g., 1 day, 2 days, 1 week, etc.), etc. In a particular embodiment, the length of the sliding time window 520 is one day (i.e., 24 hours).

[0104] The sliding time window 520 may progress continuously from the start time to the end time of the time period 510 or according to time steps. When the sliding time window 520 is described herein as progressing in real time, it should be understood that the term “real time” may include delays resulting from time steps, as well as normal latencies in data processing, communications, etc. In embodiments utilizing time steps, the time step may consist of any suitable time interval, such as one or more seconds (e.g., 1 second, 5 seconds, 10 seconds, 15 seconds, 30 seconds, etc.), one or more minutes (e.g., 1 minute, 5 minutes, 10 minutes, 15 minutes, 30 minutes, etc.), one or more hours (e.g., 1 hour, 6 hours, 12 hours, etc.), one or more days (e.g., 1 day, 2 days, 1 week, etc.), etc. In general, a time step may be defined based on the length of the time period 510 as a multiplication factor whose product is the length of the time period 510.

[0105] In embodiments utilizing time steps, the time step may be defined relative to the sliding time window 520. For example, the length of the time step may be the same as the length of the sliding time window 520, may be a multiplication factor whose product is the length of the sliding time window 520, or may be related in some other way to the length of the sliding time window 520. Alternatively, the length of the time step may be completely independent of the length of the sliding time window 520.

[0106] The sliding time window 520 divides the current time period 510 into a past portion 530, a present portion 540, and a future portion 550. The past portion 530 extends from at least the start time of the current time period 510 to before the start of the sliding time window 520. In other words, the past portion 530 is fixed to the start time of the current time period 510. In a preferred embodiment, the past portion 530 extends the entire period from the start time of the current time period 510 to the start of the sliding time window 520. However, in alternative embodiments, the past portion 530 can extend from the start time of the current time period 510 to some time that is a certain number of time units (e.g., 30 seconds, 1 minute, 10 minutes, 15 minutes, etc.) before the start of the sliding time window 520. The present portion 540 spans at least a portion of the sliding time window 520. In a preferred embodiment, the current portion 540 spans the entire sliding time window 520 from beginning to end so as to be coextensive with the sliding time window 520. The future portion 550 spans from at least the end of the sliding time window 520 to the end time of the current time period 510. In other words, the future portion 540 is fixed to the end time of the current time period 510.

[0107] It should be appreciated that at a first point in time, when the current time 512 is the start time of the current time period 510, the past portion 530 has a length of zero. Additionally, at a second point in time, when the end of the sliding time window 520 is the end time of the current time period 510, the future portion 550 has a length of zero. As the sliding time window 520 progresses from the first point in time to the second point in time, the past portion 530 increases in length, the current portion 540 maintains a constant length (i.e., corresponding to the length of the sliding time window 520), and the future portion 550 decreases in length. As the sliding time window 520 progresses from the second point in time to the end of the current time period 510, the past portion 530 continues to increase in length, while the current portion 540 decreases in length. During this time, the sliding time window 520 may decrease in length, or alternatively may remain a fixed length and extend into the next time period 510 (i.e., immediately after the current time period 510). However, it should be understood that in embodiments utilizing time steps where the length of the time steps is the same as the length of the sliding time window 520, the sliding time window 520 does not progress any further within the current time period 510 after the second point in time, but instead progresses to the next time period 510 if applicable.

[0108] After each progression of the sliding time window 520 within the current time period 510, a value of a parameter for the power infrastructure site 140 may be determined for each of the past portion 530, the present portion 540, and the future portion 550. The value for each of the past portion 530, the present portion 540, and the future portion 550 may be an extreme value of the parameter for the respective portion of the current time period 510. In one embodiment, the parameter is power demand, and the value for each of the past portion 530, the present portion 540, and the future portion 550 is a value of peak power demand for the respective portion. For example, a past value 535 of peak power demand for the past portion 530 of the current time period 510 may be determined, a present value 545 of peak power demand for the present portion 540 of the current time period 510 may be determined, and a future value 555 of peak power demand for the future portion 550 of the current time period 510 may be determined.

[0109] As used herein, "power demand" refers to power flow from grid 310 to power infrastructure site 140 through the point of common coupling, and "peak power demand" refers to the maximum power flow from grid 310 to power infrastructure site 140 through the point of common coupling over a period of time. However, it should be understood that parameters other than power demand may be determined, and values ​​other than maximum values ​​(i.e., peaks), such as minimums and averages, may be determined as the values ​​of the parameters. In general, past values ​​535, present values ​​545, and / or future values ​​555 may each be determined from the instantaneous values ​​of the parameters over respective portions of time period 510 or from a smoothing function or other function applied to the values ​​of the parameters over respective portions of time period 510. One example of a smoothing function is a rolling average of the values ​​of the parameters over a period of time (e.g., 15 minutes). For example, the peak power demand for the past portion 530, the present portion 540, and / or the future portion 550 may be defined as the maximum of the rolling average of power demand over a period of time (e.g., 15 minutes) rather than the maximum instantaneous power demand over the respective portion of the time period 510.

[0110] The past value 535 may be reliably determined based on the historical values ​​of the parameter in the past portion 530. For example, if the value of the parameter being determined is a peak power demand, then the peak power demand for the past portion 530 of the current time period 510 may be determined by simply finding the maximum power demand by the power infrastructure site 140 (e.g., via a common point of coupling with the grid 310) over the past portion 530 of the current time period 510.

[0111] The current value 545 may be determined based on the operational configuration of the power infrastructure site 140 output by the model. For example, the optimizer 410 may solve an optimization model of the power infrastructure site 140 to determine values ​​for each of one or more variables that represent the operational configuration of the power infrastructure site 140 during a current portion 540 represented by the sliding time window 520. The power consumption and / or power generation over the current portion 540 of the current time period 510 may be derived from this operational configuration. For example, values ​​of variables (e.g., setpoints of the battery energy storage system 320, the generator 330, the charging station 340, etc.) over the current portion 540 may be used to plot the power demand (e.g., power consumption minus power generation / discharge at each of multiple points in time) over the current portion 540 of the current time period 510. The peak power demand for the current portion 540 of the current time period 510 can then be determined by finding the maximum power demand among the plotted power demands. Generally, this peak power demand for the present portion 540 of the present time period 510 represents the known near-future demand over the optimization period. Thus, there may be little or no uncertainty in the present value 545.

[0112] The future value 555 may be determined based on a prediction of the parameter. The future value 555 may represent an uncertain estimate of the value of the parameter. Generally, the uncertainty associated with the future value 555 decreases as the remaining time in the current time period 510 (i.e., the length of the future portion 550) decreases.

[0113] In one embodiment, the prediction of the parameter may include a probability distribution 560 of the value of the parameter in the future portion 550 of the current time period 510. The probability distribution 560 may be derived by running one or more predictive models that output the probability distribution 560 based on historical data (e.g., past power demand) and / or forecast data (e.g., expected power demand, weather forecasts, etc.).

[0114] Determining the future value 555 may include selecting a value for the parameter from a probability distribution based on the value of the risk tolerance parameter ξ. For example, values ​​or percentiles in the probability distribution may be associated with different values ​​of the risk tolerance parameter ξ. The value of the risk tolerance parameter ξ may be received as user input via a graphical user interface provided by the EMS 110 and / or may be derived from the time remaining in the current time period 510 (i.e., the length of the future portion 550). The future value 555 may be derived as a single value within the probability distribution 560 defined by the risk tolerance parameter ξ or an average of the portion of the probability distribution 560 defined by the risk tolerance parameter ξ.

[0115] In an alternative embodiment, an estimate of power demand may be plotted over a future portion 550 of the current time period 510 based on one or more forecasting models. The peak power demand for the future portion 550 of the current time period 510 may then be determined by finding the maximum power demand among the plotted power demands.

[0116] After each progression of the sliding time window 520 within the current time period 510 or one or more progressions of the sliding time window 520, an output value of the parameter for the current time period 510 may be determined based on the past value 535, the present value 545, and the future value 555 of the current time period 510. For example, if the value of the parameter being determined is a peak power demand, the output value is the value of the peak power demand. In this case, the output value may be determined to be the maximum value of the past value 535, the present value 545, and the future value 555. More generally, the past value 535 may be an extreme value (e.g., minimum or maximum) of the parameter in the past portion 530 of the current time period 510, the present value 545 may be an extreme value of the parameter in the present portion 540 of the current time period 510, the future value 545 may be an extreme value of the parameter in the future portion 550 of the current time period 510, and the output value may be determined by selecting the most extreme value of the parameter from the past value 535, the present value 545, and the future value 555. However, it should be understood that values ​​other than the first extreme value can be used for the past value 535, the present value 545, the future value 555, and / or the output value, such as the second extreme value, the third extreme value, the average of all relevant values, the average of all extremes of relevant values, etc.

[0117] After each progression of the sliding time window 520 within the current time period 510 or one or more progressions of the sliding time window 520, the determined output value of the parameter may be used during operation of the power infrastructure site 140. For example, if the value of the parameter is a peak power demand, the determined output value may be set as the peak power demand during operation of the power infrastructure site 140 for the remainder of the current time period 510. More generally, the determined output value of the parameter may be set as a control value for the power infrastructure site 140. In one embodiment, control of the power infrastructure site 140 may be initiated based on the determined output value.

[0118]

number

[0119] Among other things, uncertainty in the future value 555 of peak power demand affects subsequent optimization by optimizer 410. In particular, if the future value 555 is lower than the eventual actual peak power demand in time period 510, potential consequences include power infrastructure site 140 paying more for energy than necessary, paying more for peak power demand in subsequent time periods 510 (e.g., due to an increased perceived risk that power infrastructure site 140 will exceed peak power demand in future time periods 510), and / or having less flexibility in operation (e.g., in responding to unexpected events). Conversely, if the future value 555 is higher than the eventual actual peak power demand in time period 510, potential consequences include power infrastructure site 140 paying more for peak power demand than necessary.

[0120] 6 illustrates an exemplary process 600 for determining a value for a parameter (e.g., peak power demand) of a power infrastructure site 140, according to one embodiment. Process 600 may be performed by optimizer 410, by software 112 in combination with optimizer 410, or by software 112 independent of optimizer 410. Although process 600 is shown with a particular arrangement and order of subprocesses, process 600 may be performed with fewer, more, or different subprocesses, as well as with a different arrangement and / or order of the subprocesses. Furthermore, even if subprocesses are described or illustrated in a particular order, it should be understood that any subprocess that is not dependent on the completion of another subprocess may be performed before, after, or in parallel with the other independent subprocess.

[0121] Process 600 iterates through one or more time periods 510 to operate on a current time period 510. The current time period 510 is the time period that includes a current time 512. In sub-process 610, once it becomes the current time period 510, it is determined whether to operate on a next time period 510. It should be understood that the next time period 510 becomes the current time period 510 as soon as it includes the current time 512. If the next time period 510 is considered (i.e., "Yes" in sub-process 610), process 600 performs one or more inner iterations of sub-processes 620-670. Otherwise, if the next time period 510 is not considered (i.e., "No" in sub-process 610), process 600 may terminate. Process 600 is generally considered to repeat continuously and indefinitely through successive time periods 510 for one or more power infrastructure sites 140 in real time until the operator interrupts or terminates process 600 (e.g., via a graphical user interface of EMS 110).

[0122] Iterations of the inner loop formed by sub-processes 620-670 may be performed according to time steps, as described elsewhere herein. In other words, an iteration of sub-processes 620-670 may be performed after each of multiple time steps within the current time period 510. Alternatively, iterations of sub-processes 620-670 may be performed continuously.

[0123] In sub-process 620, it is determined whether the current time period 510 has ended. If the current time period 510 has ended (i.e., "Yes" in sub-process 620), process 600 returns to sub-process 610, which becomes the current time period 510, to determine whether to perform another outer iteration for the next time period 510. If the current time period 510 has not ended (i.e., "No" in sub-process 620), process 600 performs an iteration of sub-processes 630-670. It should be appreciated that the current time period 510 is determined to have ended when the current time 512 is at the end time of the time period 510 under consideration, or is within one time step of that end time if time steps are used, or is subsequent to the end time of the time period 510 under consideration. Conversely, when the current time 512 is before the end time of the time period under consideration 510, or if time steps are used, is multiple time steps before the end time of the time period under consideration 510, the current time period 510 is determined to not have ended.

[0124] In sub-process 630, the sliding time window 520 is advanced continuously or by a time step, depending on the embodiment. It should be understood, therefore, that sub-processes 640-670 are performed after each of multiple advancements of the sliding time window 520 within the current time period 510. While it is generally preferred that sub-processes 640-670 be performed after every advancement of the sliding time window 520, in alternative embodiments, sub-processes 640-670 may be performed only after a certain number of advancements of the sliding time window 520 (e.g., intervals of multiple time steps), according to any other pattern, or randomly. Advancement of the sliding time window 520 increases the length of the past portion 530 of the current time period 510 and decreases the length of the future portion 550 of the current time period 510. In embodiments in which time steps are used, the length of the past portion 530 increases by the length of the time step, and the length of the future portion 550 decreases by the length of the time step.

[0125] In sub-process 640, historical values ​​535 of a parameter (e.g., peak power demand) of one or more power infrastructure sites 140 that occurred during the historical portion 530 of the current time period 510 are determined. As described elsewhere herein, the historical portion 530 may extend from at least the start time of the current time period 510 to before the start of, and preferably until the start of, the sliding time window 520. The historical values ​​535 may be reliably determined from historical values ​​of the parameter recorded and stored (e.g., in database 114) for the historical portion 530 of the current time period 510. For example, in embodiments where the historical value 535 is an extreme value (e.g., minimum or maximum) of the parameter, the historical value 535 may be easily (i.e., with very little computational cost) determined as the extreme value of the parameter's historical values. In embodiments where the parameter is power demand, the historical value 535 may be determined as the maximum of the historical values ​​of power demand during the historical portion 530 of the current time period 510.

[0126] In sub-process 650, a current value 545 of one or more power infrastructure site(s) 140 parameters (e.g., peak power demand) occurring during a current portion 540 of a current time period 510 is determined. As described elsewhere herein, the current portion 540 may span a sliding time window 520 (i.e., from the beginning to the end of the sliding time window 520). The current value 545 may be determined based on an operational configuration of the power infrastructure site(s) 140 output by a model 680 for the current portion 540 of the current time period 510. The model 680 may be an optimization model of the optimizer 410. For example, in an embodiment in which the current value 545 is an extreme value (e.g., minimum or maximum) of the parameter, the current value 545 may be determined as the expected extreme value of the parameter if the power infrastructure site 140 were to operate according to the operational configuration of the power infrastructure site 140 that solves the optimization model. In embodiments where the parameter is power demand, the current value 545 may be determined as the maximum expected power demand through the point of common coupling with the grid 310 (e.g., the difference between the expected power consumption and the expected power generation / discharge at the power infrastructure site 140) along the time span of the current portion 540 of the current time period 510.

[0127] In sub-process 660, future values ​​555 of parameters (e.g., peak power demand) of one or more power infrastructure sites 140 that are predicted to occur in a future portion 550 of the current time period 510 are determined. As described elsewhere herein, the future portion 550 may extend from at least the end of the sliding time window 520 to the end time of the current time period 510. The future values ​​555 may be determined based on predictions of the parameters. The predictions of the parameters may include the output of one or more predictive models. The predictive models may include predictive machine learning models, probability constrained optimization, etc.

[0128] In one embodiment, the prediction of the parameter includes a probability distribution 560 of the parameter's value in the future portion 550 of the current time period 510. In this case, subprocess 660 may include selecting a future value 555 of the parameter from the probability distribution 560 based on the value of a risk tolerance parameter ξ. The value of the risk tolerance parameter ξ may be received as user input via a graphical user interface provided by the EMS 110. In this case, the risk tolerance parameter ξ represents a user-adjustable bias. Alternatively or additionally, the risk tolerance parameter ξ may be based on the time remaining in the current time period 510 (i.e., the length of the future portion 550 of the current time period 510). For example, if the future value 555 is a peak power demand, the risk tolerance ξ may decrease as the amount of time remaining in the current time period 510 decreases because the potential benefit lost from a higher future value 555 of the peak power demand increases. In general, a lower risk tolerance parameter ξ may result in a more likely future value 555 of peak power demand being selected or otherwise derived from the probability distribution 560, while a higher risk tolerance parameter ξ may result in a less likely future value 555 of peak power demand being selected or otherwise derived from the probability distribution 560.

[0129] Alternatively, parameter prediction may take other forms. For example, the optimizer 410 may perform an initial optimization once per time period 510 at the beginning of each time period 510 based on a schedule for the entire time period 510 (e.g., a fixed billing period). This optimization may be performed by solving a model 680, which may include an optimization model of the optimizer 410, as described elsewhere herein. The operating configuration generated by the optimization may be used to determine values ​​for the parameters at multiple time points throughout the current time period 510. After each progression of the sliding time window 520 through the current time period 510, the values ​​of the parameters remaining in the future portion 550 may be used to determine the future values ​​555. For example, if the parameter is a power demand, the maximum value of the power demand in the future portion 550 may be determined as the future value 555.

[0130] The prediction of a parameter may be based on the parameter's historical value adjusted for one or more factors. These factors may include expected utilization of infrastructure assets (e.g., flexible load 350), forecasted weather, on-site generation and / or storage, etc. In one embodiment, the graphical user interface of EMS 110 may include one or more inputs that allow an operator of a power infrastructure site 140 to override the parameter's predicted future value 555 with a manual input value. In this case, if the operator does not want to utilize the prediction functionality of the disclosed embodiments, the operator can simply set the future value 555 to zero. This setting may be specific to each position in the sliding time window 520, specific to each time window 510, and / or global for all time windows 510. However, it should be understood that manually overriding the future value 555 often results in a suboptimal determination of peak power demand for the time period 510.

[0131] In sub-process 670, an output value of a parameter (e.g., peak power demand) of one or more power infrastructure 140 is determined based on the parameter's past values ​​535, present values ​​545, and future values ​​555. If the goal is to determine an extreme value (e.g., minimum or maximum) of the parameter, the output value may be determined as the most extreme value of the parameter from the parameter's past values ​​535, present values ​​545, and future values ​​555. For example, if the parameter's value is peak power demand, the output value may be determined as the maximum of the past values ​​535, present values ​​545, and future values ​​555. Similarly, if the extreme value is a minimum, the output value may be determined as the minimum of the past values ​​535, present values ​​545, and future values ​​555. However, it should be understood that the output value may be determined in other manners (e.g., average) depending on the intended use of the parameter and the output value.

[0132]

number

[0133]

number

[0134] In one embodiment, the parameter output value determined in sub-process 670 is provided directly to control process 690 for controlling operation of power infrastructure site 140. For example, the parameter output value may be set as a control value in control process 690 for at least a subset of the iterations of sub-process 670. Thus, control process 690 may be initiated based on the output value determined in at least a subset of the iterations of sub-process 670. However, it should be understood that control using the determined parameter output value is not required for every embodiment.

[0135]

number

[0136]

number

[0137] If the flexible load 350 is an electric vehicle, the scheduled time may be a departure time, and the target state of charge may be a state of charge required for the electric vehicle to complete the scheduled route. Charging may also include or describe preconditioning of the electric vehicle (e.g., cooling the electric vehicle before its departure, preconditioning the electric vehicle's battery, etc.), as well as charging of the electric vehicle's battery. However, preconditioning may also be a flexible operating state that can be terminated or avoided when the electric vehicle needs to be charged by its scheduled departure time, or when the optimizer 410 determines that the power can be better used elsewhere.

[0138] The control process 690 may include an optimizer 410 that generates and transmits control information to one or more control systems 142. Each control system 142 may then control the physical components within its respective power infrastructure site 140. Alternatively, the optimizer 410 may control the physical components directly (i.e., without the intervention of a control system). In either case, the control may include setting a setpoint for the physical component, actuating a switch, turning the physical component on or off, or otherwise changing the physical or operational state of the physical component. The setpoint for the physical component may include values ​​such as voltage, current, output power, input power, power flow, state of charge, temperature, etc. For example, the control may include controlling the setpoint (e.g., output power) of a non-intermittent distributed energy resource. It should be understood that intermittent distributed energy resources (e.g., solar or wind generators, or other weather-dependent generators) can be controlled in a similar manner, but intermittent distributed energy resources can typically only be controlled to output power less than their potential output power, essentially wasting energy.

[0139]

number

[0140] 7. Exemplary Embodiments The disclosed embodiments determine optimal values ​​of parameters for operation of a power infrastructure site 140 (e.g., a power depot, such as an EV depot, microgrid, or the like, with or without generation / storage) during a time period 510 in the presence of uncertainty about a future portion 550 of the time period 510. The optimal values ​​of the parameters for the entire time period 510 are determined based on certain past values ​​535, expected present values ​​545 as a result of optimization, and uncertain future values ​​555 determined by forecasting. This allows for improved utilization of the power infrastructure site's 140 common coupling point with the grid 310 (e.g., to improve EV fleet performance by ensuring on-time charging) in the presence of future uncertainty, increased consistency in power consumption by the power infrastructure site 140 that the grid 310 must support, reduced total operating costs for the power infrastructure site 140, and flexible incorporation of various data sources and / or various billing rules (e.g., energy tariffs, peak power charges, etc.) imposed by the utility operating grid 310.

[0141] Furthermore, because the disclosed embodiments only require optimization within a sliding time window 520, optimal values ​​for the parameters can be determined in a computationally fast, feasible, and scalable manner. To improve speed and scalability, the optimization may be formulated as a linear optimization model. In contrast, conventional systems that optimize over the entire time period 510 may become computationally impractical (e.g., too slow, insufficient data, etc.) as the number of variables that must be optimized increases (e.g., as the size of the EV fleet at the power infrastructure site 140 increases).

[0142] In one embodiment, the parameter value being optimized is a peak power demand. If the optimal value for the peak power demand is higher than the current set value for the peak power demand, increasing the set value for the peak power demand to the higher optimal value allows the power infrastructure site 140 to realize a higher peak benefit for the remainder of the current time period 510. For example, if the peak power demand is predicted to be higher than the current set value for the peak power demand in the future portion 550 and the predicted peak power demand value impacts the power infrastructure site 140's utility costs (e.g., via peak power rates), the peak power demand may be preemptively set to the predicted peak power demand value to utilize the higher peak power demand value for a longer period (i.e., adding the time between the current time 512 and the time when the predicted peak power demand is estimated to occur) and reduce the impact on utility costs. Without the disclosed embodiments, the benefit of the peak power demand would only be realized from the time it occurs until the end of the current time period 510. In the disclosed embodiment, the benefits of the peak power demand can be obtained from the time it occurs and the time it is predicted, whichever is earlier, until the end of the current time period 510 .

[0143] Embodiment 1: A method including: using at least one hardware processor, progressing a sliding time window in real time from a start time to an end time of the time period, the length of the sliding time window being less than a length of the time period; after each of a plurality of progressions of the sliding time window within the time period, determining past values ​​of a parameter of an electric power infrastructure site that occurred during a past portion of the time period extending at least from the start time of the time period to before the start of the sliding time window; determining current values ​​of the parameter in the sliding time window based on an operational configuration of the electric power infrastructure site output by a model for a current portion of the time period extending through the sliding time window; determining future values ​​of the parameter during a future portion of the time period extending at least from the end of the sliding time window to the end time of the time period based on a prediction of the parameter; and determining an output value of the parameter for the time period based on the past value, the present value, and the future value.

[0144] Embodiment 2: The method of claim 1, wherein the parameter is a power demand, and each of the past value, present value, future value, and output value is a value of a peak power demand.

[0145] Embodiment 3: The method of embodiment 2, further comprising using at least one hardware processor to set the determined output value after one or more of a plurality of progressions of the sliding time window within the time period as the operational peak power demand of the power infrastructure site for the remainder of the time period.

[0146] Embodiment 4: The method of any preceding embodiment, wherein the past value is an extreme value of the parameter in a past portion of the time period, the present value is an extreme value of the parameter in a current portion of the time period, and the future value is an extreme value of the parameter in a future portion of the time period, and determining the output value includes selecting the most extreme value of the parameter from the past value, the present value, and the future value.

[0147] Embodiment 5: The method of any of the preceding embodiments, wherein the time period is a fixed billing period during which a utility that supplies power to the power infrastructure site bills the operator of the power infrastructure site for the power used by the power infrastructure site.

[0148] Embodiment 6: The method of any of the preceding embodiments, wherein the start of the sliding time window corresponds to a current time and the end of the sliding time window corresponds to a future time within the time period.

[0149] Embodiment 7: The method of any of the preceding embodiments, wherein the prediction of the parameter comprises a probability distribution of the value of the parameter in a future portion of the time period.

[0150] Embodiment 8: The method of embodiment 7, wherein determining the future value includes selecting a value for the parameter from the probability distribution based on the value of the risk tolerance parameter.

[0151] Embodiment 9: The method of embodiment 8, further comprising receiving, using at least one hardware processor, a user input indicating a value for the risk tolerance parameter.

[0152] Embodiment 10: The method of any preceding embodiment, further comprising executing, using at least one hardware processor, a predictive model to generate a prediction of the parameter.

[0153] Embodiment 11: A method as described in any of the preceding embodiments, further comprising using at least one hardware processor to set the determined output value as a control value for the power infrastructure site after one or more of a plurality of progressions of a sliding time window within the time period.

[0154] Embodiment 12: The method of embodiment 11, further comprising using at least one hardware processor to initiate control of the power infrastructure site based on output values ​​determined after one or more of a plurality of progressions of the sliding time window within the time period.

[0155] Embodiment 13: A system comprising at least one hardware processor and software, the software, when executed by the at least one hardware processor, performing a method according to any of the preceding embodiments.

[0156] Embodiment 14: A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by a processor, causing the processor to perform the method of any one of embodiments 1 to 12.

[0157] The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles described herein may be applied to other embodiments without departing from the spirit or scope of the present invention. It should therefore be understood that the description and drawings presented herein represent presently preferred embodiments of the present invention and, therefore, represent the subject matter broadly contemplated by the present invention. It is further understood that the scope of the present invention fully encompasses other embodiments that may become apparent to those skilled in the art, and therefore, the scope of the present invention is not limited.

[0158] As used herein, the terms "comprising," "comprise," and "comprises" are open-ended. For example, "A comprises B" means that A can include either: (i) B alone, or (ii) B in combination with one or more, and possibly any number of, other components. In contrast, the terms "consisting of," "consist of," and "consists of" are closed-ended. For example, "A consists of B" means that A includes only B and does not include other components in the same context.

[0159] Combinations described herein, such as "at least one of A, B, or C," "one or more of A, B, or C," "at least one of A, B, and C," "one or more of A, B, and C," and "A, B, C, or any combination thereof," include any combination of A, B, and / or C, and may include multiple As, multiple Bs, or multiple Cs. Specifically, combinations such as "at least one of A, B, or C," "one or more of A, B, or C," "at least one of A, B, and C," "one or more of A, B, and C," and "A, B, C, or any combination thereof," may be A only, B only, C only, A and B, A and C, B and C, or A, B, and C, and any such combination may include one or more members of its components A, B, and / or C. For example, a combination of A and B may include one A and multiple Bs, multiple A and one B, or multiple A and multiple Bs.

Claims

1. Using at least one hardware processor, progressing a sliding time window in real time from a start time to an end time of a time period, the length of the sliding time window being less than a length of the time period; after each of a plurality of progressions of the sliding time window within the time period, determining past values ​​of a parameter of a power infrastructure site that occurred during a past portion of the time period extending from at least the start time of the time period to before the start of the sliding time window; determining a current value of the parameter in the sliding time window based on an operational configuration of the power infrastructure site output by a model for a current portion of the time period spanning the sliding time window; determining a future value of the parameter for at least a future portion of the time period extending from an end of the sliding time window to the end time of the time period based on the prediction of the parameter; determining an output value of the parameter for the time period based on the past value, the present value, and the future value; A method comprising:

2. The method of claim 1 , wherein the parameter is a power demand, and each of the past value, the present value, the future value, and the output value is a peak power demand value.

3. 3. The method of claim 2, further comprising: using the at least one hardware processor, after one or more of the plurality of progressions of the sliding time window within the time period, setting the determined output value as the peak power demand during operation of the power infrastructure site for a remainder of the time period.

4. 2. The method of claim 1 , wherein the past value is an extreme value of the parameter during the past portion of the time period, the present value is an extreme value of the parameter during the current portion of the time period, and the future value is an extreme value of the parameter during the future portion of the time period, and determining the output value comprises selecting a most extreme value of the parameter from the past, present, and future values.

5. The method of claim 1 , wherein the time period is a fixed billing period during which a utility supplying power to the power infrastructure site charges an operator of the power infrastructure site for power used by the power infrastructure site.

6. The method of claim 1 , wherein the start of the sliding time window corresponds to a current time and the end of the sliding time window corresponds to a future time within the time period.

7. The method of claim 1 , wherein the prediction of the parameter comprises a probability distribution of values ​​of the parameter in the future portion of the time period.

8. The method of claim 7 , wherein determining the future value comprises selecting a value for a risk tolerance parameter from the probability distribution based on the value of the parameter.

9. The method of claim 8 , further comprising: receiving, using the at least one hardware processor, a user input indicating the value of the risk tolerance parameter.

10. The method of claim 1 , further comprising: using the at least one hardware processor to execute a predictive model to generate the prediction of the parameter.

11. 2. The method of claim 1, further comprising: using the at least one hardware processor, setting the determined output value as a control value for the power infrastructure site after one or more of the plurality of progressions of the sliding time window within the time period.

12. 12. The method of claim 11, further comprising: using the at least one hardware processor to initiate control of the power infrastructure site based on the output value determined after one or more of the plurality of progressions of the sliding time window within the time period.

13. The method of claim 12 , wherein the at least one hardware processor initiates control of the power infrastructure site in an automatic or semi-automatic manner.

14. The method of claim 11 , wherein the parameter is a peak power demand and the output value is a peak power demand.

15. The method of claim 1 , further comprising using the output values ​​of the parameters to inform a future solution to an optimization model, the optimization model minimizing a target value.

16. at least one hardware processor; Software and wherein the software, when executed by the at least one hardware processor, progressing a sliding time window in real time from a start time to an end time of a time period, the length of the sliding time window being less than a length of the time period; after each of a plurality of progressions of the sliding time window within the time period, determining past values ​​of a parameter of an electric power infrastructure site that occurred during at least a past portion of the time period extending from the start time of the time period to before the start of the sliding time window; determining a current value of the parameter in the sliding time window based on an operational configuration of the power infrastructure site output by a model for a current portion of the time period spanning the sliding time window; determining a future value of the parameter for a future portion of the time period extending from an end of the sliding time window to the end time of the time period based on the prediction of the parameter; determining an output value of the parameter for the time period based on the past value, the present value, and the future value; A system configured to:

17. 17. The system of claim 16, wherein the parameter is a power demand, and each of the past value, the present value, the future value, and the output value is a peak power demand value.

18. 18. The system of claim 17, wherein the software is further configured to set the determined output value as the peak power demand during operation of the power infrastructure site for the remainder of the time period after one or more of the plurality of progressions of the sliding time window within the time period, the time period being a fixed billing period during which a utility supplying power to the power infrastructure site charges an operator of the power infrastructure site for power used by the power infrastructure site.

19. 17. The system of claim 16, wherein the past value is an extreme value of the parameter during the past portion of the time period, the present value is an extreme value of the parameter during the current portion of the time period, and the future value is an extreme value of the parameter during the future portion of the time period, and determining the output value comprises selecting a most extreme value of the parameter from the past value, the present value, and the future value.

20. 17. The system of claim 16, wherein the start of the sliding time window corresponds to a current time and the end of the sliding time window corresponds to a future time within the time period.

21. 17. The system of claim 16, wherein the prediction of the parameter comprises a probability distribution of values ​​of the parameter in the future portion of the time period, and determining the future value comprises selecting a value of the parameter from the probability distribution based on a value of a risk tolerance parameter.

22. 17. The system of claim 16, wherein the software is further configured to set the determined output value as a control value for the power infrastructure site after one or more of the plurality of progressions of the sliding time window within the time period.

23. 23. The system of claim 22, wherein the software is further configured to initiate control of the power infrastructure site based on the output value determined after one or more of the plurality of progressions of the sliding time window within the time period.

24. 24. The system of claim 23, wherein the software is further configured to initiate control of the power infrastructure site in an automatic or semi-automatic manner.

25. 23. The system of claim 22, wherein the parameter is a peak power demand and the output value is a peak power demand.

26. 17. The system of claim 16, wherein the software is further configured to use the output values ​​of the parameters to inform an optimization model of future solutions, the optimization model minimizing a target value.

27. A non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, progressing a sliding time window in real time from a start time to an end time of a time period, the length of the sliding time window being less than a length of the time period; after each of a plurality of progressions of the sliding time window within the time period, determining past values ​​of a parameter of an electric power infrastructure site that occurred during at least a past portion of the time period extending from the start time of the time period to before the start of the sliding time window; determining a current value of the parameter in the sliding time window based on an operational configuration of the power infrastructure site output by a model for a current portion of the time period spanning the sliding time window; determining a future value of the parameter for a future portion of the time period extending from an end of the sliding time window to the end time of the time period based on the prediction of the parameter; determining an output value of the parameter for the time period based on the past value, the present value, and the future value; a non-transitory computer-readable medium for causing the processor to perform the steps of:

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