System and method for controlling electric vehicle fleet charging or microgrid operation to extend battery life in consideration of heuristic approach

Abstract

A conventional optimization model for electric vehicle (EV) charging does not sufficiently consider battery loss. Thus, the disclosed embodiments utilize a heuristic approach to penalize an optimization scheme for deviation of an electric vehicle or other flexible load (e.g., a micro-grid battery) relative to a state of health (SoH) parameter. For example, SoH parameters may encourage the optimization model to quickly bring and maintain the state of charge of the flexibly loaded battery within an ideal state of charge (SoC) range. This approach biases the optimization model to reduce the solution of such SoH parameter deviation, thereby extending the life of the battery.

Description

Technical Field

[0001] The embodiments described herein generally relate to energy management, and more particularly to charging control using heuristic methods in power infrastructure sites such as electric vehicle charging stations or microgrids to extend battery life. Background Art

[0002] Fleets of many commercial and public transportation vehicles are transitioning to electric vehicles (EVs). Electric vehicles typically include batteries, and the cost of replacing these batteries is high. Battery aging limits the useful life of electric vehicles. As the battery ages with use (i.e., cycle aging) and time (i.e., calendar aging), its energy storage capacity and energy supply capacity decrease until the battery no longer meets its usage requirements.

[0003] Batteries have different chemical compositions and manufacturing types, and EV charging stations may have to charge various different types of electric vehicles. This makes it difficult to automatically determine which electric vehicle requires what treatment to maximize its battery and service life. Battery health, including thermal control of the battery temperature, is typically managed by the built-in battery management system (BMS) of the electric vehicle. However, the battery management system does not expose much data to the charging station. Therefore, incorporating this information into charging control is neither easy nor inexpensive. In addition, battery degradation is typically expressed as a state of health (SoH), and SoH can be defined using key performance indicators such as changes in capacity, internal battery resistance, round-trip efficiency, etc.

[0004] Since battery health is a long-term phenomenon (e.g., over several years) affected by technical and commercial factors, it should be balanced with short-term and medium-term operating criteria. Therefore, the inventors recognized that any heuristic method for battery aging should take these operating criteria into account, and an optimization formula can determine the best trade-off between battery degradation and other objectives. European Patent No. 3 702 201B1, U.S. Patent No. 8,975,866B2, and U.S. Patent No. 8,762,189B2 all describe various heuristic methods that consider the state of charge or battery degradation. The present disclosure addresses one or more problems in these methods discovered by the inventors. Summary of the Invention

[0005] Accordingly, systems, methods, and non-transitory computer-readable media are disclosed for using heuristic methods for charge control in a power infrastructure site, such as a charging station or a microgrid, to extend battery life. The aim of some embodiments is to reduce battery wear by incorporating the SoH parameter into an optimization model for the operation of one or more power infrastructure sites. Another aim of some embodiments is to define these SoH parameters at a finer granularity level, such as for a specific flexible load (e.g., an electric vehicle with a battery, a single battery in a microgrid, etc.) or the type of flexible load or battery, to provide greater flexibility and accuracy.

[0006] In one embodiment, a method includes using at least one hardware processor to: generate an optimization model from a model of at least one power infrastructure site, where the optimization model outputs a target value based on one or more variables, the one or more variables including the state of charge of an energy storage device in a flexible load in at least one power infrastructure site and inputs including a schedule for the flexible load, and where the optimization model penalizes a state of charge that deviates from a health state (SoH) parameter associated with the energy storage device in the flexible load; solve the optimization model to determine values of the one or more variables for the optimized target value; and based on the determined values of the one or more variables, initiate control of one or more physical components in at least one power infrastructure site.

[0007] The at least one power infrastructure site may include a charging station, where the flexible load includes an electric vehicle, and where the one or more physical components include one or more charging stations configured to be electrically connected to the electric vehicle to enable one or both of charging or discharging the electric vehicle. The determined values of the one or more variables may associate each electric vehicle with one or both of: a period of time or a power flow rate for charging or discharging at one of the one or more charging stations in the at least one power infrastructure site.

[0008] The method may further include receiving vehicle information of the electric vehicle from at least one external system via at least one network, where the input of the optimization model is at least derived from the vehicle information. The vehicle information may include one or more of: a planned schedule of the electric vehicle, a route of the electric vehicle, an arrival time of the electric vehicle at the at least one power infrastructure site, or a state of charge of the electric vehicle.

[0009] The target value may include at least a power cost, where the optimized target value includes minimizing the target value.

[0010] Initiating control of one or more physical components can include controlling setpoints of one or more physical components based on determined values of one or more variables. The one or more physical components can include non-intermittent distributed energy resources.

[0011] Initiating control can include communicating via at least one network with a control system of at least one electric power infrastructure site.

[0012] The SoH parameter can include a state of charge (SoC) range. At least one in the SoC range can be defined by a minimum SoC threshold greater than empty charge and a maximum SoC threshold less than full charge.

[0013] The optimization model can include an objective function in which the penalty for a state of charge deviating from the SoH parameter increases as the deviation increases. The objective function can be linear.

[0014] The optimization model can penalize one or more additional charging characteristics associated with an energy storage device in a flexible load.

[0015] At least one energy storage device in the flexible load can be associated with an SoH parameter different from that of a different energy storage device in the flexible load. At least one type of energy storage device in the flexible load can be associated with an SoH parameter different from that of a different type of energy storage device in the flexible load. Each energy storage device can be associated with an energy storage device-specific SoH parameter that is independent of the SoH parameters associated with other energy storage devices.

[0016] The optimization model can include a stochastic algorithm.

[0017] It should be understood that any feature in the above methods can be implemented alone or in any combination with any subset of other features. Thus, to the extent that the appended claims imply specific dependencies between features, the disclosed embodiments are not limited to these specific dependencies. Instead, any feature described herein can be combined with any other feature described herein or implemented in any combination of features without one or more other features described herein. Additionally, any method described above and elsewhere herein can be embodied alone or in any combination in executable software modules in a processor-based system (such as a server) and / or in executable instructions stored in a non-transitory computer-readable medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The structure and operational details of the present invention can be derived in part by studying the drawings, where like reference numerals denote like parts, and where:

[0019] Figure 1Shows an example infrastructure according to an embodiment, in which one or more of the processes described herein can be implemented;

[0020] Figure 2 Shows an example processing system according to an embodiment, through which one or more of the processes described herein can be executed;

[0021] Figure 3 Shows an example distribution network of a power infrastructure site according to an embodiment;

[0022] Figure 4 Shows an example data flow between various components of an optimized infrastructure according to an embodiment;

[0023] Figure 5 Shows an example process for using heuristic methods for charge control to extend battery life in a power infrastructure site according to an embodiment;

[0024] Figures 6A to 6C Shows an example penalty curve for deviation from health state parameters according to an alternative embodiment; and

[0025] Figure 7 Shows the effect of the objective function of charging the state of charge outside the penalty ideal SoC range according to an embodiment of the experiment. Detailed Description

[0026] In one embodiment, a system, method, and non-transitory computer-readable medium for using heuristic methods for charge control to extend battery life in a power infrastructure site (such as a charging station or a microgrid) are disclosed. After reading this specification, those skilled in the art will clearly know how to implement the present invention in various alternative embodiments and alternative applications. However, although various embodiments of the present invention will be described herein, it should be understood that these embodiments are presented only by way of example and illustration, rather than limitation. Therefore, the detailed description of various embodiments should not be construed as limiting the scope or breadth of the present invention set forth in the appended claims.

[0027] The term "power infrastructure site" refers to any infrastructure with a clear electrical boundary. Generally, a power infrastructure site will include a distribution network with interconnected loads and can be independently controlled. For example, a power infrastructure site can be a charging station and / or a microgrid including multiple infrastructure assets. However, a power infrastructure site can also consist of a single infrastructure asset. Infrastructure assets can include generators, energy storage systems, loads, and / or any other components of the power infrastructure site.

[0028] The term "power refueling station" refers to any power infrastructure site that includes at least one charging station configured to be electrically connected to a flexible load to charge and / or discharge the battery of the flexible load. It should be understood that a power refueling station may also include non-flexible loads. As an example, a power refueling station can be an electric vehicle (EV) refueling station that provides private charging for a fleet of electric vehicles of a municipal public transportation system, an enterprise, a utility provider, or other entities, provides public charging for individual electric vehicles, provides private charging for private electric vehicles (e.g., at the residence of the owner of an individual electric vehicle), and so on.

[0029] The term "microgrid" refers to any power infrastructure site that includes a local power grid capable of operating independently of the rest of the power grid. A microgrid can include multiple infrastructure assets (e.g., one or more generators, one or more energy storage systems, one or more loads, etc.) that are connected under the same point of common coupling through a local distribution network. The infrastructure assets of a microgrid typically include at least some type of generator or energy storage system, but can also include any combination of infrastructure assets. It should be understood that a power refueling station and a microgrid are not mutually exclusive, and in some cases, a power infrastructure site can include a power refueling station and a microgrid.

[0030] The term "flexible load" refers to any load that is flexible in at least one characteristic, which can be represented by a variable in an optimization model. One such characteristic can be the location where the flexible load charges or discharges. A flexible load with location flexibility can charge or discharge at any of multiple locations in one or more power infrastructure sites. Another such characteristic can be the time when the flexible load charges or discharges. A flexible load with time flexibility can charge or discharge according to different timings in terms of start time, end time, time range, duration, etc. Another such characteristic can be the power flow rate at which the flexible load charges or discharges. A flexible load with power flow rate flexibility can charge at any of a variety of different power flow rates (e.g., any power flow rate within a power flow rate range). Another such characteristic can be the operating state of the flexible load. A flexible load with operating state flexibility (e.g., water heater, air conditioner, compressor, etc.) can be converted to another state with reduced power consumption (e.g., low power mode, off, discharge, etc.) to increase the energy available for other loads and / or reduce the total energy consumed. It should be understood that these are just examples of characteristics, and even if not specifically described herein, a flexible load can be flexible in other characteristics. A flexible load can be flexible in only one characteristic, all characteristics, or any subset of characteristics. In contrast, the term "non-flexible load" refers to any load that is not flexible in any characteristic.

[0031] In an expected embodiment, the flexible load is an electric vehicle and the power infrastructure site is an EV charging station. The electric vehicle is flexible at least in terms of location. In other words, since the electric vehicle is mobile, it can generally be charged at any one of the multiple available charging stations within any one of the multiple EV charging stations. In particular, the electric vehicle includes a built-in battery that can be electrically connected to any one of the multiple available charging stations within the EV charging station. Similar types of flexible loads include, but are not limited to, drones, robotic systems, mobile machines, mobile devices, power tools, etc.

[0032] However, the disclosed method is not limited to electric vehicles or other mobile loads. Instead, the disclosed method can be applied to any load, whether mobile or stationary, as long as the load has one or more flexible characteristics that can be defined according to the variables in the optimization model. For example, the flexible load can simply consist of a fixed or mobile battery, or can be a system that consumes electricity without any onboard battery to store electricity. Stationary flexible loads are generally not flexible in terms of location, but may be flexible in terms of timing, power flow rate, operating state, etc. For the purposes of this disclosure, it is generally assumed that the flexible load includes or consists of an energy storage device. Although the energy storage device is mainly described as a battery herein, it should be understood that the energy storage device can include or consist of any other mechanism or component for storing energy, such as, for example, supercapacitors, compressed gas, etc.

[0033] The power infrastructure site can also include one or more flexible generators. The term "flexible generator" refers to any generator that can be controlled to increase and / or decrease the power generation. Examples of flexible generators include, but are not limited to, diesel generators, hydrogen fuel cells, distributed energy sources with reducible power output, etc. The optimization of the power infrastructure site can optimize the variables of the flexible load and the flexible generator.

[0034] As used herein, the term "distribution network" refers to the interconnection of electrical components within a power infrastructure site that distributes electricity to flexible loads. These electrical components can include one or more intermittent and / or non-intermittent distributed energy sources, including renewable energy sources (e.g., solar generators, wind generators, geothermal generators, hydroelectric generators, fuel cells, etc.) and / or non-renewable energy sources (e.g., diesel generators, natural gas generators, etc.), one or more battery energy storage systems (BESSs), etc. Additionally or alternatively, in the case where the power infrastructure site includes a power refueling station, these electrical components can include charging stations. Despite the term "charging", the term "charging station" includes stations that are capable of discharging a load (e.g., extracting power from a built-in battery of the load) in addition to charging the load (e.g., powering a built-in battery of the load), stations that can only charge the load, and stations that can only discharge the load. The electrical components within a power infrastructure site can be considered "nodes" of the distribution network, and the electrical connections between these electrical components can be considered "edges" that connect the nodes within the distribution network. The distribution network of a power infrastructure site can be connected to other distribution networks (e.g., during normal operation) or can be isolated (e.g., in the case of a microgrid during independent operation).

[0035] 1. Infrastructure example

[0036] Figure 1 An example infrastructure is shown in which one or more of the disclosed processes can be implemented. The infrastructure can include an Energy Management System (EMS) 110 (e.g., including one or more servers) that hosts and / or executes one or more of the various functions, processes, methods, and / or software modules described herein. The EMS 110 can include dedicated servers or can alternatively be implemented in a computing cloud where the resources of one or more servers are dynamically and elastically allocated to multiple tenants based on demand. In either case, the servers can be centrally deployed and / or distributed across different geographical locations. The EMS 110 can also include or be communicatively connected to software 112 and / or one or more databases 114. Additionally, the EMS 110 can be communicatively connected to one or more user systems 130, a power infrastructure site 140, and / or a power market 150 via one or more networks 120.

[0037] One or more networks 120 may include the Internet, and the EMS 110 may communicate with one or more user systems 130, one or more power infrastructure sites 140, and / or one or more power markets 150 over the Internet using standard transport protocols (such as, Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), File Transfer Protocol (FTP), FTP Secure (FTPS), Secure Shell FTP (SFTP), Extensible Messaging and Presence Protocol (XMPP), Open Field Message Bus (OpenFMB), IEEE Smart Energy Profile Application Protocol (IEEE 2030.5), etc.) and proprietary protocols. Although the EMS 110 is shown as being connected to various systems via a set of one or more networks 120, it should be understood that the EMS 110 may be connected to individual systems via different sets of one or more networks. For example, the EMS 110 may be connected to a subset of user systems 130, power infrastructure sites 140, and / or power markets 150 over the Internet, but may also be connected to one or more other user systems 130, power infrastructure sites 140, and / or power markets 150 via an intranet. Additionally, although only a few user systems 130 and power infrastructure sites 140, one power market 150, one software instance 112, and a set of one or more databases 114 are shown, it should be understood that the infrastructure may include any number of user systems, power infrastructure sites, power markets, software instances, and databases.

[0038] One or more user systems 130 can include any type of computing device capable of wired and / or wireless communication, including but not limited to desktop computers, laptop computers, tablet computers, smartphones, or other mobile phones, servers, gaming consoles, televisions, set-top boxes, kiosks, point-of-sale terminals, embedded controllers, programmable logic controllers (PLCs), etc. However, it is generally contemplated that one or more user systems 130 will include personal computers, mobile devices, or workstations through which operators of one or more operators of the power infrastructure site 140 can interact with the EMS 110 as users. These interactions can include providing input data for the intervention system between the graphical user interface provided by the EMS 110 or the EMS 110 and one or more user systems 130 (e.g., parameters for configuring one or more of the processes described herein) and / or receiving data (e.g., outputs of one or more of the processes described herein). The graphical user interface can include screens (e.g., web pages) that include a combination of content and elements such as text, images, videos, animations, references (e.g., hyperlinks), frames, inputs (e.g., text boxes, text areas, check boxes, radio buttons, drop-down menus, buttons, forms, etc.), scripts (e.g., JavaScript), etc., including elements containing or derived from data stored in one or more databases (e.g., database 114).

[0039] The EMS 110 can execute software 112 that includes one or more software modules that implement one or more of the disclosed processes. In addition, the EMS 110 can include, be communicatively coupled to, or otherwise access one or more databases 114 that store data input to and / or output from one or more of the disclosed processes. Any suitable database can be used, including but not limited to MySQL TM , Oracle TM , IBM TM , Microsoft SQL TM , Access TM , PostgreSQL TM , etc., including cloud-based databases, proprietary databases, and unstructured databases (e.g., MongoDB TM ).

[0040] The EMS 110 can communicate with one or more power infrastructure sites 140 and / or one or more power markets 150 via an application programming interface (API). For example, the EMS 110 can "push" (i.e., actively send data) data to each power infrastructure site 140 via the API of the control system 142 of the power infrastructure site 140. The control system 142 can be a supervisory control and data acquisition (SCADA) system. Alternatively or additionally, the control system 142 of the power infrastructure site 140 can "pull" data (i.e., initiate the transmission of data via a request) from the EMS 110 via the API of the EMS 110. Similarly, the EMS 110 can push data to the power market interface 151 of the power market 150 via the API of the power market interface 152, and / or the power market interface 153 can pull data from the EMS 110 via the API of the EMS 110.

[0041] 2. Example processing system

[0042] Figure 2 is a block diagram showing an example wired or wireless system 200 that can be used in conjunction with various embodiments described herein. For example, the system 200 can be used as or incorporated with one or more of the functions, processes, or methods described herein (e.g., storing and / or executing software 112), and can represent components of the EMS 110, one or more user systems 130, one or more power infrastructure sites 140, the control system 142, the power market interface 152, and / or other processing devices described herein. The system 200 can be a server or any conventional personal computer, or any other processor-enabled device capable of wired or wireless data communication. Those skilled in the art will appreciate that other computer systems and / or architectures can also be used.

[0043] The system 200 preferably includes one or more processors 210. The one or more processors 210 can include a central processing unit (CPU). Additional processors can be provided, such as, a graphics processing unit (GPU), a secondary processor that manages input / output, a secondary processor that performs floating-point mathematical operations, a dedicated microprocessor having an architecture suitable for rapidly executing signal processing algorithms (e.g., a digital signal processor), a secondary processor subordinate to the main processing system (e.g., a backend processor), an additional microprocessor or controller for a dual-processor or multi-processor system, and / or a coprocessor. Such secondary processors can be discrete processors or can be integrated with the processor 210. Examples of processors that can be used with the system 200 include, but are not limited to, any processor available from Intel Corporation of Santa Clara, California (e.g., Pentium TM 、Core i7 TM 、XeonTM etc.), any processor available from Advanced Micro Devices, Inc. (AMD) in Santa Clara, California, any processor available from Apple Inc. in Cupertino (e.g., A series, M series, etc.), any processor available from Samsung Electronics Co., Ltd. in Seoul, Korea (e.g., Exynos TM ), any processor available from NXP Semiconductors N.V. in Eindhoven, the Netherlands, etc.

[0044] Processor 210 is preferably connected to communication bus 205. Communication bus 205 may include data channels for facilitating information transfer between the storage devices and other peripheral components of system 200. Additionally, communication bus 205 may provide a set of signals for communicating with processor 210, including a data bus, an address bus, and / or a control bus (not shown). Communication bus 205 may include any standard or non-standard bus architecture, such as those conforming to the Industry Standard Architecture (ISA), Extended Industry Standard Architecture (EISA), Micro Channel Architecture (MCA), Peripheral Component Interconnect (PCI) local bus, standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE), including the IEEE 488 General-Purpose Interface Bus (GPIB), IEEE 696 / S-100, etc.

[0045] System 200 preferably includes main memory 215 and may also include secondary memory 220. Main memory 215 provides storage for instructions and data for programs executed on processor 210, such as one or more functions and / or modules discussed herein (e.g., software 112). It should be understood that programs stored in the memory and executed by processor 210 may be written and / or compiled in any suitable language, including but not limited to C / C++, Java, JavaScript, Perl, VisualBasic,.NET, etc. Main memory 215 is typically semiconductor-based memory, such as dynamic random access memory (DRAM) and / or static random access memory (SRAM). Other semiconductor-based memory types include, for example, synchronous dynamic random access memory (SDRAM), Rambus dynamic random access memory (RDRAM), ferroelectric random access memory (FRAM), etc., including read-only memory (ROM).

[0046] The secondary storage 220 may optionally include an internal medium 225 and / or a removable medium 230. The removable medium 230 is read and / or written in any known manner. The removable storage medium 230 may be, for example, a tape drive, a compact disc (CD) drive, a digital versatile disc (DVD) drive, other optical disc drives, a flash drive, and the like. The secondary storage 220 is a non-transitory computer-readable medium on which computer-executable code (e.g., software 112) and / or other data are stored. The computer software or data stored on the secondary storage 220 is read into the main memory 215 for execution by the processor 210.

[0047] In an alternative embodiment, the secondary storage 220 may include other similar devices for allowing a computer program or other data or instructions to be loaded into the system 200. Such devices may include, for example, a communication interface 240 that allows software and data to be transferred from an external storage medium 245 to the system 200. Examples of the external storage medium 245 may include an external hard disk drive, an external optical disc drive, an external magneto-optical drive, and the like. Other examples of the secondary storage 220 may include semiconductor-based memories such as programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), and flash memory (a block-oriented memory similar to EEPROM).

[0048] As described above, the system 200 may include a communication interface 240. The communication interface 240 allows software and data to be transferred between the system 200 and external devices (e.g., a printer), a network, or other information sources. For example, computer software or executable code may be transferred to the system 200 from a network server (e.g., platform 110) through the communication interface 240. Examples of the communication interface 240 include a built-in network adapter, a network interface card (NIC), a Personal Computer Memory Card International Association (PCMCIA) network card, a Card Bus network adapter, a wireless network adapter, a Universal Serial Bus (USB) network adapter, a modem, a wireless data card, a communication port, an infrared interface, an IEEE 1394 FireWire, and any other device capable of interfacing the system 200 with a network (e.g., one or more networks 120) or another computing device. The communication interface 240 preferably implements industry-promulgated protocol standards such as the Ethernet IEEE 802 standards, Fibre Channel, Digital Subscriber Line (DSL), Asymmetric Digital Subscriber Line (ADSL), Frame Relay, Asynchronous Transfer Mode (ATM), Integrated Services Digital Network (ISDN), Personal Communication Services (PCS), Transmission Control Protocol / Internet Protocol (TCP / IP), Serial Line Internet Protocol / Point-to-Point Protocol (SLIP / PPP), etc., but may also implement custom or non-standard interface protocols.

[0049] Software and data transmitted through communication interface 240 typically take the form of electrical communication signals 255. These signals 255 can be provided to communication interface 240 via communication channel 250. In one embodiment, communication channel 250 can be a wired or wireless network (e.g., one or more networks 120), or any other type of communication link. Communication channel 250 carries signals 255 and can be implemented using various wired or wireless communication means, including, for example, wires or cables, optical fibers, conventional telephone lines, cellular phone links, wireless data communication links, radio frequency (“RF”) links, or infrared links, etc.

[0050] Computer-executable code (e.g., a computer program such as software 112) is stored in main memory 215 and / or secondary memory 220. The computer program can also be received through communication interface 240 and stored in main memory 215 and / or secondary memory 220. When executed, such a computer program enables system 200 to perform the various functions of the disclosed embodiments described elsewhere herein.

[0051] In this specification, the term “computer-readable medium” is used to refer to any non-transitory computer-readable storage medium for providing computer-executable code and / or other data to or within system 200. Examples of such media include main memory 215, secondary memory 220 (including internal memory 225 and / or removable media 230), external storage media 245, and any peripheral device communicatively coupled to communication interface 240 (including a network information server or other network device). These non-transitory computer-readable media are means for providing executable code, programming instructions, software, and / or other data to system 200.

[0052] In embodiments implemented using software, the software can be stored on a computer-readable medium and loaded into system 200 through removable media 230, I / O interface 235, or communication interface 240. In such an embodiment, the software is loaded into system 200 in the form of electrical communication signals 255. When executed by processor 210, the software preferably causes processor 210 to perform one or more of the processes and functions described elsewhere herein.

[0053] In one embodiment, I / O interface 235 provides an interface between one or more components of system 200 and one or more input and / or output devices. Example input devices include, but are not limited to, sensors, keyboards, touchscreens or other touch-sensitive devices, cameras, biometric sensing devices, computer mice, trackballs, pen-based pointing devices, etc. Examples of output devices include, but are not limited to, other processing devices, cathode ray tubes (CRTs), plasma displays, light emitting diode (LED) displays, liquid crystal displays (LCDs), printers, vacuum fluorescent displays (VFDs), surface conduction electron emitter displays (SEDs), field emission displays (FEDs), etc. In some cases, input and output devices may be combined, such as in the case of a touch panel display (e.g., in a smartphone, tablet device, or other mobile device).

[0054] System 200 may also include optional wireless communication components that facilitate wireless communication over a voice network and / or a data network (e.g., in the case where user system 130 is a smartphone or other mobile device). The wireless communication components include antenna system 270, radio system 265, and baseband system 260. In system 200, radio frequency (RF) signals are transmitted and received in the air by antenna system 270 under the management of radio system 265.

[0055] In one embodiment, antenna system 270 may include one or more antennas and one or more multiplexers (not shown) that perform a switching function to provide transmit and receive signal paths for antenna system 270. In the receive path, the received RF signal may be coupled from the multiplexer to a low noise amplifier (not shown), which amplifies the received RF signal and sends the amplified signal to radio system 265.

[0056] In an alternative embodiment, radio system 265 may include one or more radios that are configured to communicate at various frequencies. In one embodiment, radio system 265 may combine a demodulator (not shown) and a modulator (not shown) in an integrated circuit (IC). The demodulator and modulator may also be separate components. In the input path, the demodulator strips the RF carrier signal, leaving a baseband received audio signal that is sent from radio system 265 to baseband system 260.

[0057] If the received signal contains audio information (e.g., a user system 130 including a smartphone or other mobile device), the baseband system 260 decodes the signal and converts it to an analog signal. The signal is then amplified and sent to a speaker. The baseband system 260 also receives analog audio signals from a microphone. These analog audio signals are converted to digital signals and encoded by the baseband system 260. The baseband system 260 also encodes the digital signals for transmission and generates a baseband transmitted audio signal, which is routed to the modulator section of the radio system 265. The modulator mixes the baseband transmitted audio signal with an RF carrier signal to generate an RF transmitted signal, which is routed to the antenna system 270 and can be passed through a power amplifier (not shown). The power amplifier amplifies the RF transmitted signal and routes it to the antenna system 270, where the signal is switched to an antenna port for transmission.

[0058] The baseband system 260 is also communicatively coupled to one or more processors 210. The one or more processors 210 can access data storage areas 215 and 220. The one or more processors 210 are preferably configured to execute instructions (i.e., computer programs, such as the disclosed software) that may be stored in the main memory 215 or the secondary memory 220. The computer program can also be received from the baseband processor 260 and stored in the main memory 210 or the secondary memory 220, or executed upon receipt. Such a computer program, when executed, enables the system 200 to perform the various functions of the disclosed embodiments.

[0059] 3. Example power infrastructure site

[0060] Figure 3 A single-line diagram of an example distribution network of an example power infrastructure site 140 according to an embodiment is shown. As a non-limiting example, the power infrastructure site 140 can be an EV charging station for charging electric vehicles as flexible loads. The power infrastructure site 140 is connected to the power grid 310 and can purchase power from the power grid 310 (e.g., from the power market 150). Depending on the time-of-use (ToU) rate, the purchase price of power may vary throughout the day (e.g., higher during the day than at night) and over multiple days (e.g., higher in the summer than in the winter). The ToU rate can assign peak rates, partial peak rates, and off-peak rates to individual time periods (e.g., each hourly interval of a day), representing the electricity price during those time periods.

[0061] The power distribution network of the power infrastructure site 140 may include nodes representing one or more distributed energy sources in the power infrastructure site 140, including, for example, one or more battery energy storage systems 320 and / or one or more generators 330, as shown by generators 330A and 330B. One or more generators 330 may include renewable energy sources (e.g., solar generators, wind generators, geothermal generators, hydroelectric generators, fuel cells, etc.) and / or non-renewable energy sources (e.g., diesel generators, natural gas generators, etc.). For example, generator 330A may be a solar generator including a plurality of photovoltaic cells that convert sunlight into electrical energy, and generator 330B may be a diesel generator that generates electricity by burning diesel gasoline.

[0062] The power distribution network of the power infrastructure site 140 may also include nodes representing one or more charging stations 340 in the power infrastructure site 140. In the example shown, the power infrastructure site 140 is a power refueling station including a plurality of charging stations 340A, 340B, 340C, 340D, 340E, 340F, and 340G. Each charging station 340 may include one or more chargers 342. For example, charging station 340F is shown to have two chargers 342. Each charger 342 is configured to be electrically connected to a flexible load 350 to supply power to and / or discharge from one or more energy storage devices (e.g., batteries) of the flexible load 350. In the case of supplying power to or charging the battery of the flexible load 350, power may flow from the generator 330 and / or the power grid 310 through the power distribution network to the battery of the flexible load 350. Conversely, in the case of discharging the battery of the flexible load 350, power may flow from the battery of the flexible load 350 into the power distribution network and thus into the battery energy storage system 320, the power grid 310, and / or another flexible load 350. At any given time, some chargers 342 may be connected to flexible loads 350, as shown by flexible loads 350A, 350B, 350C, 350E, 350F1, and 350F2, while other chargers 342 may not be connected to flexible loads 350 and may be available to receive incoming flexible loads 350.

[0063] In one embodiment, two or more power infrastructure sites 340 can be connected via a transmission network (not shown) or can be connected within the same distribution network. In this case, power can be transmitted between the two power infrastructure sites 340. For example, the first power infrastructure site 140 can transmit power from one or more of its infrastructure assets (e.g., one or more battery energy storage systems 320, one or more generators 330, and / or one or more flexible loads 350) to the second power infrastructure site 140 via the network to charge one or more infrastructure assets (e.g., one or more battery energy storage systems 320 and / or one or more flexible loads 350) of the second power infrastructure site 140.

[0064] One or more charging stations 340 or a single charger 342 can be configured to start charging (e.g., at a set power flow rate), terminate charging, start discharging (e.g., at a set power flow rate), terminate discharging, etc. of the flexible load 350 under the control of the control system 142, the EMS 110, and / or another controller. For example, the charging station 340 and / or the charger 342 can include a processing system 200 that receives commands from a controller (e.g., the control system 142 and / or the EMS 110) via the communication interface 240, processes the commands using the processor 210 and the main memory 215, and controls an actuator (e.g., one or more switches) to start or terminate charging or discharging according to the processed commands.

[0065] It should be understood that in addition to the flexible load 350, the power infrastructure site 140 can also include other loads. For example, the power infrastructure site 140 can include auxiliary loads for operating various functions of the power infrastructure site 140. However, if these auxiliary loads are flexible in at least one characteristic (e.g., operating state), they can also be regarded as the flexible load 350. It should also be understood that each battery energy storage system 320 can be used as a power source (i.e., when discharging) and as a load (i.e., when charging), and can also be regarded as the flexible load 350.

[0066] The parameters (e.g., voltage, current, set points, etc.) of each node within the distribution network of the power infrastructure site 140 can be monitored and / or simulated by the EMS 110 (e.g., during a load flow analysis). Thus, the EMS 110 can monitor and / or simulate the parameters of the nodes within the distribution network and control one or more physical components of the power infrastructure site 140 based on these parameters, such as one or more charging stations 340, one or more chargers 342, one or more generators 330, one or more battery energy storage systems 320, etc. This control can be performed periodically or in real time and can be direct or indirect (e.g., performed through the control system 142).

[0067] The illustrated power infrastructure site 140 includes a certain number of charging stations 340. However, it should be understood that the power infrastructure site 140 can include any number of charging stations 340, including only a single charging station 340. Additionally, each charging station 340 can include any number of chargers 342, including only a single charger 342. In its simplest form, the power infrastructure site 140 can consist of a single charging station 340 (such as a charging station 340 in a residence, public refueling station, etc.), which is composed of a single charger 342. Furthermore, although all flexible loads 350 are shown as being connected to the charging station, one or more flexible loads 350 can be connected to the distribution network without any intermediate charging station 340.

[0068] A single EMS 110 can manage a single power infrastructure site 140 or multiple power infrastructure sites 140. In the case where the EMS 110 manages multiple power infrastructure sites 140, the managed power infrastructure sites 140 can be the same, similar, or different from each other. For example, a homogeneous combination of power infrastructure sites 140 can consist of multiple power refueling stations for a fleet of one or more electric vehicles. In contrast, a heterogeneous combination of power infrastructure sites 140 can consist of including one or more power refueling stations, one or more residential charging stations 340, one or more public charging stations 340, and / or one or more microgrids, etc.

[0069] 4. Example data stream

[0070] Figure 4Shows an example data flow between various components of an optimized infrastructure according to an embodiment. Software 112 hosted on platform 110 may implement optimizer 410. Optimizer 410 may generate an optimization model for output target values based on one or more variables and inputs, the inputs including the scheduling of one or more flexible loads 350 in one or more power infrastructure sites 140. The optimization model represents a software-based specific implementation of an optimization problem. Optimizer 410 may solve the optimization model to determine the values of one or more variables of the optimization target values, where these variables are subject to zero, one, or more constraints (e.g., inequality and / or equality constraints representing physical and / or technical capacity limitations, flexibility of loads and / or generators, etc.). The optimization may include minimizing the target value (e.g., if the target value represents cost) or maximizing the target value (if the target value represents a performance metric). The determined values of the one or more variables may represent the operating configurations (e.g., set points, schedules, etc.) of one or more power infrastructure sites 140 over a period of time. Optimizer 410 may also initiate control of one or more physical components in one or more power infrastructure sites 140 based on the determined values of the one or more variables.

[0071] Optimizer 410 may update and solve the optimization model periodically based on real-time or updated data. For example, the optimizer may update and solve the optimization model for the operation of power infrastructure site 140 in a sliding time window representing an optimization period. The sliding time window advances continuously or continuously to cover the next time period for which the operation of power infrastructure site 140 is to be optimized. It should be understood that the start of the sliding time window may be the current time or some future time (e.g., 1 minute, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour, 24 hours, etc. in the future) so that the optimization is performed before the future time period included in the sliding time window, and the optimization is performed for this future time period. In one embodiment, the sliding time window may advance according to a time step (e.g., every 15 minutes) and utilize the latest data available within the time range (e.g., a time range extending into the past, the size of which is equal to the time step). The size of the sliding time window may be any suitable duration (e.g., 15 minutes, 30 minutes, 1 hour, 24 hours, etc.).

[0072] Optimizer 410 may collect the data it uses for optimization (e.g., as inputs to an optimization model) from one or more scheduling systems 420, one or more telemetry systems 430, one or more control systems 142 of one or more electric power infrastructure sites 140, and / or one or more electric power market interfaces 152 of one or more electric power markets 150. It should be understood that these are just examples of data flows, and Optimizer 410 may collect data from other systems (e.g., which may include one or more site management systems 440). For example, Optimizer 410 may receive solutions from other optimizers, and these solutions may be incorporated into the inputs of the optimization model. In such a case, Optimizer 410 may focus on one aspect of the overall optimization, so that it does not have to integrate every detail of the optimization into the optimization model.

[0073] In one embodiment, Optimizer 410 may fuse the collected data of multiple electric power infrastructure sites 140 into a single virtual site model and perform a single overall optimization on the virtual site model to optimize the operation of multiple electric power facility sites 140 as a single unit. This virtual site model may represent a multi-tenant charging system that aggregates multiple electric power infrastructure sites 140 (e.g., EV charging stations) of multiple tenants (e.g., EV fleet operators).

[0074] Optimizer 410 may output data to one or more scheduling systems 420, one or more control systems 142 of one or more electric power infrastructure sites 140, one or more site management systems 440, and / or one or more electric power market interfaces 152 of one or more electric power markets 150. It should be understood that these are just examples of data flows, and Optimizer 410 may output data to other systems (e.g., which may include one or more telemetry systems 430). For example, Optimizer 410 may output its solutions or other data to one or more other optimizers, and each optimizer may incorporate this data into the inputs of its own optimization model. In such a case, Optimizer 410 may focus on one aspect of the overall optimization, so that it does not have to integrate every detail of the optimization into the optimization model.

[0075] One or more scheduling systems 420, one or more telemetry systems 430, and / or one or more site management systems 440 may be hosted on EMS 110 or may be located outside of EMS 110. In either case, when collecting data, Optimizer 410 may receive data by extracting data from the corresponding system via the API of the corresponding system or by having the corresponding system push the data to Optimizer 410 via the API of Optimizer 410. Similarly, Optimizer 410 may output data by pushing the data to the corresponding system via the API of the corresponding system or by having the corresponding system extract the data via the API of Optimizer 410.

[0076] Each scheduling system 420 can manage the schedules and routes of flexible loads 350 (e.g., a fleet of electric vehicles). The optimizer 410 can receive scheduling information from each scheduling system 420. The scheduling information provided by the scheduling system 420 can include the planned schedules of the flexible loads 350. For example, for each flexible load 350, the planned schedule can include the scheduled arrival time at which the flexible load 350 is expected to arrive at the power infrastructure site 140, the scheduled departure time at which the flexible load 350 is expected to leave the power infrastructure site 140, an auxiliary task schedule defining the time period during which the flexible load 350 is expected to perform or undergo auxiliary tasks (e.g., preconditioning, cleaning, maintenance, or repair of the passenger cabin and / or battery of an electric vehicle, etc.), a power flow schedule defining the power flow rate or profile at which the flexible load 350 charges or discharges, the priority of the flexible load 350 relative to other flexible loads 350 (e.g., indicating the relative cost of a charging delay for the flexible load 350), the preferred power infrastructure site 140 assigned to the flexible load 350, etc. Additionally, in the case where the flexible load 350 has a route, for each flexible load 350, the scheduling information can include the route assigned to the flexible load 350.

[0077] After solving the optimization model, the optimizer 410 may output data associated with the scheduling system 420 to the scheduling system 420 or an intervention system. For example, in the case where the optimizer 410 performs optimization on multiple power infrastructure sites 140, the optimizer 410 may output a sorted list of the power infrastructure sites 140, e.g., sorted from the power infrastructure site 140 with the largest capacity to the power infrastructure site 140 with the smallest capacity. The scheduling system 420 or the intervention system may use such information to update the route assignment of the flexible loads 350 (e.g., by rerouting one or more flexible loads 350 from a power infrastructure site 140 with a smaller capacity to a power infrastructure site 140 with a larger capacity). Additionally or alternatively, the optimizer 410 may output a sorted list of the flexible loads 350, e.g., sorted from the flexible load 350 that will be charged first to an acceptable state of charge (SoC) to the flexible load 350 that will be charged last to an acceptable state of charge (e.g., reflecting which electric vehicle can be ready first or has been plugged in for the longest time). As another example, the optimizer 410 or the intervention system may provide suggestions to improve the scheduling and / or provide data from which such suggestions can be derived. For example, if the flexible load 350 is an electric vehicle, such data may identify electric vehicles that are frequently late, and thus the route of the electric vehicle may be changed (e.g., assigned to another electric vehicle, separated from another electric vehicle, etc.). More generally, the scheduling system 420 or the intervention system may analyze the data provided by the optimizer 410 to identify inefficiencies (e.g., bottlenecks, electric vehicles that require fast charging for their scheduling, etc.) and / or inaccurate parameters (e.g., the actual energy consumed during preprocessing, etc.) in the scheduling of the flexible loads 350, and update the scheduling of the flexible loads 350 (e.g., re-optimize the EV route assignment) to eliminate or mitigate these inefficiencies and / or reflect more accurate parameters.

[0078] Each telemetry system 430 may collect telemetry information of one or more flexible loads 350. The telemetry system 430 may be installed on each flexible load 350 and transmit real-time telemetry information to the optimizer 410. For example, in the case where the flexible load 350 is an electric vehicle, the telemetry system 430 may be embedded (e.g., as software or hardware) within the electronic control unit (ECU) of the electric vehicle and communicate via a wireless communication network (e.g., a cellular communication network). Alternatively, the telemetry system 430 may be external to the flexible load 350. In this case, the telemetry system 430 may collect real-time telemetry information of multiple flexible loads 350 and forward or relay the collected telemetry information to the optimizer 410 in real time. It should be understood that as used herein, the term "real-time" or "in real time" includes not only events that occur simultaneously, but also events separated by delays caused by normal delays in processing, communication, etc. and / or delays caused by using time steps (e.g., with respect to a sliding time window for optimization).

[0079] The optimizer 410 may receive telemetry information from each telemetry system 430. The telemetry information provided by the telemetry system 430 may include one or more parameters of each flexible load 350 from which the telemetry system 430 collects data. For each flexible load 350, these parameters may include the estimated arrival time of the flexible load 350 at the scheduled power infrastructure site 140, the current state of charge of the energy storage device (e.g., battery) on the flexible load 350, etc. It should be understood that these parameters may change over time, and updated telemetry information reflecting any changes may be received by the optimizer 410 periodically (e.g., and used as an input to the optimization model for the current optimization period). Real-time telemetry information reduces the uncertainty in the optimization model.

[0080] Each site management system 440 may manage infrastructure assets, including flexible loads 350, and employees within one or more power infrastructure sites 140. After solving the optimization model, the optimizer 410 may output site information to each site management system 440. For example, the site information may include a sorted list of flexible loads 350, e.g., sorted from the flexible load 350 with the longest time until it can be unplugged to the flexible load 350 with the shortest time until it can be unplugged. For example, the site management system 440 may use such information to determine which flexible loads 350 can be served, etc. Additionally or alternatively, the site information may include an assignment or mapping of flexible loads 350 to charging stations 340. Such a mapping can be used to automatically route the flexible loads 350 to their mapped charging stations 340, e.g., by sending routing instructions to the navigation system within each flexible load 350, such as the location of the mapped charging station 340.

[0081] Each control system 142 can provide independent control of the power infrastructure site 140. After solving the optimization model, the optimizer 410 can output control information associated with the control of the power infrastructure site 140 to the control system 142 associated with that power infrastructure site 140. Such control information can be provided to the control system 142 of each power infrastructure site 140 managed by the optimizer 410. The control information can include one or more control instructions that trigger one or more control operations at the control system 142 and / or other information used during one or more control operations of the control system 142. The control instructions can include, but are not limited to, instructions for controlling set points associated with physical components of the power infrastructure site 140 (e.g., the power flow rate associated with the charging station 340), instructions for charging the flexible load 350, instructions for discharging the flexible load 350, instructions for performing pre-treatment of the flexible load 350 (e.g., cooling of the passenger compartment and / or battery of an electric vehicle, heating of the passenger compartment of an electric vehicle, etc.), and the like. The other information can include, but is not limited to, a sorted list of charging stations 340 (e.g., from the charging station 340 with the largest capacity to the charging station with the smallest capacity), a charging schedule for the flexible load 350, an assignment or mapping of the flexible load 350 to the charging station 340, and the like.

[0082] The optimizer 410 can also receive data from each control system 142. For example, the control system 142 can report events detected within the corresponding power infrastructure site 140 (e.g., the charger 342 of the charging station 340 is plugged into the flexible load 350), the state of charge detected for the plugged-in flexible load 350, the actual power flow or power flow rate between the charging station 340 and the connected flexible load 350, and the like. The optimizer 410 can receive this data periodically and use this data as input to the optimization model for the current optimization period.

[0083] The power market interface 152 can provide an interface to the power market 150, where the utility company operating the power grid 310 is a participant in the power market. In particular, the power market 150 can enable the operator of the power infrastructure site 140 to purchase power from the utility or other energy providers (e.g., operating the power grid 310), sell power to energy providers (such as operating the power grid 320), obtain service commitments from energy providers, provide service commitments to energy providers, etc. The optimizer 410 can receive pricing information from the power market interface 152. For example, the pricing information can include a forecast of the electricity price from the power grid 310 within one or more future time periods (e.g., including the optimization period), one or more uncertainty parameters of the price forecast, applicable electricity prices, etc. The optimizer 410 can incorporate the pricing information into the input of the optimization model. For example, the objective value output by the optimization model can include the electricity cost derived from the pricing information. In this case, the optimization objective value may include minimizing the objective value, thereby minimizing the electricity cost.

[0084] The optimizer 410 can receive a DER generation forecast from the power market interface 152. Alternatively, the optimizer 410 can receive a weather forecast from the power market interface 152 or other external systems (such as a weather forecasting service) and derive its own DER generation forecast. It should be understood that the power generated by renewable energy sources such as solar generators and wind turbines may vary significantly due to weather. The optimizer 410 can incorporate the DER generation forecast into the input of the optimization model, for example, to determine how much DER generation is available for one or more power infrastructure sites 140 during the current optimization period.

[0085] The optimizer 410 can obtain service commitments from the power market 150 through the power market interface 152. For example, the optimizer 410 can determine the energy demand required during the optimization period based on the solution of the optimization model (e.g., including the values of one or more variables that optimize the objective value output by the optimization model). The optimizer 410 can send a request to the power market interface 152 for a commitment from the power market 150 to provide service for the determined energy demand during the corresponding optimization period. In response, the optimizer 410 can receive from the energy provider via the power market interface 152 the service commitment requested for the energy demand during the corresponding optimization period. If no energy provider on the power market 150 commits to providing service, the optimizer 410 can update and re-run the optimization model (e.g., impose constraints on the energy demand), and / or initiate or execute some other remedial measures.

[0086] Optimizer 410 may obtain other services from the power market 150 via the power market interface 152. For example, optimizer 410 may receive from the power market interface 152 the service pricing for one or more services provided by an energy provider on the power market 150. Optimizer 410 may incorporate this service pricing into the input of the optimization model. Based on the solution of the optimization model, optimizer 410 may determine to purchase one or more services and request one or more service commitments from the power market 150 via the power market interface 152. In response, optimizer 410 may receive from the power market interface 152 the service commitments requested for one or more services. If no energy provider on the power market 150 commits to providing the service, optimizer 410 may update the input of the optimization model to exclude the service pricing and re-run the optimization model, and / or initiate or perform some other remedial measures. In one embodiment, optimizer 410 may receive the scheduling of the requested service commitments via the power market interface 152.

[0087] Optimizer 410 may provide service commitments to the power market 150 via the power market interface 152. For example, optimizer 410 may provide via the power market interface 152 to the power market 150 the flexibility information of one or more power infrastructure sites 140 it manages. The flexibility information may include the price-versus-flexibility curve per kilowatt (kW) in one or more power infrastructure sites 140. Energy providers on the power market 150 may purchase flexibility from optimizer 410 via the power market interface 152. Such flexibility may be used for curtailment (e.g., demand response) when needed. In one embodiment, optimizer 410 may receive the scheduling of the provided service commitments via the power market interface 152.

[0088] It should be understood that the depicted data flows are merely examples. Optimizer 410 may interface with fewer, more, or different sets of systems than those shown. Each system may be used to collect data to be incorporated into the input of the optimization model solved by optimizer 410 and / or serve as the destination for the data output by optimizer 410. Other examples of data that may be collected and used as input to the optimization model include, but are not limited to, weather, the status of one or more power infrastructure sites 140, financial data related to one or more power infrastructure sites 140, uncertainty estimates (e.g., the planned scheduling of flexible load 350), etc.

[0089] For example, the optimizer 410 may interface with a weather forecasting service to receive weather forecasts for the optimization period from the weather forecasting service. Incorporating the weather forecast into the optimization model can improve the estimation of energy demand. For example, as described above, the weather forecast can be used to predict DER power generation, and the DER power generation can be incorporated into the input of the optimization model. Additionally or alternatively, the optimizer 410 can utilize a weather forecast with a preprocessing model to more accurately estimate the energy demand of the preprocessing flexible load 350. For example, during relatively hot or cold periods, more energy may be required to preprocess (e.g., cool or heat) an electric vehicle than during mild periods.

[0090] It should be understood that the above examples are not restrictive. The optimizer 410 can utilize other inputs and / or generate other outputs in addition to those specifically described herein. The optimizer 410 can utilize a modular "building block" architecture such that new inputs can be easily incorporated into the optimization model and / or existing inputs can be easily removed from the optimization model. Some of these inputs may represent solutions to the outputs of other optimization systems. Additionally, as discussed in the various examples, the optimizer 410 can provide additional outputs (such as a sorted list) that describe the capacity constraints of an EV power supply equipment (EVSE), such as the charging station 340. These additional outputs can be used by other systems (e.g., the scheduling system 420, the site management system 440, etc.), so that the optimizer 410 does not have to integrate every detail of the optimization.

[0091] 5. Process overview

[0092] Embodiments of a process for using heuristics for charge control in a power infrastructure site to extend battery life will now be described in detail. It should be understood that the described process can be embodied in one or more software modules executed by one or more hardware processors, e.g., as software 112 executed by the processor 210 of the EMS 110. The described process can be implemented as instructions represented in source code, object code, and / or machine code. These instructions can be directly executed by one or more hardware processors 210, or can be executed by a virtual machine or container running between the object code and the hardware processor 210. Additionally, the disclosed software can be built on top of or interface with one or more existing systems.

[0093] Alternatively, the described processes may be implemented as hardware components (e.g., general purpose processors, integrated circuits (ICs), application specific integrated circuits (ASICs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gates, or transistor logic, etc.), combinations of hardware components, or combinations of hardware and software components. For clarity of explanation of the interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described herein in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art may implement the described functionality in different ways for each particular application, but such implementation decisions should not be construed as causing a departure from the scope of the present invention. Additionally, the functional grouping within a component, block, module, circuit, or step is for ease of description. Without departing from the present invention, a particular function or step may be moved from one component, block, module, circuit, or step to another.

[0094] Battery degradation can be modeled using an empirical model that superimposes two degradation phenomena: (i) cycle aging; and (ii) calendar aging. Cycle aging is a function of depth of discharge (DoD), temperature, and charge / discharge rate. In EV applications, motor peak load and emergency rapid charging can cause cycle aging. From the perspective of the EMS 110 that manages the power infrastructure site 140 as an EV charging station, the depth of discharge and temperature are uncontrollable parameters because they mainly depend on the strategies of the EV fleet operator, the driving style of the EV driver, and the battery management systems of the electric vehicles, and these systems generally do not expose this data.

[0095] On the other hand, calendar aging is a function of time and average state of charge. For example, calendar aging increases when the battery is close to full state of charge or at a low state of charge. A lithium-ion battery that remains in an empty state of charge for a long time loses capacity, and a lithium-ion battery that remains in a full state of charge for a long time, especially at high temperatures, degrades and loses capacity faster. The time that the battery spends within these extreme SoC ranges can be managed by the EMS 110. In particular, the EMS 110 can optimize the operation of the power infrastructure site 140 to penalize extreme SoC values in the optimization model. Accordingly, the embodiments control the state of charge in the batteries of flexible loads to reduce the calendar aging of the batteries.

[0096] Figure 5FIG. 500 illustrates an example process 500 according to an embodiment for performing charging control using a heuristic approach to extend battery life in a power infrastructure site. Process 500 may be implemented by optimizer 410. Although process 500 is shown in a particular arrangement and order of sub-processes, process 500 may be implemented with fewer, more, or different sub-processes and different arrangements and / or orders of sub-processes. Additionally, it should be understood that any sub-process that does not depend on the completion of another sub-process may be executed before, after, or in parallel with other independent sub-processes, even if the sub-processes are described or illustrated in a particular order.

[0097] In sub-process 510, an optimization model is generated from models of one or more power infrastructure sites 140. For example, the model of each power infrastructure site 140 managed by optimizer 410 may be stored in a data structure in a memory (e.g., one or more databases 114). Sub-process 510 may generate an optimization model that utilizes the model of a single power infrastructure site 140 to optimize the operation of the single power infrastructure site 140, or that utilizes the models of multiple power infrastructure sites 140 to optimize the operation among the multiple power infrastructure sites 140. In one embodiment, the optimization model outputs a target value based on one or more variables and inputs, the input including a schedule for flexible load 350 in one or more power infrastructure sites 140 represented by the models of the one or more power infrastructure sites 140.

[0098] The variables may represent controllable parameters within the multiple power infrastructure sites 140. Examples of controllable parameters include, but are not limited to, set points of physical components (e.g., non-intermittent distributed energy), power flow rates for charging or discharging flexible load 350, state of charge to which flexible load 350 or battery energy storage system 320 will be charged, time to start charging or discharging flexible load 350, time to terminate charging or discharging flexible load 350, duration of charging or discharging flexible load 350, assignment or mapping of flexible load 350 to charging station 340 or power infrastructure site 140, etc. Generally, the optimization model will include multiple variables, including a mix of different types of controllable parameters. In one embodiment, one or more variables in the optimization model at least include the state of charge of an energy storage device (e.g., a battery) of flexible load 350 in the modeled power infrastructure site 140.

[0099] The scheduling of flexible load 350 may include the scheduled arrival time of each flexible load 350, the scheduled departure time of each flexible load 350, the route information of each flexible load 350, the state of charge required for each flexible load at the scheduled departure time, etc. The scheduling of flexible load 350 can be represented as a constraint (e.g., an inequality or equality constraint) in the optimization model, or in some other suitable way. It should be understood that the input of the optimization model may also include other information, such as energy pricing forecasts, weather forecasts, DER power generation forecasts, etc.

[0100] In one embodiment, the optimization model penalizes the health state (e.g., defined by the state of charge) that deviates from one or more SoH parameters associated with the energy storage device of flexible load 350. It should be understood that in this case, "penalize" means any way of increasing the overall objective value output by the optimization model. Mathematically, the optimization model can be expressed as:

[0101]

[0102] where x includes the values of all variables, f(x) is the main objective function, which uses x as the values of the operating variables to calculate the objective value estimated to be generated by the operation of power infrastructure site 140, and h(x) is the SoH objective function, which increases the objective value as the deviation from one or more SoH parameters associated with the battery of flexible load 350 increases. The main objective function f(x) represents a model of one or more power infrastructure sites 140, while the SoH objective function h(x) represents a battery loss model.

[0103] In an embodiment where the objective value includes the power cost, the main objective function f(x) takes into account one or more energy prices during the optimization period when calculating the objective value. For example, the main objective function f(x) can determine the power demand curve generated by the value x and use one or more energy prices with the power demand curve (e.g., calculate the product of the power demand curve and one or more energy prices) to determine the objective value as its output. The main objective function f(x) can also consider other costs associated with the operation of one or more power infrastructure sites 140 during the optimization period, such as grid fees, network service costs, indirect capital costs, etc.

[0104] The SoH objective function h(x) can convert the deviation from one or more SoH parameters into the same unit as the objective value output by the main objective function f(x). Therefore, in an embodiment where the objective value includes the power cost, the SoH objective function h(x) can convert the deviation from one or more SoH parameters into a monetary value, representing the cost of such deviation (e.g., the value loss of flexible load 350 due to battery loss).

[0105] One or more SoH parameters are primarily described herein as being related to the state of charge of a battery. The term "state of charge" refers to the charge level of a battery relative to its capacity. However, one or more SoH parameters can include or consist of other attributes related to battery health, such as one or more temperatures in the battery (e.g., the temperature of each individual battery cell), the rate of power flow into and / or out of the battery, the power load, the battery voltage, etc.

[0106] In one embodiment, the SoH parameter can include an ideal minimum state of charge SoC defining an ideal SoC range idealMin and an ideal maximum state of charge SoC idealMax . In this case, the deviation of a particular flexible load 350 relative to the SoH parameter can be defined as the difference between the state of charge of the flexible load 350 and the ideal SoC range. As a non-limiting example, in the case of a lithium-ion battery, the ideal minimum state of charge SoC idealMin can be 20%-30% of full charge, and the ideal maximum state of charge SoC idealMax can be 70%-80% of full charge.

[0107] Figure 6A and Figure 6B show two example penalty curves for deviations relative to the ideal SoC range according to an alternative embodiment. In Figure 6A , when the state of charge of the battery is within the ideal SoC range, the cost representing the penalty is zero, while when the state of charge of the battery is outside the SoC range, the cost jumps to a fixed value. In Figure 6B , when the state of charge of the battery is within the ideal SoC range, the cost is zero, and then it increases linearly as the state of charge drops below the ideal minimum state of charge SoC idealMin and increases linearly as the state of charge increases above the ideal maximum state of charge SoC idealMax . In other words, the cost is proportional to the amount of deviation of the state of charge of the battery relative to the ideal SoC range. It should be understood that these are just two examples, and there can be many alternative curves for the penalty relative to the ideal SoC range. For example, when the state of charge drops below the ideal minimum state of charge SoC idealMin , the cost may increase non-linearly (e.g., exponentially), and / or when the state of charge increases above the ideal maximum state of charge SoC idealMax , the cost will increase non-linearly (e.g., exponentially).

[0108] In an alternative embodiment, the SoH parameter can include or consist of a single state of charge, e.g., the average state of charge SoC of the ideal SoC range idealAvgor other desired state of charge. In this case, the deviation of a particular flexible load 350 relative to the SoH parameter can be defined as the difference between the state of charge of the flexible load 350 and a single state of charge (e.g., SoC idealAvg ). As a non-limiting example, the single desired state of charge can be 50% of full charge.

[0109] Figure 6C illustrates an example penalty curve for the deviation relative to the average desired state of charge SoC idealAvg . In this example, when the state of charge of the battery is at the average desired state of charge SoC idealAvg , the cost is zero, and then increases linearly as the distance between the state of charge of the battery and the average desired state of charge SoC idealAvg increases. In other words, the penalty is proportional to the amount of deviation of the state of charge of the battery relative to the desired state of charge. It should be understood that this is just an example, and there may be many alternative curves for the penalty with respect to the desired state of charge (e.g., SoC idealAvg ). For example, the penalty curve may be an upward parabola centered on the desired state of charge (e.g., SoC idealAvg ).

[0110] It should be understood that the penalty curve can have many other shapes besides the shapes specifically described herein. For example, the illustrated penalty curves are all symmetric (e.g., remaining constant or increasing at the same rate on either side of the SoH parameter). In this case, the penalty curve can be represented as a single cost factor applied to the absolute deviation between the state of charge of the battery and the desired state of charge or the desired SoC range. However, the penalty curve may be asymmetric (e.g., increasing at different rates on either side of the SoH parameter). In this case, the penalty curve can be represented as two cost factors: one for the cost factor of negative deviation; and one for the cost factor of positive deviation. Generally, compared to the cost assigned to a state of charge closer to the desired state of charge or the desired SoC range, the penalty curve may assign a higher cost to at least one state of charge further from the desired state of charge or the desired SoC range.

[0111] In one embodiment, each flexible load 350 or energy storage device can be associated with a different set of one or more SoH parameters (e.g., different desired SoC ranges, different SoC idealMin , different SoC idealMax , and / or different SoC idealAvg)。For example, at least one energy storage device of the flexible load 350 can be associated with different SoH parameters of a different energy storage device of the flexible load 350, or each energy storage device of the flexible load 350 can be associated with an energy storage device-specific SoH parameter that is independent of the SoH parameters associated with other energy storage devices of the flexible load 350. As another alternative, one or more types of energy storage devices and possibly each type of energy storage device can be associated with a different set of one or more SoH parameters. For example, at least one type of energy storage device of the flexible load 350 can be associated with different SoH parameters of a different type of energy storage device of the flexible load 350. In other words, the SoH parameters can be load-specific and / or energy storage device-specific.

[0112] In an embodiment using the ideal SoC range as the SoH parameter, in one example, the SoH objective function h(x) can be defined as:

[0113] h(x) = C idealMin +C idealMax

[0114]

[0115]

[0116] where each v from 1 to V represents a different flexible load 350, SoC v,t represents the state of charge of the flexible load v at time step t, represents the cost factor for the deviation from the ideal SoC range that is below the ideal SoC range for the flexible load v at time step t, represents the cost factor for the deviation from the ideal SoC range that is above the ideal SoC range for the flexible load v at time step t. It should be understood that if then the penalty curve will be asymmetric, and if then the penalty curve will be symmetric. For each flexible load v and each time step t, and / or the values of can be constant, which is specific to each flexible load v and each time step t (i.e., battery variation and time variation), specific to each flexible load v but not specific to each time step t (i.e., battery variation, but not time variation), or specific to each time step t but not specific to each flexible load (time variation, but not battery variation). The SoH objective function h(x) in this embodiment only adds three to four linear parameters for each battery or flexible load 350.

[0117] In an embodiment using a single ideal state of charge as the SoH parameter, in one example, the SoH objective function h(x) can be defined as:

[0118]

[0119] where SoC ideal is the ideal state of charge, and c v,t represents a cost factor for deviation relative to SoC ideal . The value of c v,t may be constant for each flexible load v and each time step t, which is specific to each flexible load v and each time step t (i.e., battery variation and time variation), specific to each flexible load v but not specific to each time step t (i.e., battery variation, but not time variation), or specific to each time step t but not specific to each flexible load v (time variation, but not battery variation). The SoH objective function h(x) in this embodiment only adds two linear parameters for each battery or flexible load 350.

[0120] In an alternative embodiment, other SoH parameters can be used to define the SoH objective function h(x). For example, the SoH parameter can include a paired vector of the state of charge and a relevant cost factor defining a penalty curve. In this case, for the state of charge not explicitly included in the vector, the cost factor can be interpolated (e.g., using the cost factors of the two closest explicitly included states of charge on either side of the state of charge to be interpolated). In an alternative embodiment, the deviation and / or cost factor can be determined in a more complex manner and / or as a function of any other parameter. In one embodiment, for batteries more affected by extreme states of charge, the cost factor may be relatively high, so that these batteries are preferentially corrected over those less affected by extreme states of charge. Similarly, the cost factor can be adjusted according to weather forecasts so as to increase during forecasted hotter optimization periods relative to forecasted milder or colder optimization periods (e.g., at least for heat-sensitive batteries), because batteries may experience greater degradation at high temperatures.

[0121] In the above example, the SoH objective function h(x) is expressed as a piecewise penalty (e.g., as a cost), which is added to the main objective function f(x) at each time step and can separately and explicitly define the SoH parameter (e.g., SoC idealMin and SoC idealMax , or SoC ideal ) and / or cost factor (e.g., c idealMin and c idealMax, or c). This provides flexibility in how penalties are applied to reduce battery degradation in the flexible load 350. However, it should be understood that these are merely examples and the penalty curve can be defined in any suitable manner, including in a less flexible manner.

[0122] The main objective function f(x) can consider inputs including the scheduling of the flexible load 350 and / or other information (e.g., weather forecasts, DER generation forecasts, etc.). Alternatively, the inputs can be converted into constraints. In this case, the optimization model is solved subject to constraints that can be expressed as:

[0123]

[0124] Subject to the following constraints

[0125] ec i ec(x) = 0, i = 1, …, n ec

[0126] ic j ic(x) ≤ 0, j = 1, …, n ic

[0127] where ec represents equality constraints, nec represents the number of equality constraints, ic represents inequality constraints, and nic represents the number of inequality constraints. Solving the optimization model can include a value x that minimizes the objective value output as the sum of the main objective function f(x) and the SoH objective function h(x), while satisfying all constraints (e.g., within a tolerance). In one embodiment, in contrast to hard constraints, at least some of the constraints can be probabilistic constraints. Additionally, at least some of the constraints can limit the limit parameters associated with the flexible load 350 (e.g., parameters associated with an electric vehicle).

[0128] The main objective function f(x) of the virtual site model and the SoH objective function h(x) for penalizing the value x that increases battery degradation can be constructed in any suitable manner. For example, feature engineering or other techniques can be used to evaluate potential parameters (variables, constraints or boundary conditions, weights, etc.) and select the most influential parameters for the optimization model. The user can interact with the optimizer 410 periodically (e.g., through the graphical user interface generated by the software 112), e.g., to revise the main objective function f(x) and / or the SoH objective function h(x), e.g., to change one or more parameters in the optimization model. Machine learning algorithms can learn from these interactions, e.g., to tune the parameters or default values in the optimization model based on these regular user interactions.

[0129] The main objective function f(x) and / or the SoH objective function h(x) may include mixed integer programming, linear programming, quadratic programming, mixed integer linear programming (MILP), mixed integer quadratic programming (MIQP), etc. Generally, using linear programming can improve the computational speed and scalability of the optimization model, which may be particularly beneficial when the main objective function f(x) and / or the SoH objective function h(x) include a large number of variables or other parameters (e.g., for a large number of flexible loads 350). There are many commercial and open-source modeling languages available for defining the objective function f(x) and the SoH objective function h(x).

[0130] The objective function f(x) and / or the SoH objective function h(x) can be trained in any known way. For example, supervised machine learning can be used to train the objective function f(x). In this case, an (X, Y) training dataset can be used, where X represents a set of historical values of the variables and Y represents the true objective values of these historical values of the variables. In this case, the training can be expressed as:

[0131] min(Y - f(X))

[0132] where f(X) (e.g., the weights in f(X)) is updated to minimize the error between the reference true objective value Y and the corresponding objective value output by f(X). Alternatively, the objective function f(x) can be trained in other ways using historical data. The SoH objective function h(x) can be trained in the same, similar, or different ways as the main objective function f(x), and / or trained together with the main objective function f(x).

[0133] In one embodiment, the optimization model may include multiple objective functions, including three or more objective functions:

[0134]

[0135] Subject to zero, one, or more constraints. In addition to the main objective function f(x) and the SoH objective function h(x), each g(x) represents a secondary objective function. There may be K≥1 secondary objective functions g(x). Each secondary objective function g(x) can output an objective value based on x in the same unit of measure (e.g., monetary cost) as the objective values output by the main objective function f(x) and the SoH objective function h(x). It should be understood that when solving the optimization model, the total objective value, which is the sum of the objective values output by the main objective function f(x), the SoH objective function h(x), and each secondary objective function g(x), is minimized. Similar to the SoH objective function h(x), the secondary objective function g(x) can be a penalty function that penalizes certain attributes of x, thus causing the optimization model to deviate from solving for values of x with these attributes.

[0136] While the optimization model is presented in this document as a minimization problem, it should be understood that the optimization model could also be presented in this document as a maximization problem:

[0137]

[0138] or

[0139]

[0140] subject to zero, one, or more constraints. However, for simplicity of description, it is generally assumed here that the optimization model is cast as a minimization problem. Those skilled in the art will understand how to convert between minimization and maximization problems and which problem might be more appropriate in a given situation.

[0141] In subprocess 520, the optimization model is solved to determine the values of one or more variables of the optimization objective value. In other words, the optimization model is evaluated to determine the value x that optimizes (e.g., minimizes) the objective value output by the optimization model. Solving the optimization model can include iteratively executing the optimization model with a different set of values x at each iteration until the objective value converges. After each iteration, any known technique (e.g., gradient descent) can be used to determine the value x for the next iteration. The objective value can be determined to have converged when the objective value is within a tolerance relative to a specific value, when the rate of improvement of the objective value relative to the previous iteration meets a threshold (e.g., in a minimization problem, the rate of decrease of the objective value drops below the threshold), and / or when one or more other criteria are met. Once the objective value has converged, the value x that results in the final objective value can be output as the solution to the optimization model. These values x can represent the operating configuration of the power infrastructure site 140.

[0142] It should be understood that the optimization model deviates from states that can increase battery losses through the SoH objective function h(x) (e.g., states of charge outside the ideal SoC range). In one embodiment, the SoH objective function h(x) can take into account the weather such that, relative to an optimization period in mild or cool weather, the penalty is increased during an optimization period in hot weather, at least for batteries that are more prone to greater degradation at high temperatures. Thus, during a high-temperature optimization period, the deviation in the optimization model from extreme states of charge (e.g., above the ideal SoC range) may increase, such that fewer batteries in the power infrastructure site 140 reach extreme states of charge. For example, the increase in deviation can be achieved by increasing a cost factor (e.g., increasing each c idealMax value associated with the vulnerable battery), decreasing the SoC idealMax value, decreasing the SoC ideal value, etc.

[0143] The optimization model can be a stochastic optimization model that is robust to uncertainty. Any suitable technique can be used in subprocess 520 to solve the optimization model. Such techniques include, but are not limited to, stochastic multi-stage optimization, chance-constrained optimization, Markowitz mean-variance optimization, etc. There are many commercial and open-source solvers available for solving the optimization model in subprocess 520.

[0144] In one embodiment, multiple optimization models can be solved, each optimization model including a different primary objective function f(x). Each primary objective function f(x) can represent a different scenario. Such an embodiment can be used to evaluate multiple different "what-if" scenarios. Alternatively or additionally, each primary objective function f(x) can represent a different goal. The final solution can be determined from the solutions output from the multiple optimization models by selecting one solution, combining the solutions in any suitable way, etc.

[0145] In subprocess 530, based on the solution to the optimization model, control of the power infrastructure site 140 where the optimization was performed in subprocess 520 can be initiated. The control can include configuring one or more physical components in the power infrastructure site 140 based on the determined value x representing the operating configuration. These physical components can include physical infrastructure assets such as battery energy storage system 320, generator 330, charging station 340, charger 342, flexible load 350, auxiliary load, etc.

[0146] As described above, the solution to the optimization model includes the value of one or more variables (i.e., x) that optimize the objective value in subprocess 520. The value x can represent the operating configuration of the power infrastructure site 140. For example, for each flexible load 350, the value x can include a future charging pattern (e.g., power flow rate, charging time period, etc.) to charge the flexible load 350 to a target state of charge at a scheduled time. As a result of the SoH objective function h(x), the future charging pattern can eliminate or minimize the time when the battery of the flexible load 350 has an extreme state of charge (i.e., outside the ideal SoC range), thereby reducing battery wear and extending the life of the battery and the useful life of the flexible load 350.

[0147] In the case where the flexible load 350 is an electric vehicle, the scheduled time can be the departure time, and the target state of charge can be the state of charge required for the electric vehicle to complete its scheduled route. Charging can also include or otherwise take into account preconditioning of the electric vehicle (e.g., cooling the electric vehicle before departure, preconditioning the battery of the electric vehicle, etc.), as well as charging the battery of the electric vehicle. However, preconditioning may be a flexible operating state that can be terminated or abandoned when charging is required before the scheduled departure time of the electric vehicle or when the optimizer 410 determines that the power can be better used elsewhere.

[0148] Sub - process 530 may include the optimizer 410 generating control information and transmitting it to one or more control systems 142. Then, each control system 142 may control the physical components in its respective electric power infrastructure site 140. Alternatively, the optimizer 410 may directly control the physical components (i.e., without intervening control systems). In either case, the control may include setting set - points of the physical components, actuating switches, turning physical components on or off, or otherwise changing the physical or operating state of the physical components, etc. The set - points of the physical components may include values such as voltage, current, output power, input power, power flow rate, state of charge, temperature, etc. For example, the control may include controlling the set - point (e.g., output power) of non - intermittent distributed energy resources. It should be understood that although intermittent distributed energy resources (e.g., solar or wind generators or other weather - dependent generators) may be controlled in a similar manner, intermittent distributed energy resources can generally only be controlled to reduce the output power to below its potential output power, which is essentially a waste of energy.

[0149] In one embodiment, process 500 may be executed periodically to determine a value x representing the operating configuration of the electric power infrastructure site 140 in a sliding time window and initiate control of the electric power station 140 during an optimization period represented by the sliding time window to conform to these operating configurations. The sliding time window may slide according to time steps (i.e., expiration of time intervals), and after each time step, process 500 may be executed during the optimization period currently represented by the sliding time window. For example, the time step may be one minute, five minutes, ten minutes, fifteen minutes, thirty minutes, one hour, one day, or any other time interval. The sliding time window may start at the current time and end in the future, or it may start in the future and end in the future. In the former case, process 500 is executed at the start of each optimization period, while in the latter case, process 500 is executed prior to each optimization period (e.g., one minute, five minutes, ten minutes, fifteen minutes, thirty minutes, one hour, one day, etc. in advance). The periodic execution of process 500 may be automatically executed (i.e., without user intervention) after each time step or semi - automatically executed (e.g., with user confirmation) after each time step. In either case, once the execution of process 500 is initiated, each sub - process of process 500 may be automatically executed.

[0150] In alternative or additional embodiments, process 500 may be performed in response to a triggering event rather than a time step. For example, process 500 may be performed in response to a user action. In particular, the user may execute optimizer 410 using one or more inputs of the graphical user interface provided by software 112. In this case, the initiation of control in subprocess 530 may be performed automatically, semi-automatically, or manually. In the semi-automatic case, the graphical user interface may display the operation configuration output by subprocess 520 to the user and request user confirmation before optimizer 410 executes subprocess 530. In the manual case, the graphical user interface may provide the operation configuration output by subprocess 520 to the user, and the user may manually configure power infrastructure site 140.

[0151] 6. Example embodiment

[0152] The disclosed embodiments incorporate a heuristic penalty factor for battery aging into the optimization model for controlling the operation of power infrastructure site 140 (e.g., power and time scheduling of flexible load 350). For example, the heuristic penalty factor may be used in the SoH objective function h(x) in the optimization model, which may be constrained and / or weighted for other optimization objectives (e.g., time, power level, etc.) to achieve an ideal balance between the objectives. The heuristic penalty factor may be expressed as a linear function, which has generalizability, scalability, rapidity, and computational tractability.

[0153] In the context of electric vehicles, using the heuristic penalty factor can facilitate the charging control of a fleet of commercial electric vehicles by reducing the impact of charging scheduling on battery aging within the electric vehicles. Similar issues exist for stationary batteries in a microgrid (e.g., battery energy storage system 320). In addition to extending the battery life, the disclosed embodiments are easy to configure and maintain, do not require complex system integration with the battery management system in flexible load 350, and can be applied to a range of battery chemistries, flexible loads 350, charging protocols, etc.

[0154] Figure 7 Shows the impact of the SoH objective function h(x) penalizing the state of charge outside the ideal SoC range for an experiment according to an embodiment. The impact is represented in a line graph by the state of charge of the battery over time. As shown, when using the SoH penalty during optimization, the battery spends less time in extreme states of charge than when not using the SoH penalty during optimization. Conversely, when not using the SoH penalty during the optimization process, the battery spends more time in extreme states of charge (i.e., closer to empty and full states of charge), which increases battery wear and shortens the battery life.

[0155] Generally, the optimization model will be affected by the SoH penalty to keep a flexible load 350 (e.g., an electric vehicle) within an ideal SoC range during (e.g., charging or discharging). Calendar aging can be reduced by charging an empty battery in advance and charging the battery to full only when necessary (e.g., to maintain the schedule of the flexible load 350). By appropriately setting the cost factor, the optimization model can be configured not to override higher-priority short-term goals (e.g., preparing the battery of an electric vehicle for departure and having sufficient state of charge to complete a specified route).

[0156] The SoH parameters used for the SoH penalty (e.g., incorporated into the SoH objective function h(x)) can be defined at various granularity levels. For example, the SoH parameters can be defined globally, for each power infrastructure site 140, each type of charging station 340 or charger 342, each flexible load 350, each individual flexible load 350, each type of battery, each battery, etc. The selection of the values of the SoH parameters can be informed by various sources, including manufacturers, industry averages, ambient temperature varying over time, machine learning, stochastic methods, telemetry of the flexible load 350, etc.

[0157] The values of the SoH parameters can be system settings. Alternatively, the SoH parameters can be manually edited by the user, e.g., through user input provided by a graphical user interface of the software 112. In this case, the SoH parameters may initially be populated with default values. Then, the user can edit these default values, including, for example, turning off the SoH penalty for one or more flexible loads 350, infrastructure sites 140, etc. (e.g., by reducing the associated cost factor to zero).

[0158] Example 1: A method, including using at least one hardware processor to: generate an optimization model from a model of at least one power infrastructure site, where the optimization model outputs a target value based on one or more variables, the one or more variables including the state of charge of an energy storage device (e.g., a battery) in a flexible load in at least one power infrastructure site and inputs including a schedule for the flexible load, and where the optimization model penalizes a state of charge that deviates from a health state (SoH) parameter associated with the energy storage device of the flexible load; solve the optimization model to determine values of the one or more variables of the optimization target value; and initiate control of one or more physical components in at least one power infrastructure site based on the determined values of the one or more variables.

[0159] Embodiment 2: The method according to Embodiment 1, wherein at least one power infrastructure site includes a refueling station, wherein the flexible load includes an electric vehicle, and wherein one or more physical components include one or more charging stations configured to be electrically connected to the electric vehicle to enable charging or discharging of the electric vehicle, or both.

[0160] Embodiment 3: The method according to Embodiment 2, wherein the determined value of one or more variables associates each electric vehicle with one or both of the following: the period of time or the power flow rate for charging or discharging at one or more charging stations in at least one power infrastructure site.

[0161] Embodiment 4: The method according to Embodiment 2 or 3, further comprising receiving vehicle information of the electric vehicle from at least one external system via at least one network, wherein the input of the optimization model is at least derived from the vehicle information.

[0162] Embodiment 5: The method according to Embodiment 4, wherein the vehicle information includes one or more of the following: the planned schedule of the electric vehicle, the route of the electric vehicle, the arrival time of the electric vehicle at at least one power infrastructure site, or the state of charge of the electric vehicle.

[0163] Embodiment 6: The method according to any one of Embodiments 1 to 5, wherein the target value includes at least the power cost, and wherein optimizing the target value includes minimizing the target value.

[0164] Embodiment 7: The method according to any one of Embodiments 1 to 6, wherein initiating control of one or more physical components includes controlling the set points of one or more physical components according to the determined values of one or more variables.

[0165] Embodiment 8: The method according to Embodiment 7, wherein one or more physical components include non-intermittent distributed energy.

[0166] Embodiment 9: The method according to any one of Embodiments 1 to 8, wherein initiating control includes communicating with the control system of at least one power infrastructure site via at least one network.

[0167] Embodiment 10: The method according to any one of Embodiments 1 to 9, wherein the SoH parameter includes a state of charge (SoC) range.

[0168] Embodiment 11: The method according to Embodiment 10, wherein at least one of the SoC ranges is defined by a minimum SoC threshold greater than the empty charge and a maximum SoC threshold less than the full charge.

[0169] Example 12: The method according to any one of Examples 1 to 11, wherein the optimization model includes an objective function, and the penalty for the state of charge deviating from the SoH parameter increases as the deviation increases in the objective function.

[0170] Example 13: The method according to Example 12, wherein the objective function is linear.

[0171] Example 14: The method according to any one of Examples 1 to 13, wherein the optimization model penalizes one or more additional charging characteristics associated with an energy storage device (e.g., a battery) in the flexible load.

[0172] Example 15: The method according to any one of Examples 1 to 14, wherein at least one energy storage device (e.g., a battery) in the flexible load is associated with an SoH parameter that is different from an SoH parameter of a different energy storage device (e.g., a battery) in the flexible load.

[0173] Example 16: The method according to any one of Examples 1 to 15, wherein at least one type of energy storage device (e.g., a battery) in the flexible load is associated with an SoH parameter that is different from an SoH parameter of a different type of energy storage device (e.g., a battery) in the flexible load.

[0174] Example 17: The method according to any one of Examples 1 to 16, wherein each energy storage device (e.g., a battery) is associated with an energy storage device-specific (e.g., battery-specific) SoH parameter that is independent of the SoH parameter associated with other energy storage devices (e.g., batteries).

[0175] Example 18: The method according to any one of Examples 1 to 17, wherein the optimization model includes a stochastic algorithm.

[0176] Example 19: A system, comprising: at least one hardware processor; and software configured to perform the method according to any one of Examples 1 to 18 when executed by the at least one hardware processor.

[0177] Example 20: A non-transitory computer-readable medium storing instructions, wherein when the instructions are executed by a processor, the processor is caused to perform the method according to any one of Examples 1 to 18.

[0178] The foregoing description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles described herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, it is to be understood that the description and drawings presented herein represent the current preferred embodiments of the invention and thus represent the broad subject matter contemplated by the invention. It is further understood that the scope of the invention fully encompasses other embodiments that may become obvious to those skilled in the art, and thus the scope of the invention is not limited.

[0179] Combinations described herein, such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" include any combination of A, B, and / or C, and may include multiple A's, multiple B's, or multiple C's. Specifically, combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", "A, B, C, or any combination thereof" can be A only, B only, C only, A and B, A and C, B and C, or A and B and C, and any such combination can include one or more of its components A, B, or C. For example, a combination of A and B can include one A and multiple B's, multiple A's and one B, or multiple A's and multiple B's.

Claims

1. A method comprising using at least one hardware processor to: Generate an optimized model from the models of at least one power infrastructure site, where The optimization model outputs a target value based on one or more variables, the one or more variables including the state of charge of an energy storage device in a flexible load in the at least one power infrastructure site and inputs including a schedule for the flexible load, and wherein the optimization model penalizes a state of charge that deviates from a health state (SoH) parameter associated with the energy storage device in the flexible load; Solve the optimization model to determine values of the one or more variables that optimize the target value; and Based on the determined values of the one or more variables, initiate control of one or more physical components in the at least one power infrastructure site.

2. The method according to claim 1, wherein The at least one power infrastructure site includes a refueling station, wherein the flexible load includes an electric vehicle, and wherein the one or more physical components include one or more charging stations configured to be electrically connected to the electric vehicle to effect one or both of charging the electric vehicle or discharging the electric vehicle.

3. The method according to claim 2, wherein The determined values of the one or more variables associate each electric vehicle in the electric vehicle with one or both of: a time period or a power flow rate for charging or discharging at one of the one or more charging stations in the at least one power infrastructure site.

4. The method according to claim 2, further comprising receiving vehicle information of the electric vehicle from at least one external system via at least one network, wherein the input of the optimization model is at least derived from the vehicle information.

5. The method according to claim 4, wherein The vehicle information includes one or more of: a planned schedule of the electric vehicle, a route of the electric vehicle, an arrival time of the electric vehicle at the at least one power infrastructure site, or a state of charge of the electric vehicle.

6. The method according to claim 1, wherein The target value includes at least a power cost, and wherein optimizing the target value includes minimizing the target value.

7. The method according to claim 1, wherein, Initiating control of one or more physical components includes controlling a set point of the one or more physical components according to the determined values of the one or more variables.

8. The method according to claim 7, wherein The one or more physical components include non-intermittent distributed energy.

9. The method according to claim 1, wherein Initiating control includes communicating via at least one network with a control system of the at least one power infrastructure site.

10. The method according to claim 1, wherein, The SoH parameter includes a state of charge (SoC) range.

11. The method according to claim 10, wherein, At least one of the SoC ranges is defined by a minimum SoC threshold greater than an empty charge and a maximum SoC threshold less than a full charge.

12. The method according to claim 1, wherein The optimization model includes an objective function, and the penalty for a state of charge that deviates from the SoH parameter increases with an increase in the deviation in the objective function.

13. The method according to claim 12, wherein The objective function is linear.

14. The method according to claim 1, wherein The optimization model penalizes one or more additional charging characteristics associated with the energy storage device in the flexible load.

15. The method according to claim 1, wherein At least one of the energy storage devices in the flexible load is associated with a SoH parameter different from that of a different energy storage device in the flexible load.

16. The method according to claim 1, wherein, At least one type of the energy storage device in the flexible load is associated with a SoH parameter that is different from that of a different type of the energy storage device in the flexible load.

17. The method according to claim 1, wherein Each energy storage device in the energy storage devices is associated with an energy storage device-specific SoH parameter, and the energy storage device-specific SOH parameter is independent of the SoH parameters associated with other energy storage devices.

18. The method according to claim 1, wherein, The optimization model includes a stochastic algorithm.

19. The method according to claim 1, wherein, The optimization model deviates from a state that can increase battery losses.

20. The method according to claim 1, wherein, The optimization model can be expressed as where x includes the values of the one or more variables, f(x) is the main objective function for calculating the target value, and h(x) is a SoH objective function that increases as the state of charge deviates from one or more SoH parameters.

21. The method according to claim 20, wherein, The SoH objective function h(x) is formulated as a piecewise penalty added at each time step.

22. A system, comprising: at least one hardware processor; and software configured to, when executed by the at least one hardware processor, generate an optimization model from a model of at least one power infrastructure site, where the optimization model outputs a target value based on one or more variables, the one or more variables including the state of charge of an energy storage device in a flexible load in the at least one power infrastructure site and inputs including a schedule for the flexible load, and where the optimization model penalizes a state of charge that deviates from a health state (SoH) parameter associated with the energy storage device in the flexible load; solve the optimization model to determine the values of the one or more variables that optimize the target value; and initiate control of one or more physical components in the at least one power infrastructure site based on the determined values of the one or more variables.

23. The system according to claim 22, wherein Initiating control of one or more physical components includes controlling setpoints of the one or more physical components according to the determined values of the one or more variables.

24. The system according to claim 23, wherein, The one or more physical components include non-intermittent distributed energy.

25. The system according to claim 22, wherein The optimization model includes an objective function, and the penalty for a state of charge that deviates from the SoH parameter increases as the deviation increases in the objective function.

26. The system according to claim 22, wherein, The optimization model includes a stochastic algorithm.

27. The system according to claim 22, wherein, The optimization model deviates from a state that can increase battery losses.

28. The system according to claim 22, wherein The optimization model can be expressed as where x includes the values of the one or more variables, f(x) is the main objective function for calculating the target value, and h(x) is a SoH objective function that increases as the state of charge deviates from one or more SoH parameters.

29. The system according to claim 28, wherein, The SoH objective function h(x) is formulated as a piecewise penalty added at each time step.

30. The system according to claim 22, wherein, The at least one power infrastructure site includes a refueling station, where the flexible load includes an electric vehicle, and where the one or more physical components include one or more charging stations configured to be electrically connected to the electric vehicle to enable charging or discharging of the electric vehicle, or both.

31. The system according to claim 30, wherein, The determined value of the one or more variables associates each electric vehicle in the electric vehicle fleet with a power flow rate for charging or discharging at one of the one or more charging stations in the at least one electric power infrastructure site.

32. A non-transitory computer-readable medium storing instructions thereon, wherein, The instructions, when executed by a processor, cause the processor to: Generate an optimization model from a model of at least one electric power infrastructure site, where the optimization model outputs a target value based on one or more variables, the one or more variables including the state of charge of an energy storage device in a flexible load in the at least one electric power infrastructure site and inputs including a schedule for the flexible load, and where the optimization model penalizes a state of charge that deviates from a health state (SoH) parameter associated with the energy storage device in the flexible load; Solve the optimization model to determine values of the one or more variables that optimize the target value; and Based on the determined values of the one or more variables, initiate control of one or more physical components in the at least one electric power infrastructure site.

33. The non-transitory computer-readable medium according to claim 32, wherein, Initiating control of one or more physical components includes controlling setpoints of the one or more physical components according to the determined values of the one or more variables.

34. The non-transitory computer-readable medium according to claim 33, wherein, The one or more physical components include non-intermittent distributed energy resources.

35. The non-transitory computer-readable medium according to claim 32, wherein, The optimization model includes an objective function, and the penalty for a state of charge that deviates from the SoH parameter increases with the increase in the deviation in the objective function.

36. The non-transitory computer-readable medium according to claim 32, wherein, The optimization model includes a stochastic algorithm.

37. The non-transitory computer-readable medium according to claim 32, wherein, The optimization model deviates from states that can increase battery losses.

38. The non-transitory computer-readable medium according to claim 32, wherein, The optimization model can be expressed as where x includes the values of the one or more variables, f(x) is the primary objective function that calculates the target value, and h(x) is the SoH objective function that increases as the state of charge deviates from one or more SoH parameters.

39. The non-transitory computer-readable medium according to claim 38, wherein, The SoH objective function h(x) is formulated as a piecewise penalty added at each time step.

40. The non-transitory computer-readable medium according to claim 32, wherein, The at least one electric power infrastructure site includes a refueling station, where the flexible load includes electric vehicles, and where the one or more physical components include one or more charging stations configured to be electrically connected to the electric vehicles to enable charging or discharging of the electric vehicles, or both.

41. The non-transitory computer-readable medium according to claim 40, wherein, The determined value of the one or more variables associates each electric vehicle in the electric vehicle fleet with a power flow rate for charging or discharging at one of the one or more charging stations in the at least one electric power infrastructure site.

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