Battery energy storage system optimization method and device, equipment, storage medium and product

Abstract

The invention relates to the technical field of energy, and provides a battery energy storage system optimization method and device, equipment, a storage medium and a product. The method comprises the following steps: constructing a long-term optimization model; solving the long-term optimization model to obtain the optimal power of the battery energy storage system; constructing a short-term optimization model based on the optimal power; and solving the short-term optimization model to obtain the second-level execution power when the battery energy storage system participates in the electricity market. Through the mode, the battery energy storage system can perform energy scheduling according to the second-level execution power, so that a second-level adjustment signal can be responded in time, the dynamic response capability of the battery energy storage system during participation in the electricity market is effectively improved, the battery energy storage system can effectively process sudden load changes or system faults, and the service life of the battery energy storage system is prolonged. And the stability of the battery energy storage system is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy, in particular to a battery energy storage system optimization method, device, equipment, storage medium and product. BACKGROUND

[0002] With the rapid development of the electricity market, the electricity ancillary service market mechanism is gradually improved, which provides a good environment for the battery energy storage system (BESS) to participate in the electricity market. The electricity ancillary service market allows the provision of frequency regulation, peak shaving, backup and other services, among which the frequency regulation service is more mature and the battery energy storage system has a higher participation level.

[0003] Currently, when the battery energy storage system participates in the coordinated dispatch of the electricity market, a single-stage optimization technology of the energy storage is mainly used, that is, an energy storage capacity optimization model is established with the optimal net income of the battery energy storage system as the target, a battery loss cost function is established combined with the battery cycle life data, the actual operation cost of the battery energy storage system is quantitatively calculated, and the energy storage capacity when the net income of the battery energy storage system is optimal is solved by using a particle swarm algorithm.

[0004] However, the existing single-stage optimization method of the battery energy storage system usually needs to perform energy scheduling at a long time scale (such as minutes or hours), which cannot respond to the second-level adjustment signal in time, resulting in insufficient dynamic response capability of the battery energy storage system when participating in the electricity market, and it is difficult to effectively handle sudden load changes or system failures, which easily affects the stability of the battery energy storage system. SUMMARY

[0005] The embodiments of the present application provide a battery energy storage system optimization method, device, equipment, storage medium and product to solve the technical problem that the existing single-stage optimization method of the battery energy storage system cannot respond to the second-level adjustment signal in time, resulting in insufficient dynamic response capability of the battery energy storage system when participating in the electricity market, and it is difficult to effectively handle sudden load changes or system failures, which easily affects the stability of the battery energy storage system.

[0006] In a first aspect, the embodiments of the present application provide a battery energy storage system optimization method, comprising: constructing a long-term optimization model; the long-term optimization model is a mathematical model of the battery energy storage system in a first stage, and the battery energy storage system performs minute-level energy scheduling in the first stage; solving the long-term optimization model to obtain the optimal power of the battery energy storage system; based on the optimal power, constructing a short-term optimization model; the short-term optimization model is a mathematical model of the battery energy storage system in a second stage, and the battery energy storage system performs second-level energy scheduling in the second stage; solving the short-term optimization model to obtain the second-level execution power of the battery energy storage system when participating in the electricity market.

[0007] In one embodiment, the long-term optimization model is constructed, including: determining a grid electricity purchase cost, an uncontracted capacity charge of the battery energy storage system, and a target cost of a remaining battery capacity of the battery energy storage system; the target cost of the remaining battery capacity is a penalty cost when the remaining battery capacity of the battery energy storage system deviates from a target interval; constructing the long-term optimization model based on the grid electricity purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity; wherein the long-term optimization model aims to minimize a total cost of a first stage, and the total cost of the first stage is determined based on the grid electricity purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity.

[0008] In one embodiment, the grid electricity purchase cost is determined based on a power of the grid and a unit price of electricity traded by the grid; the uncontracted capacity charge is determined based on a peak load of the grid, a preset cost coefficient, and a contract capacity of the grid; and the target cost of the remaining battery capacity is determined based on a preset penalty coefficient, the target interval, and the remaining battery capacity of the battery energy storage system, and the remaining battery capacity is determined based on a discharging power of the battery energy storage system, a charging power, a charging efficiency parameter, a discharging efficiency parameter, and an energy capacity.

[0009] In one embodiment, the short-term optimization model is constructed based on the optimal power, including: determining a tracking deviation cost of the adjustment signal, a total cost of a power reference deviation of the battery energy storage system, a grid electricity purchase cost, and a penalty cost of simultaneous charging and discharging of the battery energy storage system; constructing the short-term optimization model based on the optimal power, the tracking deviation cost of the adjustment signal, the total cost of the power reference deviation of the battery energy storage system, the grid electricity purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system; wherein the short-term optimization model aims to minimize a total cost of a second stage, and the total cost of the second stage is determined based on the tracking deviation cost of the adjustment signal, the total cost of the power reference deviation of the battery energy storage system, the grid electricity purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system.

[0010] In one embodiment, before determining the tracking deviation cost of the adjustment signal, the total cost of the power reference deviation of the battery energy storage system, the grid electricity purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system, the method further includes: determining a corrected power limit of the battery energy storage system; determining a baseline power of the battery energy storage system based on the optimal power; and determining the tracking deviation cost of the adjustment signal based on the corrected power limit of the battery energy storage system, the baseline power, and an expected response power of the adjustment signal.

[0011] In one embodiment, the total cost of the power reference deviation of the battery energy storage system is determined based on a power reference deviation penalty coefficient, the corrected power limit of the battery energy storage system, and the optimal power; and the penalty cost of simultaneous charging and discharging of the battery energy storage system is determined based on the corrected power limit of the battery energy storage system and a penalty cost coefficient of simultaneous charging and discharging of the battery energy storage system.

[0012] In a second aspect, the embodiments of the present application provide a battery energy storage system optimization device, comprising: a first construction module configured to construct a long-term optimization model; the long-term optimization model is a mathematical model of the battery energy storage system in a first stage; the battery energy storage system performs minute-level energy scheduling in the first stage; a first optimization module configured to solve the long-term optimization model to obtain an optimal power of the battery energy storage system; a second construction module configured to construct a short-term optimization model based on the optimal power; the short-term optimization model is a mathematical model of the battery energy storage system in a second stage; the battery energy storage system performs second-level energy scheduling in the second stage; and a second optimization module configured to solve the short-term optimization model to obtain a second-level execution power of the battery energy storage system when participating in the power market.

[0013] In a third aspect, the embodiments of the present application provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor; when the processor executes the computer program, the above-mentioned battery energy storage system optimization method is implemented.

[0014] In a fourth aspect, the embodiments of the present application provide a non-transitory computer readable storage medium, having a computer program stored thereon; when the computer program is executed by a processor, the above-mentioned battery energy storage system optimization method is implemented.

[0015] In a fifth aspect, the embodiments of the present application provide a computer program product, comprising a computer program; when the computer program is executed by a processor, the above-mentioned battery energy storage system optimization method is implemented.

[0016] The battery energy storage system optimization method, device, equipment, storage medium and product provided by the embodiments of the present application do not use a single-stage optimization method of the battery energy storage system, but use a two-stage optimization method of the battery energy storage system, first construct a long-term optimization model of the battery energy storage system when performing minute-level energy scheduling, solve the long-term optimization model to obtain an optimal power of the battery energy storage system, then construct a short-term optimization model of the battery energy storage system when performing second-level energy scheduling according to the optimal power, and solve the short-term optimization model to obtain a second-level execution power of the battery energy storage system when participating in the power market, so that the battery energy storage system can perform energy scheduling according to the second-level execution power, thereby responding to the second-level adjustment signal in time, effectively improving the dynamic response capability of the battery energy storage system when participating in the power market, and further enabling the battery energy storage system to effectively handle sudden load changes or system failures, and ensuring the stability of the battery energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0018] Figure 1 is a flowchart of the battery energy storage system optimization method provided by the embodiments of the present application.

[0019] Figure 2 is a schematic diagram of the target cost of the remaining battery capacity provided by the embodiments of the present application.

[0020] Figure 3 is a structural schematic diagram of the battery energy storage system optimization device provided by the embodiments of the present application.

[0021] Figure 4 is a structural schematic diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of protection of the present application.

[0023] Please refer to Figure 1 , Figure 1 is a flowchart of the battery energy storage system optimization method provided by the embodiments of the present application. As shown in Figure 1 , in the embodiments of the present application, the battery energy storage system optimization method comprises steps S110 to S140, and each step is specifically as follows: S110: Construct a long-term optimization model.

[0024] The long-term optimization model is a mathematical model of the battery energy storage system in the first stage. The battery energy storage system performs minute-level energy scheduling in the first stage.

[0025] Battery energy storage system (BESS) can provide peak shaving, frequency modulation, backup power, black start, and promote renewable energy consumption, which is conducive to improving the safety and flexibility of power grid operation. BESS is an important participant in the electricity market. BESS can rise from the minimum execution power to the maximum execution power within a few milliseconds. Through the collaborative optimization capability of BESS, it can concurrently participate in multiple businesses in the electricity market, such as peak shaving, power demand response, peak shaving and frequency modulation. However, under the premise of meeting the rules of the electricity market, how to perform collaborative optimization of BESS under the condition of limited power and energy capacity is still a challenge.

[0026] In the scenario of participating in the electricity market, BESS usually needs to implement energy scheduling of different time scales, such as determination of regulation power, base point power and capacity, which requires minute-level or hour-level optimization time window and time interval, and response regulation signal requires second-level optimization time window and time interval. According to the requirements of part of the electricity market, only BESS that can meet the requirement of responding to automatic generation control (AGC) signal once every 4 seconds is allowed to participate in power regulation service, therefore, BESS should pay close attention to AGC signal. In addition, adjustment deviation according to SOC (State Of Charge, battery remaining capacity) is allowed, which is called "base point setting". The purpose of base point setting is to keep the SOC of BESS at 50% level to ensure the continuity of BESS participating in various services of the electricity market. The energy scheduling of BESS should avoid "reverse behavior", for example, if AGC signal requires BESS to discharge, BESS should avoid performing charging operation.

[0027] Based on the above requirements of BESS participating in the electricity market, the embodiment proposes a two-stage model predictive control (MPC) collaborative optimization method under the electricity market, which is used to realize the collaborative optimization of BESS: the energy scheduling of BESS is divided into two stages, namely the first stage (also called a stage) and the second stage (also called b stage); in a stage, the battery energy storage system performs minute-level energy scheduling (i.e. energy scheduling is performed in units of minutes) to complete the following two key points: one is to calculate the power reference value of BESS (i.e. the optimal power of battery energy storage system) based on the selected target, and the other is to calculate the corrected power limit of battery energy storage system for b stage; in b stage, the battery energy storage system performs second-level energy scheduling (i.e. energy scheduling is performed in units of seconds) to complete the following three key points: one is to track the regulation signal while meeting the corrected power limit of battery energy storage system in a stage, the other is to prevent BESS from appearing reverse behavior, and the third is to combine the power reference adjustment in a stage.

[0028] Specifically, according to the two-stage model predictive control (MPC) collaborative optimization method described above, the embodiment needs to first construct a long-term optimization model.

[0029] S120: Solving the long-term optimization model to obtain the optimal power of the battery energy storage system.

[0030] The optimal power of the battery energy storage system will be used for the construction and solving of the second-stage model.

[0031] S130: Constructing a short-term optimization model based on the optimal power.

[0032] The short-term optimization model is a mathematical model of the battery energy storage system in the second stage, and the battery energy storage system performs second-level energy scheduling in the second stage.

[0033] S140: Solving the short-term optimization model to obtain the second-level execution power of the battery energy storage system when participating in the power market.

[0034] The battery energy storage system optimization method provided by the embodiment does not use a single-stage optimization method for the battery energy storage system, but uses a two-stage optimization method for the battery energy storage system. First, a long-term optimization model for the battery energy storage system to perform minute-level energy scheduling is constructed, the long-term optimization model is solved to obtain the optimal power of the battery energy storage system, and then a short-term optimization model for the battery energy storage system to perform second-level energy scheduling is constructed according to the optimal power. The short-term optimization model is solved to obtain the second-level execution power of the battery energy storage system when participating in the power market, so that the battery energy storage system can perform energy scheduling according to the second-level execution power, thereby responding to the second-level adjustment signal in a timely manner, effectively improving the dynamic response capability of the battery energy storage system when participating in the power market, and further enabling the battery energy storage system to effectively handle sudden load changes or system failures, thereby ensuring the stability of the battery energy storage system.

[0035] In some embodiments, constructing the long-term optimization model includes: determining a grid power purchase cost, an uncontracted capacity charge of the battery energy storage system, and a target cost of the remaining battery capacity of the battery energy storage system; the target cost of the remaining battery capacity is a penalty cost when the remaining battery capacity of the battery energy storage system deviates from a target interval; constructing the long-term optimization model based on the grid power purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity; wherein the long-term optimization model aims to minimize the total cost of the first stage, and the total cost of the first stage is determined based on the grid power purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity.

[0036] In some embodiments, the grid purchase cost is determined based on the grid's power output and the grid's electricity purchase and sale price per unit; the non-shrinkage capacity charge is determined based on the grid's peak load, a preset cost coefficient, and the grid's contracted capacity; the target cost of the remaining battery capacity is determined based on a preset penalty coefficient, a target range, and the remaining battery capacity of the battery storage system, and the remaining battery capacity is determined based on the battery storage system's discharge power, charging power, charging efficiency parameters, discharge efficiency parameters, and energy capacity.

[0037] Specifically, assume there exists a grid-connected system with a photovoltaic system, N (N is a positive integer) battery energy storage systems (BESS), and a total load, and all BESS systems will participate in the electricity market; therefore, all BESS systems should closely monitor regulation signals. After determining the number of BESS systems participating in the electricity market, we begin to construct the optimization problem for stage a, i.e., constructing the long-term optimization model.

[0038] During the optimization process in phase a, each battery energy storage system performs energy scheduling on a minute-by-minute basis. Assume there exists a time coefficient. ,and The duration of each time period is denoted as ∆t>0. Before performing the optimization in phase a, each battery energy storage system In each time period The market participation bid price is known, using express.

[0039] On the battery energy storage system side, the first Individual battery energy storage systems at various time periods , No. Individual battery energy storage systems at various time periods Charging power is denoted as Then the first Each battery energy storage system must meet the following constraints: in, It is a binary variable; Indicates the first Maximum power limit of individual battery energy storage systems ; Indicates the first Minimum power limit for individual battery energy storage systems ; This represents the time set of stage a.

[0040] formula The constraints also prevent the battery storage system from charging and discharging simultaneously, while satisfying the formula Under the premise of, the first Net charge / discharge power of each battery energy storage system for: Assume the first The charging efficiency parameter of a battery energy storage system is denoted as: , , No. The discharge efficiency parameter of a battery energy storage system is denoted as: , , No. The energy capacity of a battery energy storage system is denoted as , Then the first individual battery energy storage systems Remaining battery power over a period of time It can be calculated using the following formula: in, Indicates the first individual battery energy storage systems Remaining battery power over a given time period.

[0041] Understandably, the remaining battery capacity is only up to the maximum remaining battery capacity of the battery energy storage system. and minimum remaining battery power The changes between these two conditions satisfy the following constraints: Please see Figure 2 , Figure 2 This is a schematic diagram of the target cost of remaining battery power provided in an embodiment of this application.

[0042] like Figure 2 As shown, in order to ensure the continuity of electricity market services during the participation of battery energy storage systems in the electricity market, the baseline setting requires that the remaining battery power of the battery energy storage system at each time point be maintained within a given target range. Internally, when the remaining battery power of the battery storage system deviates from the target range, a penalty coefficient will be applied to the battery storage system, causing the battery storage system to generate the target cost of the remaining battery power, in order to ensure the stability of electricity market services.

[0043] In this embodiment, , The preset penalty coefficient includes a mild penalty coefficient. and severe penalty coefficient , , Based on this, the first The target cost of the remaining battery capacity of a battery energy storage system can be expressed by the following formula: wherein, denotes the center point, and is the intercept term, , , The calculation formula of is as follows: ; ; .

[0044] It can be understood that the above-mentioned first The battery remaining capacity target cost of the battery energy storage system can be expressed by the following linear constraint: For example, assuming that the target interval of the battery remaining capacity of a battery energy storage system is 30% to 70% (i.e. , 70%), the light penalty coefficient , and the heavy penalty coefficient , when the battery remaining capacity of the battery energy storage system is 25% (lower than ), the high cost prompts the battery energy storage system to charge; when the battery remaining capacity of the battery energy storage system is 45% (within the target interval), the low cost allows the battery remaining capacity of the battery energy storage system to deviate slightly; when the battery remaining capacity of the battery energy storage system is 75% (higher than ), the high cost prompts the battery energy storage system to charge.

[0045] After determining the battery remaining capacity target cost of each battery energy storage system on the battery energy storage system side, the grid power purchase cost on the grid side needs to be determined.

[0046] On the grid side, assuming represents the power of the grid, i.e. the power input / output power of the grid; represents the grid power purchase cost, i.e. the revenue cost (or revenue) of grid energy procurement, then: ; wherein, represents the unit price of buying and selling power of the grid, .

[0047] Assuming that the total solar energy of each time period is denoted as , ​​​each The total load of the time slot is denoted as , and assuming that the peak load of the grid is known before the optimization of phase a is performed, the power balance constraint is: wherein denotes the net charge-discharge power of the th battery energy storage system.

[0048] Assuming that the peak load of the grid is denoted as , the grid contract capacity is denoted as , The aggregated load of the time slot may be understood as the aggregated load of the time slot , the aggregated PV generation of the time slot may be understood as the aggregated PV generation of the time slot wherein denotes the market participation bid price of the th battery energy storage system in each time slot , i.e. the regulation bid price accepted by the th battery energy storage system, which is also considered in the calculation of the peak load of the grid.

[0049] The uncontracting capacity charge (UCC) of the battery energy storage system can be modeled as a linear function of the peak load of the grid exceeding a given contracted capacity, so that: ; wherein denotes the uncontracting capacity charge of the battery energy storage system; denotes a preset cost coefficient, ; denotes the grid contract capacity; denotes the peak load of the grid.

[0050] After determining the battery remaining capacity target cost of each battery energy storage system on the battery energy storage system side, the grid electricity purchase cost on the grid side, and the uncontracting capacity charge of the battery energy storage system, the optimization problem of phase a can be modeled to obtain a long-term optimization model , and the expression of the long-term optimization model is as follows: ; wherein .

[0051] wherein the long-term optimization model The total cost of the a stage is minimized, and the total cost of the a stage is .

[0052] Long-term optimization model It is essentially a mixed linear function problem, which can be solved using tools such as Gurobi9, thereby obtaining the optimal power of the battery energy storage system.

[0053] In some embodiments, based on the optimal power, a short-term optimization model is constructed, including: determining the adjustment signal tracking deviation cost, the total battery energy storage system power reference deviation cost, the grid power purchase cost and the penalty cost of the battery energy storage system simultaneous charging and discharging; based on the optimal power, the adjustment signal tracking deviation cost, the total battery energy storage system power reference deviation cost, the grid power purchase cost and the penalty cost of the battery energy storage system simultaneous charging and discharging, a short-term optimization model is constructed; wherein the short-term optimization model aims to minimize the total cost of the second stage, and the total cost of the second stage is determined based on the adjustment signal tracking deviation cost, the total battery energy storage system power reference deviation cost, the grid power purchase cost and the penalty cost of the battery energy storage system simultaneous charging and discharging.

[0054] In some embodiments, before determining the adjustment signal tracking deviation cost, the total battery energy storage system power reference deviation cost, the grid power purchase cost and the penalty cost of the battery energy storage system simultaneous charging and discharging, further comprising: determining the corrected battery energy storage system power limit; based on the optimal power, determining the baseline power of the battery energy storage system; based on the corrected battery energy storage system power limit, the baseline power and the adjustment signal expected response power, determining the adjustment signal tracking deviation cost.

[0055] Before entering the second stage (i.e. b stage), the power limit of the battery energy storage system needs to be corrected to meet the application requirements of the b stage, and the corrected battery energy storage system power limit is obtained, which is used to ensure that the battery remaining capacity of the b stage battery energy storage system is feasible, i.e. to meet the following formula: Wherein, is the lower limit of the corrected battery energy storage system power limit; is the upper limit of the corrected battery energy storage system power limit; and are parameters of the adjustment slope, and satisfy: ; .

[0056] Further, based on the a stage solving the long-term optimization model The optimal power is obtained, the baseline power of the battery energy storage system is determined, and the tracking deviation cost of the regulation signal is determined based on the corrected battery energy storage system power limit, the baseline power, and the expected response power of the regulation signal.

[0057] It should be noted that the data of the a stage needs to be interpolated before being used in the b stage at different time scales.

[0058] Specifically, the b stage performs energy scheduling in seconds, aiming to closely track the regulation signal. In order to avoid confusion, the time symbol of the b stage is indexed as , representing a "lower level" optimization compared to the a stage, and the time set of the b stage is denoted as , and the duration of each time period is denoted as .

[0059] On the battery energy storage system side, the charging power, discharging power, and net charging and discharging power of each battery energy storage system in the b stage are determined according to the formula and the corrected battery energy storage system power limit in the formula , then the charging power of the th battery energy storage system in the b stage , the discharging power of the th battery energy storage system in the b stage , and the net charging and discharging power of the th battery energy storage system in the b stage can be represented by the following formulas respectively: Assuming that the tracking deviation cost of the regulation signal is denoted as , the penalty coefficient of the regulation signal deviation is denoted as , and the expected response power of the regulation signal (in MW) is denoted as , then the deviation of the power of the th battery energy storage system from the expected regulation response can be modeled, and the tracking deviation cost of the regulation signal , i.e. satisfying the following formula: ; wherein is an auxiliary decision scalar, satisfying the following constraint conditions: represents the baseline power, satisfying the following formula: ; wherein represents the solution of the long-term optimization model in the a stage The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage. The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage. The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage. The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage.

[0060] In order to avoid the reverse behavior of the battery energy storage system, the power of the battery energy storage system needs to meet the signal rule of keeping the same direction, that is: In order to avoid the reverse behavior of the battery energy storage system, the power of the battery energy storage system needs to meet the signal rule of keeping the same direction, that is: In order to avoid the reverse behavior of the battery energy storage system, the power of the battery energy storage system needs to meet the signal rule of keeping the same direction, that is: In order to avoid the reverse behavior of the battery energy storage system, the power of the battery energy storage system needs to meet the signal rule of keeping the same direction, that is: The sign function of is used to represent the direction of the control signal. The sign function of is used to represent the direction of the control signal. The sign function of is used to represent the direction of the control signal.

[0061] The total cost of power reference deviation of the first battery energy storage system is denoted as , and the penalty coefficient of power reference deviation is denoted as , then the long-term optimization model in the a stage is solved according to the a stage The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage. The optimal power of the first battery energy storage system is obtained by solving the long-term optimization model in the a stage. The total cost of power reference deviation of the first battery energy storage system is denoted as : The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The auxiliary decision variable satisfies the following constraint conditions: The auxiliary decision variable satisfies the following constraint conditions: The auxiliary decision variable satisfies the following constraint conditions: In this embodiment, the penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as The penalty cost of simultaneous charging and discharging between the first battery energy storage system and other battery energy storage systems is denoted as

[0062] The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as

[0063] The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as The total cost of power reference deviation of the first battery energy storage system is denoted as Assuming that the optimization in the b stage is known, there are the following power balance constraints: wherein, represents the net charging / discharging power of the i-th battery energy storage system in the b stage; represents the power of the b-stage power grid, i.e., the power input / output of the b-stage power grid.

[0064] After determining the adjustment signal tracking deviation cost, the battery energy storage system power reference deviation total cost, the grid power purchase cost, and the penalty cost of the battery energy storage system simultaneous charging and discharging, the optimization problem in the b stage can be modeled to obtain a short-term optimization model The expression of the short-term optimization model is as follows: ; .

[0065] wherein, the short-term optimization model aims to minimize the total cost in the b stage, and the total cost in the b stage is .

[0066] Similarly, the short-term optimization model is also essentially a linear programming problem. By solving the short-term optimization model , the second-level execution power of each battery energy storage system when participating in the power market can be obtained.

[0067] Alternatively, the two-stage model predictive control (MPC) collaborative optimization method under the power market proposed in the embodiment can be represented by the following algorithm steps: (1) initialize the accepted regulatory bid in the a stage .

[0068] (2) retrieve the initial SOC at the BESS side and store it as .

[0069] (3) repeat steps (a) to (f): (a) solve the optimization problem , and the result is .

[0070] (b) determine the corrected battery energy storage system power limit according to the formula and the formula .

[0071] (c) take as the optimal solution at time , and pass the required data to the b stage.​

[0072] (d) repeating steps (A) to (C): (A) solving an optimization problem using data received from the a stage , resulting in .

[0073] (B) taking as the optimal solution for time and sending the second-level execution power to the corresponding BESS.

[0074] (C) moving the scheduling window to in the b stage.

[0075] (e) until , where is a floor division operator.

[0076] (f) moving the scheduling window to in the b stage.

[0077] (4) until .

[0078] In some embodiments, the battery energy storage system power reference deviation total cost is determined based on a power reference deviation penalty coefficient, a corrected battery energy storage system power limit, and an optimal power; and the penalty cost of simultaneous charging and discharging of the battery energy storage system is determined based on the corrected battery energy storage system power limit and a penalty cost coefficient of simultaneous charging and discharging of the battery energy storage system.

[0079] The battery energy storage system optimization method provided by the embodiments of the present application constructs a two-stage battery energy storage system optimization model and algorithm, which is used for collaborative optimization of the battery energy storage system including applications such as participating in power market peak regulation and frequency regulation; in order to comply with the participation rules of the power market, the embodiments of the present application combine the SOC deviation cost and factors such as prevention of reverse behavior to calculate the second-level power, so that the battery energy storage system can adjust the relevant parameter configuration according to the application and market rules, and respond to the second-level adjustment signal in time.

[0080] The embodiments of the present application also provide a battery energy storage system optimization device. Please refer to Figure 3 , Figure 3 is a structural schematic diagram of the battery energy storage system optimization device provided by the embodiments of the present application. In the embodiments of the present application, the battery energy storage system optimization device comprises a first construction module 310, a first optimization module 320, a second construction module 330, and a second optimization module 340.

[0081] The first construction module 310 is configured to construct a long-term optimization model.

[0082] The long-term optimization model is a mathematical model of the battery energy storage system in a first stage, and the battery energy storage system performs minute-level energy scheduling in the first stage.

[0083] The first optimization module 320 is configured to solve the long-term optimization model to obtain optimal power of the battery energy storage system.

[0084] The second construction module 330 is configured to construct a short-term optimization model based on the optimal power.

[0085] The short-term optimization model is a mathematical model of the battery energy storage system in a second stage, and the battery energy storage system performs second-level energy scheduling in the second stage.

[0086] The second optimization module 340 is configured to solve the short-term optimization model to obtain second-level execution power of the battery energy storage system when participating in the power market.

[0087] In some embodiments, the first construction module 310 is configured to determine a grid electricity purchase cost, an uncontracted capacity charge of the battery energy storage system, and a target cost of a remaining battery capacity of the battery energy storage system, the target cost of the remaining battery capacity being a penalty cost when the remaining battery capacity of the battery energy storage system deviates from a target interval, construct the long-term optimization model based on the grid electricity purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity, and determine a total cost of the first stage based on the grid electricity purchase cost, the uncontracted capacity charge, and the target cost of the remaining battery capacity, the long-term optimization model aiming to minimize the total cost of the first stage.

[0088] In some embodiments, the grid electricity purchase cost is determined based on a power of the grid and a unit price of the grid for buying and selling electricity, the uncontracted capacity charge is determined based on a peak load of the grid, a preset cost coefficient, and a contract capacity of the grid, and the target cost of the remaining battery capacity is determined based on a preset penalty coefficient, the target interval, and the remaining battery capacity of the battery energy storage system, the remaining battery capacity being determined based on a discharging power of the battery energy storage system, a charging power of the battery energy storage system, a charging efficiency parameter, a discharging efficiency parameter, and an energy capacity.

[0089] In some embodiments, the second construction module 330 is configured to determine an adjustment signal tracking deviation cost, a total cost of a power reference deviation of the battery energy storage system, a grid electricity purchase cost, and a penalty cost of the battery energy storage system for simultaneous charging and discharging, construct the short-term optimization model based on the optimal power, the adjustment signal tracking deviation cost, the total cost of the power reference deviation of the battery energy storage system, the grid electricity purchase cost, and the penalty cost of the battery energy storage system for simultaneous charging and discharging, and determine a total cost of the second stage based on the adjustment signal tracking deviation cost, the total cost of the power reference deviation of the battery energy storage system, the grid electricity purchase cost, and the penalty cost of the battery energy storage system for simultaneous charging and discharging, the short-term optimization model aiming to minimize the total cost of the second stage.

[0090] In some embodiments, the second construction module 330 is configured to determine a revised battery energy storage system power limit; determine a baseline power of the battery energy storage system based on the optimal power; and determine an adjustment signal tracking deviation cost based on the revised battery energy storage system power limit, the baseline power, and the desired response power.

[0091] In some embodiments, the battery energy storage system power reference deviation total cost is determined based on a power reference deviation penalty coefficient, the revised battery energy storage system power limit, and the optimal power; and the penalty cost of simultaneous charging and discharging of the battery energy storage system is determined based on the revised battery energy storage system power limit and a penalty cost coefficient of simultaneous charging and discharging of the battery energy storage system.

[0092] The embodiments of the present application also provide an electronic device, Figure 4 is a structural schematic diagram of the electronic device provided by the embodiments of the present application, as Figure 4 shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communications bus 440. The processor 410 can invoke the logical instructions in the memory 430 to execute the battery energy storage system optimization method.

[0093] In addition, the logical instructions in the memory 430 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0094] The embodiments of the present application also provide a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the battery energy storage system optimization method provided by each of the methods.

[0095] The embodiment of the application further provides a computer program product, the computer program product comprising a computer program, the computer program being stored in a non-transitory computer readable storage medium, and the computer program being executed by a processor, so that the computer can execute the battery energy storage system optimization method provided by each method.

[0096] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0097] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0098] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for optimizing a battery energy storage system, characterized in that, include: Construct a long-term optimization model; The long-term optimization model is the mathematical model of the battery energy storage system in the first stage, in which the battery energy storage system performs minute-level energy scheduling. The optimal power of the battery energy storage system is obtained by solving the long-term optimization model. Based on the optimal power, a short-term optimization model is constructed; the short-term optimization model is the mathematical model of the battery energy storage system in the second stage, in which the battery energy storage system performs second-level energy scheduling. The short-term optimization model is solved to obtain the second-level execution power of the battery energy storage system when participating in the electricity market.

2. The battery energy storage system optimization method according to claim 1, characterized in that, The construction of the long-term optimization model includes: The grid purchase cost, the unreduced capacity charge of the battery energy storage system, and the target cost of the remaining battery capacity of the battery energy storage system are determined; the target cost of the remaining battery capacity is the penalty cost when the remaining battery capacity of the battery energy storage system deviates from the target range. Based on the grid purchase cost, the non-shrinkage capacity charge, and the target cost of the remaining battery capacity, the long-term optimization model is constructed. The long-term optimization model aims to minimize the total cost of the first stage, which is determined based on the grid purchase cost, the non-shrinkage capacity charge, and the target cost of the remaining battery capacity.

3. The battery energy storage system optimization method according to claim 2, characterized in that, The power grid purchase cost is determined based on the power grid's output and the unit price of electricity purchased and sold by the grid. The non-shrinkage capacity charge is determined based on the peak load of the power grid, a preset cost factor, and the contracted capacity of the power grid; The target cost of the remaining battery capacity is determined based on a preset penalty coefficient, the target range, and the remaining battery capacity of the battery energy storage system. The remaining battery capacity is determined based on the discharge power, charging power, charging efficiency parameters, discharge efficiency parameters, and energy capacity of the battery energy storage system.

4. The battery energy storage system optimization method according to claim 1, characterized in that, The construction of a short-term optimization model based on the optimal power includes: Determine the cost of the adjustment signal tracking deviation, the total cost of the battery energy storage system power reference deviation, the grid electricity purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system; Based on the optimal power, the tracking deviation cost of the adjustment signal, the total cost of the power reference deviation of the battery energy storage system, the grid purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system, the short-term optimization model is constructed. The short-term optimization model aims to minimize the total cost of the second stage, which is determined based on the tracking deviation cost of the adjustment signal, the total cost of the power reference deviation of the battery energy storage system, the grid purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system.

5. The battery energy storage system optimization method according to claim 3, characterized in that, Before determining the cost of the regulating signal tracking deviation, the total cost of the battery energy storage system power reference deviation, the grid purchase cost, and the penalty cost of simultaneous charging and discharging of the battery energy storage system, the method further includes: Determine the revised power limits for battery energy storage systems; Based on the optimal power, the baseline power of the battery energy storage system is determined; The tracking deviation cost of the regulation signal is determined based on the corrected power limit of the battery energy storage system, the baseline power, and the expected response power of the regulation signal.

6. The battery energy storage system optimization method according to claim 5, characterized in that, The total cost of the power reference deviation of the battery energy storage system is determined based on the power reference deviation penalty coefficient, the corrected power limit of the battery energy storage system, and the optimal power. The penalty cost for simultaneous charging and discharging of the battery energy storage system is determined based on the modified power limit of the battery energy storage system and the penalty cost coefficient for simultaneous charging and discharging of the battery energy storage system.

7. A battery energy storage system optimization device, characterized in that, include: The first building block is used to build the long-term optimization model; The long-term optimization model is the mathematical model of the battery energy storage system in the first stage, in which the battery energy storage system performs minute-level energy scheduling. The first optimization module is used to solve the long-term optimization model to obtain the optimal power of the battery energy storage system. The second construction module is used to construct a short-term optimization model based on the optimal power; the short-term optimization model is the mathematical model of the battery energy storage system in the second stage, in which the battery energy storage system performs second-level energy scheduling. The second optimization module is used to solve the short-term optimization model to obtain the second-level execution power of the battery energy storage system when participating in the electricity market.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the battery energy storage system optimization method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the battery energy storage system optimization method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the battery energy storage system optimization method as described in any one of claims 1 to 6.

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