Power system dispatching optimization method and system based on battery loss

Abstract

The invention discloses an electric power system dispatching optimization method and system based on battery loss, and the method comprises the steps: carrying out the modeling of the relation among the charging and discharging power of a battery, the temperature of the battery and the capacity loss of the battery according to the operation data of an energy storage unit of an electric power system, and constructing a battery capacity loss constraint condition; based on a battery capacity loss constraint condition, constructing a power transaction optimization model by taking the lowest power transaction cost and the lowest battery operation and maintenance cost as targets; and performing iterative solution on the power transaction optimization model to obtain a battery optimization use strategy, and scheduling operation of the energy storage unit. According to the method, the model constraint condition is constructed according to the influence of the charging and discharging power of the battery and the generated thermal stress on the service life of the battery, and a more accurate and comprehensive battery optimization use strategy for maximizing the power market income is solved; the strategy is used to extend the battery life to maximize the power sold by the energy storage operator in the power market so as to improve economic benefits, and at the same time, the reliability and stability of the power system are improved.

Description

Technical Field

[0001] The present application belongs to the field of power system optimization scheduling and operation, and specifically relates to a power system scheduling optimization method and system based on battery loss. Background Art

[0002] In the electricity market, energy storage operators can participate in the electricity market based on price signals and realize energy arbitrage transactions. However, because energy storage batteries are frequently charged and discharged, as the number of charge and discharge times increases, not only will the electrochemical performance decline, but significant thermal stress will also be generated. This thermal stress will cause loss of energy storage capacity and accelerate the degradation process of the battery, thereby affecting the overall life and performance of the battery, and causing additional operating costs for energy storage operators. Therefore, the economic benefits of energy storage operators in the electricity market can be maximized by extending the battery life, while at the same time improving the reliability and stability of the power system.

[0003] Although the existing technology takes into account the degradation of batteries during the charging and discharging process, it does not take into account the cost of cooling the battery due to thermal stress and the impact of thermal stress on the battery capacity. Therefore, the constructed trading strategy still cannot maximize economic benefits by accurately evaluating the cost of battery loss, nor can it effectively extend the battery service life. Summary of the invention

[0004] This application proposes a method and system for optimizing power system scheduling based on battery loss. Model constraints are constructed according to the impact of battery charging and discharging power and the generated thermal stress on battery life, and a more accurate and comprehensive battery optimization strategy for maximizing power market revenue is solved. This strategy is used to extend battery life to maximize the amount of electricity sold by energy storage operators in the power market to improve economic benefits, while also improving the reliability and stability of the power system.

[0005] A first aspect of the present application provides a method for optimizing power system scheduling based on battery loss, the method comprising:

[0006] Based on the operation data of the energy storage unit of the power system, the relationship between battery charging and discharging power, battery temperature and battery capacity loss is modeled to construct the battery capacity loss constraint conditions;

[0007] Based on the battery capacity loss constraint, an electricity trading optimization model is constructed with the goal of minimizing electricity trading fees and battery operation and maintenance costs;

[0008] The power trading optimization model is iteratively solved by the alternating direction factor method to obtain the battery optimization strategy.

[0009] The operation of the energy storage unit is scheduled according to the battery optimization usage strategy.

[0010] The above scheme models the relationship between battery charging and discharging power, battery temperature and battery capacity loss, and constrains the cost calculation by constructing constraints related to battery capacity loss, thereby improving the accuracy of cost calculation and introducing the impact of battery capacity on tradable electricity. Then, an electricity trading optimization model is constructed with the goal of minimizing electricity trading fees and battery operation and maintenance costs. The battery optimization use strategy is obtained by iteratively solving the model. Because this strategy is related to battery capacity loss, it can extend the battery life by scheduling the operation of the energy storage unit, so that the battery can provide more tradable electricity for electricity market transactions, thereby indirectly increasing the amount of electricity sold by energy storage operators in the electricity market to improve economic benefits. In addition, extending the battery life can also improve the reliability and stability of the power system.

[0011] In a possible implementation method of the first aspect, according to the energy storage unit operation data of the power system, the relationship between the battery charging and discharging power, the battery temperature and the battery capacity loss is modeled, and a battery capacity loss constraint condition is constructed, specifically:

[0012] Modeling the changing relationship between the battery charging and discharging power and the battery state of charge according to the energy storage unit operation data to construct a first relationship model;

[0013] Based on the heat transfer principle, a battery temperature change function is constructed according to the energy storage unit operation data and a preset battery-thermal energy balance relationship model;

[0014] Constructing an ambient temperature change function according to the battery temperature change function, the net heat released by the energy storage unit and the preset cooling performance coefficient;

[0015] According to the first relationship model and the ambient temperature change function, a battery capacity loss constraint condition is constructed.

[0016] The above scheme first models the changing relationship between the battery charging and discharging power and the battery state of charge to construct a first relationship model that describes the effect of the battery charging and discharging power on the battery capacity change; then, based on the heat transfer principle, the heat transfer between adjacent batteries is described to construct a battery temperature change function; then, the effect of battery heat release and battery cooling to reduce battery heat on ambient temperature changes is described to construct an ambient temperature change function; under the first relationship model and the ambient temperature change function, a battery capacity loss constraint condition is finally constructed that can fully reflect the effect of the battery charging and discharging power and the generated thermal stress on the battery capacity loss.

[0017] In a possible implementation method of the first aspect, based on the heat transfer principle, according to the energy storage unit operation data and a preset battery-thermal energy balance relationship model, a battery temperature change function is constructed, specifically:

[0018] Based on the principle of heat transfer, the heat transfer between adjacent batteries is modeled according to the current data and resistance data of the energy storage unit, and a battery-thermal energy balance relationship model is constructed;

[0019] The battery-thermal energy balance relationship model is calculated based on the measured battery temperature difference change, and the battery temperature change function is constructed.

[0020] In the above scheme, based on the principle of heat transfer, because batteries between different temperatures will transfer heat, it is necessary to model the heat transfer behavior between batteries; then the battery-thermal energy balance relationship model is calculated according to the change in battery temperature difference, and a battery temperature change function is constructed to describe the temperature of the battery at each moment, providing data support for the subsequent solution of the change in the ambient temperature of the battery at each moment.

[0021] In a possible implementation method of the first aspect, the battery temperature change function, the ambient temperature change function, and the battery capacity loss constraint condition are specifically:

[0022] The battery temperature variation function has the following specific formula:

[0023]

[0024] Where, T i,t is the temperature of the ith battery at time t, U a is the heat loss transfer function, C th is the thermal capacity of the battery, t p is the preset time interval, τ is the thermal delay coefficient, I t is the current flowing through the battery at time t, T at is the ambient temperature at time t, r i,t is the equivalent resistance of the ith battery at time t;

[0025] The specific formula of the ambient temperature change function is:

[0026]

[0027]

[0028] In the formula, is the ambient temperature at time t, t p is the preset time interval size, C en is the heat capacity of the battery casing, is the heat released into the housing by all batteries at time t, is the cooling amount at time t, T env is the external ambient temperature, U envis the heat transfer rate of the heat flow path between the housing and the external environment, η is the preset cooling performance coefficient, is the cooling power of the energy storage cooling system at time t;

[0029] The battery capacity loss constraint condition is specifically formulated as follows:

[0030]

[0031]

[0032] C rem =C st -ψ;

[0033]

[0034] In the formula, S δ (δ), S T (T) are the internal pressure caused by the charge and discharge cycle δ to the charge and discharge power and battery temperature, respectively, 1 、h 2 and K T is the constant term coefficient, is the average temperature of the charge and discharge cycle, T r is the reference temperature, ψ is the net capacity loss of the battery, C b is the rated capacity of the battery, j is the jth charge and discharge cycle, L is the total number of charge and discharge cycles, n j is the equivalent number of cycles, is the S of the jth charge-discharge cycle δ (δ), is the S of the jth charge-discharge cycle T (T), C rem is the remaining capacity of the battery, C st is the initial capacity of the battery, and SoH is the battery health.

[0035] In a possible implementation method of the first aspect, based on the battery capacity loss constraint condition, an electricity trading optimization model is constructed with the goal of minimizing the electricity trading fee and the battery operation and maintenance cost, specifically:

[0036] Construct a battery operation and maintenance cost function based on battery capacity loss costs and battery cooling costs;

[0037] Based on the intraday-day real-time power market transaction data, the power transaction cost function is constructed;

[0038] Taking the battery capacity loss constraint as the constraint of the model and the minimum sum of the battery operation and maintenance cost function and the power transaction fee function as the objective function, an electricity transaction optimization model is constructed.

[0039] In the above scheme, the battery capacity loss cost and battery cooling cost are added to the cost function, and the cost of battery cooling to reduce the impact of battery thermal stress on battery capacity is introduced, so that the calculation of the total cost is more accurate; then the battery capacity loss constraint is used as the constraint of the model to construct the power trading optimization model, so that the limited and constantly changing battery capacity also needs to be considered in the process of calculating the cost; because the battery capacity will cause the tradable electricity to change, and further cause the profit of electricity trading to change, so the solution of the model can be made more in line with the actual situation.

[0040] In a possible implementation method of the first aspect, the power trading optimization model is iteratively solved by an alternating direction factor method to obtain a battery optimization use strategy, specifically:

[0041] Based on the preset peer-to-peer power market trading mechanism, the target optimization vector of the power trading optimization model in the peer-to-peer transaction is iteratively optimized by the alternating direction factor method; wherein the target optimization vector includes the day-ahead bid power, cooling power and intraday real-time market bid power of the battery;

[0042] When the target optimization vector meets a preset iteration threshold, a battery optimization usage strategy is obtained.

[0043] A second aspect of the present application provides a power system scheduling optimization system based on battery loss, the system comprising: a constraint condition building module, a model building module, a model solving module and an energy storage unit scheduling module;

[0044] The constraint condition building module is used to model the relationship between battery charging and discharging power, battery temperature and battery capacity loss according to the energy storage unit operation data of the power system, and build the battery capacity loss constraint condition;

[0045] The model building module is used to build an electricity trading optimization model based on the battery capacity loss constraint condition with the goal of minimizing the electricity trading fee and battery operation and maintenance cost;

[0046] The model solving module is used to iteratively solve the power trading optimization model through the alternating direction factor method to obtain the battery optimization use strategy;

[0047] The energy storage unit scheduling module is used to schedule the operation of the energy storage unit according to the battery optimization use strategy.

[0048] In a possible implementation of the second aspect, the constraint condition construction module includes: a battery capacity loss constraint condition construction unit;

[0049] The battery capacity loss constraint condition construction unit is used to model the changing relationship between the battery charging and discharging power and the battery state of charge according to the operating data of the energy storage unit, and construct a first relationship model; based on the heat transfer principle, according to the operating data of the energy storage unit and the preset battery-thermal energy balance relationship model, a battery temperature change function is constructed; according to the battery temperature change function, the net heat released by the energy storage unit and the preset cooling performance coefficient, an ambient temperature change function is constructed; according to the first relationship model and the ambient temperature change function, a battery capacity loss constraint condition is constructed.

[0050] In a possible implementation of the second aspect, the constraint condition construction module includes: a temperature change function construction unit;

[0051] The temperature change function construction unit is used to model the heat transfer between adjacent batteries based on the heat transfer principle and according to the current data and resistance data of the energy storage unit, and to construct a battery-thermal energy balance relationship model; the battery-thermal energy balance relationship model is calculated according to the measured battery temperature difference change to construct a battery temperature change function.

[0052] In a possible implementation of the second aspect, the model building module includes: a power trading optimization model building unit;

[0053] The power trading optimization model construction unit is used to construct a battery operation and maintenance cost function according to the battery capacity loss cost and the battery cooling cost; to construct a power trading cost function according to the intraday-day real-time power market trading data; to construct a power trading optimization model with the battery capacity loss constraint as the constraint of the model and the minimum sum of the battery operation and maintenance cost function and the power trading cost function as the objective function.

[0054] In a possible implementation of the second aspect, the model solving module includes: a battery optimization use strategy generating module;

[0055] The battery optimization use strategy generation module is used to iteratively optimize the target optimization vector of the power transaction optimization model in the point-to-point transaction based on the preset point-to-point power market transaction mechanism through the alternating direction factor method; wherein the target optimization vector includes the battery's day-ahead bid power, cooling power and intraday real-time market bid power; when the target optimization vector meets the preset iteration threshold, the battery optimization use strategy is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 It is a specific flow chart of a method for optimizing power system scheduling based on battery loss provided in a certain embodiment of the present application;

[0058] Figure 2 This is a structural diagram of a power system scheduling optimization system based on battery loss provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0061] First embodiment

[0062] Because energy storage batteries are frequently charged and discharged, as the number of charge and discharge times increases, not only will the electrochemical performance decline, but also significant thermal stress will be generated. This thermal stress will cause loss of energy storage capacity, resulting in less electricity that energy storage operators can sell in the power market. Therefore, if energy storage operators do not consider the degradation and attenuation of battery capacity caused by battery charge and discharge cycles and thermal stress during power trading, it will be difficult to estimate the actual transaction and operating costs.

[0063] Although existing technologies have also studied the impact of battery charging and discharging on degradation, they have not studied the cooling costs required for thermal stress generated by batteries during operation, resulting in inadequate design of power trading strategies. Therefore, it is worth studying how to comprehensively consider and construct electrical, thermal behavior, and battery degradation models so that energy storage operators can participate in different levels of power markets to optimize battery use, extend service life, and maximize the economic benefits of energy storage operators.

[0064] like Figure 1 As shown, Figure 1A specific flow chart of a method for optimizing power system scheduling based on battery loss is provided for a certain embodiment of the present application. The method for optimizing power system scheduling based on battery loss of this embodiment includes steps S1 to S4, which are described in detail as follows:

[0065] Step S1, based on the energy storage unit operation data of the power system, the relationship between the battery charging and discharging power, the battery temperature and the battery capacity loss is modeled to construct the battery capacity loss constraint condition.

[0066] In the embodiment of the present application, the battery charge and discharge power determines the transaction feasible domain of the battery in the day-ahead and intraday real-time power trading market, and determines the tradable power of the energy storage operator, thus having a direct impact on the economic benefits of the energy storage operator in the power trading market. The battery charge and discharge power is determined by the voltage and current of the energy storage unit, so the various voltage and current data of the battery can be obtained through the energy storage unit operation data of the power system.

[0067] First of all, because repeated charging and discharging of the battery will lead to the loss of active materials in the battery and the decrease of battery capacity, it is necessary to model the relationship between the battery charging and discharging power and the battery capacity loss, so as to accurately quantify the impact of the battery charging and discharging cycle on the battery loss. In the embodiment of the present application, the direct impact of the battery charging and discharging power on the battery state of charge is used to describe the impact of the battery charging and discharging power on the battery capacity. The state of charge (SOC) is the percentage of the remaining battery power to the rated capacity of the battery, which is used to reflect the remaining capacity of the battery. Moreover, the battery state of charge can be obtained based on the operating data of the energy storage unit, so the changing relationship between the battery charging and discharging power and the battery state of charge is modeled through the various voltage data and current data of the battery, and a first relationship model is constructed, which is more intuitive and convenient in model construction.

[0068] The specific formula of the first relationship model is:

[0069]

[0070] In the formula, ζ t is the per unit value of the battery state of charge at time t, is the open circuit voltage of the battery at time t, V t is the terminal voltage of the battery at time t; r t and I t are the equivalent resistance and current of the battery at time t, respectively. The current is positive during discharge and negative during charge; C cap is the actual charge and discharge capacity of the battery, t p For the duration of each period, and p t are the net charge and discharge power of the energy storage subunit and the energy storage unit at time t, N cis the number of batteries in an energy storage subunit, N m is the total number of energy storage subunits.

[0071] Among them, the per-unit value represents the relative value of various physical quantities and parameters in the power system analysis. In the embodiment of the present application, the per-unit value of the battery state of charge is used to illustrate the ratio of the current battery state of charge to the standard battery state of charge.

[0072] After obtaining the first relationship model that describes the relationship between battery charging and discharging power and battery capacity loss, it is necessary to model the relationship between battery temperature and battery capacity loss. Because the current generated by the battery during the charging and discharging process will heat the resistor when it flows through the resistor to generate heat, the generated heat will be transferred to the adjacent battery and the surrounding environment through conduction and convection through each battery, causing the battery temperature and ambient temperature to rise. When the difference between the battery temperature and the ambient temperature is large, the thermal stress generated will cause damage to the internal structure of the battery, thereby affecting the battery components to crack and break, causing unstable operation of the power system. For example, some studies have shown that when the temperature is highest at the geometric center of the battery, the area is very likely to expand at high temperature and be squeezed by stress to cause regional deformation, so it is necessary to build a model for the effect of battery temperature on battery capacity loss.

[0073] Therefore, the embodiment of the present application needs to construct a battery temperature change function based on the heat transfer principle, according to the change of battery temperature caused by the transfer of battery heat between batteries in the energy storage unit, and to construct an ambient temperature change function according to the change of ambient temperature caused by the transfer of battery heat to the surrounding environment.

[0074] Among them, for the transfer of battery heat, a battery-thermal energy balance relationship model is first constructed through the current data and resistance data of the energy storage unit to determine the temperature of each battery and calculate the heat that needs to be absorbed by adjacent batteries and the surrounding environment, and then calculate the corresponding cooling power; wherein, the cooling power is used to cool the battery and the surrounding environment, which can effectively prevent the deformation of the internal structure of the battery due to excessive thermal stress of the battery, and the cooling power can affect the battery operation and maintenance cost in the objective function of the embodiment of the present application, so the battery-thermal energy balance relationship model can not only affect the stability of the power system, but also indirectly affect the trading benefits in the power market.

[0075] The battery-thermal energy balance relationship model has the following specific formula:

[0076]

[0077] Where, T i is the Kelvin temperature of the ith battery, ρ is the specific heat capacity of the battery, U is the heat transfer rate of heat loss between the battery and the adjacent battery, T ais the ambient temperature, U ai is the heat transfer rate between the battery and the surrounding environment, I is the current flowing through the battery, and r is the equivalent resistance inside the battery.

[0078] Furthermore, based on the principle of heat transfer, the heat transfer process between the battery and the environment is analyzed. For a battery, if the temperature of one of its adjacent batteries is higher than the battery itself, and the temperature of the other adjacent battery is lower than the battery itself, then the battery obtains heat from the adjacent battery with a higher temperature and dissipates heat to the adjacent battery with a lower temperature. In the process of heat transfer, the magnitude of the heat flux is proportional to the temperature difference between the batteries. If the temperatures of both adjacent batteries are low, the middle battery with a higher temperature will dissipate heat to the adjacent battery with a lower temperature. For the surrounding environment, the batteries at the edge of the energy storage unit dissipate heat to the surrounding environment through the front side exposed to the air, and these batteries can be regarded as virtual batteries with lower temperatures. Therefore, according to the constructed battery-thermal energy balance relationship model, considering the heat transfer and temperature difference between batteries, the battery temperature change function is first constructed.

[0079] The specific formula of the battery temperature change function is:

[0080]

[0081] Where, T i,t is the temperature of the ith battery at time t, U a is the heat loss transfer function, C th is the thermal capacity of the battery, t p is the preset time interval, τ is the thermal delay coefficient, I t is the current flowing through the battery at time t, is the ambient temperature at time t, r i,t is the equivalent resistance of the ith battery at time t.

[0082] Furthermore, assuming that each battery releases an equal amount of heat to the surrounding environment, the net heat released by all batteries can be calculated as follows:

[0083]

[0084] In the formula, is the net heat released by all batteries at time t, N c is the number of batteries in an energy storage subunit, N m is the total number of energy storage subunits, U a is the heat loss transfer function, T i,t is the temperature of the ith battery at time t, is the ambient temperature at time t.

[0085] Because the cooling system consumes the net heat released by all batteries, the ambient temperature will be affected by the combined effect of the cooling power and the net heat released by all batteries. To address this problem, an ambient temperature change function can be constructed based on the net heat released by all batteries and the preset cooling performance coefficient.

[0086] The specific formula of the ambient temperature change function is:

[0087]

[0088] In the formula, is the ambient temperature at time t, t p is the preset time interval size, C en is the heat capacity of the battery casing, is the heat released into the housing by all batteries at time t, is the cooling amount at time t, T env is the external ambient temperature, U env is the heat transfer rate of the heat flow path between the housing and the external environment, η is the preset cooling performance coefficient, is the cooling power of the energy storage cooling system at time t.

[0089] After obtaining the first relationship model describing the relationship between the charge and discharge power and the battery capacity loss, as well as the battery temperature change function and the ambient temperature change function describing the relationship between the battery temperature and the battery capacity loss, the effects of the battery charge and discharge power and the battery temperature on the battery capacity are modeled separately to obtain the charge and discharge power influence model and the temperature influence model.

[0090] Among them, the charging and discharging power influence model has the following specific formula:

[0091]

[0092] In the formula, S δ (δ) is the internal pressure brought by the charge and discharge cycle δ to the charge and discharge power, h 1 and h 2 is the constant term coefficient.

[0093] The temperature influence model, the specific formula is:

[0094]

[0095] In the formula, S T (T) is the internal pressure of the battery temperature caused by the charge and discharge cycle δ, K T is the constant term coefficient, which is, is the average temperature of the charge and discharge cycle, T r is the reference temperature, and in the embodiment of the present application, the average value of the ambient temperature is taken.

[0096] Based on the charge and discharge power influence model and the temperature influence model, the influence of battery charge and discharge power and battery temperature on battery capacity loss is quantified. In the embodiment of the present application, the influence of battery capacity loss is mainly reflected in three aspects: battery net capacity loss, battery remaining capacity and battery health.

[0097] Exemplarily, the specific formulas for the net capacity loss, the remaining battery capacity and the battery health are:

[0098]

[0099] C rem =C st -ψ (14)

[0100]

[0101] Where ψ is the net capacity loss of the battery, C b is the rated capacity of the battery, j is the jth charge and discharge cycle, L is the total number of charge and discharge cycles, n j is the equivalent number of cycles, is the S of the jth charge-discharge cycle δ (δ), is the S of the jth charge-discharge cycle T (T), C rem is the remaining capacity of the battery, C st is the initial capacity of the battery, and SoH is the battery health.

[0102] Based on the three aspects of battery net capacity loss, battery remaining capacity and battery health, the current battery life cycle and the battery's trading feasible domain in the day-ahead and intraday real-time electricity trading market can be comprehensively measured. This has a direct impact on the economic efficiency of electricity trading, so it can be used as a constraint condition for the electricity trading optimization model, recorded as a battery capacity loss constraint condition, and the cost calculation can be constrained by introducing the impact of battery capacity on the tradable electricity, making the model calculation results more accurate and practical.

[0103] Step S2, based on the battery capacity loss constraint, construct an electricity trading optimization model with the goal of minimizing electricity trading fees and battery operation and maintenance costs.

[0104] In the embodiment of the present application, the purpose of constructing the power trading optimization model is to enable the energy storage operator to obtain greater benefits in power trading. Therefore, it is necessary to construct the objective function with the lowest power trading fee and battery operation and maintenance cost, so as to increase benefits by reducing costs.

[0105] Furthermore, energy storage operators generally participate in multi-level electricity market transactions. In the multi-level electricity market transactions, energy storage operators can generally determine the day-ahead transaction volume based on the price information of the trading market, dispatch the remaining power capacity to the real-time auxiliary service market after completing the day-ahead market transactions, and settle the revenue in real time in the order of each time interval. Therefore, the transaction feasible domain of the battery that the energy storage operator can provide is very important. It determines the level of economic benefits that the energy storage operator can obtain in multi-level electricity market transactions. Therefore, it is necessary to optimize the use of batteries to extend the battery service life and reduce the battery operation and maintenance costs, so that the battery capacity loss is reduced and the battery transaction feasible domain is improved.

[0106] The electricity trading cost is mainly related to the day-ahead market electricity price, the intraday real-time market price, the day-ahead bid electricity volume and the intraday real-time market bid current. Therefore, the electricity trading cost function can be constructed based on the intraday-day-ahead real-time electricity market trading data.

[0107] The battery operation and maintenance cost includes the battery capacity loss cost and the battery cooling cost, so according to the calculation method of the battery capacity loss cost and the battery cooling cost, a battery operation and maintenance cost function is constructed. Among them, the battery cooling cost is related to the cooling power mentioned in step S1.

[0108] The objective function is to minimize the sum of the battery operation and maintenance cost function and the electricity transaction fee function.

[0109] Then, because the battery operation and maintenance cost function is related to the battery capacity loss, the battery capacity loss constraint is used as the constraint of the model to construct the power trading optimization model.

[0110] The specific formula of the power trading optimization model is as follows:

[0111]

[0112] Wherein, st(1)-(15) represents that the power trading optimization model needs to satisfy the formulas (1)-(15) in step S1, which includes the battery capacity loss constraint condition; is the day-ahead market electricity price, is the real-time market electricity price within the day, p e 、p c and p r They are the battery's day-ahead bidding power, cooling power, and intraday real-time market bidding power. p b , are the battery power boundaries, ω is the value cost conversion parameter of the battery net capacity loss, and ψ is the net capacity loss.

[0113] In the embodiment of the present application, because the battery capacity loss cost and battery cooling cost are added to the cost function, the cost of battery cooling is introduced to reduce the impact of battery thermal stress on battery capacity, so that the calculation of the total cost is more accurate; and because the battery capacity will cause changes in the tradable electricity, further resulting in changes in the profit of electricity trading, the solution of the model can be made more in line with the actual situation.

[0114] Step S3, iteratively solving the power trading optimization model through the alternating direction factor method to obtain the battery optimization use strategy.

[0115] In an embodiment of the present application, in order to ensure that under a point-to-point trading market mechanism, the energy storage operator can conduct point-to-point electricity transactions with any subject in the electricity market transaction, a distributed alternating direction multiplication factor method is used to iteratively solve the electricity trading optimization model to obtain the battery optimization usage strategy of the energy storage operator under the point-to-point trading market mechanism.

[0116] Specifically, because the day-ahead bidding power, cooling power and intraday real-time market bidding power of the battery are directly related to the benefits that the energy storage operator can obtain in power trading, iterative optimization of these parameters in the power trading optimization model through the distributed alternating direction multiplication factor method is a battery optimization strategy that can maximize the power market benefits. In the embodiment of the present application, the day-ahead bidding power, cooling power and intraday real-time market bidding power of the battery are recorded as the target optimization vector.

[0117] Among them, the target optimization vector of the power trading optimization model is iteratively solved by the alternating direction multiplication factor method. The specific process is as follows:

[0118]

[0119] In the formula, ρ is the iteration parameter of the alternating direction multiplication factor method, and its initial value is 1. * Yes e 、p c and p r The vector group, st(1)-(16) represents that the power trading optimization model needs to satisfy the formulas (1)-(16) in steps S1-S2.

[0120] During the iterative solution process, input ω and is the first iteration p * After the k+1th iteration, calculate Whether the preset iteration threshold is met, if so, output p e (k) 、p c (k) and pr (k) As the final result, it is delivered to the energy storage operator Construct a battery optimization usage strategy; otherwise, perform the k+2th iteration calculation. In some embodiments, the iteration threshold is 1e-5.

[0121] Finally, a battery optimization strategy is constructed based on the output target optimization vector.

[0122] Step S4, scheduling the operation of the energy storage unit according to the battery optimization use strategy.

[0123] In the embodiment of the present application, because the battery optimization usage strategy is related to cooling power consumption, the battery optimization usage strategy is used to adjust parameters such as the cooling power in the energy storage unit, which can reduce the degree of battery capacity loss while extending the battery life.

[0124] Implementing the embodiments of the present application has the following beneficial effects:

[0125] The embodiment of the present application models the relationship between battery charge and discharge power, battery temperature and battery capacity loss respectively, constructs the battery capacity loss constraint condition of the battery charge and discharge power and battery temperature on the battery capacity, fully describes the heat transfer between adjacent batteries and the thermal stress generated by the heat transfer of the battery to the surrounding environment, realizes the introduction of the impact of battery capacity on the tradable power, and also considers the impact of cooling cost on power trading cost, and improves the calculation accuracy of the economic benefits of energy storage operators in the power market. In addition, in the construction of the objective function, the battery capacity loss cost and battery cooling cost are added, and the cost of battery cooling to reduce the impact of battery thermal stress on battery capacity is introduced, so that the calculation of the total cost is more accurate; then the power trading optimization model is constructed with the battery capacity loss constraint condition as the constraint condition of the model, so that the limited and constantly changing battery capacity needs to be considered in the process of calculating the cost; because the battery capacity will cause the tradable power to change, and further cause the profit of power trading to change, so that the solution of the model can be more in line with the actual situation. Finally, the power trading optimization model is iteratively solved by the alternating direction factor method, and the battery optimization use strategy that can maximize the power market benefits under the point-to-point trading mechanism is obtained. Adjusting the operating parameters of energy storage units through battery optimization strategies can not only reduce battery operation and maintenance costs, but also extend battery life. This can maximize the amount of electricity sold by energy storage operators in the power market to improve economic benefits while also improving the reliability and stability of the power system.

[0126] Second embodiment

[0127] Further, in order to execute the power system dispatch optimization system based on battery loss corresponding to the above method embodiment to achieve the corresponding functions and technical effects, Figure 2 A structural diagram of a power system dispatch optimization system based on battery loss is provided. For ease of description, only the parts related to this embodiment are shown. The power system dispatch optimization system based on battery loss provided by the embodiment of the present application includes:

[0128] The constraint condition building module 201 is used to model the relationship between battery charging and discharging power, battery temperature and battery capacity loss according to the energy storage unit operation data of the power system, and build the battery capacity loss constraint condition.

[0129] In the embodiment of the present application, the first relationship model is constructed based on the relationship between the battery charging and discharging power and the battery capacity loss, in view of the fact that repeated charging and discharging of the battery will lead to the loss of active materials in the battery and the decrease in battery capacity. Then, the current generated by the battery during the charging and discharging process will heat the resistor when it flows through the resistor, and the generated heat will be transferred to the adjacent batteries and the surrounding environment through conduction and convection through each battery, generating thermal stress and causing loss of battery capacity. The battery temperature change function and the ambient temperature change function are constructed. Finally, based on the first relationship model, the battery temperature change function and the ambient temperature change function, the battery capacity loss constraint condition is constructed.

[0130] The model building module 202 is used to build an electricity trading optimization model based on the battery capacity loss constraint condition and with the goal of minimizing the electricity trading fee and battery operation and maintenance cost.

[0131] In the embodiment of the present application, the purpose of constructing the power trading optimization model is to enable energy storage operators to obtain greater benefits in power trading, so it is necessary to construct the objective function with the lowest power trading fee and battery operation and maintenance cost, and increase benefits by reducing costs. The power trading optimization model is constructed with the lowest sum of power trading fees and battery operation and maintenance costs as the objective function of the model, and the battery capacity loss constraint as the constraint of the model.

[0132] The model solving module 203 is used to iteratively solve the power trading optimization model through the alternating direction factor method to obtain the battery optimization use strategy.

[0133] In the embodiment of the present application, in order to ensure that the energy storage operator can conduct point-to-point electricity transactions with any subject in the electricity market transaction under the point-to-point trading market mechanism, the distributed alternating direction multiplication factor method is used to iteratively solve the electricity trading optimization model to obtain the battery optimization use strategy of the energy storage operator under the point-to-point trading market mechanism. Because the battery's day-ahead bid power, cooling power and intraday real-time market bid power are directly related to the benefits that the energy storage operator can obtain in the electricity transaction, iterative optimization of these parameters in the electricity trading optimization model through the distributed alternating direction multiplication factor method is a battery optimization use strategy that can maximize the benefits of the electricity market.

[0134] The energy storage unit scheduling module 204 is used to schedule the operation of the energy storage unit according to the battery optimization use strategy.

[0135] In the embodiment of the present invention, the constraint condition building module 201 further includes:

[0136] A battery capacity loss constraint condition construction unit is used to model the changing relationship between the battery charging and discharging power and the battery state of charge according to the operating data of the energy storage unit, and construct a first relationship model; based on the heat transfer principle, a battery temperature change function is constructed according to the operating data of the energy storage unit and a preset battery-thermal energy balance relationship model; an ambient temperature change function is constructed according to the battery temperature change function, the net heat released by the energy storage unit and a preset cooling performance coefficient; and a battery capacity loss constraint condition is constructed according to the first relationship model and the ambient temperature change function.

[0137] The temperature change function construction unit is used to model the heat transfer between adjacent batteries based on the heat transfer principle and according to the current data and resistance data of the energy storage unit, and to construct a battery-thermal energy balance relationship model; the battery-thermal energy balance relationship model is calculated according to the measured battery temperature difference change to construct a battery temperature change function.

[0138] In the embodiment of the present application, the direct influence of the battery charging and discharging power on the battery state of charge is used to describe the influence of the battery charging and discharging power on the battery capacity. Therefore, the changing relationship between the battery charging and discharging power and the battery state of charge is modeled through various voltage data and current data of the battery to construct a first relationship model.

[0139] After obtaining the first relationship model that describes the relationship between battery charging and discharging power and battery capacity loss, it is necessary to model the relationship between battery temperature and battery capacity loss. Because the current generated by the battery during the charging and discharging process will heat the resistor when it flows through the resistor to generate heat, the generated heat will be transferred to the adjacent battery and the surrounding environment through conduction and convection through each battery, causing the battery temperature and ambient temperature to rise. When the difference between the battery temperature and the ambient temperature is large, the thermal stress generated will cause damage to the internal structure of the battery, thereby affecting the battery components to crack and break, causing unstable operation of the power system. For example, some studies have shown that when the temperature is highest at the geometric center of the battery, the area is very likely to expand at high temperature and be squeezed by stress to cause regional deformation, so it is necessary to build a model for the effect of battery temperature on battery capacity loss.

[0140] Therefore, the embodiment of the present application needs to construct a battery temperature change function based on the heat transfer principle, according to the change of battery temperature caused by the transfer of battery heat between batteries in the energy storage unit, and to construct an ambient temperature change function according to the change of ambient temperature caused by the transfer of battery heat to the surrounding environment.

[0141] Among them, for the transfer of battery heat, a battery-thermal energy balance relationship model is first constructed through the current data and resistance data of the energy storage unit to determine the temperature of each battery and calculate the heat that needs to be absorbed by adjacent batteries and the surrounding environment, and then calculate the corresponding cooling power; wherein, the cooling power is used to cool the battery and the surrounding environment, which can effectively prevent the deformation of the internal structure of the battery due to excessive thermal stress of the battery, and the cooling power can affect the battery operation and maintenance cost in the objective function of the embodiment of the present application, so the battery-thermal energy balance relationship model can not only affect the stability of the power system, but also indirectly affect the trading benefits in the power market.

[0142] Furthermore, based on the principle of heat transfer, the heat transfer process between the battery and the environment is analyzed. For a battery, if the temperature of one of its adjacent batteries is higher than the battery itself, and the temperature of the other adjacent battery is lower than the battery itself, then the battery obtains heat from the adjacent battery with a higher temperature and dissipates heat to the adjacent battery with a lower temperature. In the process of heat transfer, the magnitude of the heat flux is proportional to the temperature difference between the batteries. If the temperatures of both adjacent batteries are low, the middle battery with a higher temperature will dissipate heat to the adjacent battery with a lower temperature. For the surrounding environment, the batteries at the edge of the energy storage unit dissipate heat to the surrounding environment through the front side exposed to the air, and these batteries can be regarded as virtual batteries with lower temperatures. Therefore, according to the constructed battery-thermal energy balance relationship model, considering the heat transfer and temperature difference between batteries, the battery temperature change function is first constructed.

[0143] Because the cooling system consumes the net heat released by all batteries, the ambient temperature will be affected by the combined effect of the cooling power and the net heat released by all batteries. In this case, an ambient temperature change function can be constructed based on the net heat released by all batteries and the preset cooling performance coefficient.

[0144] After obtaining the first relationship model describing the relationship between charge and discharge power and battery capacity loss, as well as the battery temperature change function and the ambient temperature change function describing the relationship between battery temperature and battery capacity loss, the impact of battery charge and discharge power and battery temperature on battery capacity is modeled to obtain a charge and discharge power impact model and a temperature impact model. Based on the charge and discharge power impact model and the temperature impact model, the impact of battery charge and discharge power and battery temperature on battery capacity loss is quantified. In the embodiment of the present application, the impact of battery capacity loss is mainly reflected in three aspects: the net capacity loss of the battery, the remaining capacity of the battery, and the battery health.

[0145] Finally, based on the three aspects of battery net capacity loss, battery remaining capacity and battery health, the current battery life cycle and the battery's trading feasible domain in the day-ahead and intraday real-time electricity trading market can be comprehensively measured, which has a direct impact on the economic efficiency of electricity trading. Therefore, it can be used as a constraint condition for the electricity trading optimization model, recorded as a battery capacity loss constraint condition, and the cost calculation can be constrained by introducing the impact of battery capacity on the tradable electricity, making the model calculation results more accurate and practical.

[0146] In the embodiment of the present invention, the model building module 202 further includes:

[0147] The power trading optimization model construction unit is used to construct a battery operation and maintenance cost function based on the battery capacity loss cost and the battery cooling cost; to construct a power trading cost function based on the intraday-day real-time power market trading data; to construct a power trading optimization model with the battery capacity loss constraint as the constraint of the model and the minimum sum of the battery operation and maintenance cost function and the power trading cost function as the objective function.

[0148] Among them, energy storage operators generally participate in multi-level electricity market transactions. In the multi-level electricity market transactions, energy storage operators can generally determine the day-ahead transaction volume based on the price information of the trading market, dispatch the remaining power capacity to the real-time auxiliary service market after completing the day-ahead market transactions, and settle the income in real time in the order of each time interval. Therefore, the transaction feasible domain of the battery that the energy storage operator can provide is very important. It determines the level of economic benefits that the energy storage operator can obtain in multi-level electricity market transactions. Therefore, it is necessary to optimize the use of batteries to extend the battery service life and reduce the battery operation and maintenance costs, so that the battery capacity loss is reduced and the battery transaction feasible domain is improved.

[0149] The electricity trading cost is mainly related to the day-ahead market electricity price, the intraday real-time market price, the day-ahead bid electricity volume and the intraday real-time market bid current. Therefore, the electricity trading cost function can be constructed based on the intraday-day-ahead real-time electricity market trading data.

[0150] The battery operation and maintenance cost includes battery capacity loss cost and battery cooling cost, so the battery operation and maintenance cost function is constructed based on the calculation method of battery capacity loss cost and battery cooling cost.

[0151] The objective function is to minimize the sum of the battery operation and maintenance cost function and the electricity transaction fee function.

[0152] Then, because the battery operation and maintenance cost function is related to the battery capacity loss, the battery capacity loss constraint is used as the constraint of the model to construct the power trading optimization model.

[0153] In the embodiment of the present invention, the model solving module 203 further includes:

[0154] The battery optimization use strategy generation module is used to iteratively optimize the target optimization vector of the power transaction optimization model in the point-to-point transaction based on the preset point-to-point power market transaction mechanism through the alternating direction factor method; wherein the target optimization vector includes the battery's day-ahead bid power, cooling power and intraday real-time market bid power; when the target optimization vector meets the preset iteration threshold, the battery optimization use strategy is obtained.

[0155] In an embodiment of the present application, in order to ensure that under a point-to-point trading market mechanism, the energy storage operator can conduct point-to-point electricity transactions with any subject in the electricity market transaction, a distributed alternating direction multiplication factor method is used to iteratively solve the electricity trading optimization model to obtain the battery optimization usage strategy of the energy storage operator under the point-to-point trading market mechanism.

[0156] Specifically, because the day-ahead bidding power, cooling power and intraday real-time market bidding power of the battery are directly related to the benefits that the energy storage operator can obtain in power trading, iterative optimization of these parameters in the power trading optimization model through the distributed alternating direction multiplication factor method is a battery optimization strategy that can maximize the power market benefits. In the embodiment of the present application, the day-ahead bidding power, cooling power and intraday real-time market bidding power of the battery are recorded as the target optimization vector.

[0157] After the iterative solution is completed, the battery optimization strategy is constructed based on the output target optimization vector. Because the battery optimization strategy is related to cooling power consumption, the battery optimization strategy is used to adjust parameters such as the cooling power in the energy storage unit, which can reduce the loss of battery capacity while extending the battery life.

[0158] Implementing the embodiments of the present application has the following beneficial effects:

[0159] The embodiment of the present application fully considers the impact of the two factors of battery charging and discharging power and battery temperature on the battery life, and constructs the influence relationship model to comprehensively measure the current battery life cycle through the three aspects of battery net capacity loss, battery remaining capacity and battery health, and constructs the battery capacity loss constraint condition to indicate the limitation of battery capacity loss on the economic benefits of power trading. At the same time, the objective function of the model is constructed by minimizing the sum of power trading costs and battery operation and maintenance costs, because the transaction feasible domain of the battery that the energy storage operator can provide has a direct impact on power trading, which determines the level of economic benefits that the energy storage operator can obtain in multi-level power market transactions. Therefore, it is necessary to derive a power trading optimization model that can optimize the battery life to extend the battery service life and reduce the battery operation and maintenance costs. Finally, by adjusting the parameters such as the cooling power in the energy storage unit through the battery optimization use strategy, the degree of battery capacity loss can be reduced on the basis of extending the battery service life, while maximizing the amount of electricity sold by the energy storage operator in the power market to improve economic benefits, while improving the reliability and stability of the power system, and reducing the probability of battery fracture due to thermal stress.

[0160] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for optimizing power system dispatch based on battery loss, characterized in that: include: Based on the operation data of the energy storage unit of the power system, the relationship between battery charging and discharging power, battery temperature and battery capacity loss is modeled to construct the battery capacity loss constraint conditions; Based on the battery capacity loss constraint, an electricity trading optimization model is constructed with the goal of minimizing electricity trading fees and battery operation and maintenance costs; The power trading optimization model is iteratively solved by the alternating direction factor method to obtain the battery optimization strategy. The operation of the energy storage unit is scheduled according to the battery optimization usage strategy.

2. The method for optimizing power system dispatch based on battery loss according to claim 1, characterized in that: According to the energy storage unit operation data of the power system, the relationship between the battery charging and discharging power, the battery temperature and the battery capacity loss is modeled to construct the battery capacity loss constraint condition, which is specifically: Modeling the changing relationship between the battery charging and discharging power and the battery state of charge according to the energy storage unit operation data to construct a first relationship model; Based on the heat transfer principle, a battery temperature change function is constructed according to the energy storage unit operation data and a preset battery-thermal energy balance relationship model; Constructing an ambient temperature change function according to the battery temperature change function, the net heat released by the energy storage unit and the preset cooling performance coefficient; According to the first relationship model and the ambient temperature change function, a battery capacity loss constraint condition is constructed.

3. The method for optimizing power system dispatch based on battery loss according to claim 2, characterized in that: Based on the heat transfer principle, according to the energy storage unit operation data and the preset battery-thermal energy balance relationship model, a battery temperature change function is constructed, specifically: Based on the principle of heat transfer, the heat transfer between adjacent batteries is modeled according to the current data and resistance data of the energy storage unit, and a battery-thermal energy balance relationship model is constructed; The battery-thermal energy balance relationship model is calculated based on the measured battery temperature difference change, and the battery temperature change function is constructed.

4. The method for optimizing power system dispatch based on battery loss according to claim 2, characterized in that: The battery temperature change function, the ambient temperature change function and the battery capacity loss constraint condition are specifically: The battery temperature variation function has the following specific formula: Where, T i,t is the temperature of the ith battery at time t, U a is the heat loss transfer function, C th is the thermal capacity of the battery, t p is the preset time interval, τ is the thermal delay coefficient, I t is the current flowing through the battery at time t, is the ambient temperature at time t, r i,t is the equivalent resistance of the ith battery at time t; The specific formula of the ambient temperature change function is: In the formula, is the ambient temperature at time t, t p is the preset time interval size, C en is the heat capacity of the battery casing, is the heat released into the housing by all batteries at time t, is the cooling amount at time t, T env is the external ambient temperature, U env is the heat transfer rate of the heat flow path between the housing and the external environment, η is the preset cooling performance coefficient, is the cooling power of the energy storage cooling system at time t; The battery capacity loss constraint condition is specifically formulated as follows: C rem =C st -ψ; In the formula, S δ (δ), S T (T) are the internal pressure caused by the charge and discharge cycle δ to the charge and discharge power and battery temperature, h1, h2 and K T is the constant term coefficient, is the average temperature of the charge and discharge cycle, T r is the reference temperature, ψ is the net capacity loss of the battery, C b is the rated capacity of the battery, j is the jth charge and discharge cycle, L is the total number of charge and discharge cycles, n j is the equivalent number of cycles, is the S of the jth charge-discharge cycle δ (δ), is the S of the jth charge-discharge cycle T (T), C rem is the remaining capacity of the battery, C st is the initial capacity of the battery, and SoH is the battery health.

5. The method for optimizing power system dispatch based on battery loss according to claim 1, characterized in that: The power transaction optimization model is constructed based on the battery capacity loss constraint condition with the goal of minimizing power transaction fees and battery operation and maintenance costs, specifically: Construct a battery operation and maintenance cost function based on battery capacity loss costs and battery cooling costs; Based on the intraday-day real-time power market transaction data, the power transaction cost function is constructed; Taking the battery capacity loss constraint as the constraint of the model and the minimum sum of the battery operation and maintenance cost function and the power transaction fee function as the objective function, an electricity transaction optimization model is constructed.

6. The method for optimizing power system dispatch based on battery loss according to claim 1, characterized in that: The power trading optimization model is iteratively solved by the alternating direction factor method to obtain the battery optimization strategy, which is specifically: Based on the preset point-to-point electricity market trading mechanism, the target optimization vector of the electricity trading optimization model in the point-to-point transaction is iteratively optimized by the alternating direction factor method; wherein the target optimization vector includes the day-ahead bid power, cooling power and intraday real-time market bid power of the battery; When the target optimization vector meets a preset iteration threshold, a battery optimization usage strategy is obtained.

7. A power system dispatch optimization system based on battery loss, characterized in that: include: Constraint building module, model building module, model solving module and energy storage unit scheduling module; The constraint condition building module is used to model the relationship between battery charging and discharging power, battery temperature and battery capacity loss according to the energy storage unit operation data of the power system, and build the battery capacity loss constraint condition; The model building module is used to build an electricity trading optimization model based on the battery capacity loss constraint condition with the goal of minimizing the electricity trading fee and battery operation and maintenance cost; The model solving module is used to iteratively solve the power trading optimization model through the alternating direction factor method to obtain the battery optimization use strategy; The energy storage unit scheduling module is used to schedule the operation of the energy storage unit according to the battery optimization use strategy.

8. The power system dispatch optimization system based on battery loss according to claim 7, characterized in that: The constraint condition building module includes: a battery capacity loss constraint condition building unit; The battery capacity loss constraint condition construction unit is used to model the changing relationship between the battery charging and discharging power and the battery state of charge according to the operating data of the energy storage unit, and construct a first relationship model; based on the heat transfer principle, according to the operating data of the energy storage unit and the preset battery-thermal energy balance relationship model, a battery temperature change function is constructed; according to the battery temperature change function, the net heat released by the energy storage unit and the preset cooling performance coefficient, an ambient temperature change function is constructed; according to the first relationship model and the ambient temperature change function, a battery capacity loss constraint condition is constructed.

9. The power system dispatch optimization system based on battery loss according to claim 8, characterized in that: The constraint condition building module includes: a temperature change function building unit; The temperature change function construction unit is used to model the heat transfer between adjacent batteries based on the heat transfer principle and according to the current data and resistance data of the energy storage unit, and to construct a battery-thermal energy balance relationship model; the battery-thermal energy balance relationship model is calculated according to the measured battery temperature difference change to construct a battery temperature change function.

10. The power system dispatch optimization system based on battery loss according to claim 7, characterized in that: The model building module includes: a power transaction optimization model building unit; The power trading optimization model construction unit is used to construct a battery operation and maintenance cost function according to the battery capacity loss cost and the battery cooling cost; to construct a power trading cost function according to the intraday-day real-time power market trading data; to construct a power trading optimization model with the battery capacity loss constraint as the constraint of the model and the minimum sum of the battery operation and maintenance cost function and the power trading cost function as the objective function.

11. The power system dispatch optimization system based on battery loss according to claim 7, characterized in that: The model solving module includes: a battery optimization use strategy generation module; The battery optimization use strategy generation module is used to iteratively optimize the target optimization vector of the power transaction optimization model in the point-to-point transaction based on the preset point-to-point power market transaction mechanism through the alternating direction factor method; wherein the target optimization vector includes the battery's day-ahead bid power, cooling power and intraday real-time market bid power; when the target optimization vector meets the preset iteration threshold, the battery optimization use strategy is obtained.

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