Method and system for evaluating performance of optical storage power station

Abstract

The invention relates to the technical field of optical storage power stations, and provides an optical storage power station performance evaluation method and system, and the method comprises the steps: collecting the operation data of an optical storage power station; based on the physical characteristics of the photovoltaic module, the storage battery pack and the inverter, establishing a photovoltaic energy storage equivalent model to construct an energy storage prediction model, inputting the operation data of the optical storage power station into the energy storage prediction model, and outputting energy storage charge state prediction data before and after the storage battery pack executes the automatic power generation control instruction; a frequency modulation strategy is matched based on the energy storage charge state prediction data, and a frequency modulation control instruction of the optical storage power station is output; in the execution process of the frequency modulation control instruction, collecting performance indexes of the optical storage power station, inputting the performance indexes into the comprehensive regulation performance index model, and outputting comprehensive regulation performance indexes for evaluating the performance of the optical storage power station; the accuracy and reliability of performance evaluation of the optical storage power station can be improved.

Description

Technical Field

[0001] This disclosure belongs to the field of photovoltaic and energy storage power station technology, and in particular relates to a method and system for evaluating the performance of photovoltaic and energy storage power stations. Background Technology

[0002] A photovoltaic (PV) power station is a comprehensive energy solution integrating photovoltaic power generation and energy storage systems, designed to improve solar energy utilization and grid stability. The PV power generation component converts solar energy into electricity using solar panels, while the energy storage component typically uses lithium-ion batteries to store the generated energy for use during periods of insufficient sunlight or peak electricity demand. The main components of a PV power station include photovoltaic modules, inverters, energy storage systems (including battery management systems), energy management systems (EMS), and related power distribution equipment.

[0003] The performance of a photovoltaic (PV) and energy storage (ESS) power station can be defined and evaluated through multiple dimensions. First, there's power generation efficiency, which refers to the efficiency with which the PV system converts solar energy into electrical energy, typically expressed as photoelectric conversion efficiency (PCE). Second, there's energy storage efficiency, which refers to the degree of energy loss during battery charging and discharging, usually expressed as coulombic efficiency and energy efficiency. Furthermore, system stability and reliability are also important performance indicators, including the system's operational stability under different load and environmental conditions, battery cycle life, and state of health (SoH). Economic evaluation is also a crucial aspect of performance assessment, including the system's payback period, internal rate of return (IRR), and net present value (NPV). Performance evaluation helps understand the system's operating status and efficiency, providing data support for optimizing system design and operating strategies. Secondly, performance evaluation can identify weaknesses and potential failures in the system, enabling timely maintenance and upgrades, thus improving system reliability and lifespan. Moreover, performance evaluation is the foundation of economic analysis; by assessing the system's operating costs and benefits, reasonable investment and operational decisions can be made, ensuring the project's sustainable development.

[0004] Current performance evaluation methods for photovoltaic (PV) and energy storage (ESS) power plants mainly include data monitoring, modeling and simulation, and experimental testing. Data monitoring uses sensors and data acquisition devices installed in the system to monitor the real-time operating data of the PV power generation, energy storage system, and inverter, and performs real-time analysis and evaluation. Modeling and simulation utilize computer models to simulate the operation of the PV-ESS power plant, evaluating the system's performance and efficiency under different conditions. Experimental testing verifies the system's actual performance and stability through laboratory and field tests.

[0005] While current performance evaluation methods are relatively mature, they still have some limitations and shortcomings. First, data monitoring and modeling simulations are limited by data accuracy and model precision in practical applications, making it difficult to fully reflect the actual operating state of the system. Second, experimental testing is constrained by experimental conditions and equipment, and test results may differ from actual operating conditions. Furthermore, performance evaluation typically requires significant time and resources, resulting in high costs and hindering large-scale application. Finally, with continuous technological advancements, the complexity and diversity of photovoltaic-storage power station systems are constantly increasing, and existing evaluation methods and tools may be insufficient to fully adapt to and cover all aspects. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure provides a method and system for evaluating the performance of photovoltaic-storage power stations. The method employs a technical solution that uses the physical characteristics of photovoltaic modules, battery banks, and inverters to construct equivalent photovoltaic energy storage models and energy storage prediction models. It establishes a comprehensive regulation performance index model covering the photovoltaic side, energy storage side, and joint frequency regulation dimensions. This solution can overcome the shortcomings of existing evaluation methods, such as insufficient data accuracy, limited model precision, and difficulty in adapting to complex systems.

[0007] The following is the technical content of this disclosure: A method for evaluating the performance of a photovoltaic-storage power station includes: Collect operational data from photovoltaic and energy storage power stations; Based on the physical characteristics of photovoltaic modules, battery banks and inverters, an equivalent model of photovoltaic energy storage is established to construct an energy storage prediction model. The operation data of the photovoltaic-energy storage power station is input into the energy storage prediction model, and the predicted data of the energy storage state of charge before and after the battery bank executes the automatic power generation control command are output. Based on the predicted state of charge data of energy storage, a frequency regulation strategy is matched, and frequency regulation control commands for photovoltaic-storage power stations are output. During the execution of frequency modulation control commands, the performance indicators of the photovoltaic-storage power station are collected and input into the comprehensive regulation performance indicator model, and the comprehensive regulation performance indicator used to evaluate the performance of the photovoltaic-storage power station is output. The comprehensive regulation performance index model is as follows:

[0008] In the formula, Z represents the comprehensive regulation performance index; This indicates the measured power generation rate of the photovoltaic module; This represents the average standard regulation rate of photovoltaic modules under ideal baseline conditions across all operating scenarios covered by the frequency regulation strategy. Indicates the measured response delay time of the photovoltaic module; Indicates the standard delay time of photovoltaic modules; This indicates the measured energy absorption rate of the battery pack; This indicates the standard energy absorption rate of the battery pack. This indicates the measured energy release rate of the battery pack; This indicates the standard energy release rate of the battery pack; This indicates the measured adjustment error of the photovoltaic module; Indicates the allowable adjustment error of photovoltaic modules; The weighting factor represents the optoelectronic indicators; The weighting factor represents the energy storage-side indicators; This represents the weighting factor of the joint frequency modulation index.

[0009] Furthermore, The construction of the comprehensive regulation performance index model includes: A primary performance index is defined, which includes photovoltaic-side index, energy storage-side index, and joint frequency regulation index. Define the secondary indicators included in each primary performance indicator, wherein the secondary indicators include: The secondary indicators corresponding to the photovoltaic side indicators are: measured power generation rate of photovoltaic modules, measured response delay time of photovoltaic modules, and measured adjustment error of photovoltaic modules. The secondary indicators corresponding to the energy storage side indicators are: measured energy absorption rate of the battery pack and measured energy release rate of the battery pack. A grey relational decision matrix is ​​used to obtain the weight factors of each primary and secondary performance index, and a comprehensive adjustment performance index model is constructed based on the weight factors.

[0010] Furthermore, The construction of the energy storage prediction model includes: Based on the current-voltage characteristic curve of photovoltaic modules, an equivalent model of photovoltaic power generation is established; Based on the charging and discharging characteristics, cycle life, and health status of the battery pack, an equivalent model of the energy storage circuit of the battery pack is established. Based on the conversion efficiency and losses of the inverter, an efficiency equivalent model of the inverter is established. The photovoltaic power generation model, energy storage circuit equivalent model, and efficiency equivalent model are combined into a photovoltaic energy storage equivalent model for the photovoltaic energy storage power station, and then imported into the simulation environment for simulation. Based on the simulation results of the energy storage capacity and charging / discharging power distribution of the battery pack, an energy storage prediction model is established. The energy storage prediction model is used to predict the energy storage state of charge of the battery pack before and after executing the automatic power generation control command.

[0011] Furthermore, Based on the simulation results of the battery pack's energy storage capacity and charge / discharge power distribution, an energy storage prediction model is established, including: Obtain the initial energy storage capacity, state of charge decay parameters, energy storage discharge mileage and total energy storage mileage of the battery pack, and calculate the real-time energy storage capacity of the battery pack. The system monitors automatic power generation control commands. After the battery pack receives the automatic power generation control command, it calculates the real-time remaining capacity of the battery pack and selects individual battery cells in the battery pack that meet the upper and lower limit thresholds based on the energy storage thresholds. Using the selected battery cells as the main power output, the power of each cell is allocated according to its remaining state of charge to obtain the power allocation ratio. The real-time energy storage capacity of the battery pack, the information of individual battery cells that meet the upper and lower thresholds, and the power distribution ratio are imported into the photovoltaic energy storage equivalent model. The response time of the photovoltaic module and the battery pack after receiving the automatic power generation control command is obtained through simulation. Based on the power difference caused by the response time difference, the energy storage prediction model of the battery pack is established. The expression for the energy storage prediction model is:

[0012] In the formula, This represents the state of charge of the i-th battery cell when it begins to respond. This indicates the state of charge of the energy storage at the end of the automatic control command for the i-th battery cell; This represents the state of charge of the i-th battery cell at the predicted time t; This represents the power of the i-th battery cell at the predicted time t; This indicates the number of individual battery cells in the battery pack that participated in this power output; This represents the battery cell power command for the i-th battery cell. This represents the state of charge of the j-th battery cell; This represents the state of charge of the i-th battery cell; This indicates the real-time energy storage capacity of the battery pack; Indicates the response time of the photovoltaic module; This indicates the response speed of the photovoltaic module.

[0013] Furthermore, The formula for calculating the real-time energy storage capacity is as follows:

[0014] In the formula, M represents the real-time energy storage capacity of the battery pack; M represents the energy storage and discharge range of the battery pack. Indicates the total energy storage range of the battery pack; β represents the initial energy storage capacity of the battery pack; β represents the state-of-charge decay parameter.

[0015] Furthermore, The frequency modulation strategy based on energy storage state of charge prediction data is matched to output frequency modulation control commands for the photovoltaic-storage power station; including: Obtain predicted data of the energy storage state of charge of the battery pack and individual battery cells, and detect whether the energy storage state of charge of the individual battery cells exceeds the limit based on the predicted data. If there is an over-limit phenomenon, obtain the full life cycle cost of the photovoltaic-storage power station, take frequency regulation demand as an opportunity constraint, take minimizing operating cost as the optimization objective, and establish a joint frequency regulation optimization model; The optimal solution of the joint frequency regulation optimization model is obtained by using the antlion optimization algorithm, which serves as the joint frequency regulation strategy under the current automatic generation control command. Based on the joint frequency regulation strategy, joint frequency regulation control commands are sent to the battery pack.

[0016] Furthermore, The establishment of the joint frequency modulation optimization model includes: To obtain the unit cost of power generation of photovoltaic modules, the unit cost of battery packs, the operation and maintenance costs, and the battery replacement costs during the operation of a photovoltaic and energy storage power station; The photovoltaic power generation output, battery pack output power, and battery pack energy storage state of charge of the photovoltaic power station are collected during the frequency regulation process. Using frequency regulation demand as an opportunity constraint and minimizing operating costs as the optimization objective, a joint frequency regulation optimization model is established. The expression for the joint frequency regulation optimization model is as follows:

[0017] In the formula, This indicates the unit cost of photovoltaic power generation for photovoltaic modules; This indicates the unit cost of the battery pack. Indicates operating and maintenance costs; L represents the battery pack replacement cost; T represents the battery pack lifespan; and T represents the total number of batteries at time t. This represents the output power of photovoltaic power generation at time t; This represents the output power of the battery pack at time t; This represents the grid load demand at time t; Indicates the efficiency of the photovoltaic system; Indicates the charging and discharging efficiency of the battery pack; Indicates inverter efficiency; This represents the probability of default on frequency modulation (FM) demand.

[0018] Furthermore, In the antlion optimization algorithm: The population size of antlions and ants corresponds to the number of candidate strategies and the number of working condition verification samples, respectively. The positions of the ants and antlions correspond to the operating condition sample parameters of a single set of verification candidate strategies and the specific decision variable values ​​of a single set of candidate joint frequency modulation strategies, respectively. Each ant corresponds to a working condition sample, and each antlion corresponds to a frequency modulation strategy.

[0019] A system for evaluating the performance of a photovoltaic-storage power station includes: The data monitoring module is used to collect operational data from the photovoltaic-storage power station. The state prediction module is used to establish an equivalent model of photovoltaic energy storage based on the physical characteristics of photovoltaic modules, battery packs and inverters to build an energy storage prediction model. The module inputs the operating data of the photovoltaic-energy storage power station into the energy storage prediction model and outputs the energy storage state of charge prediction data before and after the battery pack executes the automatic power generation control command. The strategy matching module is used to match frequency regulation strategies based on energy storage state of charge prediction data and output frequency regulation control commands for photovoltaic-storage power stations. The performance evaluation module is used to collect the performance indicators of the photovoltaic-storage power station during the execution of frequency regulation control commands, input them into the comprehensive regulation performance indicator model, and output the comprehensive regulation performance indicator for evaluating the performance of the photovoltaic-storage power station. The comprehensive regulation performance index model is as follows:

[0020] In the formula, Z represents the comprehensive regulation performance index; This indicates the measured power generation rate of the photovoltaic module; This represents the average standard regulation rate of photovoltaic modules under ideal baseline conditions across all operating scenarios covered by the frequency regulation strategy. Indicates the measured response delay time of the photovoltaic module; Indicates the standard delay time of photovoltaic modules; This indicates the measured energy absorption rate of the battery pack; This indicates the standard energy absorption rate of the battery pack. This indicates the measured energy release rate of the battery pack; This indicates the standard energy release rate of the battery pack; This indicates the measured adjustment error of the photovoltaic module; Indicates the allowable adjustment error of photovoltaic modules; The weighting factor represents the optoelectronic indicators; The weighting factor represents the energy storage-side indicators; This represents the weighting factor of the joint frequency modulation index.

[0021] Furthermore, The expression for the energy storage prediction model is:

[0022] In the formula, This represents the state of charge of the i-th battery cell when it begins to respond. This indicates the state of charge of the energy storage at the end of the automatic control command for the i-th battery cell; This represents the state of charge of the i-th battery cell at the predicted time t; This represents the power of the i-th battery cell at the predicted time t; This indicates the number of individual battery cells in the battery pack that participated in this power output; This represents the battery cell power command for the i-th battery cell. This represents the state of charge of the j-th battery cell; This represents the state of charge of the i-th battery cell; This indicates the real-time energy storage capacity of the battery pack; Indicates the response time of the photovoltaic module; This indicates the response speed of the photovoltaic module.

[0023] Compared with the prior art, this disclosure has the following advantages: This disclosure utilizes a photovoltaic energy storage equivalent model and an energy storage prediction model to accurately capture changes in the state of charge of the battery pack. Combined with an optimization algorithm, it achieves optimal matching of the frequency regulation strategy and completes dynamic evaluation through a multi-dimensional comprehensive regulation performance index model. This model integrates photovoltaic-side secondary indicators and weighting factors for evaluating the operating efficiency of photovoltaic modules, energy storage-side secondary indicators and weighting factors for evaluating the energy storage efficiency of the battery pack, and a joint frequency regulation index and weighting factor for evaluating the synergistic frequency regulation effect of photovoltaic and energy storage. These indicators can respectively measure: Among them, the secondary indicators on the photovoltaic side can measure the power generation efficiency, response timeliness and regulation accuracy of photovoltaic modules in frequency regulation scenarios, and directly reflect the photovoltaic side's ability to execute frequency regulation commands. Secondary indicators on the energy storage side can measure the energy storage efficiency and energy output efficiency of battery packs, and accurately quantify the charging and discharging performance and energy utilization level of energy storage systems. The joint frequency regulation index can measure the degree of coordination between photovoltaic modules and battery banks when executing frequency regulation commands, and determine whether the overall system meets the dynamic requirements of grid frequency regulation. The integrated design of the above multi-dimensional indicators and weights allows the comprehensive adjustment performance indicator model to fully cover the three performance dimensions of "component-system-coordination", solving the problems of existing evaluation methods that are difficult to reflect the actual operating status of the system, have poor adaptability, and are costly.

[0024] Other features and advantages of this disclosure will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic diagram of the method of this disclosure is shown; Figure 2 A schematic diagram of the system disclosed herein is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0028] Figure 1 A schematic diagram of the method according to this disclosure is shown. Specific implementation details of this disclosure include: S1. Real-time collection of operating data of photovoltaic and energy storage power stations through distributed monitoring nodes; S2. Based on the physical characteristics of photovoltaic modules, battery packs and inverters, establish an equivalent model for photovoltaic energy storage and construct an energy storage prediction model to predict the energy storage state of charge of the battery pack. S3. Based on the predicted state of charge of the energy storage, combined with the opportunity constraints of the photovoltaic-storage power station, the optimal joint frequency regulation strategy is matched, and the joint frequency regulation control command of the photovoltaic-storage power station is output. S4. Set evaluation indicators for photovoltaic-storage power stations, and dynamically evaluate the energy storage performance of photovoltaic-storage power stations by real-time online monitoring of the execution process of joint frequency regulation control commands.

[0029] Based on the physical characteristics of photovoltaic modules, battery banks, and inverters, an equivalent photovoltaic energy storage model is established, and an energy storage prediction model is constructed to predict the energy storage state of charge of the battery bank, including the following steps: S21. Based on the current-voltage characteristic curve of photovoltaic modules, establish an equivalent model for photovoltaic power generation; S22. Based on the charging and discharging characteristics, cycle life, and health status of the battery pack, establish an equivalent model of the energy storage circuit of the battery pack. S23. Based on the conversion efficiency and losses of the inverter, establish an efficiency equivalent model for the inverter; S24. Integrate the photovoltaic power generation model, energy storage circuit equivalent model, and efficiency equivalent model, merge them into a photovoltaic energy storage equivalent model for a photovoltaic-energy storage power station, and import them into the simulation environment. S25. Analyze the energy storage capacity and charging / discharging power distribution of the battery pack, establish an energy storage prediction model, and predict the energy storage state of charge before and after the battery pack executes the automatic power generation control command.

[0030] The analysis of the battery pack's energy storage capacity and charging / discharging power distribution, and the establishment of an energy storage prediction model to predict the battery pack's state of charge before and after executing automatic power generation control commands, includes the following steps: S251. Obtain the initial energy storage capacity, state of charge decay parameters, energy storage discharge mileage and total energy storage mileage of the battery pack, and calculate the real-time energy storage capacity of the battery pack. S252. Real-time monitoring of the battery pack and automatic power generation control commands. After receiving the automatic power generation control commands, calculate the real-time remaining capacity of the battery pack, set the upper and lower limit thresholds for energy storage, and filter out battery cells in the battery pack that do not meet the upper and lower limit thresholds. S253. Select battery cells that meet the upper and lower thresholds to participate in the output of the current automatic power generation control command, and allocate the power of the cells according to their remaining state of charge. S254. Through simulation of the photovoltaic energy storage equivalent model, obtain the response time of the photovoltaic module and the battery pack after receiving the automatic power generation control command, and based on the power difference caused by the response time difference, establish the energy storage prediction model of the battery pack to predict the energy storage state of charge when the battery pack starts to respond to the automatic control command and the energy storage state of charge after the automatic power generation control command ends.

[0031] Furthermore, the expression for the energy storage prediction model includes:

[0032] In the formula, This represents the state of charge of the i-th battery cell when it begins to respond. This indicates the state of charge of the energy storage at the end of the automatic control command for the i-th battery cell; This represents the state of charge of the i-th battery cell at the predicted time t; This represents the power of the i-th battery cell at the predicted time t; This indicates the number of individual battery cells in the battery pack that participated in this power output; This represents the battery cell power command for the i-th battery cell. This represents the state of charge of the j-th battery cell; This represents the state of charge of the i-th battery cell; This indicates the real-time energy storage capacity of the battery pack; Indicates the response time of the photovoltaic module; This indicates the response speed of the photovoltaic module.

[0033] Furthermore, based on the predicted state of charge of the energy storage, combined with the opportunity constraints of the photovoltaic-storage power station, the optimal joint frequency regulation strategy is matched, and the joint frequency regulation control command of the photovoltaic-storage power station is output, including the following steps: S31. Obtain the predicted state of charge of the battery pack and individual battery cells, and detect whether there is any over-limit phenomenon in the individual battery cells. If there is an over-limit phenomenon in the individual battery cells, adjust the power allocation command. If there is no over-limit phenomenon in the individual battery cells, maintain the current power allocation command. S32. Obtain the full life cycle cost of the photovoltaic-storage power station, and establish a joint frequency regulation optimization model with frequency regulation demand as an opportunity constraint and minimizing operating cost as the optimization objective. S33. The optimal solution of the joint frequency regulation optimization model is obtained by using the antlion optimization algorithm, which serves as the joint frequency regulation strategy under the current automatic generation control command. S34. Based on the joint frequency modulation strategy, send a joint frequency modulation control command to the battery pack.

[0034] Furthermore, to obtain the total life-cycle cost of the photovoltaic-storage power station, and using frequency regulation demand as an opportunity constraint and minimizing operating costs as the optimization objective, a joint frequency regulation optimization model is established, including the following steps: S321. Obtain the unit cost of photovoltaic modules, the unit cost of battery packs, the operation and maintenance costs, and the battery replacement costs during the operation of the photovoltaic-storage power station. S322. Collect the photovoltaic power generation output power, battery pack output power, and battery pack energy storage state of charge of the photovoltaic power station during the frequency regulation process. S323. Taking frequency regulation demand as a chance constraint and minimizing operating cost as the optimization objective, establish a joint frequency regulation optimization model, wherein the expression of the joint frequency regulation optimization model is:

[0035] In the formula, This indicates the unit cost of photovoltaic power generation for photovoltaic modules; This indicates the unit cost of the battery pack. Indicates operating and maintenance costs; L represents the battery pack replacement cost; T represents the battery pack lifespan; and T represents the total number of batteries at time t. This represents the output power of photovoltaic power generation at time t; This represents the output power of the battery pack at time t; This represents the grid load demand at time t; Indicates the efficiency of the photovoltaic system; Indicates the charging and discharging efficiency of the battery pack; Indicates inverter efficiency; This represents the probability of default on frequency modulation (FM) demand.

[0036] Furthermore, the optimal solution of the joint frequency regulation optimization model is obtained by using the antlion optimization algorithm, which serves as the joint frequency regulation strategy under the current automatic generation control command. This includes the following steps: S331. Input the population size of antlions and ants, the number of iterations of the algorithm, the dimension of variables and the upper and lower bounds of variables, and initialize the basic parameters of the algorithm. S332. Use a random method to generate a set of initial configuration strategies as the initial positions of ants and antlions; S333. Record the positions of each ant and antlion in the population through simulation calculations; S334. Determine whether the current positions of the ants and antlions meet the constraints. If the constraints are met, proceed to step S355. If the constraints are not met, update the positions of the ants and antlions and rebuild the traps to generate a new generation of population. Then proceed to step S333. S335. Select the antlion that satisfies the constraints, and use the position corresponding to the antlion as the objective function value. S336. Perform elite selection on antlions in the population; S337. Update the positions of ants and antlions in the population and rebuild the traps to generate a new generation of ant and antlion populations. S338. Iterate through the ant and antlion populations until the maximum number of iterations is reached, then terminate the algorithm and use the best antlion position as the optimal joint frequency modulation strategy.

[0037] Furthermore, setting evaluation indicators for photovoltaic-storage power stations and dynamically evaluating their energy storage performance through real-time online monitoring of the execution process of joint frequency regulation control commands includes the following steps: S41. Based on the frequency regulation characteristics and combined frequency regulation characteristics of the photovoltaic-storage power station, set the primary performance indicators, which include photovoltaic side indicators, energy storage side indicators and combined frequency regulation indicators. S42. Based on the adjustment speed, adjustment accuracy, response time and energy conversion rate of the photovoltaic-storage power station executing automatic substation control commands, set the secondary indicators included in each primary performance indicator; S43. Using the grey relational decision matrix, the weight factors of each primary and secondary performance index are obtained, and a comprehensive adjustment performance index model is set. S44. Monitor online data during the joint frequency regulation strategy operation of photovoltaic and energy storage power stations in real time, input the online data into the comprehensive regulation performance index model, and dynamically evaluate the comprehensive performance of photovoltaic and energy storage power stations.

[0038] Furthermore, the expression for the comprehensive regulation performance index model is as follows:

[0039] In the formula, Z represents the comprehensive regulation performance index; This indicates the measured power generation rate of the photovoltaic module; This represents the average standard regulation rate of photovoltaic modules under ideal baseline conditions across all operating scenarios covered by the frequency regulation strategy. Indicates the measured response delay time of the photovoltaic module; Indicates the standard delay time of photovoltaic modules; This indicates the measured energy absorption rate of the battery pack; This indicates the standard energy absorption rate of the battery pack. This indicates the measured energy release rate of the battery pack; This indicates the standard energy release rate of the battery pack; This indicates the measured adjustment error of the photovoltaic module; Indicates the allowable adjustment error of photovoltaic modules; The weighting factor represents the optoelectronic indicators; The weighting factor represents the energy storage-side indicators; This represents the weighting factor of the joint frequency modulation index.

[0040] Secondly, this disclosure also provides an online performance evaluation system for photovoltaic-storage power plants based on a joint frequency modulation strategy, the system comprising: The data monitoring module is used to collect real-time operating data of the photovoltaic and energy storage power station through distributed monitoring nodes; The state prediction module is used to establish an equivalent model of photovoltaic energy storage based on the physical characteristics of photovoltaic modules, battery packs and inverters, and to build an energy storage prediction model to predict the state of charge of the battery pack. The strategy matching module is used to match the optimal joint frequency regulation strategy based on the prediction results of the energy storage state of charge and the opportunity constraints of the photovoltaic-storage power station, and output the joint frequency regulation control command of the photovoltaic-storage power station. The dynamic evaluation module is used to set evaluation indicators for photovoltaic-storage power stations. By monitoring the execution process of joint frequency regulation control commands in real time, it dynamically evaluates the energy storage performance of photovoltaic-storage power stations.

[0041] The following are specific examples of this disclosure: S1. Real-time collection of operational data from photovoltaic and energy storage power stations through distributed monitoring nodes.

[0042] The operational data of the photovoltaic-storage power station includes the following aspects: Output power of photovoltaic modules; Parameters of the battery pack, such as voltage, current, and temperature; Inverter operating status; Environmental parameters (such as light intensity, temperature, humidity, etc.); Power grid frequency and load conditions.

[0043] S2. Based on the physical characteristics of photovoltaic modules, battery packs and inverters, establish an equivalent model for photovoltaic energy storage and construct an energy storage prediction model to predict the energy storage state of charge of the battery pack.

[0044] In the description of this disclosure, based on the physical characteristics of photovoltaic modules, battery banks, and inverters, an equivalent photovoltaic energy storage model is established, and an energy storage prediction model is constructed to predict the energy storage state of charge of the battery bank, including the following steps: S21. Based on the current-voltage characteristic curve of photovoltaic modules, establish an equivalent model for photovoltaic power generation.

[0045] Specifically, based on the photovoltaic module's datasheet or experimental data, determine the relevant parameters of the photovoltaic module, including photocurrent (affected by light intensity), dark saturation current, series resistance, parallel resistance, diode factor, output current, and thermal voltage, etc. Then, establish models of the influence of temperature and light intensity on these parameters. Next, numerically solve the above equations to simulate the output characteristics of the photovoltaic module under different environmental conditions, thereby constructing an equivalent model of photovoltaic power generation.

[0046] S22. Based on the charging and discharging characteristics, cycle life, and health status of the battery pack, establish an equivalent model of the energy storage circuit of the battery pack.

[0047] Specifically, the parameters required for the equivalent model of the energy storage circuit of a battery pack include battery terminal voltage, open-circuit voltage, charging and discharging current, ohmic internal resistance, dynamic resistance, and time constant. The state of charge (SOC) of the battery pack is calculated using the current integration method, and the voltage and SOC changes of the battery pack under different charging and discharging conditions are simulated to construct the equivalent model of the energy storage circuit.

[0048] S23. Based on the conversion efficiency and loss of the inverter, establish an efficiency equivalent model for the inverter.

[0049] Specifically, the inverter efficiency equivalent model mainly focuses on the inverter's efficiency and conversion characteristics. It needs to simulate the inverter's output power and efficiency under different input power conditions based on the inverter's fixed losses and loss coefficients.

[0050] S24. Integrate the photovoltaic power generation model, energy storage circuit equivalent model, and efficiency equivalent model, merge them into a photovoltaic energy storage equivalent model for a photovoltaic-energy storage power station, and import them into the simulation environment.

[0051] S25. Analyze the energy storage capacity and charging / discharging power distribution of the battery pack, establish an energy storage prediction model, and predict the energy storage state of charge before and after the battery pack executes the automatic power generation control command.

[0052] The English name for Automatic Generation Control (AGC) is Automatic generation control, and the English name for State of Charge (SOC) is State of charge.

[0053] In the description of this disclosure, the analysis of the energy storage capacity and charge / discharge power distribution of the battery pack, the establishment of an energy storage prediction model, and the prediction of the energy storage state of charge of the battery pack before and after the execution of automatic generation control commands include the following steps: S251. Obtain the initial energy storage capacity, state of charge decay parameters, energy storage discharge mileage and total energy storage mileage of the battery pack, and calculate the real-time energy storage capacity of the battery pack.

[0054] The formula for calculating real-time energy storage capacity is as follows:

[0055] In the formula, M represents the real-time energy storage capacity of the battery pack; M represents the energy storage and discharge range of the battery pack. Indicates the total energy storage range of the battery pack; β represents the initial energy storage capacity of the battery pack; β represents the state-of-charge decay parameter.

[0056] S252. Real-time monitoring of the battery pack and automatic power generation control commands. After receiving the automatic power generation control commands, calculate the real-time remaining capacity of the battery pack, set the upper and lower limit thresholds for energy storage, and filter out battery cells in the battery pack that do not meet the upper and lower limit thresholds.

[0057] S253. Select battery cells that meet the upper and lower thresholds to participate in the output of the current automatic power generation control command, and allocate the power of the cells according to their remaining state of charge.

[0058] Specifically, the first step is to determine the total power required for the automatic power generation control command. Then, based on the remaining state of charge (SPC) of the selected battery cells, the total power is allocated to each battery cell. Power allocation must be based on the proportion of the SPC, and after allocation, the SPC of each battery cell at the next time step is calculated. A pseudocode example of power allocation is shown below: # Initialize parameters SoC_max=0.95 SoC_min=0.20 delta_t=1 # Time step (hours) P_total=100 # Total power (kW) required by the AGC command battery_capacity=[10,15,20,10,5]# Rated capacity (kWh) of each battery cell SoC=[0.9,0.85,0.5,0.4,0.3]# Current state of charge of each battery cell #Filter battery cells that meet the threshold conditions selected_batteries=[i for i in range(len(SoC)) if SoC_min<=SoC[i]<=SoC_max] #Calculate the total remaining state of charge total_SoC=sum([SoC[i] for i in selected_batteries]) #Power Distribution P = [0] * len(SoC) for i in selected_batteries: P[i]=P_total * (SoC[i] / total_SoC) #Update the state of charge of each battery cell for i in selected_batteries: SoC[i]=SoC[i]- (P[i] * delta_t / battery_capacity[i]) # Output results print("Power Allocation:", P) print("Updated state of charge:",SoC) S254. Through simulation of the photovoltaic energy storage equivalent model, obtain the response time of the photovoltaic module and the battery pack after receiving the automatic power generation control command, and based on the power difference caused by the response time difference, establish the energy storage prediction model of the battery pack to predict the energy storage state of charge when the battery pack starts to respond to the automatic control command and the energy storage state of charge after the automatic power generation control command ends.

[0059] In the description of this disclosure, the expressions for the energy storage prediction model include:

[0060] In the formula, This represents the state of charge of the i-th battery cell when it begins to respond. This indicates the state of charge of the energy storage at the end of the automatic control command for the i-th battery cell; This represents the state of charge of the i-th battery cell at the predicted time t; This represents the power of the i-th battery cell at the predicted time t; This indicates the number of individual battery cells in the battery pack that participated in this power output; This represents the battery cell power command for the i-th battery cell. This represents the state of charge of the j-th battery cell; This represents the state of charge of the i-th battery cell; This indicates the real-time energy storage capacity of the battery pack; Indicates the response time of the photovoltaic module; This indicates the response speed of the photovoltaic module.

[0061] S3. Based on the predicted state of charge of the energy storage, combined with the opportunity constraints of the photovoltaic-storage power station, the optimal joint frequency regulation strategy is matched, and the joint frequency regulation control command of the photovoltaic-storage power station is output.

[0062] In the description of this disclosure, based on the predicted state of charge of the energy storage, combined with the opportunity constraints of the photovoltaic-storage power station, the optimal joint frequency regulation strategy is matched, and the joint frequency regulation control command of the photovoltaic-storage power station is output, including the following steps: S31. Obtain the predicted state of charge of the battery pack and individual battery cells, and detect whether there are any battery cells exceeding the limit. If there are any battery cells exceeding the limit, adjust the power allocation command. If there are no battery cells exceeding the limit, maintain the current power allocation command.

[0063] S32. Obtain the total life cycle cost of the photovoltaic-storage power station, and establish a joint frequency regulation optimization model with frequency regulation demand as an opportunity constraint and minimizing operating costs as the optimization objective.

[0064] In the description of this disclosure, the life-cycle cost of a photovoltaic-storage power station is obtained, and a joint frequency regulation optimization model is established with frequency regulation demand as an opportunity constraint and minimizing operating costs as the optimization objective. The model includes the following steps: S321. Obtain the unit cost of photovoltaic modules, the unit cost of battery packs, the operation and maintenance costs, and the battery replacement costs during the operation of the photovoltaic-storage power station.

[0065] S322. Collect the photovoltaic power generation output power, battery pack output power, and battery pack energy storage state of charge of the photovoltaic power station during the frequency regulation process.

[0066] S323. Taking frequency regulation demand as a chance constraint and minimizing operating cost as the optimization objective, establish a joint frequency regulation optimization model, wherein the expression of the joint frequency regulation optimization model is:

[0067] In the formula, This indicates the unit cost of photovoltaic power generation for photovoltaic modules; This indicates the unit cost of the battery pack. Indicates operating and maintenance costs; L represents the battery pack replacement cost; T represents the battery pack lifespan; and T represents the total number of batteries at time t. This represents the output power of photovoltaic power generation at time t; This represents the output power of the battery pack at time t; This represents the grid load demand at time t; Indicates the efficiency of the photovoltaic system; Indicates the charging and discharging efficiency of the battery pack; Indicates inverter efficiency; This represents the probability of default on frequency modulation (FM) demand.

[0068] S33. The optimal solution of the joint frequency regulation optimization model is obtained by using the antlion optimization algorithm, which serves as the joint frequency regulation strategy under the current automatic generation control command.

[0069] Among them, the Ant Lion Optimizer (ALO) is an emerging swarm intelligence optimization algorithm inspired by the behavior of antlions hunting ants. Antlions are insects that catch ants by digging traps in the sand. ALO solves complex optimization problems by simulating this hunting behavior.

[0070] In the description of this disclosure, the optimal solution of the joint frequency regulation optimization model is obtained using the antlion optimization algorithm, which serves as the joint frequency regulation strategy under the current automatic generation control command, and includes the following steps: S331. Input the population size of antlions and ants, the number of iterations of the algorithm, the dimension of variables, and the upper and lower bounds of variables to initialize the basic parameters of the algorithm.

[0071] S332. Use a random method to generate a set of initial configuration strategies as the initial positions of ants and antlions.

[0072] S333. Record the positions of each ant and antlion in the population through simulation calculations.

[0073] S334. Determine whether the current positions of the ants and antlions meet the constraints. If the constraints are met, proceed to step S355. If the constraints are not met, update the positions of the ants and antlions and rebuild the traps to generate a new generation of population, then proceed to step S333.

[0074] S335. Select the antlion that satisfies the constraints, and use the position corresponding to the antlion as the objective function value.

[0075] S336, Perform elite selection operation on antlions in the population.

[0076] S337. Update the positions of ants and antlions in the population and rebuild the traps to generate a new generation of ant and antlion populations.

[0077] S338. Iterate through the ant and antlion populations until the maximum number of iterations is reached, then terminate the algorithm and use the best antlion position as the optimal joint frequency modulation strategy.

[0078] S34. Based on the joint frequency modulation strategy, send a joint frequency modulation control command to the battery pack.

[0079] Among them, the population size of antlions and ants corresponds to the number of candidate strategies and the number of working condition verification samples, respectively. The positions of the ants and antlions correspond to the operating condition sample parameters of a single set of verification candidate strategies and the specific decision variable values ​​of a single set of candidate joint frequency modulation strategies, respectively. Each ant corresponds to a working condition sample, and each antlion corresponds to a frequency modulation strategy.

[0080] S4. Set evaluation indicators for photovoltaic-storage power stations, and dynamically evaluate the energy storage performance of photovoltaic-storage power stations by real-time online monitoring of the execution process of joint frequency regulation control commands.

[0081] In the description of this disclosure, the evaluation indicators for photovoltaic-storage power stations are set, and the energy storage performance of the photovoltaic-storage power stations is dynamically evaluated by real-time online monitoring of the execution process of joint frequency regulation control commands, including the following steps: S41. Based on the frequency regulation characteristics and combined frequency regulation characteristics of the photovoltaic-storage power station, set the primary performance indicators, which include photovoltaic side indicators, energy storage side indicators and combined frequency regulation indicators.

[0082] S42. Based on the adjustment speed, adjustment accuracy, response time and energy conversion rate of the photovoltaic-storage power station executing automatic substation control commands, set the secondary indicators included in each primary performance indicator.

[0083] S43. Using the grey relational decision matrix, the weight factors of each primary and secondary performance index are obtained, and a comprehensive adjustment performance index model is set.

[0084] In the description of this disclosure, the expression for the comprehensive regulation performance index model is as follows:

[0085] In the formula, Z represents the comprehensive regulation performance index; This indicates the measured power generation rate of the photovoltaic module; This represents the average standard regulation rate of photovoltaic modules under ideal baseline conditions across all operating scenarios covered by the frequency regulation strategy. Indicates the measured response delay time of the photovoltaic module; Indicates the standard delay time of photovoltaic modules; This indicates the measured energy absorption rate of the battery pack; This indicates the standard energy absorption rate of the battery pack. This indicates the measured energy release rate of the battery pack; This indicates the standard energy release rate of the battery pack; This indicates the measured adjustment error of the photovoltaic module; Indicates the allowable adjustment error of photovoltaic modules; The weighting factor represents the optoelectronic indicators; The weighting factor represents the energy storage-side indicators; This represents the weighting factor of the joint frequency modulation index.

[0086] S44. Monitor online data during the joint frequency regulation strategy operation of photovoltaic and energy storage power stations in real time, input the online data into the comprehensive regulation performance index model, and dynamically evaluate the comprehensive performance of photovoltaic and energy storage power stations.

[0087] Based on the method of this disclosure, embodiments of this disclosure also provide a system corresponding to the above method (such as...). Figure 2 As shown), it includes: The data monitoring module is used to collect operational data from the photovoltaic-storage power station. The state prediction module is used to establish an equivalent model of photovoltaic energy storage based on the physical characteristics of photovoltaic modules, battery packs and inverters to build an energy storage prediction model. The module inputs the operating data of the photovoltaic-energy storage power station into the energy storage prediction model and outputs the energy storage state of charge prediction data before and after the battery pack executes the automatic power generation control command. The strategy matching module is used to match frequency regulation strategies based on energy storage state of charge prediction data and output frequency regulation control commands for photovoltaic-storage power stations. The performance evaluation module is used to collect the performance indicators of the photovoltaic-storage power station during the execution of frequency regulation control commands, input them into the comprehensive regulation performance indicator model, and output the comprehensive regulation performance indicator for evaluating the performance of the photovoltaic-storage power station. The comprehensive regulation performance index model is as follows:

[0088] In the formula, Z represents the comprehensive regulation performance index; This indicates the measured power generation rate of the photovoltaic module; This represents the average standard regulation rate of photovoltaic modules under ideal baseline conditions across all operating scenarios covered by the frequency regulation strategy. Indicates the measured response delay time of the photovoltaic module; Indicates the standard delay time of photovoltaic modules; This indicates the measured energy absorption rate of the battery pack; This indicates the standard energy absorption rate of the battery pack. This indicates the measured energy release rate of the battery pack; This indicates the standard energy release rate of the battery pack; This indicates the measured adjustment error of the photovoltaic module; Indicates the allowable adjustment error of photovoltaic modules; The weighting factor represents the optoelectronic indicators; The weighting factor represents the energy storage-side indicators; This represents the weighting factor of the joint frequency modulation index.

[0089] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method of evaluating performance of a light storage plant, characterized by, The method comprises the following steps: Collecting operation data of the photovoltaic storage power station; Based on the physical characteristics of the photovoltaic module, the battery pack and the inverter, an equivalent model of photovoltaic storage is established to construct a storage prediction model, the operation data of the photovoltaic storage power station is input into the storage prediction model, and the storage state of charge prediction data before and after the battery pack executes the automatic generation control instruction is output; Based on the storage state of charge prediction data, a frequency modulation strategy is matched, and a frequency control instruction of the photovoltaic storage power station is output; In the execution process of the frequency control instruction, the performance index of the photovoltaic storage power station is collected and input into a comprehensive adjustment performance index model, and a comprehensive adjustment performance index for evaluating the performance of the photovoltaic storage power station is output; The comprehensive adjustment performance index model is: In the formula, Z represents a comprehensive adjustment performance index; represents the measured power generation rate of the photovoltaic assembly; represents the average standard adjustment rate of the photovoltaic assembly under the ideal reference state under all operating scenarios covered by the frequency modulation strategy; represents the measured response delay time of the photovoltaic assembly; represents the standard delay time of the photovoltaic assembly; represents the measured energy absorption rate of the battery pack; represents the standard energy absorption rate of the battery pack; represents the measured electric energy release rate of the battery pack; represents the standard electric energy release rate of the battery pack; represents the measured adjustment error of the photovoltaic assembly; represents the allowed adjustment error of the photovoltaic assembly; represents the weight factor of the photovoltaic side index; represents the weight factor of the energy storage side index; represents the weight factor of the joint frequency modulation index.

2. The method for evaluating performance of a light storage power station according to claim 1, wherein, The construction of the comprehensive adjustment performance index model comprises: Setting a primary performance index, the primary performance index comprises a photovoltaic side index, a storage side index and a joint frequency modulation index; Setting secondary indexes contained in each primary performance index, the secondary indexes comprise: The secondary indexes corresponding to the photovoltaic side index: the actual power generation rate of the photovoltaic module, the actual response delay time of the photovoltaic module and the actual adjustment error of the photovoltaic module; The secondary indexes corresponding to the storage side index: the actual energy absorption rate of the battery pack and the actual electric energy release rate of the battery pack; Using a grey correlation decision matrix, weight factors of each primary performance index and secondary performance index are obtained, and a comprehensive adjustment performance index model is constructed based on the weight factors.

3. The method of claim 1, wherein the method further comprises: The construction of the storage prediction model comprises: Based on the volt-ampere characteristic curve of the photovoltaic module, an equivalent model of photovoltaic power generation is established; Based on the charging and discharging characteristics, cycle life and health state of the battery pack, an equivalent model of the storage circuit of the battery pack is established; Based on the conversion efficiency and loss of the inverter, an efficiency equivalent model of the inverter is established; After the photovoltaic power generation model, the storage circuit equivalent model and the efficiency equivalent model are combined as the equivalent model of photovoltaic storage of the photovoltaic storage power station, the equivalent model is imported into a simulation environment for simulation; Based on the simulation results of the storage capacity and the charging and discharging power distribution of the battery pack, a storage prediction model is established, and the storage prediction model is used to predict the storage state of charge of the battery pack before and after the execution of the automatic generation control instruction.

4. The method of claim 3, wherein the method further comprises: The establishment of the storage prediction model based on the simulation results of the storage capacity and the charging and discharging power distribution of the battery pack comprises: Obtaining the initial storage capacity, the state of charge attenuation parameter, the storage discharge mileage and the total storage mileage of the battery pack, and calculating the real-time storage capacity of the battery pack; Monitoring the automatic generation control instruction, after the battery pack receives the automatic generation control instruction, the real-time residual capacity of the battery pack is calculated, and based on the upper and lower threshold values of the storage, the battery cells in the battery pack that meet the upper and lower threshold values are screened out; Taking the screened battery cells as the output main body, the residual state of charge of each battery cell is used for single battery power distribution, and a power distribution ratio is obtained; The real-time storage capacity of the battery pack, the information of the battery cells meeting the upper and lower threshold values and the power distribution ratio are input into the equivalent model of photovoltaic storage, the response time of the photovoltaic module and the battery pack after receiving the automatic generation control instruction is obtained through simulation simulation, and based on the power difference caused by the response time difference, a storage prediction model of the battery pack is established; An expression of the energy storage prediction model is: In the formula, represents the state of charge of the energy storage at the beginning of the response of the i-th battery cell; represents the state of charge of the energy storage at the end of the automatic control command of the i-th battery cell; represents the state of charge of the energy storage at the predicted time t of the i-th battery cell; represents the cell power of the i-th battery cell at the predicted time t; represents the number of battery cells participating in the current power output in the battery pack; represents the cell power command of the i-th battery cell; represents the state of charge of the j-th battery cell; represents the state of charge of the i-th battery cell; represents the real-time energy storage capacity of the battery pack; represents the response time of the photovoltaic module; represents the response speed of the photovoltaic module.

5. The method for evaluating performance of a light storage power station according to claim 4, wherein, A calculation formula of the real-time energy storage capacity is: wherein, represents the real-time energy storage capacity of the battery pack; M represents the energy storage discharge range of the battery pack; represents the total energy storage range of the battery pack; represents the initial energy storage capacity of the battery pack; β represents the state of charge decay parameter.

6. The method of claim 1, wherein, The frequency modulation strategy is matched based on the prediction data of the energy storage state of charge, and a frequency modulation control instruction of the light storage power station is outputted; the prediction data of the energy storage state of charge of the battery pack and the battery cell is acquired, and whether the energy storage state of charge of the battery cell exists an out-of-limit phenomenon is detected based on the prediction data; If the out-of-limit phenomenon exists, a life cycle cost of the light storage power station is acquired, the frequency modulation demand is taken as an opportunity constraint, a minimum operation cost is taken as an optimization target, and a joint frequency modulation optimization model is established; An optimal solution of the joint frequency modulation optimization model is solved by using the lion-ant optimization algorithm, and is taken as a joint frequency modulation strategy under a current automatic generation control instruction; Based on the joint frequency modulation strategy, a joint frequency modulation control instruction is sent to the battery pack. The joint frequency modulation optimization model is established; the establishment includes:

7. The method of claim 6, wherein the method further comprises: A generation unit cost of the photovoltaic module, a unit cost of the battery pack, an operation and maintenance cost and a battery replacement cost in an operation process of the light storage power station are acquired; A photovoltaic generation output power, a battery pack output power and an energy storage state of charge of the battery pack in a frequency modulation process of the light storage power station are collected; The joint frequency modulation optimization model is established by taking the frequency modulation demand as an opportunity constraint and taking the minimum operation cost as an optimization target, and an expression of the joint frequency modulation optimization model is: In the lion-ant optimization algorithm, wherein, represents a photovoltaic power generation unit cost of the photovoltaic module; represents a storage battery pack unit cost of the storage battery pack; represents an operation and maintenance cost; represents a storage battery pack replacement cost; L represents a life cycle of the storage battery pack; and T represents a total number at time t; represents a photovoltaic power generation output power at time t; represents a storage battery pack output power at time t; represents a grid load demand at time t; represents a photovoltaic system efficiency; represents a storage battery pack charge and discharge efficiency; represents an inverter efficiency; represents a violation probability of frequency modulation demand.

8. The method of claim 1, wherein, A population size of the lion and the ant respectively corresponds to a number of candidate strategies and a number of working condition verification samples; A position of the ant and the lion respectively corresponds to an operation working condition sample parameter of a single verification candidate strategy and a specific decision variable value of a single set of candidate joint frequency modulation strategies; Each ant corresponds to a working condition sample, and each lion corresponds to a frequency modulation strategy. The data monitoring module is used for collecting operation data of the light storage power station; 9. A method system for performance evaluation of a solar power plant, characterized by, The state prediction module is used for establishing a photovoltaic energy storage equivalent model based on physical characteristics of the photovoltaic module, the battery pack and the inverter to construct an energy storage prediction model, inputting the operation data of the light storage power station into the energy storage prediction model, and outputting prediction data of the energy storage state of charge of the battery pack before and after the automatic generation control instruction is executed; The strategy matching module is used for matching the frequency modulation strategy based on the prediction data of the energy storage state of charge, and outputting the frequency modulation control instruction of the light storage power station; The performance evaluation module is used for collecting a performance index of the light storage power station in an execution process of the frequency modulation control instruction, inputting the performance index into a comprehensive adjustment performance index model, and outputting an evaluation comprehensive adjustment performance index of the performance of the light storage power station; The comprehensive adjustment performance index model is: An expression of the energy storage prediction model is: ​ In the formula, Z represents a comprehensive adjustment performance index; represents the measured power generation rate of the photovoltaic assembly; represents the average standard adjustment rate of the photovoltaic assembly under the ideal reference state under all operating scenarios covered by the frequency modulation strategy; represents the measured response delay time of the photovoltaic assembly; represents the standard delay time of the photovoltaic assembly; represents the measured energy absorption rate of the battery pack; represents the standard energy absorption rate of the battery pack; represents the measured electric energy release rate of the battery pack; represents the standard electric energy release rate of the battery pack; represents the measured adjustment error of the photovoltaic assembly; represents the allowed adjustment error of the photovoltaic assembly; represents the weight factor of the photovoltaic side index; represents the weight factor of the energy storage side index; represents the weight factor of the joint frequency modulation index.

10. The system for evaluating performance of a photovoltaic power station according to claim 9, wherein, ​ In the formula, represents the state of charge of the i-th battery cell at the beginning of the response; represents the state of charge of the i-th battery cell at the end of the automatic control instruction; represents the state of charge of the i-th battery cell at the predicted time t; represents the cell power of the i-th battery cell at the predicted time t; represents the number of battery cells participating in the current power output in the battery pack; represents the battery cell power instruction of the i-th battery cell; represents the state of charge of the j-th battery cell; represents the state of charge of the i-th battery cell; represents the real-time energy storage capacity of the battery pack; represents the response time of the photovoltaic module; represents the response speed of the photovoltaic module.

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