Systems and methods for optimizing energy dispatch

Abstract

A method, system, apparatus, and non-temporary computer-readable medium for optimizing energy dispatch are provided. In one implementation, the computer-readable medium includes instructions causing a processor to generate multiple energy dispatch patterns for utilizing one or more energy storage units, to generate a recommended energy dispatch pattern based on data determined for each of the multiple energy dispatch patterns, and to adjust one or more energy storage units to dispatch electricity according to the recommended energy dispatch pattern.

Description

Technical Field

[0001]

[0001] This disclosure relates to the field of energy storage. More specifically, this disclosure relates to optimizing energy dispatch and anomaly detection in an energy storage system.

Background Art

[0002]

[0002] An energy storage system can receive, store, and output energy. An energy storage system can be connected to, for example, a power grid, a power plant, a building, or any other type of facility, receive energy, store it, and later supply the energy. An operator of an energy storage system can configure how the energy storage system operates. An inappropriate configuration of an energy storage system can lead to an undesirable rate of degradation of the energy sources within the energy storage system. For example, if the rate or time at which energy is received or output by an energy storage system is not configured efficiently, the energy storage system can undergo unnecessary degradation and / or can lead to a reduction in the overall lifespan of the energy storage system.

[0003]

[0003] Therefore, as will be described in more detail herein, there is a need for systems and methods directed to, for example, improving the efficiency of using an energy storage system for energy input or output by evaluating a plurality of generated energy dispatch patterns.

Summary of the Invention

[0004]

[0004] The disclosed embodiments can relate to an energy storage system. Embodiments consistent with this disclosure provide systems, methods, and apparatuses associated with energy storage.

[0005]

[0005] The disclosed embodiments may include systems, methods, apparatus and non-temporary computer-readable media for optimizing energy dispatch. For example, the disclosed embodiments may include generating a plurality of energy dispatch patterns for utilizing one or more energy storage units using a computing device; generating a recommended energy dispatch pattern based on data determined for each of the plurality of energy dispatch patterns; and dispatching electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern. Some of these embodiments may include, for each of the plurality of energy dispatch patterns, determining an estimate of the energy dispatch pattern based on expected energy market data; determining one or more state variables of one or more energy storage units based on environmental data for one or more energy storage units; determining an estimated limit degradation of one or more energy storage units based on one or more state variables; determining an estimated cost of the energy dispatch pattern based on the estimated limit degradation; and calculating an estimated net value of the energy dispatch pattern based on the estimate and estimated cost. Some of these embodiments may include generating a recommended energy dispatch pattern based on the estimated net value of multiple energy dispatch patterns.

[0006]

[0006] The disclosed embodiments may include systems, methods, apparatus and non-transient computer-readable media for anomaly detection in energy storage systems. Some of these embodiments may include, by means of a computing device, receiving usage data of batteries located in one or more energy storage units during a time frame; inputting the usage data into a machine learning model; generating a predicted temperature of the batteries at the end of the time frame based on the machine learning model's processing of the usage data; receiving the measured temperature of the batteries at the end of the time frame from a battery temperature sensor; determining the difference between the predicted temperature and the measured temperature; transmitting a battery state instruction based on the determined difference; and configuring battery usage based on the battery state. Some of these embodiments may include transmitting an instruction that a battery anomaly has been detected based on a difference that meets a threshold; and adjusting battery usage based on the detection of a battery anomaly.

[0007]

[0007] Consistent with the disclosed embodiments, a non-temporary computer-readable medium may store instructions that, when executed by at least one processor, cause at least one processor to execute any of the processes described herein.

[0008]

[0008] The above general description and the following detailed description are illustrative and descriptive only and do not limit the scope of the claims.

[0009]

[0009] The accompanying drawings incorporated into and constituting part of this disclosure illustrate various disclosed embodiments. [Brief explanation of the drawing]

[0010] [Figure 1] This document presents an exemplary system for managing an energy storage system, consistent with some embodiments of the present disclosure. [Figure 2]This document illustrates an exemplary computing device consistent with several embodiments of this disclosure. [Figure 3] An exemplary energy storage unit consistent with several embodiments of this disclosure is shown. [Figure 4] A flowchart illustrating an exemplary method for optimizing energy dispatch, consistent with some embodiments of this disclosure, is shown. [Figure 5] An exemplary user interface associated with energy dispatch optimization, consistent with several embodiments of this disclosure, is shown. [Figure 6] A flowchart illustrating an exemplary method for anomaly detection in an energy storage system, consistent with some embodiments of this disclosure, is shown. [Figure 7] This document presents an exemplary user interface associated with anomaly detection in an energy storage system, consistent with several embodiments of the present disclosure. [Modes for carrying out the invention]

[0011]

[0017] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. While several exemplary embodiments are described herein, modifications, adaptations, and other forms of implementation are possible. For example, components shown in the drawings may be replaced, added, or modified, and exemplary methods described herein may be modified by replacing, rearranging, deleting, or adding steps to the disclosed methods. Thus, the following detailed description is not limited to specific embodiments and examples, but includes general principles described herein and shown in the drawings, in addition to the general principles encompassed by the accompanying claims.

[0012]

[0018] Figure 1 shows an exemplary system for managing an energy storage system 100, consistent with several embodiments of the present disclosure. The system 100 may include an energy storage system 110, a network 114, a user device 116, an energy market device 118, a weather data device 120, and one or more power lines (e.g., power line 122). The energy storage system 110 may include one or more energy storage units (e.g., 112A, 112B, and 112C).

[0013]

[0019] Network 114 may include one or more of the various types of networks for information communication, such as cellular networks (e.g., 2G, 3G, 4G, or 5G), satellite networks, Wi-Fi networks, WiMAX networks, Bluetooth networks, near-field communication (NFC) networks, low-power wide-area networks (LPWAN) networks, mobile networks, terrestrial microwave networks, wireless ad-hoc networks, Ethernet networks, telephone networks, power line communication (PLC) networks, coaxial cable networks, and / or fiber optic networks. Network 114 may include wired networks or wireless networks. Network 114 may include personal area networks, local area networks, metropolitan area networks, wide area networks, global area networks, space networks, or any other type of computer network that may use data connectivity between network nodes. In some examples, network 114 may include Internet Protocol (IP) based networks. Network 114 may connect energy storage units 112A, 112B, 112C, user device 116, energy market device 118, and / or weather data device 120 using interconnected communication links.

[0014]

[0020] The energy storage system 110 may refer to any system configured to store energy. The energy storage system 110 may include a centralized system or a distributed system. The energy storage system 110 may include one or more energy storage units (e.g., 112A, 112B, and 112C). In some examples, the energy storage units 112A, 112B, and 112C may be located in a single physical location, such as a site. In some examples, the energy storage units 112A, 112B, and 112C may be distributed across multiple physical locations. The energy storage units 112A, 112B, and 112C may be interconnected via a network and configured to function as a system.

[0015]

[0021] The energy storage system 110 may be coupled to one or more power lines (e.g., power line 122). In some examples, power line 122 may be associated with a power grid. In some examples, power line 122 may be associated with a power plant (e.g., a solar power plant, solar farm, wind power plant, wind farm, hydroelectric power plant). In some examples, power line 122 may be associated with any type of facility (e.g., a building, factory, hospital, or school). The energy storage system 110 may receive energy through power line 122, store the received energy, and / or output energy through power line 122. For example, the energy storage system 110 may be grid energy storage within a power grid. The energy storage system 110 may store electrical energy when the demand for electricity is low and output electrical energy to the grid when the demand for electricity is high. In another example, the energy storage system 110 may be located next to a solar farm and may be configured to receive electrical energy from the solar farm and store it for later output. As another example, the energy storage system 110 may be located next to the facility and may be configured to store electrical energy and supply electrical energy to the facility at appropriate times.

[0016]

[0022] An energy storage system 110, including energy storage units (or more), can store energy in one or more of various forms, such as electrochemical, chemical, mechanical, electrical, electromagnetic, biological, and / or thermal. In some examples, the energy storage system 110, including energy storage units (or more), may store energy using rechargeable batteries. An example of an energy storage unit will be described in more detail with reference to Figure 3.

[0017]

[0023] The user device 116 may include any type of computing device configured to perform one or more of the embodiments described herein (for example, to optimize energy dispatch and / or to detect anomalies in the energy storage system). For example, the user device 116 may include at least one processor and memory for storing instructions that, when executed by the at least one processor, cause the at least one processor to perform one or more of the embodiments described herein. The user device 116 may include, for example, a computer, laptop computer, desktop computer, mainframe computer, tablet, smartphone, mobile phone, mobile device, server device, client device, automotive electronics device, augmented reality headset, smartwatch, Internet of Things (IoT) device, or any other type of computing device. In some examples, the user device 116 may be configured to receive data from various sources (for example, via network 114) and / or manage the energy storage system 110. For example, the user device 116 may receive operational data from energy storage units 112A, 112B, and 112C. Additionally or alternatively, user device 116 may receive data from energy market device 118, including, for example, historical energy market data and / or projected energy market data. Energy market device 118 may include any type of computing device capable of storing data associated with energy markets (e.g., wholesale energy markets). Additionally or alternatively, user device 116 may receive data from weather data device 120, including, for example, historical weather data and / or projected weather data. Weather data device 120 may include any type of computing device capable of storing data associated with weather. Based on the received data, user device 116 may manage the energy storage system 110.For example, as will be described in more detail herein, the user device 116 may optimize the dispatch of energy from the energy storage units 112A, 112B, and 112C, detect anomalies in the energy storage units 112A, 112B, and 112C, and / or trigger the display of a user interface(s) that may enable a user (e.g., an administrator of the energy storage system 110) to control the energy storage system 110.

[0018]

[0024] Figure 2 shows an exemplary computing device 210 consistent with several embodiments of the present disclosure. The computing device 210 may include, for example, at least one processor 212, at least one memory 214, at least one network interface 216, one or more input devices 218, and / or one or more output devices 220. Devices described herein (e.g., the user device 116, energy market device 118, and weather data device 120 shown in Figure 1, and the computing device 312 shown in Figure 3) may similarly include these components and / or be implemented in a similar manner to the computing device 210. In some examples, the computing device 210 including one or more of the components may be implemented using virtualization and / or cloud computing technologies.

[0019]

[0025] The processor 212 can execute instructions of a computer program to perform any of the functions described herein. The processor 212 may include, for example, an integrated circuit, a microchip, a microcontroller, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), or other units suitable for executing instructions or logical operations. The processor 212 may include a single core or a multi-core processor (e.g., dual-core, quad-core, or any desired number of cores). The processor 212 may provide the ability to execute, control, implement, or store multiple processes, applications, or programs. In some examples, the processor 212 may be configured to provide parallel processing capabilities that enable devices associated with the processor to execute multiple processes simultaneously. In some examples, the processor 212 may be configured using virtualization techniques. Other types of processor configurations may be implemented to provide the characteristics described herein.

[0020]

[0026] Memory 214 may include a non - transient computer - readable medium that can store instructions that cause at least one processor to execute one or more of the processes described herein when executed by the at least one processor. The non - transient computer - readable medium may include any type of physical memory in which information or data readable by at least one processor can be stored. The non - transient computer - readable medium may include, for example, random access memory (RAM), read - only memory (ROM), compact disc read - only memory (CD - ROM), digital versatile disc (DVD), programmable read - only memory (PROM), erasable programmable read - only memory (EPROM), non - volatile random access memory (NVRAM), volatile memory, non - volatile memory, hard drive, flash drive, disk, cache, register, optical data storage media, physical media having patterns, or networked versions thereof. The non - transient computer - readable medium may include multiple structures and may be located at a local position or a remote position.

[0021]

[0027] The network interface 216 may include, for example, a network card and / or a modem, and may be configured to provide data communication (e.g., bidirectional data communication) with a network (e.g., network 114). The network interface 216 may be a wireless interface, a wired interface, or a combination of both. The specific design and implementation of the network interface 216 may depend on the communication network through which the computing device 210 is intended to operate. For example, the network interface 216 may include a wireless local area network (WLAN) card, an Integrated Services Digital Network (ISDN) card, a cellular modem, a satellite modem, a modem configured to provide data communication connectivity over the Internet, a network card with an Ethernet port, a device having a radio frequency receiver and transmitter, and / or a device having an optical receiver and transmitter. In some examples, the network interface 216 may be designed to operate over network 114. The network interface 216 may be configured to transmit and receive electrical, electromagnetic, or optical signals that may represent various types of data.

[0022]

[0028] The input device 218 can include, for example, a keyboard, a mouse, a touchpad, a touch screen, one or more buttons, a joystick, a microphone, and / or any other device configured to detect and / or receive input. In some examples, the input device 218 may include one or more of various types of sensors, such as an image sensor, a temperature sensor, a humidity sensor, a position sensor, or any other type of sensor. The output device 220 can include, for example, a light indicator, a light source, a display (e.g., a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a liquid crystal display (LCD), or a dot matrix display), a screen, a touch screen, a speaker, headphones, a device configured to provide a tactile cue, a vibrator, and / or any other device configured to provide output.

[0023]

[0029] The memory 214 can store instructions that cause at least one processor to perform one or more of the processes described herein when executed by the at least one processor. The instructions can include, for example, software instructions, computer programs, computer code, executable instructions, source code, machine instructions, machine language programs, or any other type of instructions for a computing device. The instructions can be based on one or more of various types of desired programming languages and can include (e.g., embody) various processes for optimizing the energy dispatch described herein and / or detecting anomalies in an energy storage system.

'002'4

[0030] Figure 3 shows an exemplary energy storage unit 310 consistent with several embodiments of the present disclosure. The energy storage unit 310 may include, for example, a computing device 312, a cooling system 314, one or more sensors 316, and / or one or more batteries (e.g., 318A-318H). The energy storage unit 310 may be one or more examples of energy storage units 112A, 112B, 112C. In some examples, the energy storage unit 310 may have an enclosure, and the components of the energy storage unit 310 may be housed within the enclosure. The enclosure may be of any desired shape and / or may be constructed using any desired material. For example, the enclosure may have a rectangular or cubic shape.

[0025]

[0031] Computing device 312 may be implemented in a similar manner to computing device 210. Computing device 312 may be local to the energy storage unit 310 (for example, it may be located within the enclosure of the energy storage unit 310). Computing device 312 may be configured to manage other components of the energy storage unit 310. Computing device 312 may also be configured to communicate with another computing device (for example, user device 116) for managing the energy storage unit 310, either additionally or alternatively. For example, computing device 312 may transmit operational data of the energy storage unit 310 to other computing devices, receive instructions from other computing devices, and execute received instructions.

[0026]

[0032] A battery (for example, any one of batteries 318A to 318H) may refer to a battery and may include a power source containing one or more electrochemical cells with external connections. Batteries 318A to 318H may be rechargeable and may be discharged and recharged multiple times. Batteries 318A to 318H may include one or more of various types of batteries, such as lithium-ion batteries, lithium iron phosphate batteries, silver oxide batteries, nickel-zinc batteries, nickel-metal hydride batteries, lead-acid batteries, nickel-cadmium batteries, lithium nickel-manganese-cobalt oxide (NMC) batteries, lithium nickel-cobalt-aluminum oxide (NCA) batteries, lithium-ion manganese oxide (LMO) batteries, lithium-cobalt oxide batteries, fuel cells, or other types of batteries. Although the energy storage unit 310 is shown to include batteries 318A to 318H, more or fewer batteries may be included in the energy storage unit 310 as needed.

[0027]

[0033] Batteries 318A to 318H may have associated control components. These control components may be configured, for example, to manage the charging and discharging of batteries 318A to 318H. The control components may include, for example, a battery management system (BMS). In some examples, each of batteries 318A to 318H may have its own control component (e.g., a battery management system). The control components for each of batteries 318A to 318H may be implemented by a computing device and / or communicate with a central management component (e.g., one for the energy storage unit 310, implemented by computing device 312). Additionally or alternatively, the central management component may collectively manage the charging and discharging of batteries 318A to 318H. The charging and discharging of batteries 318A to 318H may be controlled using appropriate techniques such as a circuit with switch control, a charge or discharge controller, a charge or discharge regulator, and / or a battery regulator, so that batteries 318A to 318H can be controlled to receive electricity from a source at a specific rate, output electricity to a load at a specific rate, or be in an idle state.

[0028]

[0034] The cooling system 314 may include any type of device configured to remove heat from the energy storage unit 310. The cooling system 314 may use air, liquid, and / or any other type of suitable medium or material to remove heat. In some examples, the cooling system 314 may include a heat sink and / or cooling fins. In some examples, the cooling system 314 may include a fan (e.g., for moving air in air cooling), a pump (e.g., for moving liquid in liquid cooling), a compressor (e.g., for vapor compression refrigeration), or any other type of device for cooling. The cooling system 314 may use any type of desired configuration, positioning, and / or distribution. In some examples, each of the batteries 318A to 318H may have a cooling element associated with each of the batteries 318A to 318H individually as part of the cooling system 314.

[0029]

[0035] One or more sensors 316 may include any type of sensor configured to collect information associated with the energy storage unit 310 (for example, to measure the operation of the energy storage unit 310). For example, sensors 316 may include temperature sensors, humidity sensors, position sensors, current sensors, voltage sensors, and / or other types of sensors. Sensors 316 may use any type of desired configuration, positioning, and / or distribution. In some examples, each of the batteries 318A to 318H may have sensors associated with each of the batteries 318A to 318H individually. In some examples, the energy storage unit 310 may include sensors that may be applicable collectively to the batteries 318A to 318H.

[0030]

[0036] The computing device 312 may communicate with and control the batteries 318A-318H (e.g., via control components for each battery), the cooling system 314, and the sensor 316(or more). In some examples, the computing device 312 may control the components based on instructions from another computing device (e.g., user device 116). Additionally or alternatively, data associated with the energy storage unit 310 may be collected, including, for example, data measured by the sensor 316(or more), data used by the energy storage unit 310 (e.g., parameters for controlling the batteries 318A-318H, or parameters for controlling the cooling system 314 such as fan speed, pump utilization, or compressor utilization), or any other type of data. The collected data may be processed by the computing device 312 and / or another computing device (e.g., user device 116) for one or more embodiments described herein.

[0031]

[0037] The disclosed embodiments, including methods, systems, apparatus, and non-temporary computer-readable media, may relate to the optimization of energy dispatch. Energy dispatch may refer to the input of energy to and / or the output of energy from energy storage units. The disclosed embodiments include a system for optimizing energy dispatch, the system including one or more energy storage units and a computing device including at least one processor and memory, wherein instructions cause the computing device to perform the functions described herein when executed by at least one processor. An energy storage unit may refer to a physical object for storing energy. An energy storage unit may store energy in one or more of various forms, such as electrochemical, chemical, mechanical, electrical, electromagnetic, biological, and / or thermal. In some examples, an energy storage unit may store energy using a rechargeable battery. An example of an energy storage unit is described with reference to Figure 3. In some embodiments, one or more energy storage units include one or more battery units. In some embodiments, each battery unit of one or more battery units includes an enclosure containing a plurality of batteries.

[0032]

[0038] The disclosed embodiments include receiving expected energy market data from a computing device associated with a wholesale energy market. For example, a first computing device (e.g., user device 116, computing devices in energy storage units 112A, 112B, 112C, or any other computing device) may receive expected energy market data from a computing device associated with a wholesale energy market. The wholesale energy market may include the sale of energy from an entity to resellers (e.g., energy storage operators who store the energy they receive for later output, and / or power companies that distribute the electricity to end consumers). The computing device associated with the wholesale energy market may take in information about and from the wholesale energy market and may forecast energy market data based on a model (e.g., based on machine learning). For example, the expected energy market data may be generated based on historical energy market data. In some examples, the expected energy market data may show predicted energy prices for each of several time intervals within a time frame (e.g., predicted energy prices per minute in a 24-hour period). In some examples, the computing device associated with the wholesale energy market may include an energy market device 118. A computing device associated with the wholesale energy market may transmit expected energy market data to a first computing device.

[0033]

[0039] Figure 4 shows a flowchart of an exemplary method 400 for optimizing energy dispatch, consistent with some embodiments of the present disclosure. Referring to Figure 4, in step 410, a first computing device may receive expected energy market data.

[0034]

[0040] The disclosed embodiments include receiving environmental data for one or more energy storage units from a computing device associated with a weather forecast. For example, a first computing device may receive environmental data for one or more energy storage units from a computing device associated with a weather forecast. In some embodiments, the environmental data for one or more energy storage units includes one or more of the expected ambient temperature of the area in which the one or more energy storage units are located, or the expected ambient humidity of the area in which the one or more energy storage units are located. The environmental data may also include any other type of data that may reflect the environment of the area in which the one or more energy storage units are located (e.g., wind speed in the area and / or the degree of solar radiation in that area). Solar radiation may refer to a measure of the solar energy incident on a particular area over a set period of time. The environmental data may also include expected data that may be generated based on historical data. In some examples, the environmental data may show environmental parameters for each of several time intervals of a time frame (e.g., predicted temperature or predicted humidity per minute in a 24-hour period). In some examples, the computing device associated with the weather forecast may include a weather data device 120. The computing device associated with the weather forecast may transmit the environmental data to the first computing device. Referring to Figure 4, in step 412, the first computing device may receive environmental data.

[0035]

[0041] The disclosed embodiments include generating a plurality of energy dispatch patterns for utilizing one or more energy storage units. For example, a first computing device may generate a plurality of energy dispatch patterns for utilizing one or more energy storage units. In some embodiments, an energy dispatch pattern among the plurality of energy dispatch patterns includes a plurality of segments of a time frame configured for one or more energy storage units to charge or discharge at a specific rate. For example, an energy dispatch pattern may represent the rates at which one or more energy storage units are configured to charge or discharge for each of the plurality of segments of a time frame (e.g., charge at 5 megawatts for the first minute of a 24-hour period, charge at 5 megawatts for the second minute of a 24-hour period, charge at 10 megawatts for the third minute of a 24-hour period, discharge at 20 megawatts for the fourth minute of a 24-hour period, etc.). Each of the plurality of energy dispatch patterns may be for a time frame of any desired length (e.g., 24 hours, 48 ​​hours, 72 hours, etc.). In some examples, each of multiple energy dispatch patterns may be generated for the same specific time frame (e.g., the next 24 hours of the following day).

[0036]

[0042] Multiple energy dispatch patterns may differ from one another. For example, multiple energy dispatch patterns may differ in the number of charge-discharge cycles (e.g., charge-then-discharge cycles, or discharge-then-charge cycles). A charge-then-discharge cycle may refer to a time frame in which the energy storage unit is charging, followed by a time frame in which the energy storage unit is discharging. In some examples, there may be a time frame in between the two time frames in which the energy storage unit is idle. A discharge-then-charge cycle may refer to a time frame in which the energy storage unit is discharging, followed by a time frame in which the energy storage unit is charging. In some examples, there may be a time frame in between the two time frames in which the energy storage unit is idle. Additionally or alternatively, multiple energy dispatch patterns may differ in the rates of charge or discharge and / or have different time segments in which charge or discharge occurs. Multiple energy dispatch patterns may differ from one another in any other desired manner.

[0037]

[0043] The disclosed embodiments include generating multiple energy dispatch patterns by performing an optimization technique based on an objective function and a number of different constraints. In some embodiments, the optimization technique includes linear programming or mixed-integer linear programming. The optimization technique may include any other desired method for optimization. In some embodiments, the number of different constraints include different amounts of charge-discharge cycles that should be included in the energy dispatch patterns. For example, if a constraint on the optimization technique requires that a first energy dispatch pattern contain one charge-discharge cycle, the optimization technique may be performed to generate the first energy dispatch pattern; if a constraint on the optimization technique requires that a second energy dispatch pattern contain two charge-discharge cycles, the optimization technique may be performed to generate the second energy dispatch pattern. It is intended that one or more of these or additional energy dispatch patterns can be generated using constraints that require any number of charge-discharge cycles.

[0038]

[0044] In some embodiments, the optimization technique determines a particular energy dispatch pattern by maximizing an estimate of that particular energy dispatch pattern based on market price forecasts for a time frame. For example, the input data to the optimization technique may include expected energy market data showing predicted energy prices for each of several time intervals of the time frame (e.g., predicted energy prices for every minute of a 24-hour period). In some examples, based on the expected energy market data, the optimization technique may generate a particular energy dispatch pattern such that the objective function of the optimization technique can be maximized (e.g., under certain constraints). The objective function may be, for example, an estimate of the particular energy dispatch pattern (e.g., the revenue received by discharging (or selling) energy by one or more energy storage units, minus the cost of charging (or purchasing) energy for one or more energy storage units, based on the expected energy market data). Referring to Figure 4, in step 414, the first computing device may generate multiple energy dispatch patterns for utilizing one or more energy storage units.

[0039]

[0045] The disclosed embodiments include performing one or more functions for each of a plurality of energy dispatch patterns. For example, the first computing device may perform one or more functions for each of a plurality of energy dispatch patterns. One or more functions are described in more detail below. Referring to Figure 4, in step 416, the first computing device may select an energy dispatch pattern from a plurality of energy dispatch patterns for processing (for example, including performing one or more functions (for example, steps 418, 420, 422, 424, and / or 426) for the selected energy dispatch pattern). In step 428, after performing one or more functions for the selected energy dispatch pattern, the first computing device may determine whether each of the plurality of energy dispatch patterns has been processed. If the first computing device determines that each of the plurality of energy dispatch patterns has been processed (step 428: yes), the method may proceed to step 430. However, if the first computing device determines that none of the multiple energy dispatch patterns have been processed (step 428: no), the method may return to step 416. In step 416, the first computing device may select another energy dispatch pattern from the multiple energy dispatch patterns for processing.

[0040]

[0046] In some embodiments, one or more functions performed for each of a plurality of energy dispatch patterns include determining an estimate of the energy dispatch pattern based on expected energy market data. For example, a first computing device may determine an estimate of a particular energy dispatch pattern among the plurality of energy dispatch patterns based on expected energy market data. The expected energy market data may represent predicted energy prices for each of a plurality of time intervals within a time frame (e.g., predicted energy prices for each minute of a 24-hour period). A particular energy dispatch pattern may represent the rates at which one or more energy storage units are configured to charge or discharge for each of a plurality of time intervals within a time frame (e.g., charging at 5 megawatts for the first minute of a 24-hour period, charging at 5 megawatts for the second minute of a 24-hour period, charging at 10 megawatts for the third minute of a 24-hour period, discharging at 20 megawatts for the fourth minute of a 24-hour period, etc.). The estimates for a particular energy dispatch pattern may, for example, correspond to the total revenue received (from the discharge or sale of energy) or the total cost (from the charging or purchase of energy) for each of several intervals within the time frame of the particular energy dispatch pattern. Referring to Figure 4, in step 418, the first computing device may determine the estimates for the energy dispatch pattern selected in step 416 based on the expected energy market data.

[0041]

[0047] In some embodiments, one or more functions performed for each of a plurality of energy dispatch patterns include determining one or more state variables of one or more energy storage units based on environmental data for one or more energy storage units. For example, a first computing device may determine one or more state variables of one or more energy storage units based on environmental data for one or more energy storage units. In some embodiments, the one or more state variables of one or more energy storage units include the temperature of one or more energy storage units in one or more enclosures for one or more energy storage units. In some examples, the temperature may include the temperature of the batteries of one or more energy storage units. In some examples, the temperature may be expressed individually for each battery in one or more energy storage units. In some examples, the temperature may be expressed aggregated for the batteries of one or more energy storage units (e.g., average battery temperature, maximum battery temperature, median battery temperature, or any other statistic or metric). In some examples, the temperature may be a time series of data points showing the temperature for each of multiple intervals of a time frame. Additionally or alternatively, one or more state variables of one or more energy storage units may include any other parameters that may affect or indicate the degradation of one or more energy storage units (e.g., auxiliary loads such as total power consumption and maximum power consumption to support one or more energy storage units).

[0042]

[0048] Determining one or more state variables can be based on any preferred method. Environmental data for one or more energy storage units may be input data for the method. For example, one or more state variables may be predicted based on environmental data using thermodynamic modeling for one or more energy storage units. In some examples, a machine learning model (e.g., random forest mode or neural network) may be used to predict one or more state variables based on environmental data, as described in more detail herein (e.g., in relation to anomaly detection in energy storage systems). The disclosed embodiments include determining one or more state variables for one or more energy storage units based on an energy dispatch pattern. For example, a particular energy dispatch pattern may be input to a model for predicting one or more state variables, additionally or alternatively. The first computing device may calculate the heat generated by the batteries of one or more energy storage units based on the electrical resistance of the batteries and the amount of current that the batteries can output. The amount of current may be determined based on a particular energy dispatch pattern. The heat remaining in the batteries of one or more energy storage units may correspond to the amount of heat generated minus the amount of heat dissipated through air or other cooling mechanisms (e.g., calculated using thermodynamic modeling). The battery temperature may be calculated based on the residual heat.

[0043]

[0049] Referring to Figure 4, in step 420, the first computing device may determine one or more state variables of one or more energy storage units based on environmental data for one or more energy storage units.

[0044]

[0050] In some embodiments, one or more functions performed for each of a plurality of energy dispatch patterns include determining an estimated limit degradation of one or more energy storage units based on one or more state variables. For example, a first computing device may determine the estimated limit degradation of one or more energy storage units based on one or more state variables. The estimated limit degradation may, for example, indicate the amount of decrease in the capacity of one or more energy storage units for storing energy. For example, if the capacity of one or more energy storage units decreases from 50 megawatt-hours (MWh) to 49.99 MWh, the limit degradation may be 0.01 MWh (e.g., 50 MWh minus 49.99 MWh). The disclosed embodiments include determining the estimated limit degradation of one or more energy storage units based on an energy dispatch pattern. The disclosed embodiments include determining the estimated limit degradation by performing a lookup in a database that maps the parameters of the energy dispatch pattern and the battery temperature to the expected degree of degradation. The parameters of the energy dispatch pattern may include, for example, the energy throughput of the energy dispatch pattern, the number of charge-discharge cycles included in the energy dispatch pattern, the depth of the charge-discharge cycles (e.g., discharge to 80% capacity and then recharge, discharge to 60% capacity and then recharge, discharge to 40% capacity and then recharge, discharge to 20% capacity and then recharge, discharge to 0% capacity and then recharge, etc.), the distribution of charging or discharging activity over the time span of the energy dispatch pattern (e.g., the length of time between two charge-discharge cycles of the energy dispatch pattern, the length of time between charging and discharging activity of the energy dispatch pattern, etc.), and / or any other metrics or parameters. As an example, an equation may be used in which the associated temperature and energy throughput (which can be calculated based on the energy dispatch pattern) can be mapped to the degree of degradation.In some examples, the mapping may be notified from the battery supplier for one or more energy storage units. Additionally or alternatively, the critical degradation may be calculated using other methods (e.g., machine learning models). Referring to Figure 4, in step 422, the first computing device may determine the estimated critical degradation of one or more energy storage units based on one or more state variables.

[0045]

[0051] In some embodiments, one or more functions performed for each of a plurality of energy dispatch patterns include determining the estimated cost of the energy dispatch pattern based on estimated limit degradation. For example, a first computing device may determine the estimated cost of an energy dispatch pattern based on estimated limit degradation. Disclosed embodiments include determining the estimated cost by calculating the replacement cost to replace the estimated limit degradation. The replacement cost may be, for example, the price to acquire a certain amount of energy storage capacity (e.g., $100,000 per MWh) multiplied by the amount of estimated limit degradation (e.g., 0.01 MWh). Disclosed embodiments also include determining the estimated cost by calculating the opportunity cost associated with the estimated limit degradation. The opportunity cost may be, for example, calculated as discounted future revenue lost because the estimated limit degradation is greater than the planned degradation, or conversely, as future revenue recovered if the estimated limit degradation is less than the planned degradation. The planned degradation may be composed of or determined by the operator of one or more energy storage units (for example, the operator may plan to use one or more energy storage units such that they may degrade to 50% of their original capacity in 30 years). The estimated cost of the energy dispatch pattern may be determined using any other suitable cost model. Referring to Figure 4, in step 424, the first computing device may determine the estimated cost of the energy dispatch pattern selected in step 416 based on the estimated limit degradation.

[0046]

[0052] In some embodiments, one or more functions performed for each of a plurality of energy dispatch patterns include calculating the estimated net value of the energy dispatch pattern based on an estimate and an estimated cost. For example, a first computing device may calculate the estimated net value of an energy dispatch pattern based on an estimate and an estimated cost of the energy dispatch pattern. The estimated net value may refer to, for example, the predicted net profit (e.g., monetary value) of the energy dispatch pattern. The estimated net value may correspond to, for example, the estimated value of the energy dispatch pattern minus the estimated cost of the energy dispatch pattern. Referring to Figure 4, in step 426, the first computing device may calculate the estimated net value of an energy dispatch pattern based on an estimate and an estimated cost of the energy dispatch pattern selected in step 416.

[0047]

[0053] The disclosed embodiments include generating a recommended energy dispatch pattern based on data determined for each of a plurality of energy dispatch patterns. For example, a first computing device may generate a recommended energy dispatch pattern based on data determined for each of a plurality of energy dispatch patterns. The data determined for each of a plurality of energy dispatch patterns may include, for example, the estimated net value of the plurality of energy dispatch patterns. Additionally or alternatively, the data determined for each of a plurality of energy dispatch patterns may include the estimated benefit of the plurality of energy dispatch patterns, the estimated value or estimated revenue of the plurality of energy dispatch patterns, the estimated cost of the plurality of energy dispatch patterns, the estimated marginal degradation of the plurality of energy dispatch patterns, or any other type of data suitable for generating a recommended energy dispatch pattern. The disclosed embodiments include generating a recommended energy dispatch pattern based on the estimated net value for a plurality of energy dispatch patterns. For example, a first computing device may generate a recommended energy dispatch pattern based on the estimated net value for a plurality of energy dispatch patterns. The disclosed embodiments include generating a recommended energy dispatch pattern by selecting from a plurality of energy dispatch patterns an energy dispatch pattern whose estimated net value is greater than the estimated net value of the other energy dispatch patterns among the plurality of energy dispatch patterns and which is greater than a configured threshold.The configured threshold may be determined, for example, by the operators of one or more energy storage units, and therefore, if any energy dispatch pattern results in a net loss or a net profit below a threshold amount (e.g., $0, $500, $1,000, $2,000, or any other desired amount), that energy dispatch pattern may not be recommended to the operator. Additionally or alternatively, generating a recommended energy dispatch pattern may be based on considering other factors such as the number of charge-discharge cycles included in the energy dispatch pattern, the depth of the charge-discharge cycles, and / or any other factors. Various weights may be assigned to the factors, and for each of the multiple energy dispatch patterns, a weighted score may be calculated indicating how close it is to a preferred setting for the factors. The recommended energy dispatch pattern may correspond to the energy dispatch pattern with the highest weighted score among the multiple energy dispatch patterns. Referring to Figure 4, in step 430, the first computing device may generate a recommended energy dispatch pattern based on the data determined for each of the multiple energy dispatch patterns (e.g., the estimated net value for the multiple energy dispatch patterns).

[0048]

[0054] The disclosed embodiments include dispatching electricity by adjusting one or more energy storage units according to a recommended energy dispatch pattern. For example, a first computing device may dispatch electricity by adjusting one or more energy storage units according to a recommended energy dispatch pattern. The first computing device may, for example, instruct control components for one or more energy storage units to charge or discharge (or leave idle for a specific time) one or more energy storage units (e.g., their internal batteries) for a specific time and at a specific rate as indicated in the recommended energy dispatch pattern. The charging and discharging of one or more energy storage units may be controlled using appropriate techniques such as a circuit with switch control, a charge or discharge controller, a charge or discharge regulator, and / or a battery regulator, so that one or more energy storage units can be controlled to be in a state where they are receiving electricity from a source at a specific rate, outputting electricity to a load at a specific rate, or are idle. Referring to Figure 4, in step 432, the first computing device may dispatch electricity by adjusting one or more energy storage units according to a recommended energy dispatch pattern.

[0049]

[0055] The disclosed embodiments include displaying a user interface showing a recommended energy dispatch pattern. For example, a first computing device may display a user interface showing a recommended energy dispatch pattern. The user interface may, for example, show the charging, discharging, or idle state of one or more energy storage units in a time series of data points. Figure 5 shows an exemplary user interface 500 associated with energy dispatch optimization, consistent with some embodiments of the present disclosure. The user interface may show an energy storage system identified by identifier 510 (e.g., “exemplary site-100MW 100MWh”). Figure 512 shows data points of an expected energy price and an energy dispatch pattern (recommended based on the expected energy price) over a 24-hour period. Figure 512 shows an energy dispatch pattern showing charging at a specific time (e.g., the 10th hour) and discharging at a specific time (e.g., the 15th hour). Figure 514 shows the temperature associated with one or more energy storage units over a 24-hour period. Figure 516 shows the State of Charge (SOC) of one or more energy storage units over a 24-hour period. Identifier 518 indicates the replacement cost for the energy dispatch pattern. Identifier 520 indicates the opportunity cost for the energy dispatch pattern. Identifier 522 indicates the total degradation of one or more energy storage units for the energy dispatch pattern. Identifier 524 indicates the expected benefit of the energy dispatch pattern considering replacement costs (e.g., the estimated net value of the energy dispatch pattern). Identifier 526 indicates the expected benefit of the energy dispatch pattern considering opportunity costs (e.g., the estimated net value of the energy dispatch pattern). Identifier 528 indicates the return on the energy dispatch pattern (e.g., the estimated value of the energy dispatch pattern).The pull-down menu 530 may allow the user to select an energy dispatch pattern to be displayed on the user interface (for example, "Energy Dispatch Pattern 1" may be displayed on the user interface, and other energy dispatch patterns may be selected to be displayed on the user interface). Using a button, pull-down menu, or some other trigger, the user may compare two or more energy dispatch patterns by selecting an energy dispatch pattern from the pull-down menu. The user interface shown in Figure 5 is illustrative, and other types of displays are possible. The user interface may group various related information items on a single screen, enabling the user to quickly determine the best energy dispatch pattern.

[0050]

[0056] In some embodiments, a method for optimizing energy dispatch includes: generating multiple energy dispatch patterns for utilizing one or more energy storage units using a computing device; generating a recommended energy dispatch pattern based on data determined for each of the multiple energy dispatch patterns; and dispatching electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern. In some embodiments, the method includes, for each of the multiple energy dispatch patterns, determining an estimate of the energy dispatch pattern based on expected energy market data; determining one or more state variables of one or more energy storage units based on environmental data for one or more energy storage units; determining an estimated limit degradation of one or more energy storage units based on one or more state variables; determining an estimated cost of the energy dispatch pattern based on the estimated limit degradation; and calculating an estimated net value of the energy dispatch pattern based on the estimate and estimated cost. In some embodiments, the method also includes generating a recommended energy dispatch pattern based on the estimated net value for the multiple energy dispatch patterns.

[0051]

[0057] In some embodiments, a non-temporary computer-readable medium stores instructions for optimizing energy dispatch. When executed by at least one processor, the instructions cause at least one processor to generate multiple energy dispatch patterns for utilizing one or more energy storage units, generate a recommended energy dispatch pattern based on data determined for each of the multiple energy dispatch patterns, and dispatch electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern. In some embodiments, when executed by at least one processor, the instructions cause at least one processor to determine an estimate of the energy dispatch pattern for each of the multiple energy dispatch patterns based on expected energy market data, determine one or more state variables for one or more energy storage units based on environmental data for one or more energy storage units, determine an estimated limit degradation of one or more energy storage units based on one or more state variables, determine an estimated cost of the energy dispatch pattern based on the estimated limit degradation, and calculate an estimated net value of the energy dispatch pattern based on the estimates and estimated costs. In some embodiments, when the instruction is executed by at least one processor, it causes at least one processor to generate a recommended energy dispatch pattern based on the estimated net value of a plurality of energy dispatch patterns.

[0052]

[0058] The disclosed embodiments, including methods, systems, apparatus, and non-transient computer-readable media, may relate to anomaly detection in energy storage systems. Anomalies in an energy storage system may include any operation, function, or implementation form outside the expected range of the energy storage system. In some embodiments, a system for anomaly detection in an energy storage system includes one or more energy storage units and a computing device including at least one processor and memory for storing instructions, wherein when an instruction is executed by at least one processor, the computing device causes the computing device to perform the functions described herein. An energy storage unit may refer to a physical object for storing energy. An energy storage unit may store energy in one or more forms, such as electrochemical, chemical, mechanical, electrical, electromagnetic, biological, and / or thermal. In some examples, an energy storage unit may store energy using a rechargeable battery. An example of an energy storage unit is described with reference to Figure 3. In some embodiments, each energy storage unit of one or more energy storage units includes an enclosure containing a plurality of batteries. The enclosure may be of any desired shape and / or may be constructed using any desired material. For example, the enclosure may have a rectangular or cubic shape. In some embodiments, the computing device is associated with a cloud architecture. For example, the computing device may be implemented using virtualization and / or cloud computing technologies. For example, the components of the computing device (e.g., processor, memory, network interface, input device, and / or output device) may be implemented virtually (e.g., to function as a virtual machine on a physical computing device). In some embodiments, the computing device is local to one or more energy storage units.For example, the computing device may be located within the enclosure of one or more energy storage units, or within the site area of ​​one or more energy storage units. In some examples, the computing device may be located remotely from one or more energy storage units. For example, the computing device may be located in a data center connected to one or more energy storage units via a network.

[0053]

[0059] The disclosed embodiments include receiving usage data for batteries located in one or more energy storage units during a time frame. For example, a computing device (e.g., user device 116, computing devices in energy storage units 112A, 112B, 112C, or any other computing device) may receive usage data for batteries located in one or more energy storage units during a time frame. The time frame may have any desired length (e.g., 1 minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 24 hours, 48 ​​hours, etc.). In some embodiments, the battery usage data includes one or more of the following: the battery's initial charge state for the time frame, the battery's final charge state for the time frame, the sum of the squares of the battery's current for the time frame, the battery's voltage pattern associated with the time frame, the dispatch pattern associated with the battery for the time frame, the battery's measured temperature at the start of the time frame, the ambient temperature or humidity associated with the battery during the time frame, one or more utilization parameters of the cooling system for the battery during the time frame, or the battery's relative position within the enclosure of the energy storage unit containing the battery. The charge state may refer to the charge level of the battery relative to its capacity. The starting charge state for a time frame may be the charge state of the battery at the beginning of the time frame. The ending charge state for a time frame may be the charge state of the battery at the end of the time frame. The sum of the squares of the battery currents during a time frame may correspond, for example, to the integral or cumulative of the squared battery currents over the span of the time frame, and may represent the amount of energy supplied to or from the battery. The battery voltage pattern may include, for example, a time series of data points of the battery voltage over the time frame. The dispatch pattern associated with the battery for a time frame may include, for example, the energy dispatch pattern for the battery for a time frame as described herein.The ambient temperature or humidity associated with the battery during the time frame may include, for example, the temperature or humidity within the area in which the battery is located during the time frame. One or more utilization parameters of the battery's cooling system during the time frame may include, for example, the extent to which the cooling system can be used during the time frame (e.g., fan speed, pump utilization, or compressor utilization). The relative position of the battery within the enclosure of the energy storage unit containing the battery may be measured, for example, using three-dimensional coordinates, using the battery's position relative to other batteries in the energy storage unit, or by any other preferred method. In some examples, utilization data may be received from sensors or other systems associated with the battery. Figure 6 shows a flowchart of an exemplary method 600 for anomaly detection in an energy storage system, consistent with some embodiments of the present disclosure. Referring to Figure 6, in step 610, a computing device may receive utilization data for batteries located in one or more energy storage units during the time frame.

[0054]

[0060] The disclosed embodiments include inputting usage data into a machine learning model. For example, a computing device may input usage data into a machine learning model. Inputting usage data into a machine learning model may include, for example, retrieving usage data stored in the memory of the computing device (e.g., stored in a database) and configuring the machine learning model to process the retrieved usage data. Additionally or alternatively, the computing device may read usage data stored by the computing device and configure the machine learning model to process the usage data. In some embodiments, the machine learning model includes one of a random forest model or a neural network. Additionally or alternatively, any other suitable machine learning model may be used. In some examples, other types of models or methods may be used to predict the battery temperature, such as a lookup table or mapping, an equation that can model the heat transfer process of a battery, or other suitable algorithms. The disclosed embodiments include receiving specific usage data of a battery during periods in which the battery is manually considered to be exhibiting normal behavior, receiving measured temperature data of the battery at the end of each period, and training a machine learning model using the specific usage data and measured temperature data. For example, an individual who can operate the battery may indicate a time frame in which the battery is considered to be behaving normally, and battery usage data may be collected during this time frame. The battery temperature may be measured by a sensor at the end of the time frame. Training the machine learning model may be done using any suitable training algorithm. The dataset for training the machine learning model may be based, for example, on historical battery usage data for one or more time frames in which the battery is considered to be behaving normally, and the corresponding battery temperature measured at the end of each of the one or more time frames. Validation and testing of the trained machine learning model may be performed, for example, using a validation dataset or a test dataset.The validation or test dataset may be established in a similar manner to the training dataset, and may use data from different timeframes than those included in the training dataset. Referring to Figure 6, in step 612, the computing device may input usage data into the machine learning model.

[0055]

[0061] The disclosed embodiments include generating a predicted battery temperature at the end of a time frame based on the processing of usage data by a machine learning model. For example, a computing device may include generating a predicted battery temperature at the end of a time frame based on the processing of usage data by a machine learning model. For example, the machine learning model may include several input nodes, one or more intermediate nodes, and an output node (relationships or interconnections between nodes may be configured during training of the machine learning model). Each of the input nodes may correspond to a specific item in the usage data. The value of each input node may be set to the value of the corresponding item in the usage data for the time frame. Based on the relationships or interconnections between the nodes of the machine learning model, the input values ​​to the input nodes may trigger other nodes of the machine learning model to cause the output nodes to generate a value corresponding to the predicted battery temperature at the end of the time frame. Referring to Figure 6, in step 614, the computing device may generate a predicted battery temperature at the end of a time frame based on the processing of usage data by a machine learning model.

[0056]

[0062] The disclosed embodiments include receiving the measured temperature of the battery at the end of a time frame from a battery temperature sensor. For example, a computing device may receive the measured temperature of the battery at the end of a time frame from a battery temperature sensor. The temperature sensor may include, for example, a thermometer, a bimetallic strip, a thermistor, a thermocouple, a resistance thermometer, a silicon bandgap temperature sensor, or any other type of temperature sensor. The temperature sensor may be located inside the battery, on the surface of the battery, or in any other suitable location. In some examples, the temperature sensor may measure the temperature using, for example, infrared radiation, or may be located away from the battery to measure the battery temperature. The battery temperature sensor may be instructed to measure the temperature of the battery at the end of a time frame and may be configured to transmit the measured temperature to a computing device. For example, data indicating the measured temperature may be transmitted to the computing device via a wired or wireless connection between the temperature sensor and the computing device. Referring to Figure 6, in step 616, the computing device may receive the measured temperature of the battery at the end of a time frame from the battery temperature sensor.

[0057]

[0063] The disclosed embodiments include determining the difference between a predicted temperature and a measured temperature. For example, a computing device may determine the difference between a predicted temperature and a measured temperature. The difference may include, for example, a subtraction or ratio between the predicted temperature and the measured temperature. For example, a ratio closer to 1 may indicate a smaller difference, and a ratio further from 1 may indicate a larger difference. Additionally or alternatively, the difference may include signed values ​​or absolute values. Battery anomalies may be detected, for example, based on determining that the difference meets a threshold (e.g., configured by the battery operator). In some examples, battery anomalies may be detected based on comparing the measured temperature with the predicted temperature and determining that the measured temperature is higher or lower than the predicted temperature. In some examples, the calculation may be repeated over multiple time frames, and the average or maximum value over multiple time frames may be used to calculate or compare the difference to determine whether a battery anomaly has been detected. Referring to Figure 6, in step 618, the computing device may determine the difference between the predicted temperature and the measured temperature.

[0058]

[0064] The disclosed embodiments include transmitting a battery state indication based on a determined difference between a predicted temperature and a measured temperature. For example, a computing device may transmit a battery state indication based on a determined difference between a predicted temperature and a measured temperature. The indication may be in any desired form (e.g., email notification, pop-up window, text message, graphic display, pie chart, bar graph, icon, etc.). The indication may be transmitted to any suitable device (e.g., a device associated with the battery operator). The battery state may include, for example, a normal state, an abnormal state, or any other arbitrary state of the battery. In some examples, the battery state may include the normality of the battery. The battery state may be determined based on the difference between a predicted temperature and a measured temperature. For example, a computing device may determine that the battery may be in a normal state when the temperature difference is within a certain range (e.g., -1.5 degrees Celsius to 1.5 degrees Celsius, or any other desired range), and that the battery may be in an abnormal state when the temperature difference is outside that range. Additionally or alternatively, the computing device may determine the normality of the battery inversely proportional to the magnitude of the temperature difference, for example. In some examples, the battery's health may be determined inversely proportional to the distance between the temperature difference and a reference value (e.g., 0 degrees Celsius or any other desired value). Disclosed embodiments include sending an instruction that a battery abnormality has been detected based on a difference that satisfies a threshold. For example, a computing device may send an instruction that a battery abnormality has been detected based on a difference that satisfies a threshold. The computing device may compare the difference to a threshold and determine whether the difference satisfies (e.g., satisfies or exceeds) the threshold. The instruction may include, for example, a notification to an individual (e.g., the battery operator). The instruction may be in any desired form (e.g., an email notification, a pop-up window, a text message, a graphic display, a pie chart, a bar graph, an icon, etc.). The instruction may be presented to an individual (e.g., the operator associated with the battery).The instructions may be displayed on any suitable device (e.g., a user device, a computing device, etc.). In some examples, the instructions may trigger an alarm, a flashing light, or any other type of signal (e.g., in a control room for an operator associated with the battery). In some examples, the instructions may include the difference between the predicted temperature and the measured temperature, a threshold, and / or other information. In some examples, the instructions may include the severity of the detected anomaly based on the difference between the predicted temperature and the measured temperature, and / or the amount by which the difference between the predicted temperature and the measured temperature can exceed a threshold. Referring to Figure 6, in step 620, the computing device may transmit an instruction on the battery status based on the determined difference between the predicted temperature and the measured temperature. For example, the computing device may transmit an instruction that a battery anomaly has been detected based on a difference that meets a threshold.

[0059]

[0065] The disclosed embodiments include configuring battery use based on the state of the battery. For example, a computing device may configure battery use based on the state of the battery. Configuring battery use may include, for example, continuing battery use or adjusting battery use. For example, a computing device may continue existing methods in which the battery may be used when the battery state indicates a normal state of the battery (for example, the computing device may not make any changes to existing methods), or it may adjust existing methods in which the battery may be used when the battery state indicates an abnormal state of the battery. Additionally or alternatively, configuring battery use may be based on the health of the battery. For example, the extent to which the way in which the battery may be used may be changed may be configured to be inversely proportional to the health of the battery (for example, the extent of change may be made smaller when the health of the battery is higher, and the extent of change may be made larger when the health of the battery is lower). The disclosed embodiments include adjusting battery use based on the detection of a battery abnormality. For example, a computing device may adjust battery use based on the detection of a battery abnormality. The disclosed embodiments include adjusting battery usage by one or more of the following: pausing battery usage, reducing battery usage, or modifying battery usage patterns. A computing device may, for example, instruct a control component for the battery to adjust battery usage. For example, the battery current, current flow rate, voltage, duration of use, or any other usage parameter may be adjusted. In some examples, the adjustment of battery usage may be based on the severity of detected anomalies. For example, if the severity of detected anomalies is higher, battery usage may be significantly reduced.Modifying the battery usage pattern may include, for example, modifying the depth of charge-discharge cycles for the battery (e.g., switching between shallower and deeper cycles), changing the number of charge-discharge cycles within a time frame for the battery (e.g., 1, 2, 3, 4, etc.), and / or changing any other parameters of the battery usage pattern. The depth of charge-discharge cycles for the battery may refer to, for example, the extent to which the battery can discharge or charge within a charge-discharge cycle (e.g., discharge to 80% capacity and then recharge, discharge to 60% capacity and then recharge, discharge to 40% capacity and then recharge, discharge to 20% capacity and then recharge, discharge to 0% capacity and then recharge, etc.). Referring to Figure 6, in step 622, the computing device may configure battery usage based on the state of the battery. For example, the computing device may adjust battery usage based on the detection of a battery anomaly.

[0060]

[0066] The disclosed embodiments include, for each particular battery among a plurality of batteries of one or more energy storage units, using a machine learning model to generate a predicted temperature of the particular battery at the end of a time frame, receiving the measured temperature of the particular battery at the end of the time frame, and determining the temperature difference between the predicted temperature of the particular battery and the measured temperature of the particular battery. For example, a computing device may determine the temperature difference between the predicted temperature and the measured temperature for each of the plurality of batteries of one or more energy storage units. In some examples, the plurality of batteries may have relative positions that may differ from one another. Their relative positions may be input to a machine learning model so that the machine learning model can take into account the relative positions of the batteries when generating predicted temperatures for the batteries. Each of the plurality of batteries may be associated with a sensor (which may have similar characteristics and / or functions to the temperature sensors described above) for measuring the temperature of the corresponding battery.

[0061]

[0067] The disclosed embodiments include displaying a user interface that shows a plurality of batteries, the relative positions of the plurality of batteries, and the temperature difference for each of the plurality of batteries. In some examples, the user interface may enumerate the plurality of batteries and show their respective positions and temperature differences (e.g., by listing). The user interface may have any desired format (e.g., a pop-up window, a tile listing, etc.). A tile listing may include, for example, organizing the display into frames that do not overlap each other. In some embodiments, the user interface shows the relative positions of the plurality of batteries in a three-dimensional viewpoint and shows the temperature difference for each of the plurality of batteries using a color scale. The temperature difference for each of the plurality of batteries may be shown using other desired methods. Figure 7 shows an exemplary user interface 700 associated with anomaly detection in an energy storage system, consistent with some embodiments of the present disclosure. The user interface shows a three-dimensional view 710. The user interface shows several batteries (e.g., battery 712) as boxes. A scale 716 showing the degree of temperature difference may use a pattern scale, a color scale, or any other type of appropriate indication. Each battery, represented as a box within the user interface, may be displayed in association with a corresponding indication of the degree of temperature difference for the battery (e.g., pattern, color, etc.). Window 714 may be invoked, for example, when a cursor (e.g., controlled by the user) clicks or hovers over a particular battery. Additionally or alternatively, Window 714 may be invoked via the touchscreen, by speaking into the microphone and / or by speech recognition and processing, by typing text into a text input box, by selecting from a pull-down menu, or by any other desired method.Window 714 may indicate the location of the battery (e.g., indicated by x, y, and z coordinates), the battery identifier (e.g., energy storage unit 3, rack 14, battery module 8), or the temperature difference for the battery (e.g., -1.1°C). The user interface shown in Figure 7 is illustrative, and other types of displays are possible. For example, the user interface may be a tabular listing, an image of the racks (where batteries may be located) color-coded by temperature difference, a pie chart showing the number of batteries with different temperature differences, or a line graph. In some examples, selecting a specific battery may display a time-series plot of temperature data or a tabular listing of temperature data. The user interface may present relevant information on a single screen without requiring the user to navigate to multiple screens. Other buttons, list boxes, pull-down menus, and / or other functions may be present to allow the user to zoom in or out on the data through a single screen.

[0062]

[0068] The disclosed embodiments include determining the difference between a predicted temperature and a measured temperature for each of a plurality of periods for a battery, and determining, based on the difference for each of the plurality of periods, whether a battery anomaly has been detected. For example, the predicted temperature may be determined based on a machine learning model, and the measured temperature may be measured using a sensor for the battery. The plurality of periods may be any desired time frames, such as consecutive time frames, time frames of equal length, time frames of different lengths, time frames separated by equal lengths from each other, time frames separated by different lengths from each other, a specific time period spanning several days, and / or time frames having any other desired configuration. In some examples, the computing device may calculate an average of the differences for each of the plurality of periods and use the average to determine, (for example, by comparing the average to a threshold) whether a battery anomaly has been detected. In some examples, the computing device may determine whether the differences determined for the plurality of periods show an increasing trend in the degree of difference that suggests a battery anomaly. Additionally or alternatively, the maximum value of the determined differences over multiple periods, the minimum value of the determined differences over multiple periods, a value calculated based on an operational function or gradient applied to the determined differences over multiple periods, and / or any other desired metric based on the determined differences over multiple periods may be used to determine whether a battery anomaly has been detected (for example, by comparing the metric to a threshold).

[0063]

[0069] The disclosed embodiments include inputting usage data into a second machine learning model; generating a predicted battery voltage at the end of a time frame based on the second machine learning model's processing of the usage data; receiving a measured battery voltage at the end of the time frame from a battery voltage sensor; determining the voltage difference between the predicted voltage and the measured voltage; and sending an instruction that a battery anomaly has been detected when the voltage difference meets a threshold. The second machine learning model may include, for example, a random forest model, a neural network, or any other suitable model. The second machine learning model may be trained using battery voltage data measured during time frames in which the battery was manually considered to be exhibiting normal behavior. The threshold from which the voltage difference is compared may be set to any desired value (e.g., 1 volt, 2 volts, 5 volts, 10 volts, etc.) by the operator associated with the battery. In some examples, the computing device may retrieve usage data stored in the computing device's memory and configure the second machine learning model to process the usage data. The second machine learning model may include, for example, input nodes, intermediate nodes, and / or output nodes. The values ​​of the input nodes may be set to the values ​​of corresponding items in usage data. The input values ​​to the input nodes may trigger other nodes of the second machine learning model, which may cause the output nodes to generate values ​​corresponding to the predicted battery voltage at the end of the time frame. The voltage sensor may measure the battery voltage at the end of the time frame and transmit data indicating the measured voltage to a computing device (for example, via a connection between the voltage sensor and the computing device). The computing device may receive data indicating the measured voltage. The computing device may calculate the voltage difference between the measured voltage and the predicted voltage (for example, by subtracting the measured voltage from the predicted voltage, calculating a ratio between the measured voltage and the predicted voltage, or calculating any other desired metric).The computing device may compare the voltage difference to a threshold and send an instruction that a battery anomaly has been detected when the voltage difference meets (e.g., satisfies or exceeds) the threshold. The detection of a battery anomaly may be based on both the battery voltage and temperature, or on the battery temperature, or on the battery voltage, or on any other desired parameter of the battery.

[0064]

[0070] In some embodiments, a method for detecting anomalies in an energy storage system includes: receiving usage data of batteries located in one or more energy storage units during a time frame by a computing device; inputting the usage data into a machine learning model; generating a predicted battery temperature at the end of the time frame based on the machine learning model's processing of the usage data; receiving the measured battery temperature at the end of the time frame from a battery temperature sensor; determining the difference between the predicted temperature and the measured temperature; transmitting a battery state instruction based on the determined difference; and configuring battery usage based on the battery state. In some embodiments, the method includes transmitting an instruction that a battery anomaly has been detected based on a difference that meets a threshold; and adjusting battery usage based on the detection of a battery anomaly. In some embodiments, each of the one or more energy storage units includes an enclosure containing multiple batteries. In some embodiments, the method includes, for each specific battery of a plurality of batteries in one or more energy storage units, using a machine learning model to generate a predicted temperature for that particular battery at the end of a time frame, receiving a measured temperature for that particular battery at the end of the time frame, and determining the temperature difference between the predicted temperature and the measured temperature of that particular battery. In some embodiments, the method includes displaying a user interface that shows the plurality of batteries, the relative positions of the plurality of batteries, and the temperature difference for each of the plurality of batteries.

[0065]

[0071] In some embodiments, a non-temporary computer-readable medium stores instructions for anomaly detection in an energy storage system. When executed by at least one processor, the instructions cause at least one processor to receive usage data for batteries located in one or more energy storage units during a time frame, input the usage data into a machine learning model, generate a predicted battery temperature at the end of the time frame based on the machine learning model's processing of the usage data, receive the measured battery temperature at the end of the time frame from a battery temperature sensor, determine the difference between the predicted temperature and the measured temperature, send a battery state instruction based on the determined difference, and configure battery usage based on the battery state. In some embodiments, when executed by at least one processor, the instructions cause at least one processor to send an instruction that a battery anomaly has been detected based on a difference that meets a threshold, and adjust battery usage based on the detection of a battery anomaly. In some embodiments, when the instruction is executed by at least one processor, it causes at least one processor to use a machine learning model to generate a predicted temperature for each of a plurality of batteries in one or more energy storage units at the end of a time frame, receive the measured temperature of the particular battery at the end of the time frame, and determine the temperature difference between the predicted temperature and the measured temperature of the particular battery. In some embodiments, when the instruction is executed by at least one processor, it causes at least one processor to display a user interface showing the plurality of batteries, the relative positions of the plurality of batteries, and the temperature difference for each of the plurality of batteries.

[0066]

[0072] Implementations of the methods and systems of this disclosure may involve performing or completing certain selected tasks or steps manually, automatically, or in combination thereof. Furthermore, according to the actual instruments and equipment of preferred embodiments of the methods and systems of this disclosure, some selected steps may be implemented by hardware (HW) or software (SW) on any operating system of any firmware, or by a combination thereof. For example, as hardware, the selected steps of this disclosure may be implemented as a chip or circuit. As software or algorithms, the selected steps of this disclosure may be implemented as a set of software instructions executed by a computer using any suitable operating system. In any case, the selected steps of the methods and systems of this disclosure may be described as being performed by a data processor, such as a computing device for executing a set of instructions.

[0067]

[0073] The various implementations of the systems and techniques described herein can be realized in digital electronic circuits, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be dedicated or general-purpose, coupled to receive and transmit data and instructions from a storage system, at least one input device, and at least one output device.

[0068]

[0074] The systems and techniques described herein may be implemented in computing systems that include backend components (e.g., as data servers), middleware components (e.g., application servers), or frontend components (e.g., client computers having a graphical user interface or web browser that allows users to interact with implementations of the systems and techniques described herein), or any combination of such backend, middleware, or frontend components. The components of the system may be interconnected by digital data communications (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), and the Internet. The computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact via communication networks. The relationship between a client and a server is established by computer programs running on each computer and having a client-server relationship with each other.

[0069]

[0075] While the specific features of the described implementations are shown as described herein, those skilled in the art will be able to conceive of many modifications, substitutions, alterations, and equivalents. Therefore, it should be understood that the appended claims are intended to encompass all such modifications and alterations that fall within the scope of the implementations. They are presented only as examples and not as limitations, and various changes in form and detail are possible. Any part of the apparatus and / or method described herein may be combined in any combination, except for mutually exclusive combinations. The implementations described herein may include various combinations and / or partial combinations of the functions, components, and / or features of the different implementations described herein.

[0070]

[0076] The above description is provided for illustrative purposes only. It is not exhaustive and is not limited to the exact form or embodiment disclosed. Modifications and adaptations of the embodiments will become apparent from considering the specification and practice of the disclosed embodiments. For example, while the described implementations include hardware and software, systems and methods consistent with this disclosure may be implemented as hardware only.

[0071]

[0077] It should be understood that the embodiments described above may be implemented by hardware, software (program code), or a combination of hardware and software. When implemented by software, the embodiments described above may be stored in the computer-readable medium described above. The software can perform the disclosed methods when executed by a processor. The computing units and other functional units described in this disclosure may be implemented by hardware, software, or a combination of hardware and software. Those skilled in the art will also understand that several of the above modules / units may be combined into a single module or unit, and each of the above modules / units may be further divided into several submodules or subunits.

[0072]

[0078] The block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware or software products according to various exemplary embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should be understood that in some alternative implementations, the functions shown in the blocks may occur in an order different from the order shown in the figure. For example, two consecutively shown blocks may be executed and implemented substantially simultaneously, depending on the associated functionality, or sometimes the two blocks may be executed in reverse order. Also, some blocks may be omitted. It should also be understood that each block in the block diagram, and combinations of blocks, may be implemented by a dedicated hardware-based system that performs a specified function or operation, or by a combination of dedicated hardware and computer instructions.

[0073]

[0079] In the aforementioned specification, embodiments are described with reference to numerous specific details that may differ depending on the implementation. Specific adaptations and modifications of the described embodiments can be made. Other embodiments may be apparent to those skilled in the art from the considerations herein and the practice of the invention disclosed herein. This specification and examples are intended to be considered illustrative only, and the true scope and spirit of the invention are set forth by the following claims. Furthermore, the series of steps shown in the figures are for illustrative purposes only and are not intended to be limited to any particular series of steps. Thus, those skilled in the art will understand that these steps may be performed in different orders while implementing the same method.

[0074]

[0080] It will be understood that the embodiments of this disclosure are not limited to the exact structures described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from their scope. Furthermore, other embodiments will be apparent to those skilled in the art from the discussion herein and the practice of the embodiments disclosed herein. This specification and examples are intended to be for illustrative purposes only, and the true scope and spirit of the disclosed embodiments are indicated by the accompanying claims.

[0075]

[0081] Furthermore, while exemplary embodiments are described herein, the scope includes all embodiments having equivalent elements, modifications, omissions, combinations, adaptations, or changes (e.g., aspects across various embodiments) based on this disclosure. The elements within the claims should be interpreted broadly in accordance with the language adopted within the claims and are not limited to the examples described herein or during the examination of the application. These examples should be interpreted as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any way, including rearranging or inserting or deleting steps. Thus, this specification and the examples are for illustrative purposes only, and the true scope and spirit are intended to be shown by the entire scope of the following claims and their equivalents.

Claims

1. One or more energy storage units, A computing device comprising at least one processor and memory for storing instructions, wherein when an instruction is executed by the at least one processor, the computing device has To generate multiple energy dispatch patterns for utilizing one or more of the aforementioned energy storage units, Based on the data determined for each of the plurality of energy dispatch patterns, a recommended energy dispatch pattern is generated, and Dispatching electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern. A computing device that performs this task, A system equipped with these features.

2. The system according to claim 1, wherein the one or more energy storage units include one or more battery units, and each of the one or more battery units comprises an enclosure containing a plurality of batteries.

3. When the instruction is executed by the at least one processor, the computing device: Display a user interface showing the recommended energy dispatch pattern. The system according to claim 1.

4. When the instruction is executed by the at least one processor, the computing device: Multiple energy dispatch patterns are generated by performing optimization techniques based on an objective function and multiple different constraints. The system according to claim 1.

5. The system according to claim 4, wherein the optimization technique includes linear programming or mixed-integer linear programming.

6. The system according to claim 4, wherein the plurality of different constraints include different amounts of charge-discharge cycles within the energy dispatch pattern.

7. The system according to claim 4, wherein the optimization technique determines a specific energy dispatch pattern by maximizing an estimate of the specific energy dispatch pattern based on market price forecasts for a time frame.

8. The system according to claim 1, wherein one of the plurality of energy dispatch patterns includes a plurality of intervals of a time frame configured such that one or more energy storage units charge or discharge at a specific rate.

9. When the instruction is executed by the at least one processor, the computing device is given the recommended energy dispatch pattern. For each of the energy dispatch patterns of the plurality of energy dispatch patterns, Based on expected energy market data, estimates of the energy dispatch pattern are determined. Based on environmental data for the one or more energy storage units, determine one or more state variables of the one or more energy storage units. Based on the one or more state variables, determine the estimated limit degradation of the one or more energy storage units. Based on the estimated limit degradation, the estimated cost of the energy dispatch pattern is determined, and Based on the aforementioned estimates and estimated costs, calculate the estimated net value of the energy dispatch pattern. Based on the estimated net value of the plurality of energy dispatch patterns, the recommended energy dispatch pattern is generated. The system according to claim 1, which is generated by

10. When the instruction is executed by the at least one processor, the computing device: Determining one or more state variables of one or more energy storage units based on the energy dispatch pattern, and Determining the estimated limit degradation of one or more energy storage units based on the energy dispatch pattern. The system according to claim 9, which causes the following to be performed.

11. When the instruction is executed by the at least one processor, the computing device: To receive the expected energy market data from a computing device associated with the wholesale energy market, The system according to claim 9.

12. The environmental data for one or more energy storage units is The expected ambient temperature of the region in which one or more energy storage units are located, or Expected ambient humidity of the area where one or more energy storage units are located The system according to claim 9, comprising one or more of the following.

13. When the instruction is executed by the at least one processor, the computing device: The environmental data is received from a computing device associated with weather forecasting. The system according to claim 12.

14. The system according to claim 9, wherein the one or more state variables of the one or more energy storage units include the temperature of the one or more energy storage units in one or more enclosures for the one or more energy storage units.

15. When the instruction is executed by the at least one processor, the computing device is given an estimated limit degradation. This is determined by performing a search in a database that maps the parameters of the energy dispatch pattern and the battery temperature to the expected degree of degradation. The system according to claim 9.

16. When the instruction is executed by the at least one processor, the computing device: The estimated cost is determined by calculating the replacement cost to replace the estimated limit degradation. The system according to claim 9.

17. When the instruction is executed by the at least one processor, the computing device: The estimated cost is determined by calculating the opportunity cost associated with the estimated limit degradation. The system according to claim 9.

18. When the instruction is executed by the at least one processor, the computing device: The recommended energy dispatch pattern is generated by selecting an energy dispatch pattern from among multiple energy dispatch patterns whose estimated net value is greater than the estimated net value of the other energy dispatch patterns among the multiple energy dispatch patterns, and which is greater than a configured threshold. The system according to claim 9.

19. A computing device generates multiple energy dispatch patterns for utilizing one or more energy storage units, A step of generating a recommended energy dispatch pattern based on the data determined for each of the plurality of energy dispatch patterns, Dispatching electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern, A method that includes this.

20. A non-temporary computer-readable medium for storing instructions, wherein when the instructions are executed by at least one processor, the at least one processor is configured to store instructions. To generate multiple energy dispatch patterns for utilizing one or more energy storage units, Based on the data determined for each of the plurality of energy dispatch patterns, a recommended energy dispatch pattern is generated, and Dispatching electricity by adjusting one or more energy storage units according to the recommended energy dispatch pattern. To have them do it, A non-temporary computer-readable medium.

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