Energy storage strategy evaluation method and device

Abstract

This specification provides an energy storage strategy evaluation method and apparatus. The method includes: using a dynamic simulation model of the battery temperature, heat generation power, and heat dissipation power input temperature of the energy storage battery at the current moment; sequentially simulating the battery temperature at multiple moments within a target period using the simulation model to generate corresponding battery temperature simulation results; calculating the high-temperature aging marginal cost and cycle aging marginal cost of the energy storage battery at multiple moments based on the battery temperature simulation results; obtaining the energy storage strategy to be evaluated corresponding to the energy storage battery, and determining the interaction power between the energy storage battery and the grid at multiple moments based on the strategy to be evaluated; calculating the simulation benefit of the energy storage battery within the target period based on the interaction power, high-temperature aging marginal cost, and cycle aging marginal cost; calculating the confidence level based on the simulation benefit and the actual benefit of the energy storage battery within the target period, and determining the calculation result as the evaluation result corresponding to the energy storage strategy to be evaluated.

Description

Technical Field

[0001] This application relates to the field of energy storage control technology, and in particular to an energy storage strategy evaluation method. Background Technology

[0002] As a key device for smoothing renewable energy fluctuations, peak shaving and valley filling, and improving grid flexibility, the rationality of energy storage systems' scheduling strategies directly affects grid operating efficiency and the economics of energy storage systems. Existing technologies for optimizing energy storage strategies generally suffer from several problems: reliance on manual experience in feature selection leads to strong subjectivity; key influencing factors are easily overlooked; and redundant features are difficult to remove, resulting in low prediction accuracy and poor computational efficiency in subsequent models. Furthermore, existing optimization schemes often focus on a single objective, using fixed weights for multi-objective trade-offs without considering constraints such as power plant agency purchase prices and energy storage battery characteristics, or simulating the actual execution process of the strategy to predict theoretical returns. This ultimately leads to discrepancies between idealized strategies and actual implementation, making return predictions less likely to closely reflect reality. Summary of the Invention

[0003] In view of this, embodiments of this specification provide a method for evaluating energy storage strategies. One or more embodiments of this specification also relate to an energy storage strategy evaluation apparatus, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, an energy storage strategy evaluation method is provided, comprising: The dynamic temperature simulation model of the energy storage battery at the current moment, including battery temperature, heat generation power, and heat dissipation power, is used to simulate the battery temperature at multiple moments within the target cycle, generating corresponding battery temperature simulation results. Based on the battery temperature simulation results, the high-temperature aging marginal cost and cycle aging marginal cost of the energy storage battery at the multiple times are calculated. Obtain the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated; The simulated revenue of the energy storage battery during the target period is calculated based on the interaction power, the high-temperature aging marginal cost, and the cycle aging marginal cost. The confidence level is calculated based on the simulated benefits and the actual benefits of the energy storage battery during the target period, and the calculation result is determined as the evaluation result corresponding to the energy storage strategy to be evaluated.

[0005] Optionally, the step of simulating the battery temperature at multiple moments within the target cycle using a dynamic temperature simulation model to generate corresponding battery temperature simulation results includes: The battery temperature, heat generation power, and heat dissipation power at the current moment are processed by the temperature dynamic simulation model to generate the battery temperature simulation result at the i-th moment within the target period, where i starts from 1 and is an integer from 1 to n-1, and n is the total number of multiple moments within the target interval. The battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time point are processed by the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time point within the target period. If i is incremented by 1, and if i is determined to be less than or equal to n-1, the process returns to the step of processing the battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time moment through the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time moment within the target period.

[0006] Optionally, the energy storage strategy evaluation method further includes: Calculate the capacity decay rate of the energy storage battery at the various times based on the battery temperature simulation results; The high-temperature aging marginal cost of the energy storage battery at the multiple times is calculated based on the capacity decay rate of the energy storage battery at each of the multiple times.

[0007] Optionally, the expression for the high-temperature aging marginal cost calculation process is as follows: in, Let t be the marginal cost of high-temperature aging. The initial investment cost of energy storage batteries, This refers to the rated total capacity of the energy storage battery. Cost per unit capacity of battery Let t be the capacity decay rate at time t.

[0008] Optionally, the expression for the capacity decay rate calculation process is as follows: in, The calendar aging frequency factor is determined by the battery material properties and is calibrated through high-temperature storage experiments. The activation energy of the calendar aging reaction reflects the sensitivity of temperature to the aging rate. Let be the ideal gas constant. The simulation results show the battery temperature of the energy storage battery at time t. Let t be the state of charge (SOC) of the energy storage battery at time t, and z be the exponent of the SOC's impact on calendar aging, typically taking a value between t and z. between.

[0009] Optionally, the energy storage strategy evaluation method further includes: Based on the battery temperature simulation results and the depth of discharge of the energy storage battery at each time, the number of cycles corresponding to the energy storage battery at each of the multiple time points is determined. The marginal cost of cycle aging of the energy storage battery at the multiple time points is calculated based on the number of cycles corresponding to the energy storage battery at each of the multiple time points.

[0010] Optionally, the expression for the calculation process of the cyclic aging marginal cost is as follows: in, The initial investment cost of energy storage batteries, The number of cycles for the energy storage battery at time t, based on the depth of discharge at time t. Simulation results of battery temperature Sure, Let be the absolute value of the power exchanged between the energy storage battery and the power grid at time t. The interval between any two adjacent moments within the target period is the duration of time.

[0011] Optionally, the temperature dynamic simulation model is constructed based on the battery temperature, heat generation power, heat dissipation power, battery heat capacity, and the interval between any two adjacent moments within the target period of the energy storage battery at any given time. The specific expression is as follows: in, The simulation results show the battery temperature of the energy storage battery at time t. for The heat generation power inside the energy storage battery at all times. for The heat dissipation capacity of the energy storage battery to the environment at all times. For the heat capacity of the energy storage battery, The interval between any two adjacent moments within the target period is the duration of time.

[0012] Optionally, the expression for the heat dissipation power calculation process is as follows: in, The convective heat transfer coefficient is... For heat dissipation area, The ambient temperature; in, The Nusselt number is a dimensionless number that characterizes the intensity of convective heat transfer. The thermal conductivity of the cooling fluid. This refers to the battery length.

[0013] According to a second aspect of the embodiments of this specification, an energy storage strategy evaluation device is provided, comprising: The simulation module is configured to input the battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment into the temperature dynamic simulation model. Through the temperature dynamic simulation model, the battery temperature of the energy storage battery at multiple moments within the target cycle is simulated sequentially to generate the corresponding battery temperature simulation results. The first calculation module is configured to calculate the high-temperature aging marginal cost and the cycle aging marginal cost of the energy storage battery at multiple times based on the battery temperature simulation results. The first determining module is configured to acquire the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated. The second calculation module is configured to calculate the simulated revenue of the energy storage battery in the target period based on the interaction power, the high-temperature aging marginal cost, and the cycle aging marginal cost. The second determining module is configured to perform confidence calculation based on the simulation benefits and the actual benefits of the energy storage battery during the target period, and determine the calculation result as the evaluation result corresponding to the energy storage strategy to be evaluated.

[0014] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement any of the steps of the energy storage strategy evaluation method.

[0015] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of any of the energy storage strategy evaluation methods described herein.

[0016] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described energy storage strategy evaluation method.

[0017] This specification's embodiments use a dynamic temperature simulation model to simulate the battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment. This model is then used to simulate the battery temperature at multiple moments within a target period, generating corresponding battery temperature simulation results. Based on these simulation results, the marginal cost of high-temperature aging and the marginal cost of cyclic aging are calculated at multiple moments. The corresponding energy storage strategy to be evaluated is obtained, and the interaction power between the energy storage battery and the grid at multiple moments is determined based on this strategy. The simulated benefits of the energy storage battery within the target period are calculated based on the interaction power, the marginal cost of high-temperature aging, and the marginal cost of cyclic aging. Confidence levels are calculated based on the simulated benefits and the actual benefits of the energy storage battery within the target period, and the calculation results are used as the evaluation results corresponding to the energy storage strategy to be evaluated.

[0018] The energy storage strategy evaluation method provided in the embodiments of this specification has the following beneficial effects: (1) Physical consistency: By introducing the marginal cost of high temperature aging and the marginal cost of cycle aging, the industry pain point of "artificially high" simulation benefits is solved; (2) Risk prevention: Before the energy storage strategy is issued, each energy storage strategy is evaluated. In this way, aggressive strategies that will cause the energy storage battery to overheat can be identified in advance, which is conducive to improving the safety of the energy storage system. Attached Figure Description

[0019] Figure 1 This is a flowchart of an energy storage strategy evaluation method provided in one embodiment of this specification; Figure 2 This is a schematic diagram of the structure of an energy storage strategy evaluation device provided in one embodiment of this specification; Figure 3 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0020] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0021] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0022] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0023] This specification specifically relates to a simulation evaluation method that utilizes virtual twin technology to simulate the coupling relationship between the internal physical state and economic efficiency of energy storage batteries before the issuance of energy storage strategies. Current energy storage strategy simulations mostly employ a "watt-hour" energy balance model, which only considers... This approach neglects the temperature rise effect of the battery under high-rate charge and discharge and the resulting nonlinear aging costs. This leads to simulation-predicted benefits often exceeding actual operational benefits (because in actual operation, the battery will incur hidden costs due to overheating derating or accelerated aging), resulting in inflated strategy confidence. This application aims to construct a virtual twin incorporating both electrochemical and thermal characteristics, introducing "aging penalty costs" and "temperature constraints" during the simulation phase, and outputting the net benefit after deducting physical losses, thereby improving the fit between simulation and reality.

[0024] This specification provides a method for evaluating energy storage strategies, and also relates to an energy storage strategy evaluation device, a computing device, a computer-readable storage medium, and a computer program, which will be described in detail in the following embodiments.

[0025] Figure 1 A flowchart of an energy storage strategy evaluation method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0026] Step 102: Input the battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment into the temperature dynamic simulation model. Use the temperature dynamic simulation model to simulate the battery temperature at multiple moments within the target cycle in sequence, and generate the corresponding battery temperature simulation results.

[0027] In one optional implementation, the temperature dynamic simulation model is constructed based on the battery temperature, heat generation power, heat dissipation power, battery heat capacity, and the interval between any two adjacent moments within the target period of the energy storage battery at any given time. The specific expression is as follows: in, The simulation results show the battery temperature of the energy storage battery at time t. for The heat generation power inside the energy storage battery at all times. for The heat dissipation capacity of the energy storage battery to the environment at all times. For the heat capacity of the energy storage battery, The interval between any two adjacent moments within the target period is the duration of time.

[0028] Furthermore, the expression for the heat dissipation power calculation process is as follows: in, The convective heat transfer coefficient is... For heat dissipation area, The ambient temperature; in, is the Nusselt number, a dimensionless number representing the intensity of convective heat transfer. The thermal conductivity of the cooling fluid. This refers to the battery length.

[0029] In one optional implementation, the step of simulating the battery temperature at multiple moments within the target cycle using a dynamic temperature simulation model to generate corresponding battery temperature simulation results includes: The battery temperature, heat generation power, and heat dissipation power at the current moment are processed by the temperature dynamic simulation model to generate the battery temperature simulation result at the i-th moment within the target period, where i starts from 1 and is an integer from 1 to n-1, and n is the total number of multiple moments within the target interval. The battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time point are processed by the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time point within the target period. If i is incremented by 1, and if i is determined to be less than or equal to n-1, the process returns to the step of processing the battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time moment through the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time moment within the target period.

[0030] The core of the embodiments in this specification lies in constructing a "net benefit-oriented virtual twin simulation model", which specifically requires first constructing an electrochemical-thermal coupled state equation (temperature dynamic simulation model).

[0031] At each interval of the simulation. Internally, it not only updates the state of charge ( It also updates the battery core temperature simultaneously. ).

[0032] Taking a target period of one day and an interval of 15 minutes between any two adjacent moments within the target period as an example, the starting point of the target period is the current moment. Therefore, the target period contains a total of 96 moments to be simulated. Given that moment t is the current moment... This represents the actual battery temperature (average temperature inside the battery cell) at the current moment.

[0033] Furthermore, the specific expression of the temperature dynamic simulation model is shown in formula (1): Formula (1) in, The simulation results show the battery temperature of the energy storage battery at time t, assuming time t is the current time. The average internal temperature of the battery cell at the current moment, in Kelvin (K). ) or degrees Celsius (°C); for The heat generation capacity inside a constant energy storage battery, measured in watts (W). The calculation formula is: ,in For current, For internal resistance, The heat capacity of an energy storage battery represents the amount of heat required to raise the battery temperature by 1 degree Celsius, measured in joules per kelvin (J / K). ); The interval between any two adjacent moments within the target period, in minutes; for The heat dissipation power of a battery to the environment at any given time, measured in watts. ), The calculation process is shown in formula (2): Formula (2) in, For heat dissipation area, The ambient temperature (obtained by a sensor) The convective heat transfer coefficient h is calculated as shown in formula (3): Formula (3) in, The Nusselt number is a dimensionless number that characterizes the intensity of convective heat transfer. Thermal conductivity of the cooling fluid (air or coolant), unit: ; Battery length, unit: meters ( ).

[0034] In practical applications, It can be obtained through a combination of "baseline lookup table + interpolation".

[0035] The construction method of the benchmark two-dimensional table is as follows: In a laboratory environment, the internal resistance data of the battery cell at different states of charge (SOC, such as 0%-100%) and different temperatures (such as -20℃ to 60℃) are obtained through tests such as hybrid pulse power characteristic test (HPPC), and the data are discretized and stored as a two-dimensional look-up table.

[0036] The specific implementation of real-time indexing and interpolation: During system operation, the simulation results are based on the real-time collected State of Charge (SOC) and battery temperature at various times. The four adjacent reference points in the table are located. Since the actual values ​​are usually located between grid points, a bilinear interpolation algorithm is used to perform weighted calculations on these four points to obtain a continuous and smooth internal resistance value, avoiding the step error caused by directly looking up the table.

[0037] Following the previous example, the battery temperature of the energy storage battery at multiple moments within the target period is simulated sequentially using a temperature dynamic simulation model to generate corresponding battery temperature simulation results. Specifically, the actual battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment are input into formula (1) for processing to generate the battery temperature simulation result for the first moment to be simulated within the target period. Then, the battery temperature simulation result, heat generation power, and heat dissipation power of the energy storage battery at the first moment to be simulated are input into formula (1) for processing to generate the battery temperature simulation result for the second moment to be simulated within the target period. This process continues until the battery temperature simulation result for the 96th moment to be simulated within the target period (n=96) is generated. After that, step 104 can be executed to perform the relevant marginal cost calculation.

[0038] Step 104: Based on the battery temperature simulation results, calculate the high-temperature aging marginal cost and cycle aging marginal cost of the energy storage battery at the multiple times.

[0039] In one optional implementation, the energy storage strategy evaluation method further includes: Calculate the capacity decay rate of the energy storage battery at the various times based on the battery temperature simulation results; The high-temperature aging marginal cost of the energy storage battery at the multiple times is calculated based on the capacity decay rate of the energy storage battery at each of the multiple times.

[0040] Furthermore, the expression for the high-temperature aging marginal cost calculation process is as follows: in, For time t, due to high temperature and high temperature Marginal cost of calendar aging (marginal cost of high-temperature aging), unit: yuan; The initial investment cost of the energy storage battery, in yuan. The rated total capacity of the energy storage battery, in units of: , Cost per unit capacity of battery, unit: yuan / , Let t be the capacity decay rate due to calendar aging at time t.

[0041] Furthermore, the expression for the capacity decay rate calculation process is as follows: in, The calendar aging frequency factor (precursor index) is determined by the battery material properties and calibrated through high-temperature storage experiments. The activation energy of the calendar aging reaction, in units of: This reflects the sensitivity of temperature to the aging rate. Let be the ideal gas constant, with a value of approximately . , The simulation results show the battery temperature of the energy storage battery at time t. The state of charge of the energy storage battery at time t (values) z is the SOC impact index on calendar aging, typically taking a value between 1 and 2. between.

[0042] In addition, based on the battery temperature simulation results at multiple times, the marginal cost of cyclic aging of the energy storage battery at multiple times can be calculated, so as to combine the marginal cost of high temperature aging and the marginal cost of cyclic aging to calculate the simulation benefit of the energy storage battery within the target period.

[0043] Step 106: Obtain the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated.

[0044] Specifically, the energy storage strategy to be evaluated for the energy storage battery typically includes at least the following information: operating sequence (time period / electricity price calendar), SOC upper and lower limits, maximum charge and discharge power, power ramp-up limits, and charge / discharge trigger / termination conditions. Therefore, after obtaining the energy storage strategy to be evaluated for the energy storage battery, the charge and discharge time periods included in the energy storage strategy and the maximum charge and discharge power corresponding to each charge and discharge time period can be determined. Based on the charge and discharge time periods and the maximum charge and discharge power, the interaction power between the energy storage battery and the grid at multiple moments within the target cycle can be determined. (Discharging is positive, charging is negative), unit: kilowatt (kW) ).

[0045] In addition, the interaction power between the energy storage battery and the grid at multiple times, determined by the strategy to be evaluated, can also be used to calculate the marginal cost of cyclic aging of the energy storage battery at multiple times.

[0046] In one optional implementation, the energy storage strategy evaluation method further includes: Based on the battery temperature simulation results and the depth of discharge of the energy storage battery at each time, the number of cycles corresponding to the energy storage battery at each of the multiple time points is determined. The marginal cost of cycle aging of the energy storage battery at the multiple time points is calculated based on the number of cycles corresponding to the energy storage battery at each of the multiple time points.

[0047] Furthermore, the expression for the calculation process of the cyclic aging marginal cost is as follows: in, The initial investment cost of energy storage batteries, The number of cycles for the energy storage battery at time t, based on the depth of discharge at time t. Simulation results of battery temperature Sure, Let be the absolute value of the power exchanged between the energy storage battery and the power grid at time t. The interval between any two adjacent moments within the target period is the duration of time.

[0048] Specifically, Based on the depth of discharge of the energy storage battery at time t Simulation results of battery temperature The number of cycles for a given energy storage battery at time t is determined; generally, the lifespan of the battery decreases exponentially with increasing temperature. A numerical correspondence table is established based on data accumulated during power plant operation, and the number of cycles is determined by looking up the table. For intervals where data was not collected, the difference method is used to complete the data.

[0049] Step 108: Calculate the simulated revenue of the energy storage battery in the target period based on the interactive power, the high-temperature aging marginal cost, and the cycle aging marginal cost.

[0050] Specifically, traditional simulations only calculate the electricity price difference, while the embodiments in this specification introduce a cost deduction mechanism based on an empirical aging model to calculate the net simulation benefit that takes into account aging costs.

[0051] Furthermore, net simulation revenue ( The calculation process is expressed as shown in formula (4): Formula (4) in, Net simulated revenue during the execution period of the energy storage strategy to be evaluated, in yuan; for The instantaneous power interaction between the energy storage battery and the power grid (positive for discharging, negative for charging), unit: kilowatt (kW). ); for Electricity price at any time, unit: yuan / ; The value is 0-1, representing the charge / discharge efficiency. for Marginal cost of cyclic aging at any given time, in yuan; for Marginal cost of high-temperature aging at any given time, in yuan.

[0052] Step 110: Calculate the confidence level based on the simulated benefits and the actual benefits of the energy storage battery during the target period, and determine the calculation result as the evaluation result corresponding to the energy storage strategy to be evaluated.

[0053] Specifically, the simulated gains are substituted into the confidence function shown in formula (5) for calculation: Formula (5) in, For the actual returns of energy storage batteries within the target period, This represents the net simulated return corresponding to the energy storage strategy to be evaluated within the target period.

[0054] After calculating the corresponding confidence level, the confidence level can be used as the evaluation result for the energy storage strategy to be evaluated. This evaluation result can be used to characterize the merits of the energy storage strategy to be evaluated.

[0055] because The value has already accounted for physical losses, so it is closer to... Therefore, the formula calculates It will be significantly higher than traditional methods and more sensitive to abnormal fluctuations.

[0056] This specification's embodiments use a dynamic temperature simulation model to simulate the battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment. This model is then used to simulate the battery temperature at multiple moments within a target period, generating corresponding battery temperature simulation results. Based on these simulation results, the marginal cost of high-temperature aging and the marginal cost of cyclic aging are calculated at multiple moments. The corresponding energy storage strategy to be evaluated is obtained, and the interaction power between the energy storage battery and the grid at multiple moments is determined based on this strategy. The simulated benefits of the energy storage battery within the target period are calculated based on the interaction power, the marginal cost of high-temperature aging, and the marginal cost of cyclic aging. Confidence levels are calculated based on the simulated benefits and the actual benefits of the energy storage battery within the target period, and the calculation results are used as the evaluation results corresponding to the energy storage strategy to be evaluated.

[0057] The energy storage strategy evaluation method provided in the embodiments of this specification has the following beneficial effects: (1) Physical consistency: By introducing the marginal cost of high temperature aging and the marginal cost of cycle aging, the industry pain point of "artificially high" simulation benefits is solved; (2) Risk prevention: Before the energy storage strategy is issued, each energy storage strategy is evaluated. In this way, aggressive strategies that will cause the energy storage battery to overheat can be identified in advance, which is conducive to improving the safety of the energy storage system.

[0058] It should be noted that, before evaluating the energy storage strategy to be evaluated, the embodiments in this specification may first consider alternative strategies. The optimal energy storage charging and discharging strategy is selected from the energy storage charging and discharging strategies, and then used as the energy storage strategy to be evaluated. The evaluation process is carried out using the aforementioned energy storage strategy evaluation method. The specific implementation process for selecting the optimal energy storage charging and discharging strategy is as follows: Step S1: Obtain future alternatives A storage charging and discharging strategy is proposed, and the energy is tested under multiple constraint verification conditions. The simulation of each energy storage charging and discharging strategy was performed to obtain the simulation correction power corresponding to each energy storage charging and discharging strategy.

[0059] Step S2: Calculate the simulation benefit of each energy storage charging and discharging strategy based on the simulation correction power corresponding to each energy storage charging and discharging strategy, and obtain the optimal energy storage charging and discharging strategy for the future based on the simulation benefit of each energy storage charging and discharging strategy.

[0060] Step S3: Obtain the actual benefit of the optimal energy storage charging and discharging strategy in the previous cycle, and calculate the simulation confidence level of the optimal energy storage charging and discharging strategy using the actual benefit and the simulated benefit of the optimal energy storage charging and discharging strategy.

[0061] Step S4: Calculate the comprehensive confidence score of the optimal energy storage charging and discharging strategy based on the simulation confidence score, and distribute the optimal energy storage charging and discharging strategy to the energy storage power station based on the comprehensive confidence score.

[0062] In one specific embodiment of this specification, the plurality of constraint verification conditions include energy storage body power constraint verification, transformer overload protection constraint verification, and battery capacity and SCO boundary constraint verification.

[0063] The power constraint verification of the energy storage body includes: Among them, the For the first energy storage power station The first intermediate power after power limiting at any given time; For the first energy storage power station The planned charge / discharge power at any given time; This represents the maximum charging and discharging power.

[0064] The transformer overload protection constraint verification includes: Among them, the For the first energy storage power station The second intermediate power after the transformer capacity verification at any time; the For the first energy storage power station The actual / predicted power load at the station at any given time; The maximum allowable load power of the transformer; This is a function that takes the minimum value.

[0065] The battery capacity and SCO boundary constraint verification includes: Calculate the upper and lower capacity thresholds of the energy storage power station based on its maximum capacity and the preset upper and lower SOC thresholds. According to the energy storage power station The battery capacity at time t and the second intermediate power are used to calculate the first power of the energy storage power station. Battery capacity at +1 moment; If the energy storage power station is the first If the battery capacity at time +1 is not within the upper and lower capacity thresholds of the energy storage power station, then for the energy storage power station at time +1... The power is corrected by applying the second intermediate power at time +1 to obtain the power of the energy storage power station. The second intermediate power corrected at time +1, and the energy storage power station's first... The second intermediate power, corrected at time +1, is used as the first... Simulated corrected power at time +1.

[0066] In one specific embodiment of this specification, obtaining the optimal energy storage charging and discharging strategy based on the simulation benefits corresponding to each energy storage charging and discharging strategy includes: Calculate the average simulation benefit based on the simulation benefits corresponding to the historical best energy storage charging and discharging strategies with a pre-set number of parameters. Based on the average simulation gain, a maximization function is used to select from the future candidates. From the n strategy numbers of the energy storage charging and discharging strategy, select the optimal strategy number that has the highest expected future return and controllable risk, and take the energy storage charging and discharging strategy corresponding to the optimal strategy number as the optimal energy storage charging and discharging strategy in the future.

[0067] In one specific embodiment of this specification, calculating the simulation confidence level of the optimal energy storage charging and discharging strategy using the real and simulated benefits includes: Based on the actual and simulated benefits of the optimal energy storage charging and discharging strategy, calculate the benefit achievement rate of the optimal energy storage charging and discharging strategy. Based on the return achievement rate and return volatility of the optimal energy storage charging and discharging strategy, the simulation confidence level of the optimal energy storage charging and discharging strategy is calculated.

[0068] In one specific embodiment of this specification, the step of distributing the optimal energy storage charging and discharging strategy to the energy storage power station based on the comprehensive confidence score includes: obtaining the comprehensive confidence scores corresponding to a preset number of historical optimal energy storage charging and discharging strategies, and calculating the average comprehensive confidence score of the comprehensive confidence scores corresponding to the preset number of historical optimal energy storage charging and discharging strategies; if the comprehensive score of the optimal energy storage charging and discharging strategy is greater than the average comprehensive confidence score, then the optimal energy storage charging and discharging strategy is distributed to the energy storage power station; if the comprehensive score of the optimal energy storage charging and discharging strategy is not greater than the average comprehensive confidence score, then the optimal energy storage charging and discharging strategy is discarded.

[0069] In addition, before the optimal energy storage charging and discharging strategy is distributed to the energy storage power station, the optimal energy storage charging and discharging strategy can be evaluated and processed using the aforementioned energy storage strategy evaluation method.

[0070] In summary, the embodiments in this specification construct a high-fidelity simulation environment: before the strategy is issued, the actual execution process of the strategy is simulated by considering the power purchase price of the power station agent, the characteristics of the energy storage battery, and the transformer capacity constraints, and the theoretical returns are predicted. A confidence assessment mechanism is introduced: by comparing the simulated returns with the actual returns, the return achievement rate and confidence level of the strategy are calculated, and model bias is identified. Dynamic strategy optimization is achieved: based on the confidence assessment results, the strategy with the highest expected future returns and controllable risks is selected from multiple candidate strategies and issued for execution, realizing the intelligent operation of the energy storage power station.

[0071] The overall execution steps of the embodiments in this specification include: policy candidate set and parameter initialization: obtaining future candidates. A set of energy storage charging and discharging strategies are implemented, and the necessary parameters for simulation are loaded simultaneously; the strategies are simulated in parallel: for... Each candidate strategy is simulated and analyzed, and the simulation results for each strategy are calculated and output. Benefit evaluation and optimal decision-making: Based on the simulation results, the expected benefit of each strategy is calculated, and the strategy that maximizes the benefit is selected as the future execution strategy of the system. Historical backtesting and confidence assessment: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The optimal strategy for each day is calculated, along with its execution deviation and confidence index (generated daily). (Group evaluation value). By combining historical operational data, the deviation and confidence level of the strategy to be implemented are estimated to ensure the reliability of the decision.

[0072] 1. Basic Data Modeling and Parameter Acquisition Electricity pricing model: Obtain the local electricity purchase price policy for the power plant location and construct a time-of-use electricity price set. It includes electricity prices for four periods: peak, off-peak, and valley, and the corresponding time period divisions.

[0073] Strategy Model: Input energy storage power station Power of energy storage charging and discharging strategy at any time .

[0074] Energy storage power station model: Defines the physical constraints of the energy storage system, including rated capacity. Maximum charge and discharge power Charge and discharge efficiency and the permissible SOC charge / discharge range ( , ).

[0075] Transformer Model: Obtaining the Rated Capacity of Power Station Transformers and safety capacity factor Calculate the maximum allowable load power of the transformer. .

[0076] 2. Simulation Strategy Execution Algorithm Define variables and parameters: Time granularity: Each time point represents 15 minutes (i.e.) Hour).

[0077] Input strategy: 1 Time-based planned charge / discharge strategy power ( For discharge, (For charging).

[0078] Actual / Forecasted Load: 1 Actual / predicted power load at the power station at any given time The basis for determining the actual / predicted load is historical time / future time. For the sake of algorithm scalability, if the target time is historical time, then the actual load is obtained; otherwise, the predicted load is obtained.

[0079] Energy storage status: 1st Battery state of charge at any given time ; Maximum capacity; This represents the maximum charging and discharging power.

[0080] Transformer constraints: Rated capacity of transformers at the power station and safety capacity factor Calculate the maximum allowable load power of the transformer. .

[0081] Efficiency parameters: efficiency parameters Include and ;in For discharge efficiency; For charging efficiency.

[0082] The specific execution process is as follows: S2.1 Power Constraint Verification of Energy Storage Unit First, determine the strategic power of the energy storage power station. Does it exceed the maximum allowable charge / discharge power of the energy storage converter (PCS) and battery? .

[0083] right arrive Perform the traversal: in, This is the first intermediate power after power limiting.

[0084] S2.2 Transformer overload protection constraint verification This step ensures that the energy storage charging process, when combined with the load, will not cause transformer overload; and that the energy storage discharge process matches the power demand of industrial and commercial users.

[0085] Discharge scenario ( Energy storage discharge needs to match the electricity demand of industrial and commercial users; the maximum discharge value cannot exceed the demand value. ,Right now ; Charging scenarios ( Energy storage charging will consume transformer capacity. If the absolute value of "actual / predicted load - energy storage charging power" (i.e., the total power drawn from the grid) exceeds... The limit necessitates a reduction in charging power. Otherwise, simply use electricity according to the plan.

[0086] This is the second intermediate power after the transformer capacity has been verified.

[0087] S2.3 Battery Capacity and SOC Boundary Constraint Verification This step is based on the previous moment. Calculate whether the current strategy will lead to overcharging or over-discharging.

[0088] Set the upper and lower limits of SOC as follows and ,For example and The capacity range of energy storage power stations is... ,Right now = , = ; Calculate the theoretical battery capacity of the energy storage power station at the next time step: * Among them, when hour, ; hour, . This represents the battery capacity of the energy storage power station at time t.

[0089] Power correction: like This indicates overcharging; the corrected power is: = - / ;like This indicates over-discharge; the corrected power is: = - / .

[0090] The power rating for battery capacity and SOC boundary constraint verification correction is named as follows: .

[0091] S2.4 Output: After the above steps, the corrected all-day execution strategy sequence is output. This sequence represents the "simulation strategy execution status" that satisfies all physical and safety constraints, and is used for profit calculations in subsequent steps.

[0092] 3. Strategy Evaluation and Optimization Algorithm This algorithm is used to evaluate the reliability of a strategy and select the optimal solution. The specific steps are as follows: Step S3.1: Simulation revenue calculation: in accordance with Calculate the corresponding simulation benefits The calculation formula is as follows: Step S3.2: Strategy Optimization Algorithm Calculate the simulation profit for each strategy, and select the strategy with the highest simulation profit over the three days prior to the historical date. Indicates the first The strategy in the first The total simulated revenue for each historical date. K represents the number of historical dates selected.

[0093] Calculate average simulation revenue : Optimal strategy selection (argmax function): Based on the calculated average return, the strategy index with the best overall performance is selected using a maximization function. : Right now: index as The strategy was determined as the future execution strategy, and its power plans for 96 time points were included. ={ }, where N is the number of strategies.

[0094] Step S3.3: Bias Analysis and Confidence Calculation: Obtain the actual return of the S3.2 strategy in the previous cycle during actual operation. .

[0095] Calculate the profit achievement rate: ,in .Note: This indicates that the simulation is extremely accurate; The greater the deviation from 1, the greater the deviation in strategy returns. It's the strategy number that was selected on a particular day.

[0096] Calculate the confidence score: Construct the confidence score function (), This function is related to the rate of return achievement. Positively correlated with, and negatively correlated with, return volatility. For example, if If the value is close to 1 and has small historical fluctuations, the confidence level is high; if A large deviation from 1 (indicating a large discrepancy between the simulation model and reality, or significant interference with strategy execution) indicates a low confidence level.

[0097] The strategy in the Confidence level of historical dates It consists of two parts: the achievement rate fidelity term and the volatility stability term. Its formula expression is: Formula explanation: Achievement rate guarantee items: Using absolute value Measurement bias. Regardless of whether the achievement rate is too high ( () or too low () (The confidence level will decrease as long as it deviates from 1).

[0098] when At that time, this item takes the maximum value of 1. It should be noted that regarding... Explanation: If A value significantly less than 1 (e.g., 0.6) indicates that actual implementation is hindered (e.g., transformer power outages, battery failures) or the simulation model is overly optimistic, making the strategy unreliable. If A confidence level significantly greater than 1 (e.g., 1.4) indicates that the simulation model is severely distorted (e.g., it underestimates electricity prices or overestimates load). Although the returns are high, it represents a "false prosperity" with extremely high future implementation risks, so the confidence level must be reduced.

[0099] This is the first adjustment factor (e.g., 2.0), used to control the penalty for deviation.

[0100] Fluctuation stability term: Using volatility Measure risk. The greater the volatility, the faster the exponential decay, and the lower the confidence level. To quantify the grid-friendliness of the strategy, statistical characteristics of load fluctuations at the grid connection point are introduced.

[0101] Where t represents the total number of time steps in the scheduling cycle, with a value range of 1-96; k represents the date code; This represents the actual / predicted power load at the power station on day k at time i. This represents the average load at 96:00 on day k. When the strategy returns are extremely stable ( When ), the term approaches 1.

[0102] This is the second adjustment factor (e.g., 5.0), used to control sensitivity to fluctuations.

[0103] Weighting coefficients: If model accuracy is of greater importance, it can be set... If you prioritize stable returns, you can set it in the opposite direction.

[0104] This invention introduces volatility penalties to select strategies that are "most stable and easiest to implement, even if the returns may not be the absolute highest".

[0105] Dimensional balance treatment: To facilitate horizontal comparison between different strategies, it is possible to... Normalization is performed to obtain the strategy at the . Final confidence level of historical dates : Industrial and commercial loads fluctuate greatly. If a strategy’s returns have been fluctuating wildly over the past three days, it indicates that the strategy is extremely sensitive to the accuracy of load forecasting and has poor robustness.

[0106] Step S5: Strategy Evaluation and Distribution 1. Strategy Distribution and Confidence Assessment Formula The optimal strategy selected on a certain day The strategy is then distributed, and a comprehensive confidence score is calculated. The calculation formula is: in, The representative strategy in the first Confidence score on the evaluation dimension of each historical date.

[0107] 2. Anomaly warning and manual review mechanism Set the average strategy score over the past 30 days as the baseline threshold. When a new strategy is issued Below that historical average (i.e.) When this happens, the system will trigger an anomaly warning, requiring manual intervention to determine whether any strategies have exhibited anomalies within the past 3 days.

[0108] 3. Explanation of Exemptions for Special Circumstances During the manual review process, it is necessary to check whether the low score is due to temporary adjustments to the strategy caused by the following special circumstances: Production scheduling requirements at the site: Due to production planning, energy storage equipment is required to be taken out of grid-connected operation during specific time periods; Other objective limitations: Force majeure events such as power grid dispatch instructions, equipment maintenance, or extreme weather may force the strategy to be downgraded or temporarily changed.

[0109] If it is confirmed that the situation falls under the above special circumstances, the low score strategy can be considered a reasonable adjustment, and the decision can be made with a note of the reason; otherwise, it is necessary to further investigate whether there are any abnormalities in the strategy model or data source.

[0110] These commands are then translated into specific control instructions and sent to the energy management system (EMS) of the energy storage power station for execution via a communication protocol.

[0111] Corresponding to the above method embodiments, this specification also provides embodiments of an energy storage strategy evaluation device. Figure 2 A schematic diagram of an energy storage strategy evaluation device according to one embodiment of this specification is shown. Figure 2 As shown, the device includes: The simulation module 202 is configured to input the battery temperature, heat generation power and heat dissipation power of the energy storage battery at the current moment into the temperature dynamic simulation model, and to simulate the battery temperature of the energy storage battery at multiple moments within the target cycle through the temperature dynamic simulation model, thereby generating the corresponding battery temperature simulation results. The first calculation module 204 is configured to calculate the high-temperature aging marginal cost and the cycle aging marginal cost of the energy storage battery at multiple times based on the battery temperature simulation results. The first determining module 206 is configured to obtain the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated. The second calculation module 208 is configured to calculate the simulated revenue of the energy storage battery in the target period based on the interaction power, the high-temperature aging marginal cost, and the cycle aging marginal cost. The second determining module 210 is configured to perform confidence calculation based on the simulation benefits and the actual benefits of the energy storage battery during the target period, and determine the calculation result as the evaluation result corresponding to the energy storage strategy to be evaluated.

[0112] Optionally, the simulation module 202 is further configured to: The battery temperature, heat generation power, and heat dissipation power at the current moment are processed by the temperature dynamic simulation model to generate the battery temperature simulation result at the i-th moment within the target period, where i starts from 1 and is an integer from 1 to n-1, and n is the total number of multiple moments within the target interval. The battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time point are processed by the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time point within the target period. If i is incremented by 1, and if i is determined to be less than or equal to n-1, the process returns to the step of processing the battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time moment through the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time moment within the target period.

[0113] Optionally, the energy storage strategy evaluation device further includes a processing module configured to: Calculate the capacity decay rate of the energy storage battery at the various times based on the battery temperature simulation results; The high-temperature aging marginal cost of the energy storage battery at the multiple times is calculated based on the capacity decay rate of the energy storage battery at each of the multiple times.

[0114] Optionally, the expression for the high-temperature aging marginal cost calculation process is as follows: in, Let t be the marginal cost of high-temperature aging. The initial investment cost of energy storage batteries, This refers to the rated total capacity of the energy storage battery. Cost per unit capacity of battery Let t be the capacity decay rate at time t.

[0115] Optionally, the expression for the capacity decay rate calculation process is as follows: in, The calendar aging frequency factor is determined by the battery material properties and is calibrated through high-temperature storage experiments. The activation energy of the calendar aging reaction reflects the sensitivity of temperature to the aging rate. Let be the ideal gas constant. The simulation results show the battery temperature of the energy storage battery at time t. Let t be the state of charge (SOC) of the energy storage battery at time t, and z be the exponent of the SOC's impact on calendar aging, typically taking a value between t and z. between.

[0116] Optionally, the processing module is further configured to: Based on the battery temperature simulation results and the depth of discharge of the energy storage battery at each time, the number of cycles corresponding to the energy storage battery at each of the multiple time points is determined. The marginal cost of cycle aging of the energy storage battery at the multiple time points is calculated based on the number of cycles corresponding to the energy storage battery at each of the multiple time points.

[0117] Optionally, the expression for the calculation process of the cyclic aging marginal cost is as follows: in, The initial investment cost of energy storage batteries, The number of cycles for the energy storage battery at time t, based on the depth of discharge at time t. Simulation results of battery temperature Sure, Let be the absolute value of the power exchanged between the energy storage battery and the power grid at time t. The interval between any two adjacent moments within the target period is the duration of time.

[0118] Optionally, the temperature dynamic simulation model is constructed based on the battery temperature, heat generation power, heat dissipation power, battery heat capacity, and the interval between any two adjacent moments within the target period of the energy storage battery at any given time. The specific expression is as follows: in, The simulation results show the battery temperature of the energy storage battery at time t. for The heat generation power inside the energy storage battery at all times. for The heat dissipation capacity of the energy storage battery to the environment at all times. For the heat capacity of the energy storage battery, The interval between any two adjacent moments within the target period is the duration of time.

[0119] Optionally, the expression for the heat dissipation power calculation process is as follows: in, The convective heat transfer coefficient is... For heat dissipation area, The ambient temperature; in, is the Nusselt number, a dimensionless number representing the intensity of convective heat transfer. The thermal conductivity of the cooling fluid. This refers to the battery length.

[0120] The above is a schematic scheme of an energy storage strategy evaluation device according to this embodiment. It should be noted that the technical solution of this energy storage strategy evaluation device and the technical solution of the above-described energy storage strategy evaluation method belong to the same concept. For details not described in detail in the technical solution of the energy storage strategy evaluation device, please refer to the description of the technical solution of the above-described energy storage strategy evaluation method.

[0121] Figure 3 A structural block diagram of a computing device 300 according to one embodiment of this specification is shown. The components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 330, and a database 350 is used to store data.

[0122] The computing device 300 also includes an access device 340, which enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 340 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0123] In one embodiment of this specification, the aforementioned components of the computing device 300 and Figure 3 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 3 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0124] The computing device 300 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 300 can also be a mobile or stationary server.

[0125] The processor 320 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-mentioned energy storage strategy evaluation method.

[0126] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the energy storage strategy evaluation method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the energy storage strategy evaluation method described above.

[0127] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the energy storage strategy evaluation method described above.

[0128] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the energy storage strategy evaluation method described above. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the energy storage strategy evaluation method described above.

[0129] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, it causes the computer to perform the steps of the above-described energy storage strategy evaluation method.

[0130] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-described energy storage strategy evaluation method belong to the same concept. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-described energy storage strategy evaluation method.

[0131] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0132] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0133] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for evaluating energy storage strategies, comprising: The dynamic temperature simulation model of the energy storage battery at the current moment, including battery temperature, heat generation power, and heat dissipation power, is used to simulate the battery temperature at multiple moments within the target cycle, generating corresponding battery temperature simulation results. Based on the battery temperature simulation results, the high-temperature aging marginal cost and cycle aging marginal cost of the energy storage battery at the multiple times are calculated. Obtain the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated; Based on the interactive power, the high-temperature aging marginal cost, and the cycle aging marginal cost, calculate the simulated revenue of the energy storage battery within the target period; The confidence level is calculated based on the simulated benefits and the actual benefits of the energy storage battery during the target period, and the calculation result is determined as the evaluation result corresponding to the energy storage strategy to be evaluated.

2. The energy storage strategy evaluation method according to claim 1, wherein the step of simulating the battery temperature of the energy storage battery at multiple moments within the target cycle using a temperature dynamic simulation model to generate corresponding battery temperature simulation results includes: The battery temperature, heat generation power, and heat dissipation power at the current moment are processed by the temperature dynamic simulation model to generate the battery temperature simulation result at the i-th moment within the target period, where i starts from 1 and is an integer from 1 to n-1, and n is the total number of multiple moments within the target interval. The battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time point are processed by the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time point within the target period. If i is incremented by 1, and if i is determined to be less than or equal to n-1, the process returns to the step of processing the battery temperature simulation results, heat generation power, and heat dissipation power at the i-th time moment through the temperature dynamic simulation model to generate the battery temperature simulation results at the (i+1)-th time moment within the target period.

3. The energy storage strategy evaluation method according to claim 1 further includes: Calculate the capacity decay rate of the energy storage battery at the various times based on the battery temperature simulation results; The high-temperature aging marginal cost of the energy storage battery at the multiple times is calculated based on the capacity decay rate of the energy storage battery at each of the multiple times.

4. The energy storage strategy evaluation method according to claim 1 or 3, wherein the expression for the high-temperature aging marginal cost calculation process is as follows: in, Let t be the marginal cost of high-temperature aging. The initial investment cost of energy storage batteries, This refers to the rated total capacity of the energy storage battery. Cost per unit capacity of battery Let be the capacity decay rate at time t. The interval between any two adjacent moments within the target period is the duration of time.

5. The energy storage strategy evaluation method according to claim 4, wherein the expression for the capacity decay rate calculation process is as follows: in, The calendar aging frequency factor is determined by the battery material properties and is calibrated through high-temperature storage experiments. The activation energy of the calendar aging reaction reflects the sensitivity of temperature to the aging rate. Let be the ideal gas constant. The simulation results show the battery temperature of the energy storage battery at time t. Let t be the state of charge (SOC) of the energy storage battery at time t, and z be the exponent of the SOC's impact on calendar aging, with a value ranging from... between.

6. The energy storage strategy evaluation method according to claim 1 further includes: Based on the battery temperature simulation results and the depth of discharge of the energy storage battery at each time, the number of cycles corresponding to the energy storage battery at each of the multiple time points is determined. The marginal cost of cycle aging of the energy storage battery at the multiple time points is calculated based on the number of cycles corresponding to the energy storage battery at each of the multiple time points.

7. The energy storage strategy evaluation method according to claim 1 or 6, wherein the expression for the calculation process of the cyclic aging marginal cost is as follows: in, The initial investment cost of energy storage batteries, The number of cycles for the energy storage battery at time t, based on the depth of discharge at time t. Simulation results of battery temperature Sure, Let be the absolute value of the power exchanged between the energy storage battery and the power grid at time t. The interval between any two adjacent moments within the target period is the duration of time.

8. The energy storage strategy evaluation method according to claim 1, wherein the temperature dynamic simulation model is constructed based on the battery temperature, heat generation power, heat dissipation power, battery heat capacity, and the interval between any two adjacent times within the target period of the energy storage battery at any given time, and the specific expression is as follows: in, The simulation results show the battery temperature of the energy storage battery at time t. for The heat generation power inside the energy storage battery at all times. for The heat dissipation capacity of the energy storage battery to the environment at all times. For the heat capacity of the energy storage battery, The interval between any two adjacent moments within the target period is the duration of time.

9. The energy storage strategy evaluation method according to claim 1 or 8, wherein the expression for the heat dissipation power calculation process is as follows: in, The convective heat transfer coefficient is... For heat dissipation area, Let be the ambient temperature at time t. The simulation results show the battery temperature of the energy storage battery at time t. in, The Nusselt number is a dimensionless number that characterizes the intensity of convective heat transfer. The thermal conductivity of the cooling fluid. This refers to the battery length.

10. An energy storage strategy evaluation device, comprising: The simulation module is configured to input the battery temperature, heat generation power, and heat dissipation power of the energy storage battery at the current moment into the temperature dynamic simulation model. Through the temperature dynamic simulation model, the battery temperature of the energy storage battery at multiple moments within the target cycle is simulated sequentially to generate the corresponding battery temperature simulation results. The first calculation module is configured to calculate the high-temperature aging marginal cost and the cycle aging marginal cost of the energy storage battery at multiple times based on the battery temperature simulation results. The first determining module is configured to acquire the energy storage strategy to be evaluated corresponding to the energy storage battery, and determine the interaction power between the energy storage battery and the power grid at the multiple times based on the energy storage strategy to be evaluated. The second calculation module is configured to calculate the simulated revenue of the energy storage battery in the target period based on the interaction power, the high-temperature aging marginal cost, and the cycle aging marginal cost. The second determining module is configured to perform confidence calculation based on the simulation benefits and the actual benefits of the energy storage battery during the target period, and determine the calculation result as the evaluation result corresponding to the energy storage strategy to be evaluated.

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