System and method for intelligent optimization bidding in battery energy storage systems

Abstract

The system includes multiple energy storage nodes and at least one processor, the at least one processor configured to operate according to programming and acquire state data for the system's components, and the state data for at least multiple battery modules and at least one inverter of the multiple energy storage nodes is measured and stored in memory. The acquired state data is analyzed to determine whether the values ​​of the data exceed the prediction limits provided by the algorithm for previous bidding information. If the values ​​of the acquired state data exceed the prediction limits, the cost function model is applied to at least one of the system's battery modules, inverters, and operating efficiency. The optimization gain values ​​of the algorithm for the next energy bid are adjusted based on the acquired state data and cost reduction values.

Description

[Technical Field]

[0001] Cross-reference of related applications This application, filed on 29 September 2023, claims priority under 35 U.S.C § 120 to U.S. Patent Application No. 63 / 541,540, titled "System and Method for Intelligent Optimized Bidding in a Battery Energy Storage System," the entire disclosure of which is incorporated herein by reference.

[0002] This subject relates to an example of a system and method for optimizing bidding on energy managed by a battery energy storage system (BESS) and the control of the BESS using multi-factor analysis beyond revenue maximization. [Background technology]

[0003] Battery energy storage systems, combined energy storage systems, and some energy supply systems provide and absorb energy to and from the power grid based on the current price of energy in the grid; that is, energy supply systems sell or supply energy when the price per kilowatt is high, or battery energy storage systems purchase or absorb energy when the price per kilowatt is low. In doing so, energy storage systems help the power grid provide energy efficiently during periods of oversubscribed or undersubscribed users, while maximizing energy returns for the owners of energy supply systems.

[0004] However, existing bidding optimization algorithms in modern energy supply systems are revenue-based, meaning that these algorithms do not consider the operating costs and lifespan of the energy supply system. This presents a problem, as the end-of-life (EOL) of batteries in battery energy storage systems is greatly influenced by power control strategies; for example, rapidly charging and discharging batteries will degrade them at accelerated rates across charge-discharge within nominal rates. The EOL of a battery can be improved if the battery is controlled using an optimized method. Ultimately, the overall benefit of an energy supply system, such as a battery energy storage system, is based not only on energy revenue but also on the EOL of its battery and the efficiency of the battery's operating point.

[0005] The end-of-life (EOL) of an inverter in an energy supply system is also primarily influenced by operating conditions. Stress factors such as DC link voltage, current, and temperature can affect the inverter's lifespan. Operating costs can also include the overall efficiency of the system, such as the battery, inverter, and logic power in a battery energy storage system, as well as the cost of utilizing auxiliary power, and the impact of all these factors on the overall efficiency of the battery energy storage system can be significant. [Overview of the project]

[0006] Therefore, to efficiently maximize the lifespan of components, there is a need for systems and methods to improve energy bidding strategies instead of energy supply systems. The life extension bidding technology disclosed herein optimizes the control of energy supply systems, such as battery energy storage systems, based on the total customer benefit during the lifespan of the energy supply system. The life extension bidding technology uses additional optimization functions that take into account the degradation factors of inverters and batteries. The life extension bidding technology takes into account the operational efficiency of the energy supply system. Each bid has additional information from the energy supply system and optimizes the bid to maximize customer benefit. The life extension bidding technology includes a machine learning method that self-adjusts after each bid and receives system state variables.

[0007] Overall, life extension bidding technology increases the overall profitability of energy supply system operators, improves the operational efficiency of energy supply systems, and delays the need for battery and inverter degradation and upgrades.

[0008] In the first example, the system for intelligent optimized bidding includes an energy storage system 100 comprising a plurality of energy storage nodes 110A-N, and a performance monitoring system element 105. Each of the plurality of storage nodes 110A-N comprises a plurality of battery modules 412A-N. The performance monitoring system element 105 includes at least one processor 712, 752 coupled to the energy storage nodes 110A-N, and memories 713, 753 configured to store data and programming. At least one processor 712, 752 of the performance monitoring system element 105 is configured to perform operations in accordance with the execution of programming 500 and to acquire previous energy bidding information, including at least one of the unit price and total energy purchased in the last energy bid. The processor is further configured to acquire status data of the components of the energy storage system. Status data of at least a plurality of battery modules 412A-N and at least one inverter 104 of the plurality of energy storage nodes 110A-N is measured and stored in memories 712, 753 coupled to at least one processor. At least one processor is further configured to analyze acquired state data and determine whether the acquired data values ​​exceed the prediction limits provided by the algorithm for previous energy bidding information. If the acquired data values ​​do not exceed the predetermined values, the result of the decision is reported. If the acquired state data values ​​exceed the prediction limits, at least one processor is configured to apply a cost function model to at least one of the multiple battery modules 412A~N of the energy storage system, at least one inverter 104, and the operating efficiency. At least one processor is further configured to adjust the optimization gain values ​​of the algorithm for the next energy bid based on the acquired state data and cost reduction values ​​of the multiple battery modules, at least one inverter, and the operating efficiency of the energy storage system.

[0009] In the second example, the non-temporary computer-readable medium includes an intelligent optimized bidding (IOB) module 500. The execution of the IOB module 500 by one or more processors 712, 752 is configured to perform the following actions: retrieve previous energy bidding information 520, which includes at least one of the unit price and total energy purchased in the last energy bid; and retrieve status data 525 for the components of an energy storage system 100, which includes a plurality of energy storage nodes 110A-N and at least one inverter 104, each energy storage node 110A-N including a plurality of battery modules 412A-N. The status data for at least a plurality of battery modules 412A-N and at least one inverter 104 of the plurality of energy storage nodes 110A-N is measured and stored in memories 713, 753 coupled to one or more processors 712, 752. One or more computing devices are further configured to analyze (535) the retrieved status data to determine whether the values ​​of the retrieved data exceed the predictive limits provided by the algorithm for the previous energy bidding information. If the acquired data values ​​do not exceed predetermined values, the result of the decision is reported. If the acquired state data values ​​exceed prediction limits, one or more computing devices are configured to apply cost function models 602, 604, and 606 to at least one of the multiple battery modules 412A to N of the energy storage system 100, at least one inverter 104, and the operating efficiency, and to adjust the optimization gain values ​​of the algorithm for the next energy bid based on the acquired state data and cost reduction values ​​of the multiple battery modules 412A to N of the energy storage system 100, at least one inverter 104, and the operating efficiency.

[0010] In the third example, the method for intelligent optimized bidding includes obtaining previous energy bidding information 520, which includes at least one of the unit price and total energy purchased in the last energy bid, and obtaining status data of components 525 of an energy storage system 100, which includes a plurality of energy storage nodes 110A-N and at least one inverter 104, each energy storage node 110A-N including a plurality of battery modules 412A-N. The status data of at least a plurality of battery modules 412A-N and at least one inverter 104 of the plurality of energy storage nodes are measured and stored in memories 713, 753 coupled to the energy storage nodes 110A-N. The method further includes analyzing the obtained status data 525 to determine whether the value of the obtained data exceeds a predictive limit provided by the algorithm for previous energy bidding information. If the value of the obtained data does not exceed a predetermined value, the method includes reporting the result of the decision 530. If the acquired state data values ​​exceed the prediction limit, the method further includes applying cost function models 535, 540 to at least one of the multiple battery modules 412A~N of the energy storage system, at least one inverter 104, and the operating efficiency, and adjusting the optimization gain value of the algorithm for the next energy bid based on the acquired state data and cost reduction values ​​of the multiple battery modules, at least one inverter, and the operating efficiency of the energy storage system.

[0011] The additional purposes, advantages, and novel features of the example are described in part in the following description and in part may become apparent to those skilled in the art by examining the following and the accompanying drawings, or by generating or manipulating the example. The purposes and advantages of this disclosure may be realized and achieved by the methods, means, and combinations specifically indicated in the accompanying claims.

[0012] The figures in the drawings illustrate one or more embodiments of the present concept, not as limitations, but merely as examples. In the figures, similar reference numerals refer to identical or similar elements. [Brief explanation of the drawing]

[0013] [Figure 1A] This is an isometric view of a battery energy storage system including multiple energy storage nodes, a central control system element, and an external power grid. [Figure 1B] This is an isometric view of a single energy storage node, multiple optional energy storage nodes, and the external power grid. [Figure 2] This is an electrical diagram of a battery energy storage system similar to Figures 1A and 1B, showing the flow of information and operating power. [Figure 3] This is a system diagram of a battery energy storage system similar to Figures 1A and 1B, illustrating the boost converter controller and the decentralization of the battery energy storage system. [Figure 4] Figure 1B is a semi-transparent isometric view of an energy storage node that includes a battery bank of multiple battery modules. [Figure 5] This is a flowchart of the Intelligent Optimized Bidding (IOB) control process. [Figure 6] This figure shows cost models for battery degradation, inverter degradation, and operating efficiency. [Figure 7] Figures 1A and 1B are high-level functional block diagrams of the energy storage system, showing components of a control system including performance monitoring system elements and energy storage nodes for controlling power flow, overall operation, and the implementation of the Intelligent Optimized Bidding (IOB) protocol. [Explanation of symbols]

[0014] 100 Battery Energy Storage Systems (BESS) 102 Energy Sources 104 Power Conversion System (PCS) 105 Performance Monitoring System Element 106 Connected Load 108 Control Subsystem 109A~N Battery Data 110A~N Energy Storage Node 111 Energy Storage Element 112 Required Power Flow 113 External Power Grid 114A~C Distributed PCS 115 Overall Operation 150 Battery Array 155A~C Battery Core 200 Battery Energy Storage System 211 Core Controller 212 Power Plant Controller 251 High Voltage (HV: High Voltage) Bus 252 Medium Voltage (MV: Medium Voltage) Bus 254 Point of Connection (POC) 255A~N Data Collection Sensor 256A~N Meter Reading 257 HV / MV Transformer 258A~X Core Transformer 259A~X Core 260A~X Circuit Breaker (CB_Core) 261 HV Circuit Breaker (CB_HV) 262 Array 410A~F Battery String 412A~N Battery Module 413 Battery Bank 500 Intelligent Optimized Bidding (IOB) Process 602 Cost Model for Battery Degradation 604 Cost Model for PCS Degradation 606 Cost Model for Operational Efficiency 705 Network 710 Physical Space 711 Network Communication Interface 712 Processors 713 memory 716A~N Battery Status 751 Network Communication Interface 752 processors 753 memory BMS Battery Management System APS Apparent Power System Controller Node SDU Node Storage Dispatch Unit MDU Market Dispatch Unit RTAC Real-Time Automation Controller [Modes for carrying out the invention]

[0015] In the embodiments for carrying out the following inventions, numerous specific details are given in order to provide a complete understanding of the relevant teachings. However, it should be obvious to those skilled in the art that these teachings can be put into practice without such specific details. In other examples, well-known methods, procedures, components, transfer functions, and / or circuits are described at a relatively high level without detail in order to avoid unnecessarily obscuring the embodiments of these teachings.

[0016] As used herein, the term “coupled” means any logical, physical, electrical, or optical connection, link, etc., through which electricity, output, signal, or light generated or supplied by one system element is transferred to another coupled element. Unless otherwise stated, coupled elements or devices are not necessarily directly connected to one another and may be separated by intermediate components, elements, or communication media that can modify, manipulate, or carry electricity, output, light, or signals.

[0017] Unless otherwise specified, all measurements, values, ratings, locations, sizes, dimensions, angles, and other designations described herein, including in the following claims, are approximate and not strict. Such quantities are intended to have a reasonable range that is consistent with the functions to which they relate and with those that are common in the art to which they relate. For example, unless expressly specified otherwise, parameter values, etc., may vary by approximately ±5% or ±10% from the stated quantities. The terms “approximately,” “significantly,” or “substantially” mean that parameter values, etc., may vary by up to ±25% from the stated quantities.

[0018] The orientation of any complete device, such as a battery energy storage system, combined energy storage system, or modular energy storage system, incorporating battery nodes, cores, arrays, racks, elements, modules, submodules, strings, banks, or cells, associated components, circuits, and / or battery nodes, racks, elements, modules, submodules, strings, banks, or cells as shown in any of the drawings, is given for illustrative and explanatory purposes only. In operation for a particular battery energy storage application, battery nodes, cores, arrays, racks, elements, modules, submodules, strings, banks, or cells may be oriented in any other orientation suitable for the particular application of the battery energy storage system, e.g., upright, lateral, or any other orientation. Furthermore, to the extent used herein, terms describing any direction, such as left, right, front, rear, back, end, top, bottom, upward, downward, top, bottom, and side, are used only as examples and are not limited to the direction or orientation of any energy storage system or battery node, rack, element, module, submodule, string, bank, or cell, or the components of any example energy storage system or battery node, rack, element, module, submodule, string, bank, or cell shown in the accompanying drawings and described below.

[0019] Unless otherwise specified, any multiple components such as energy storage nodes 110A-N, battery strings 410A-F, or battery modules 412A-N may include any number of the above components, including as few as one, and are not limited by the number of components shown. Unless otherwise indicated, any coupled electrical components may be connected in series or parallel. In the case of energy storage nodes 110A-N or battery modules 412A-N, components may be connected in both series and / or parallel, depending on the state of the switches or submodules.

[0020] Figure 1A is an isometric view of a battery energy storage system (BESS) 100, which includes a plurality of energy storage nodes 110A-N, a control system including a performance monitoring system element 105, and an external power grid 113. The battery energy storage system 100 includes a plurality of energy storage nodes 110A-N optionally connected to a power conversion system (PCS) 104. The energy storage nodes 110A-N include batteries of any existing or future reusable battery technology, including, for example, lithium-ion, flow batteries, or mechanical storage, such as flywheel energy storage, compressed air energy storage, pumped-storage hydroelectric power, gravity potential energy, or hydraulic accumulators. The energy storage nodes 110A-N are capable of supplying and discharging DC electricity to external loads, such as connected loads 106, both collectively and individually, as well as receiving and charging DC electricity from external sources, such as energy source 102.

[0021] The energy source 102 can be part of any suitable system for generating electrical energy. In one example, the system may be a renewable energy system that can replenish the energy source 102. Such a renewable energy source 102 may include solar power, wind power, geothermal power, biomass, and hydropower. For example, a renewable energy system may be implemented as an array of photovoltaic modules. Photovoltaic (PC) modules may include concentrated photovoltaic systems that use crystalline silicon, amorphous silicon, copper-indium gallium selenide (CIGS) thin films, cadmium telluride (CdTe) thin films, and lenses and curved mirrors to focus sunlight onto small but highly efficient multi-junction solar cells. In another example, the energy system for the energy source 102 may be a non-renewable energy system in which the energy source 102 includes non-renewable energy sources such as fossil fuels.

[0022] To facilitate the supply and reception of DC power, energy storage nodes 110A-N can be connected to a central power conversion system 104. The power conversion system 104 is configured to standardize power input and output with the energy storage nodes 110A-N. The power conversion system 104 may include (1) inverters that convert the DC sources of the energy storage nodes 110A-N to AC waveforms and vice versa, (2) DC / DC converters that convert the DC sources of the energy storage nodes 110A-N to different DC source characteristics, (3) other known power conversion elements, or (4) a combination thereof.

[0023] When energy storage nodes 110A-N provide DC, power conversion system 104 converts the DC to AC for use by the external power grid 113 and normalizes the amperage from the battery modules (not shown) of energy storage nodes 110A-N to the external power grid 113. Furthermore, when energy storage nodes 110A-N require DC, power conversion system 104 converts AC from the external power grid 113 to DC and normalizes the amperage from the external power grid 113 to energy storage nodes 110A-N. As shown in Figure 1B, energy storage nodes 110A-N are coupled in groups to distributed power conversion systems 114A-C, and the distributed power conversion systems 114A-C can perform some or all of the tasks of power conversion system 104, potentially making the use of central power conversion system 104 completely unnecessary.

[0024] The battery energy storage system 100, which includes energy storage nodes 110A-N (and the power conversion system 104 if it is not omitted), is shown to have a single connection to the external power grid 113. In scenarios where the external power grid 113 is complex and connects to multiple energy sources 102 and connected loads 106, such as a power grid including consuming devices, a single connection to the battery energy storage system 100 can absorb energy generated by the energy sources in the external power grid 113 in excess of the demand of the connected loads in the external power grid 113, or supply energy to the connected loads in the external power grid 113 in excess of the capacity of the energy sources in the external power grid 113. Alternatively, a separate line may extend to the isolated energy sources and connected loads or to the external power grid 113. A separate line may be advantageous in scenarios where the isolated energy sources are inconsistent, such as wind or solar-based energy sources. In such a scenario, power from an energy source is pushed to energy storage nodes 110A~N, which then either charge or discharge, providing consistent energy to connected loads or the external power grid 113 via other electrical paths.

[0025] The energy source 102 can be any suitable system for generating electrical energy, such as a turbine or solar cell. The external power grid 113 can include smaller local loads, such as a power grid or backup power system, for facilities such as hospitals, manufacturing sites, homes, or other suitable establishments.

[0026] The power conversion system 104 can facilitate the normalization of input or output wattage or voltage to provide a consistent output and protect energy storage nodes 110A~N or the external power grid 113 from damage. The power conversion system 104 can perform this normalization in cooperation with a control system including a performance monitoring system element 105. The performance monitoring system element 105 also communicates with and controls the energy storage nodes 110A~N to adjust their electrical output as well as their electrical capacity or intake. The performance monitoring system element 105 has components, as shown in Figure 3, that operate independently at each level. Thus, the performance monitoring system element 105 and distributed control system elements (e.g., BMS, APS controller, SDU, MDU, and RTAC (see Figure 3)) are configured to operate in a combination of independent and centralized operation.

[0027] Generally, the energy storage nodes 110A-N of the battery energy storage system 100, connected to an external power grid 113, work in coordination to supply and discharge power to the external power grid 113, or to receive and charge power from the external power grid 113. This cooperation is coordinated by a performance monitoring system element 105 and other control units, such as a market dispatch unit (MDU) or real-time automatic controller (RTAC), which are not shown in Figures 1A and 1B. Further methods and systems relating to the management and maintenance of the energy storage nodes 110A-N (e.g., battery modules 412A-N) of the battery energy storage system 100 are disclosed in U.S. Patent Application No. 17 / 810,983, filed on 6 July 2022, and currently U.S. Patent No. 11,789,086, issued on 27 September 2023, and are incorporated herein by reference in their entirety.

[0028] Figure 1B is an isometric view of energy storage node 110A, several optional energy storage nodes 110B-N, and the external power grid 113. Energy storage node 110A includes an energy storage element 111.

[0029] The energy storage element 111 may include (1) a single battery cell, (2) a cell group including several battery cells in a parallel configuration, (3) a battery submodule or module 412A (see Figure 4) including several battery cells in parallel and series configurations, (4) a battery string 410A (see Figure 4) including several battery modules 412A~N in series, (5) a battery bank 413 (see Figure 4) including several battery strings 410A~F in parallel, (6) other known energy storage elements, or (7) a combination thereof.

[0030] The energy storage node 110A may include an HVAC heating or cooling element to regulate the temperature of the energy storage node 110A, particularly the energy storage element 111.

[0031] The energy storage nodes are organized into node clusters 110A-E, 110F-J, and 110K-N, with each cluster paired with a distributed power conversion system 114A-C. The group of nodes 110A-E, which has a distributed power conversion system 114A, constitutes a battery core 155A.

[0032] The distributed power conversion systems 114A-C may include (1) an inverter that converts the DC source of the energy storage element 111 to an AC waveform and vice versa, (2) a DC / DC converter that converts the DC source of the energy storage element 210 to different DC source characteristics, (3) other known power conversion elements, or (4) a combination thereof. The distributed power conversion systems 114A-C can serve individual energy storage nodes 110A, or any number of energy storage nodes 110A-N. Multiple energy storage nodes 110A-N are generally arranged in series, but other wiring sequences are also possible. A distributed power conversion system 114A that serves multiple energy storage nodes 110A-E may be a battery core 155A, which can be controlled by a core controller 212 (see Figure 2). The core controller 212 can coordinate with the node controllers present in each of the energy storage nodes 110A-E associated with it.

[0033] The entire battery energy storage system 100 can use physical data acquisition sensors 255A~N and data logging to collect operational and environmental data, particularly component voltage, current, temperature, or power frequency, from components of the battery energy storage system, such as energy storage nodes 11A and distributed PCS 114A~C, to provide data for analysis and control of the BESS.

[0034] Figure 2 is an electrical diagram of a battery energy storage system 200 similar to the battery energy storage system 100 in Figure 1, showing the flow of information and operating power.

[0035] The battery energy storage system 200 is connected to a power grid, including both an energy source 102 and a connected load 106, via a point of connection (POC) 254. The POC 254 is coupled to a high-voltage (HV) bus 251, which is an electric bus rated and designed to a high voltage that matches the voltage expected by the power grid. The HV bus 251 may allow multiple battery energy storage systems 200 or power storage or power generation equipment to be connected in series or parallel before being connected to the power grid via the POC 254.

[0036] The battery energy storage system 200 includes an HV circuit breaker 261 designed to selectively isolate the rest of the battery energy storage system 200 from the HV bus 251. The HV circuit breaker 261 may be hardwired to trip under certain circumstances, or it may be controlled by the power plant controller 212 or other controller.

[0037] An HV / Medium Voltage (MV) transformer 257 is coupled between the HV bus 251 and the MV bus 252. The HV / MV transformer steps down the voltage experienced at the HV bus connection terminal 251 to the voltage expected at the MV bus 252 connection terminal, and steps up the voltage from the MV bus 252 connection terminal to the voltage expected at the HV bus 251 connection terminal.

[0038] The MV bus 252 is located within the boundaries of array 262. Array 262 includes power plant controllers 212 to facilitate the operation of one or more cores 259A-X. Multiple arrays 262 can be coupled in series or parallel to the MV bus 252, but this example shows only a single array 262 with a single power plant controller 212.

[0039] Core 259A is coupled to the MV bus 252 by a core transformer 258A and a core circuit breaker 260A. Multiple cores 259A~X are connected to a single MV bus 252, and each core has its own core transformer 259A~X and its own core circuit breaker 260A~X, but in this figure only a single core 259A is shown in detail.

[0040] The MV circuit breaker 260A is designed to selectively isolate the rest of the core 259A from the MV bus 252. The MV circuit breaker 260A may be hardwired to trip under specific circumstances, or it may be controlled by the power plant controller 212, the core controller 211, or other controllers.

[0041] The core transformer 258A is coupled between the MV bus 252 and the core 259A. The core transformer 258A steps down the voltage experienced at the MV bus connection terminal 252 to the voltage expected at the core 259A connection terminal, and steps up the voltage from the core 259A connection terminal to the voltage expected at the MV bus 252 connection terminal.

[0042] Core 259A includes a power conversion system 104, which includes all the hardware and controls for bidirectional conversion between direct current (DC) power and alternating current (AC) power. The power conversion system 104 exchanges AC power with the MV bus 252 and DC power with battery nodes (e.g., cubes) 110A~N.

[0043] At least one data acquisition sensor, for example, meter 255, may be connected near the HV bus 251, for example, for the purpose of collecting at least measurements relevant to the determination of vibration, such that instantaneous voltage, current, and power frequency, instantaneous power, and rate of change of frequency are all values ​​that can be notified to the power plant controller 212 and the performance monitoring system element 105 when a decaying power oscillation occurs.

[0044] Meter readings 256A~N are collected continuously or periodically by the meter and provided to the power plant controller 212.

[0045] Figure 3 is a high-level system diagram of a battery energy storage system similar to Figures 1A-B, showing a boost converter controller and the decentralization of the battery energy storage system. Energy storage nodes, each containing multiple battery modules, are electrically connected to a power conversion system (PCS). The PCS are electrically connected to each other via buses, then electrically connected to another bus via a three-winding transformer, and the other bus is electrically connected to an HV voltage network via a transformer. The energy storage nodes may be controlled by a battery management system (BMS), which communicates with the apparent power system controller (APS) together with the PCS. The APS and BMS communicate with a node storage dispatch unit (SDU). The node SDU interfaces with and monitors the connected BMS, PCS, and other hardware, as well as higher-level control. The node SDU communicates with a core SDU, which dispatches active and reactive power to the nodes based on their operating conditions, and provides, for example, telemetry values ​​to the node SDUs and core-level system operation data to the array SDU and node SDUs. The core SDU communicates with the array SDU, which provides measured and system operation data to the market dispatch unit (MDU) and real-time automated controller (RTAC). The array SDU also dispatches active and reactive power to the core SDU based on the core-level stored energy. The MDU performs the active and reactive power applications, and the RTAC communicates with the customer's control system using a variety of adjustable interfaces.

[0046] Physical data acquisition sensors and data logging can be used throughout the battery energy storage system to collect operational and environmental data from components of the battery energy storage system, such as energy storage nodes, PCS, BMS, APS, node SDU, core SDU, array SDU, MDU, and RTAC. The collected data may include, but is not limited to, the charge status, differential voltage, current, or temperature of the energy storage nodes (e.g., battery cells, battery modules 412A-N, battery strings 410A-F, and battery bank 413), PCS, BMS, APS, node SDU, core SDU, array SDU, MDU, or RTAC. The collected data is used to derive values ​​for use in the operation and implementation of the intelligent optimized bidding control process (see Figure 5).

[0047] Figure 4 is a translucent isometric view of the energy storage node 110A of Figure 1B, which includes a battery bank 413 containing multiple battery modules 412A to N. The energy storage node 110A houses multiple battery strings 410A to F, both as a battery bank 413 and as energy storage elements 111. The energy storage node 110A is both the physical housing for the energy storage elements 111 and the logical and electrical assembly of the battery banks 413 that constitute the energy storage elements 111. The energy storage node 110A physically houses the battery banks 413, and the electrical performance of the battery banks 413, including the energy storage elements 111, may be attributed to the energy storage node 110A itself. For example, if battery string 410A of battery bank 413 can store 102 kilowatt-hours of energy, and battery bank 413 contains six battery strings 410A-F, then energy storage node 110A (and energy storage element 111) can be understood and described as storing 612 kilowatt-hours of energy. Energy storage node 110A, energy storage element 111, and battery bank 413 may contain more or fewer battery strings 410A-F than shown in the figure.

[0048] A given battery string 410A includes multiple battery modules 412A~N. Similar to the relationship between the energy storage node 110A and the battery bank 413 it contains, the battery string 410A is both a physical collection of battery modules 412A~N and a logical and electrical collection of battery modules 412A~N. For example, if a battery module 412A can store 6 kilowatt-hours of energy, and the battery string 410A includes 17 battery modules 412A~N, then the battery string 410A can be understood and described as storing 102 kilowatt-hours of energy. The battery string 410A may contain more or fewer battery modules 412A than shown in the figure.

[0049] Since the battery string 410A is a logical and electrical assembly of battery modules 412A-N, this assembly is not necessarily defined by the physical structure or order of the battery cells 412A-N, and other arrangements are possible besides the series wiring of the battery modules 412A-N of the component, as in this example. Therefore, the battery string 410A may alternatively be described as a battery rack, battery subrack, or battery array, and each of these terms (element, rack, subrack, array) can be a category of the battery string 410A. In some embodiments, there are finer levels of encapsulation within the battery module 412A, which can be identified as battery groups within the battery module 412. These battery groups may also include finer levels of encapsulation, which can be identified as battery cells within a battery group containing prism, pouch, or cylindrical battery cells.

[0050] In this example, the energy storage node 110A represents a single physical fixture, the maximum size of which may be limited by the mass or volume that a person, forklift, or vehicle can transport as a single atomic unit. The battery bank 413 within the battery module 110A represents a physical organizational structure for organizing and wiring the battery cells, groups, battery modules 412A-N, and battery strings 410A-F within the energy storage node 110A. A battery cell is generally the largest manufacturing unit that a battery manufacturer can produce, capable of charging and discharging electricity at a chemical level. In some examples, battery cells are packaged together as battery modules 412A-N, representing the smallest unit that a particular operator can remove or replace within the battery energy storage system 100, while in examples where multiple battery cells are packaged together, individual battery cells are too small or too sensitive to perform individual on-site maintenance, and instead the entire package of battery cells (e.g., battery module 412A) is repaired or replaced as a whole.

[0051] Energy storage node 110A may be similar to the features presented in the energy storage system described in international application PCT / US2021 / 30551, “Energy Storage System with Removable, Adjustable, and Lightweight Plenums,” filed on 4 May 2021 (published on 11 November 2021 as WO2021226011), which is incorporated herein by reference in its entirety.

[0052] Figure 5 is a flowchart of the profit-based intelligent optimization bidding (IOB) control process 500. The IOB control process 500 is not a method to maximize the revenue of operation of BESS100, but a method to maximize the profit of operation over the lifetime of BESS100. The profit of BESS100 is greatly influenced by the lifespan of the PCS104 inverter, energy storage nodes 110A~N, and the need for enhancement and operational efficiency. The IOB control process 500 maximizes profit by adding these cost functions (shown in cost modules 602, 604, and 606 in Figure 6) to the existing bidding algorithm for previous bidding information. From a system hierarchy perspective, this top-level optimization ensures that the bids generated by the IOB control process optimize the profit of operation.

[0053] The lifespan of the BESS100 is affected by various factors. The battery modules of the energy storage nodes 110A~N and the inverter of the PCS104 are two major components that affect the lifespan of the BESS100. The average lifespan of lithium-ion batteries is approximately 20 years, while the average lifespan of inverters varies from 10 to 20 years.

[0054] Implementing the IOB control process 500 on existing BESS100 systems enhances overall operational benefits by improving the lifespan of the battery modules in energy storage nodes 110A-N and the inverters in PCS104, as well as the operational efficiency of the BESS100 systems. For example, in a scenario where the purchase price of electricity from the Electric Reliability Council in Texas (ERCOT) is 10 cents / kWh at one point, compared to 40 cents / kWh at another, it is far more economical to stress the BESS100 systems at the higher price rather than the lower price. Higher returns justify higher costs, while lower returns are only justifiable if the costs are lower.

[0055] The degradation models for the battery modules 412A-N of the energy storage nodes 110A-N and PCS inverter 104 (see 602 and 604 in Figure 6) can be derived from various sources, such as re-search-based battery models or experimental data. The operating efficiency curves for inverter 104 and batteries 110A-N (see 606 in Figure 6) can also be derived from the manufacturing datasheet.

[0056] The multidimensional function and efficiency function of the degradation variables are shown in Figure 6 and are expanded as follows to optimize the bid. The collective system state variables are defined to describe the degradation cost of the entire BESS at any operating point. Battery degradation is influenced by various important parameters of battery data and battery state, including temperature, cycle depth, cycling frequency, charge / discharge current magnitude, rate of change of state of charge (SOC), and terminal voltage. Implementing an optimized control strategy for the batteries of energy storage nodes 110A~N can lead to a significant reduction in degradation. Furthermore, by optimizing the performance of BESS100, degradation can be significantly improved without impacting revenue generation. Table 1 shows the degradation coefficients of the batteries in the energy storage nodes.

[0057] Table 1. Ideal Battery Operating Profile [Table 1]

[0058] Using a multidimensional function approach, the following cost functions are developed for the active and idle operating modes:

number

[0059] Gains k1 to k8 are configurable penalty gains. The total index n is the number of battery modules in energy storage nodes 110A to N, and i is a number representing the starting value of the corresponding variable, e.g., the sum of 1 to n, or the initial temperature (T) or voltage (V). min This is the desired time to achieve full SOC. The last term k8(tt min ) is used to incorporate the need for shorter recharge times to ensure the maximum capacity available in the event of a discharge event. C is the number of cyclins (charges) per day. The collective system state variables of BESS100 can be defined as a function of the individual subsystem state variables. For example, the stress index for the entire BESS100 can be described using the sum of the deviations from the optimal point of each battery module and the inverter of the energy storage nodes 110A-N and PCS104. The cost function increases as the collective system state variables exceed the optimal profile. If the state variable is exactly at the optimal point, its effect is zero, and if the state variable is negative, it indicates a more relaxed BESS100, which allows room for higher stressed bids. Depending on the implementation, the negative term can be zero to consider only cost and ignore additional room or factors of stress.

[0060] The inverter lifespan of the PCS104 is a function of stress factors, primarily affecting the filter capacitors and switching semiconductors (insulated-gate bipolar transistors (IGBTs), field-effect transistors (FETs), etc.). The degradation of capacitors and semiconductor switches can be delayed by reducing the number and duration of stress factors. Table 2 shows these parameters. The following CI equation is a model of the cost function of inverter lifespan. Table 2. Ideal Inverter Operation Profile [Table 2]

number

[0061] The total index n is the number of inverters in PCS104, and i is a number representing the starting value of the corresponding variable, e.g., the sum of 1 to n. This cost function increases as each inverter stress coefficient deviates from the optimal point. If the state variable is zero, it is not a source of stress; if the state variable is negative, it indicates that BESS100 can accept bids with higher stress. The above battery cost function CB batt Similarly, depending on the implementation, the negative term can be set to zero to consider only costs and ignore any additional room or factors of stress.

[0062] Furthermore, the efficiency of the BESS100 is the primary factor in the overall benefit of the BESS100 for its operation. The inverter of the PCS104 and the battery modules of the energy storage nodes 110A~N have efficiency curves that show the most efficient operating mode. The inverter of the PCS104 typically operates most efficiently at 75% load. Using these efficiency graphs, an efficiency function can be constructed for the entire BESS100. The following CE equation models the plant efficiency cost function.

number

[0063] P inv This is the calculated individual power of each inverter 104, and P batt This is the calculated individual power of each battery module in energy storage nodes 110A~N, and P aux This is auxiliary power for the intended bid. Function

number

number

number

number

[0064] In optimal control, the state variable x(t) is the battery degradation variable, and the power command is the control input u(t). Using this approach, optimization can be formulated as follows:

number

[0065] The above cost function (see FIG. 6, 602, 604, 606) can be directly used as a model for the degradation of the battery modules of the energy storage nodes 110A to N, the degradation of the inverter of PCS 104, and the operating efficiency. As described above, FIG. 6 illustrates this modeling approach. Each of the models 602, 604, 606 can be an analytical model or an artificial intelligence-based model. The input is T for the ambient temperature amb , P for the power to be bid 入札 , t for the discharging or charging time d , SOC for the current collective SOC C , V for the current collective terminal voltage of the batteries 110A to N c , and C for the number of daily cycles to be executed. The outputs of the models 602, 604, 606 are respectively the costs of battery degradation per bid, inverter degradation, and operating efficiency. Other outputs are the degradation / stress variables predicted in the model after the power dispatch is completed, the battery temperature T batt , the inverter temperature T inv、 the DC link voltage V dc , the end SOC SOC end , and the actual SOC change rate ΔSOC during dispatch. In another approach, an intermediate step may be added to first process the degradation variables in the defined model, and these variables may be used to calculate the above cost function. The advantage of the first approach is that the process has an improved compression method. The above cost function can be defined by different functions such as higher-order terms.

[0066] The cost function described above must be created using analytical data derived from the system's collected data, or by artificial intelligence methods such as neural networks. However, modeling can potentially have inaccuracies. Online machine learning methods update the optimization function for a specific project and the BESS100 architecture, for example. Therefore, after each bid, for example in block 520 of Figure 5, all optimization variables are detected and the cost function is updated. In block 525, if the detected variable exceeds its prediction limit by the model (e.g., algorithm or artificial intelligence method), then in block 535, the detected stressor and the associated penalty gain based on the corresponding function model must be updated. This update ensures that a more accurate representation of the bid cost is considered in the bid for the next bid.

[0067] To implement the logic described above, the profit-based intelligent optimization bidding IOB control process 500 includes the functional blocks shown in Figure 5. First, in block 505, the IOB control process 500 determines whether to enable the IOB control process 500. If the IOB control process 500 is disabled by the operator or by a higher control layer, the process proceeds to block 510, and the remainder of the IOB control process 500 is not performed.

[0068] When the IOB control process 500 is activated by the operator or by a higher control layer, the IOB control process 500 is initiated. In block 520, last bid information, including price and energy per unit, is obtained from records that may be stored in memory or obtained externally, and system feedback sensed from the BESS100, battery modules 412A~N, and the inverter of PCS104, as well as other components and subcomponents, is collected. In block 525, based on the sensed system feedback, the IOB control process 500 determines whether any stress or state variables (e.g., operating conditions that may affect lifetime, such as the variables in Tables 1 and 2) have reached their respective limits or predicted limits. If the respective limits or predicted limits have not been reached, in block 530, a report detailing such results is generated, stored, or provided to the operator. However, if any system state or stress variable has reached its respective limit or predicted limit in block 525, the process proceeds to block 535, in which the IOB control process 500 updates the relevant cost function terms. For example, if the battery module of energy storage node 110A is stressed due to a high discharge rate, then C above batt The cost function term k5 in the equation is updated to indicate that the BESS100 should require a proportionally higher electricity price to operate this particular battery 110A in this manner. After this update, the process proceeds to block 540, where the update to the relevant cost function is reported to the operator.

[0069] Figure 7 is a high-level functional block diagram of the energy storage system of Figures 1A and 1B, showing components of a control system including performance monitoring system elements and energy storage nodes for controlling power flow, overall operation, and the implementation of the Intelligent Optimized Bidding (IOB) protocol. As shown, the multiple energy storage nodes 110A-N include battery modules 412A-N, a power conversion system 104, and a control subsystem 108 that receives battery data 109A-N from environmental and battery sensors 255, battery modules 412A-N, the power conversion system 104, or a combination thereof.

[0070] The control system, including the performance monitoring system element 105, energy storage nodes 110A-N, the external power grid 113, and other components of the energy storage system 100, can communicate via network 705 or one or more networks 705A-N. Networks 705A-N can be local area networks, wide area networks, or a combination thereof. For example, the performance monitoring system element 105 can be connected to the energy storage nodes 110A-N and the external power grid 113 via a local area network. Alternatively or additionally, the performance monitoring system element 105 can be connected to the energy storage nodes 110A-N and the external power grid 113 via a wide area network. Or, the performance monitoring system element 105 can be connected to the components of the energy storage system 100, including the energy storage nodes 110A-N, via a combination of networks, for example, via a local area network, and connected to the external power grid 113 via a wide area network.

[0071] The performance monitoring system element 105 includes a network communication interface 711 configured for wired or wireless communication over a network 705. The performance monitoring system element 105 further includes a memory 713 and a processor 712 coupled to the network communication interface 711 and the memory 713. As shown, the memory 713 of the performance monitoring system element 105 is configured to store battery data 109A-N, power demand 112, overall operation 115, and battery status 716A-N. The performance monitoring system element 105 may also include sensors 255 coupled to the processor 712 to detect or monitor various system parameters such as power, temperature, voltage, current, resistance, and / or impedance. For example, the sensors 255 may be coupled to an HV bus 251.

[0072] The performance monitoring system element 105 is configured to receive or store the required power flow 112 or the overall operation 115. The required power flow 112 may include discharge or charge requirements for active power, reactive power, or total system power. The required power flow 112 may be power commands from the external power grid 113 based on customer or independent system operator requests received from the external power grid 113 via the network 705, in which case the power commands are determined externally.

[0073] The overall operation 115 can be a power command for the external power grid 113 based on parameters in a customer or independent system operator request received from the external power grid 113 via the network 705. The performance monitoring system element 105, the control subsystem 108, or both can obtain the parameters of the overall operation 115 and attempt to best implement it. In this case, the power command to achieve the overall operation 115 is internally determined by the performance monitoring system element 105, for example, based on satisfying the customer or independent system operator's request to the external power grid 113.

[0074] As described above in Figure 5, the performance monitoring system element 105 can, for example, acquire the requested power flow 112 required from the external power grid 103 as requested by a customer or software application, or as required during a market bid, or separately, manage the asset to optimize the performance of BESS 100 without affecting revenue output and improve the degradation of components within the system.

[0075] Energy storage nodes 110A to N include a control subsystem 108, battery modules 412A to N, and a power conversion system 104. The control subsystem 110 of the energy storage nodes 110A to N includes a network communication interface 751 configured for wired or wireless communication over a network 705. The control subsystem 108 further includes a memory 753 and a processor 752 coupled to the network communication interface 751 and the memory 753. As shown, the memory 753 of the control subsystem 108 is configured to store battery data 109A to N, battery status 716A to N, and local required power flows 112A to N.

[0076] The control subsystem 108 further includes environmental sensors 255A-N and battery sensors 255A-N coupled to the processor 752. The environmental sensors 255A-N can, for example, measure humidity and temperature inside the housings of the energy storage nodes 110A-N. The battery sensors 255A-N may include voltage sensors, current sensors, and temperature sensors for measuring readings of battery data 109A-N, such as voltage, current, temperature, or other physical phenomena occurring within the battery modules 412A-N.

[0077] In addition to implementing an intelligent optimization bidding process 500 (see Figure 5) for components within the energy storage system 100, the control subsystem 108 or performance monitoring system element 105 may further be configured to determine at least one battery state 716A-O of one or more of the energy storage nodes 110A-N from battery data 109A-N. The battery states 716A-N can be estimates algorithmically determined from battery data 109A-N, readings from sensors 255A-N that monitor various system parameters on, for example, the HV bus 251, or a combination thereof.

[0078] Each of the energy storage nodes 110A to N may include a power conversion system 104 to control one of the local required power flows 112A to N. Battery data 109A to N may include, for example, voltage, current, temperature, or other physical phenomena occurring within the battery module 412A, or a combination thereof.

[0079] As described above, the battery energy storage system 100, the energy storage node 110A, the power conversion system 104, the performance monitoring system element 105, and various controllers may depend on at least one processor, e.g., 713, 752. The processor functions to perform various operations according to instructions or programming that can be executed by the processor, for example. While a processor may be constructed using hardwired logic, a typical processor is a general-purpose processing circuit constructed by the execution of programming. A processor can typically include elements structured and configured to perform one or more processing functions, such as various data processing functions. Individual logic components can also be used, but the example utilizes components that form a programmable CPU. A processor includes, for example, one or more integrated circuit (IC) chips that incorporate electronic elements to perform the functions of a CPU. For example, a processor may be based on any known or available microprocessor architecture, such as Reduced Instruction Set Computing (RISC) using the ARM architecture, as is commonly used in mobile devices and other portable electronic devices. Naturally, other processor circuits can be used to form a CPU or processor hardware. While each example of a processor focuses on only one microprocessor, multiprocessor architectures can also be used for convenience. Digital signal processors (DSPs) or field-programmable gate arrays (FPGAs) can be suitable alternatives to processors, but they may consume more power as complexity increases.The processor may also include, in part or in whole, (1) a single-board computer used for local calculations, processing, and control of the battery energy storage system 100, the energy storage node 110A, the power conversion system 104, and various controllers; (2) an application-specific integrated circuit used for local calculations, processing, and control of the battery energy storage system 100, the energy storage node 110A, the power conversion system 104, and various controllers; (3) other known distributed control system elements; or (4) a combination thereof.

[0080] The memories 713 and 753 can be coupled to the processors 712 and 752. Memory devices are for storing data and programming. In this example, memory devices may include flash memory (non-volatile or persistent memory) and / or random access memory (RAM) (volatile memory). RAM functions as short-term storage for instructions and data being processed by the processor, for example, as working data processing memory. Flash memory typically provides longer-term storage.

[0081] Needless to say, other storage devices or configurations may be added to or replace the storage devices or configurations in the examples. Such other storage devices may be implemented using any type of storage medium having computer or processor-readable instructions or programming stored therein, and may include, for example, any or all of the tangible memory of a computer, processor, or related modules.

[0082] Network interfaces such as network interfaces 711 and 751 can be coupled to the processor. The network interfaces of the battery energy storage system 100, energy storage node 110A, power conversion system 104, performance monitoring system element 105, and various controllers are configured to communicate with each other.

[0083] The scope of protection is limited solely by the claims that follow this specification, which are intended and should be interpreted to encompass all structural and functional equivalents, insofar as they are consistent with the ordinary meaning of the language used in the claims, when interpreted in light of this specification and the following application history. Nevertheless, none of the claims are intended, nor should they be interpreted, to encompass subject matter that does not meet the requirements of Sections 101, 102, or 103 of the Patent Act. Any unintended inclusion of such subject matter is disallowed herein.

[0084] Except as stated immediately prior to this, nothing described or illustrated, whether or not it is described in the claims, is intended, nor should it be construed, to give to the public any component, step, feature, purpose, benefit, advantage, or equivalent.

[0085] The terms and expressions used herein shall be understood to have the ordinary meanings given to such terms and expressions in relation to the respective areas of the corresponding investigations and studies, unless a specific meaning is otherwise explained herein. Relative terms such as first and second, or obvious and alternative, may be used solely to distinguish one entity or action from another, and do not necessarily require or imply any actual relationship or order between such entities or actions. The terms “comprises,” “comprising,” “includes,” “including,” or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises or includes a list of elements or steps may include other elements or steps that are not explicitly listed or are specific to such process, method, article, or apparatus, rather than including only those elements or steps. The element preceded by “a” or “an” does not, unless further constraints apply, exclude the presence of additional identical elements in a process, method, article, or apparatus that comprises that element.

[0086] Furthermore, in the modes for carrying out the invention described above, it can be understood that various features are grouped together in various examples in order to streamline this disclosure. This method of disclosure should not be interpreted as reflecting an intention that the claimed examples require more features than are explicitly stated in each claim. Rather, as reflected in the following claims, the subject matter to be protected is not in all features of any single disclosed example. Thus, the following claims are incorporated here into the modes for carrying out the invention, and each claim is based on itself as separately claimed subject matter.

[0087] While the best forms and / or other examples have been described above, it will be understood that various modifications are possible, that the subject matter disclosed herein can be implemented in various forms and examples, and that these may apply to numerous applications, of which only a portion are described herein. The following claims are intended to claim all possible modifications and variations that fall within the true scope of the Concept.

Claims

1. A system for intelligent optimization bidding, An energy storage system comprising a plurality of energy storage nodes and at least one inverter, wherein each energy storage node comprises a plurality of battery modules, and the energy storage system comprises: The energy storage node includes at least one processor coupled to it, and a memory configured to store data and programming, wherein the at least one processor is The operation is performed in accordance with the execution of the aforementioned program, To obtain previous energy auction information, including at least one of the unit price and total amount of electricity purchased in the last energy auction, The acquisition of state data of the components of the energy storage system, wherein the state data of at least the plurality of battery modules of the plurality of energy storage nodes and the at least one inverter is measured and stored in the memory coupled to the at least one processor, Analyzing the acquired state data to determine whether the value of the acquired state data exceeds the prediction limit provided by the algorithm for the previous energy bidding information, If the value of the acquired state data does not exceed the prediction limit, the result of the decision is reported. If the value of the acquired state data exceeds the prediction limit, the cost function model of at least the plurality of battery modules, the at least one inverter, and the operating efficiency of the energy storage system is applied to provide the respective cost reduction values ​​of the plurality of battery modules, the inverter, and the operating efficiency of the energy storage system, and the determination is made. The system is configured to perform the following: adjusting an optimized gain value for the next energy bid based on the plurality of battery modules of the energy storage system, the at least one inverter, and the acquired state data and cost reduction value of the operating efficiency.

2. The system according to claim 1, wherein the plurality of energy storage nodes are arranged collectively and individually to provide direct current electricity to an external load.

3. The system according to claim 1, wherein the plurality of energy storage nodes are configured to receive DC electricity from an external source.

4. The system according to claim 1, wherein at least one sensor is located within the energy storage system to collect operational data and environmental data from the component as measured state data.

5. The system according to claim 1, wherein the measured state data includes at least one of instantaneous voltage, current, power frequency, instantaneous power, and rate of change of frequency related to attenuating power oscillation.

6. The optimization gain values ​​are as follows: [Math 1] The system according to claim 1, where is a reduction cost function minimized for the next energy bid, and is a gain vector penalty applied to each of the reduction states.

7. The cost function model for the aforementioned plurality of battery modules is as follows: [Math 2] The factors in the cost function model for the plurality of battery modules are temperature (T), current magnitude (|I|), state of charge (SOC), rate of change of SOC (ΔSOC), DC link voltage (V), and cycling (C). ｄａｉｌｙｍａｘ ) including, Here, k １ ~k ８ The system according to claim 1, wherein n is a configurable penalty gain, and n is the sum index of the number of battery modules in each of the battery nodes.

8. The cost function model for the at least one inverter is as follows: [Math 3] The factors in the cost function model of the at least one inverter include temperature (T), current magnitude (|I|), and DC link voltage (V), The system according to claim 1, where n is the total number of inverters.

9. The cost function model for the operating efficiency of the energy storage system is as follows: [Math 4] Here, P ｉｎｖ This is the calculated individual power of each inverter, P ｂａｔｔ P is the calculated individual power of each battery module. ａｕｘ This is auxiliary power for the intended bid. Here, [Math 5] The system according to claim 1, wherein the parameter is a value related to the deviation from a predetermined efficient operating point.

10. The system according to claim 1, wherein the operation of at least one of the plurality of battery modules is based on the optimized gain value.

11. A non-temporary computer-readable medium including an Intelligent Optimized Bidding (IOB) module, The execution of the IOB module by one or more processors involves one or more computing devices. To obtain previous energy auction information, including at least one of the unit price and total amount of electricity purchased in the last energy auction, Acquiring state data for components of an energy storage system including multiple energy storage nodes and at least one inverter, wherein each energy storage node includes multiple battery modules, and the state data for at least the multiple battery modules and the at least one inverter is measured and stored in a memory coupled to the at least one processor. Analyzing the acquired state data to determine whether the value of the acquired state data exceeds the prediction limit provided by the algorithm for the previous energy bidding information, If the value of the acquired state data does not exceed the prediction limit, the result of the decision is reported. If the value of the acquired state data exceeds the prediction limit, the cost function model of at least the plurality of battery modules, the at least one inverter, and the operating efficiency of the energy storage system is applied to provide the respective cost reduction values ​​of the plurality of battery modules, the inverter, and the operating efficiency of the energy storage system, and the determination is made. A non-temporary computer-readable medium configured to perform the following: adjusting an optimized gain value for the next energy bid based on the plurality of battery modules of the energy storage system, the at least one inverter, and the acquired state data and cost reduction value of the operating efficiency.

12. The optimization gain values ​​are as follows: [Math 6] Herein, is a reduction cost function minimized for the following energy bid, and is a gain vector penalty applied to each of the reduction states, the non-transient computer-readable medium according to claim 11.

13. The cost function model for the aforementioned plurality of battery modules is as follows: [Number 7] The factors in the cost function model for the plurality of battery modules are temperature (T), current magnitude (|I|), state of charge (SOC), rate of change of SOC (ΔSOC), DC link voltage (V), and cycling (C). ｄａｉｌｙｍａｘ ) including, Here, k １ to k ８ is a configurable penalty gain, and n is the total index of the number of battery modules of each of the battery nodes, the non-transitory computer-readable medium according to claim 11.

14. The cost function model for the at least one inverter is as follows: [Number 8] The factors in the cost function model of the at least one inverter include temperature (T), current magnitude (|I|), and DC link voltage (V), Herein, n is the total number of inverters, the non-temporary computer-readable medium according to claim 11.

15. The cost function model for the operating efficiency of the energy storage system is as follows: [Number 9] Here, P ｉｎｖ This is the calculated individual power of each inverter, P ｂａｔｔ P is the calculated individual power of each battery module. ａｕｘ This is auxiliary power for the intended bid. Here, [Number 10] The non-temporary computer-readable medium according to claim 11, wherein the parameter is a value related to a deviation from a predetermined efficient operating point.

16. A method for intelligent optimization bidding, To obtain previous energy auction information, including at least one of the unit price and total amount of electricity purchased in the last energy auction, The acquisition of state data of components of an energy storage system, wherein the energy storage system includes a plurality of energy storage nodes and at least one inverter, each energy storage node includes a plurality of battery modules, and the state data of at least the plurality of battery modules and the at least one inverter of the plurality of energy storage nodes is measured and stored in a memory coupled to the energy storage node. The process involves analyzing the acquired state data to determine whether the value of the acquired state data exceeds the prediction limit in the previous energy bidding information algorithm, If the value of the acquired state data does not exceed the prediction limit, the result of the decision is reported. If the value of the acquired state data exceeds the prediction limit, the cost function model of at least the plurality of battery modules, at least one inverter, and the operating efficiency of the energy storage system is applied to provide the respective cost reduction values ​​for the battery modules, the inverter, and the operating efficiency of the battery storage system, and the determination is made. The method comprising adjusting an optimized gain value for the next energy bid based on the plurality of battery modules of the energy storage system, the at least one inverter, and the acquired state data and cost reduction value of the operating efficiency.

17. The optimization gain values ​​are as follows: [Math 11] The method according to claim 16, wherein J is a reduction cost function that is minimized for the next energy bid, and is a gain vector penalty applied to each of the K reduction states.

18. The cost function model for the aforementioned plurality of battery modules is as follows: [Math 12] The factors in the cost function model for the plurality of battery modules are temperature (T), current magnitude (|I|), state of charge (SOC), rate of change of SOC (ΔSOC), DC link voltage (V), and cycling (C). ｄａｉｌｙｍａｘ ) including, Here, k １ ~k ８ The method according to claim 16, wherein n is a configurable penalty gain, and n is the sum index of the number of battery modules in each of the battery nodes.

19. The cost function model for the at least one inverter is as follows: [Number 13] The factors in the cost function model of the at least one inverter include temperature (T), current magnitude (|I|), and DC link voltage (V), The method according to claim 16, wherein n is the total number of inverters.

20. The cost function model for the operating efficiency of the energy storage system is as follows: [Number 14] Here, P ｉｎｖ This is the calculated individual power of each inverter, P ｂａｔｔ P is the calculated individual power of each battery module. ａｕｘ This is auxiliary power for the intended bid. Here, [Number 15] The method according to claim 16, wherein the parameter is a value related to the deviation from a predetermined efficient operating point.

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