Multi-scenario configuration method and device under renewable energy, equipment and storage medium

Abstract

The application discloses a renewable energy under multiple scenarios Configuration method, device and equipment and storage medium, the method comprises: determining the fluctuation range of wind power output under the preset signal level, determining the expected value of wind power fluctuation rate; with reducing the abandoned wind rate as the main target, the objective function is constructed; the weight coefficient of the abandoned wind rate in the objective function is determined based on the expected value; the optimization configuration model is constructed with the rated power constraint, the rated capacity constraint and the like as the constraint conditions; the rated power constraint and the rated capacity constraint are determined based on the fluctuation range; the model is solved to obtain the energy storage power and capacity configuration scheme; the application scenarios are divided, and the application scenarios are simulated, and the energy storage power and capacity configuration scheme are adjusted based on the operation effect index. Therefore, the scientificity of value evaluation can be balanced by multiple factors, the energy storage system can be reasonably configured, the fluctuation of wind power output can be better matched, the wind power utilization rate is improved, and energy waste is reduced.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of energy storage system value evaluation and configuration, and particularly relate to a multi-scenario configuration method and device under renewable energy, equipment and storage medium. BACKGROUND

[0002] In terms of energy storage system value evaluation and configuration, traditional methods are often based on deterministic models, and simply plan the power and capacity of the energy storage system according to the average output of wind power or the maximum and minimum historical output. For example, the power demand of the energy storage system is determined by the difference between the maximum output of wind power and the maximum accommodation capacity of the power grid. This approach can easily lead to unreasonable configuration of the power and capacity of the energy storage system. Or in the traditional method, a single target is used to evaluate the energy storage system or it is difficult to maximize the comprehensive benefits of the energy storage system in multi-target evaluation.

[0003] In summary, the prior art has defects in the scientificity of value evaluation and the rationality of configuration of the energy storage system. SUMMARY

[0004] Embodiments of the present application provide a multi-scenario configuration method and device under renewable energy, equipment and storage medium, which can balance multiple factors to achieve scientific value evaluation, improve power system stability and reliability, and ensure safe operation of the power system. The energy storage system can be reasonably configured, the fluctuation of wind power output can be better matched, the utilization rate of wind power can be improved, and energy waste can be reduced.

[0005] In a first aspect, embodiments of the present application provide a multi-scenario configuration method under renewable energy, comprising:

[0006] determining the fluctuation range of wind power output under a preset confidence level based on a probability distribution model of wind power output, and determining the expected value of the wind power output fluctuation rate based on the fluctuation range;

[0007] constructing a weighted combination function of the value evaluation of the energy storage system as a target function, with the main target of reducing the wind curtailment rate, and combining the investment cost and operation cost of the energy storage system and the stability benefit of the power system; wherein the weight coefficient of the wind curtailment rate in the target function is determined based on the expected value;

[0008] constructing an optimal configuration model with the target function as a constraint condition, with the constraints of charge and discharge power, remaining capacity, power system operation, rated power and rated capacity; wherein the rated power and rated capacity constraints are determined based on the fluctuation range;

[0009] solving the optimal configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system;

[0010] The application scenarios are divided based on wind power penetration, load characteristics and electricity price mechanism, each application scenario is simulated by using the optimization configuration model, and an operation effect index is calculated, and the energy storage power and capacity configuration scheme is adjusted based on the operation effect index.

[0011] In a second aspect, the embodiments of the present application provide a configuration device for multiple scenarios under renewable energy, including:

[0012] A determination module is configured to determine a fluctuation range of wind power output under a preset confidence level based on a probability distribution model of wind power output, and determine an expected value of wind power output fluctuation rate based on the fluctuation range.

[0013] A target function determination module is configured to construct a weighted combination function of energy storage system value evaluation as a target function by taking reducing curtailment rate as a main target and combining investment cost and operation cost of the energy storage system and stability benefit of the power system, wherein a weight coefficient of the curtailment rate in the target function is determined based on the expected value.

[0014] A model construction module is configured to construct an optimization configuration model by combining the target function and taking charge and discharge power constraints, residual capacity constraints, power system operation constraints, rated power constraints and rated capacity constraints as constraint conditions, wherein the rated power constraints and the rated capacity constraints are determined based on the fluctuation range.

[0015] A solution module is configured to solve the optimization configuration model to obtain an energy storage power and capacity configuration scheme of the energy storage system.

[0016] An adjustment module is configured to divide application scenarios based on wind power penetration, load characteristics and electricity price mechanism, simulate each application scenario by using the optimization configuration model, calculate an operation effect index, and adjust the energy storage power and capacity configuration scheme based on the operation effect index.

[0017] In a third aspect, the embodiments of the present application provide an electronic device, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the method provided by the embodiments of the present application.

[0018] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and when the computer program is executed in a computer, the computer executes the method provided by the embodiments of the present application.

[0019] The technical solution provided in this application determines the fluctuation range of wind power output under a preset confidence level through a probability distribution model of wind power output. This range is then used to determine the expected value of the wind power output volatility. With reducing wind curtailment as the primary objective, a weighted combination function for evaluating the value of the energy storage system is constructed, considering the investment cost, operating cost, and stability benefits of the power system. This function serves as the objective function, and is constrained by charging / discharging power constraints, remaining capacity constraints, power system operation constraints, rated power constraints, and rated capacity constraints. Combining these constraints, an optimal configuration model is constructed to solve for the energy storage power and capacity configuration scheme. This approach balances multiple factors to achieve scientific value evaluation, maximizes the comprehensive benefits of the energy storage system, and allows for reasonable configuration of the energy storage system. Specifically, the rated power constraints and the rated capacity constraints... The objective function is determined based on the expected value of wind power output volatility. The weighting coefficient of the wind curtailment rate is based on the fluctuation range. By considering the fluctuation range and the expected value of volatility, a precise basis can be provided for the power and capacity configuration of the energy storage system, effectively addressing the uncertainty of wind power output, thereby reducing the wind curtailment rate, improving wind power utilization, and reducing energy waste. Application scenarios are divided based on wind power penetration, load characteristics, and electricity pricing mechanisms. An optimized configuration model is used to simulate each application scenario and calculate operational performance indicators. Adjusting the energy storage power and capacity configuration scheme based on these indicators enables value assessment and targeted configuration optimization of the energy storage system in different scenarios. This improves the applicability and economy of the energy storage system under diverse operating conditions, achieving optimized configuration and efficient utilization of the energy storage system in the wind power environment. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a multi-scenario configuration method for renewable energy provided for the implementation of this application;

[0021] Figure 2 A structural block diagram of a configuration device for multiple scenarios under renewable energy provided for the implementation of this application;

[0022] Figure 3 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0023] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Figure 1 This is a flowchart of a configuration method for multiple scenarios under renewable energy provided in an embodiment of this application. The method can be executed by a configuration device for multiple scenarios under renewable energy, and the device can be configured in an electronic device such as a computer.

[0025] like Figure 1As shown, the method provided in this application embodiment may include the following steps:

[0026] S110: Determine the fluctuation range of wind power output under a preset confidence level based on the probability distribution model of wind power output, and determine the expected value of wind power output volatility based on the fluctuation range.

[0027] In this embodiment, a large amount of historical wind power output data and corresponding meteorological data such as wind speed, wind direction, and temperature can be collected. The data time granularity can be set to 15 minutes, 30 minutes, or 1 hour, etc., according to actual needs. Let the collected wind power output data sequence be... ,in Indicates time Wind power output, This represents the number of time points corresponding to the total data duration; the wind speed data sequence is... The data is cleaned to remove outliers and missing values. For missing values, linear interpolation is used. Assume that at time [time value missing]... arrive There are missing wind power output data, it is known. The wind power output at any given time is , The wind power output at any given time is Then the missing moment ( Wind power output It can be calculated using the following formula:

[0028] ;

[0029] Therefore, by ensuring the integrity and accuracy of the data, a reliable data foundation is provided for the establishment of the probability distribution model of wind power output, avoiding inaccurate analysis of wind power output characteristics due to data problems.

[0030] In this embodiment, the maximum likelihood estimation method can be used to analyze the processed data and determine the probability distribution characteristics of wind power output. Assuming that wind power output follows a Weibull distribution, its probability density function is:

[0031] ;

[0032] in, To provide power for wind power, For shape parameters, The scaling parameter is obtained by the maximum likelihood estimation method. and Likelihood function for:

[0033] ;

[0034] To facilitate the solution, we take the logarithm of the likelihood function to obtain the log-likelihood function. :

[0035] ;

[0036] Among them, respectively for and Find the partial derivatives, set them to zero, and solve the system of equations to obtain... and The estimated value is obtained, thus the probability distribution model of wind power output is obtained. By determining the probability distribution model, the uncertainty law of wind power output can be described, and a mathematical basis can be provided for accurately calculating the expected value of wind power output volatility.

[0037] In this embodiment, the concept of confidence level can be introduced, and combined with a probability distribution model, the fluctuation range of wind power output under a preset confidence level can be calculated, thereby obtaining the expected value of the wind power output volatility. Taking a 95% confidence level as an example, let... and These represent the lower and upper limits of wind power output, respectively, obtained by integrating the probability density function of the Weibull distribution:

[0038] ;

[0039] The above equations can be solved using numerical integration methods to obtain... and .

[0040] In this embodiment, the expected value of the wind power output volatility is determined based on the following formula:

[0041] ;

[0042] in, and These are the lower limit and the upper limit of the wind power output, respectively; This represents the expected value of the wind power output volatility. This represents the average value of the wind power output;

[0043] in, ,in, Indicates time The wind power output; N is the number of time points corresponding to the total duration of wind power output.

[0044] Therefore, by determining the fluctuation range of wind power output under a pre-set information level, the uncertainty of wind power output is quantified. Compared with traditional methods, the uncertainty of wind power output is grasped more accurately, providing a reliable basis for the optimal configuration of energy storage systems and enabling energy storage systems to better adapt to the fluctuation of wind power output.

[0045] S120: With the primary objective of reducing wind curtailment rate, a weighted combination function for evaluating the value of the energy storage system is constructed by combining the investment cost, operating cost, and stability benefits of the power system, and this function serves as the objective function; wherein, the weighting coefficient of wind curtailment rate in the objective function is determined based on the expected value of the wind power output volatility.

[0046] In this embodiment, the objective function is:

[0047] ;

[0048] in, Wind curtailment rate; Investment cost for energy storage systems; For the operating costs of energy storage systems; For the stability benefits of the power system; , , , Here, are the corresponding weighting coefficients, , , , The value of F can be determined comprehensively based on the needs of specific application scenarios, system optimization objectives, the balance between economy and safety, and engineering experience and policy verification. This allows the objective function F to accurately reflect the comprehensive benefits of energy storage systems under different scenarios.

[0049] In this embodiment, optionally, the weighting coefficient of the wind curtailment rate in the objective function is determined based on the expected value of the wind power output volatility, including: if the expected value is less than a first preset volatility threshold, the weighting coefficient of the wind curtailment rate is the first preset weighting coefficient; if the expected value is between the first preset volatility threshold and a second preset volatility threshold, the weighting coefficient of the wind curtailment rate is the second preset weighting coefficient; if the expected value is greater than the second preset volatility threshold, the weighting coefficient of the wind curtailment rate is a third preset weighting coefficient; wherein, the second preset volatility threshold is greater than the first preset volatility threshold, and the third preset weighting coefficient is greater than the second preset weighting coefficient; the second preset weighting coefficient is greater than the first preset weighting coefficient. For example, the first preset volatility threshold can be 0.4, and the second preset volatility threshold can be 0.6. For example, if the expected value is greater than 0.6, This can be considered a high-volatility scenario, such as when the fluctuation range exceeds 60% of the average at a 95% confidence level, indicating a higher risk of wind curtailment. Therefore, the weight of the wind curtailment rate should be increased. (For example, increasing from 0.4 to 0.5), b=0.25, c=0.15, d=0.1.

[0050] For example, if the expected value is between 0.4 and 0.6, i.e. 0.4 ≤ A value ≤0.6 can be considered a medium volatility scenario, where wind power output fluctuates moderately (fluctuation range is 40%~60% of the average), requiring a balance between curtailment rate and cost control. Among these, a=0.4 (weighting coefficient for curtailment rate): curtailment rate remains the core objective, but its weight is slightly lower in a high volatility scenario; b=0.3 (weighting coefficient for investment cost): cost control takes priority; c=0.2 (weighting coefficient for operating cost): together with investment cost, it constitutes a cost constraint; d=0.1 (weighting coefficient for stability benefits): maintaining the basic weight to ensure system stability.

[0051] For example, if the expected value is less than 0.4, that is This can be considered a low-volatility scenario with low wind curtailment risk and smooth fluctuations in wind power output (fluctuation range less than 40% of the average). Cost control becomes the core objective. Therefore, a=0.3 (weighting coefficient for wind curtailment rate): the weighting coefficient can be further reduced because the small fluctuation makes the wind curtailment rate easy to control; b=0.35 (weighting coefficient for investment cost): investment cost has the highest weight to avoid over-allocation; c=0.25 (weighting coefficient for operating cost): together with investment cost, it dominates cost constraints; d=0.1 (weighting coefficient for stability benefits): maintain the basic weight to ensure basic stable demand.

[0052] In this embodiment, ;in, The unit power investment cost, The rated power of the energy storage system is [missing information]. The unit capacity investment cost The rated capacity of the energy storage system is given.

[0053] In this embodiment, ; Cost per unit of charging For unit discharge cost, and They are time points The charging and discharging power of the energy storage system. Among them, ;in, and These are the weighting coefficients for the improvement of voltage deviation and frequency deviation, respectively. and The time intervals before and after the energy storage system is put into operation are respectively: Voltage deviation, and The time intervals before and after the energy storage system is put into operation are respectively: Frequency deviation. Among them, the weighting coefficients for voltage deviation and frequency deviation improvement ( and This is a scenario adaptability parameter, and its value is primarily based on the power system's sensitivity to voltage / frequency stability. For example, in scenarios with high wind power penetration, wind power output fluctuates drastically, making system active power balancing difficult and frequency deviations more likely to exceed limits. Usually, the value is higher (e.g.) =0.6, =0.4).

[0054] S130: An optimization configuration model is constructed using charging and discharging power constraints, remaining capacity constraints, power system operation constraints, rated power constraints, and rated capacity constraints as constraints, combined with the objective function; wherein, the rated power constraints and the rated capacity constraints are determined based on the fluctuation range.

[0055] In this embodiment, the charging and discharging power constraint is that the charging and discharging power of the energy storage system cannot exceed its rated power limit, that is:

[0056] ;

[0057] ;

[0058] in, and They are time points The charging and discharging power of the energy storage system , , , These are the lower limit for charging power, the upper limit for charging power, the lower limit for discharging power, and the upper limit for discharging power of the energy storage system. This constraint ensures that the energy storage system operates within a safe power range, preventing damage to equipment or safety accidents caused by excessive power.

[0059] In this embodiment, the remaining capacity constraint is that the remaining capacity of the energy storage system must be kept within a reasonable range to avoid overcharging or over-discharging, that is:

[0060] ;

[0061] in, For a moment The state of charge of an energy storage system is calculated using the following formula:

[0062] ;

[0063] in, For charging efficiency, For discharge efficiency, For time intervals, and These are the lower limit and upper limit of the state of charge, respectively. This constraint ensures the lifespan and safety of the energy storage system and prevents irreversible damage to the energy storage equipment caused by overcharging and discharging.

[0064] In this embodiment, power system operation constraints ensure that the integration of the energy storage system does not affect the normal operation of the power system; for example, node voltage and system frequency must meet relevant standards. For instance, node voltage must meet...

[0065] ;

[0066] The system frequency must meet the following requirements:

[0067] ;

[0068] in, For a moment node voltage, For a moment The system frequency, , , , These are the lower limit of node voltage, the upper limit of node voltage, the lower limit of system frequency, and the upper limit of system frequency. This constraint ensures that the operation of the energy storage system will not have a negative impact on the stability and reliability of the power system, thus guaranteeing the safe and stable operation of the power system.

[0069] In this embodiment, the rated power constraint and the rated capacity constraint are determined based on the fluctuation range, including: determining a minimum power threshold based on the fluctuation range, and setting the rated power of the energy storage system to be no less than the minimum power threshold; and determining a minimum capacity threshold based on the fluctuation range and the duration of the fluctuation range, and setting the rated capacity of the energy storage system to be no less than the minimum capacity threshold. Specifically, the minimum power threshold... (For example, if the fluctuation range is 300kW, the rated power must be ≥300kW). The fluctuation range, combined with the fluctuation duration (obtained through volatility and historical data statistics, such as 4 consecutive hours of high volatility), can be used to calculate the minimum capacity threshold. (e.g., 300kW×4h=1200kWh), the rated capacity of the energy storage system shall not be less than the minimum capacity threshold, and the rated power shall not be less than the minimum power threshold.

[0070] S140: Solve the optimized configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system.

[0071] In this embodiment, solving the optimization configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system includes: using the particle swarm optimization algorithm to solve the optimization configuration model to obtain the rated power and rated capacity configuration scheme of the energy storage system.

[0072] Specifically, in the particle swarm optimization algorithm, each particle represents a set of energy storage power and capacity configuration schemes (configuration schemes of rated power and rated capacity), that is, the particle's position vector. ,in, Indicates the first There are 10 particles. The velocity vector of each particle is... The historical best position for each particle is... The historical best position for the entire group is .

[0073] In each iteration, the particle velocity update formula is:

[0074] ;

[0075] ;

[0076] in, Inertial weights are used to balance global and local search capabilities; and This is the learning factor, which is usually a constant greater than 0; and In order to be in A random number between [a certain number of points]. and Let be the velocity vector values ​​of the i-th particle at time t+1 in the direction of rated power and the direction of rated capacity, respectively. and Let be the velocity vector values ​​of the i-th particle at time t in the rated power direction and the rated capacity direction, respectively. and Let t represent the optimal vector values ​​of the entire population in the rated power direction and rated capacity direction, respectively; and These are the optimal vector values ​​for the i-th particle in the rated power direction and the rated capacity direction, respectively.

[0077] The particle position update formula is:

[0078] ;

[0079] ;

[0080] in, and These are the position vector values ​​of the particle in the rated power direction at time t and time t+1, respectively; and Let t and t+1 represent the position vector values ​​of the particles in the rated capacity direction, respectively. During the process of solving the optimal configuration model using the particle swarm optimization algorithm, the initial particle positions are based on the minimum power threshold and the minimum capacity threshold. If the fluctuation rate of wind power output is greater than a first preset fluctuation rate threshold, the updated particle positions are shifted by increasing the first preset percentage (e.g., 300kW-330kW) based on the baseline. If the fluctuation rate of wind power output is greater than a second preset fluctuation rate threshold, the updated particle positions are shifted by increasing the second preset percentage (300kW-360kW) based on the baseline. The second preset percentage is greater than the first preset percentage. The second preset percentage can be +20%, and the first preset percentage can be +10%. This ensures that the initial solution covers the reasonable configuration range corresponding to the fluctuation rate, reducing the number of algorithm iterations.

[0081] After each position update, the fitness value of the particle (i.e., the objective function value) is calculated based on the objective function and constraints, and the historical best position of the particle and the historical best position of the swarm are updated. After multiple iterations, the optimal solution that minimizes the objective function is found, i.e., the best energy storage power and capacity configuration scheme. The particle swarm optimization algorithm can efficiently search for the optimal energy storage configuration scheme in a complex solution space, improving the scientificity and rationality of energy storage system configuration. Thus, by constructing a multi-factor objective function, setting constraints, and solving using the particle swarm optimization algorithm, an optimized energy storage power and capacity configuration scheme is obtained. This scheme can better match wind power output fluctuations, significantly reduce wind curtailment rates and improve wind power utilization in different scenarios, reduce energy waste, and, considering power system operation constraints, can quickly adjust power during wind power output fluctuations to maintain power system power balance, reduce voltage and frequency fluctuations, improve power system stability and reliability, and ensure the safe operation of the power system.

[0082] S150: Based on wind power penetration rate, load characteristics, and electricity pricing mechanism, application scenarios are divided. The optimized configuration model is used to simulate each application scenario and calculate the operation performance index. Based on the operation performance index, the energy storage power and capacity configuration scheme is adjusted.

[0083] In this embodiment, wind power penetration rate includes high, medium, and low wind power penetration rates; load characteristics include high load peak-valley difference and stable load; electricity pricing mechanism includes time-of-use pricing mechanism and fixed pricing mechanism; wherein, when the peak-valley load ratio is greater than 2, the load characteristic is high load peak-valley difference; when the load fluctuation coefficient is less than or equal to 0.1, the load characteristic is stable load; the application scenario is determined by selecting any one of the following sub-items from the wind power penetration rate, the load characteristics, and the electricity pricing mechanism and combining them.

[0084] In this embodiment, the wind power penetration rate is set as follows: ,in For a moment The load power. For example, scenario one is high wind power penetration ( Peak-valley difference (peak-valley load ratio) Scenario 1 is a time-of-use electricity pricing scenario; Scenario 2 is for medium wind power penetration ( Stable load (load fluctuation coefficient) ), fixed electricity price scenarios, etc. For different scenarios, the value assessment and configuration optimization of energy storage systems are carried out separately. Scenario classification takes into account the impact of different actual operating conditions on energy storage systems, making energy storage system configuration more targeted and practical.

[0085] During the simulation, historical wind power output data sequences were used. Load data And scenario-specific data (such as electricity prices for different time periods under time-of-use pricing scenarios) The energy storage power and capacity configuration scheme obtained through the particle swarm optimization algorithm is input into the optimization configuration model to simulate the scenario and calculate the operation performance indicators.

[0086] Among them, high wind power penetration ( Peak-valley difference (peak-valley load ratio) Taking the time-of-use pricing scenario as an example, the specific simulation and evaluation process is as follows: The rated power of the energy storage system obtained by the particle swarm optimization algorithm is used... and rated capacity The configuration scheme is substituted into the model. Specifically, in each time interval... Internally, based on the wind power output at time t Load power and the state of charge of the energy storage system Based on charging and discharging power constraints, remaining capacity constraints, and power system operation constraints, the charging and discharging strategy of the energy storage system is determined. During periods of low load and excess wind power output (… and The energy storage system operates at maximum charging power. Charging; during peak load periods ( and The energy storage system discharges at maximum power. Discharge is performed.

[0087] In this embodiment, the operational performance indicators may include wind curtailment rate, economic benefits, and power system stability benefits. Optionally, adjusting the energy storage power and capacity configuration scheme based on the operational performance indicators includes: if the wind curtailment rate does not meet the first preset requirement, increasing the rated power or rated capacity of the energy storage system; if the economic benefits do not meet the second preset requirement, increasing the rated capacity of the energy storage system; if the power system stability benefits do not meet the third preset requirement, increasing the rated power or rated capacity of the energy storage system.

[0088] Specifically, it can be done according to the formula The system calculates the wind curtailment rate for each time period in real time and accumulates the wind curtailment rate for the entire simulation cycle. ; Let t be the amount of wind power that the power grid can absorb at time t.

[0089] If the wind curtailment rate during the entire simulation cycle does not meet the first preset requirement, then the rated power or rated capacity of the energy storage system should be increased. Specifically, for instantaneous wind curtailment (high frequency, short duration): this indicates insufficient energy storage power to cope with the sudden increase in wind power. The rated power should be adjusted to ≥ the historical maximum instantaneous wind curtailment power (e.g., from 300kW to 400kW), while complying with grid connection restrictions and ensuring cost control. For continuous wind curtailment (low frequency, long duration): this indicates insufficient energy storage capacity to absorb continuous excess wind power. The rated capacity should be adjusted to ≥ the longest curtailment duration × average curtailment power (e.g., 5 hours × 300kW = 1500kWh) to ensure a reasonable state of charge.

[0090] Specifically, the economic benefits of energy storage systems include revenue from peak-valley electricity price differences and the potential benefits from reduced wind curtailment. Peak-valley electricity price differences... The calculation formula is: .

[0091] in, For a moment The discharge electricity price, For a moment The charging electricity price; the potential benefits of reducing wind curtailment Based on the current wind power feed-in tariff And the calculation of wind curtailment, i.e. Then the total economic benefits Specifically, if the economic benefits do not meet the second preset requirements, for example, if the economic benefits are low, it may be due to insufficient capacity leading to insufficient peak discharge. In this case, the rated capacity can be increased to cover the peak discharge gap. If the cost is high (the ratio of investment cost to total economic benefits exceeds 5), it is generally due to power / capacity redundancy, which results in wasted investment. In this case, redundancy can be reduced by decreasing the rated capacity of energy storage and retaining 10%-17% redundancy to cope with fluctuations.

[0092] Specifically, the calculation of the stability benefits of a power system can be performed according to the formula. Calculate the stability benefits brought about by the improvement in voltage and frequency deviations before and after the energy storage system is put into operation. Specifically, if the stability benefits of the power system do not meet the third preset requirement, increase the rated power or rated capacity of the energy storage system. Specifically, if the instantaneous deviation is large: insufficient power, unable to respond quickly to voltage / frequency fluctuations. Adjust the rated power of the energy storage to ≥ the required regulation power to ensure millisecond-level response. If the continuous regulation is weak: insufficient energy storage capacity, difficult to maintain long-term stability. Adjust the rated capacity of the energy storage to ≥ the continuous regulation power × duration to ensure a state of charge ≥ 20%. Specifically, when the power system experiences instantaneous voltage deviations from the rated value or frequency deviations from the standard value due to wind power output fluctuations, the energy storage system needs to compensate for these deviations through rapid charging and discharging to restore the voltage and frequency to the allowable range. This power regulation amount necessary to control the deviation within the safety threshold is called the "required regulation power." When the power system experiences continuous voltage deviations or frequency fluctuations, the energy storage system needs to continuously charge and discharge for a continuous period to compensate for the deviations; the "duration" is the time span required for this continuous regulation process.

[0093] By adjusting the rated power and rated capacity of the energy storage system and substituting them into the optimization configuration model, it is possible to retest whether the operating performance indicators meet the requirements. If there are conflicts in the operating performance indicators, the parameters of rated power and rated capacity can be fine-tuned to find a balance point.

[0094] For other scenarios, such as moderate wind power penetration ( Stable load (load fluctuation coefficient) In the fixed-price scenario, the simulation analysis follows the same process of data input, indicator calculation, and scheme adjustment and optimization. However, in this scenario, due to stable load and fixed electricity price, the main role of the energy storage system is to smooth wind power output fluctuations and maintain power system balance. Therefore, the charging and discharging strategies and benefit assessment focus differ from those in scenarios with high wind power penetration, high load peak-valley differences, and time-of-use pricing. Through detailed simulation and evaluation of each scenario, a comprehensive understanding of the operational performance and value of the energy storage system under different conditions can be achieved, enabling optimized configuration and efficient utilization of the energy storage system in high-penetration wind power environments.

[0095] It should be noted that the rated power and rated capacity configuration scheme obtained through the particle swarm optimization algorithm convergence ( , After that, the scheme can be simulated by optimizing the configuration model. It operates within the corresponding fluctuation range, and checks whether the wind curtailment rate is less than a preset value, which can be 10%. If the wind curtailment rate does not meet the standard, the minimum capacity threshold and the minimum power threshold can be adjusted until the rated power and rated capacity configuration scheme are adapted to the fluctuation range of wind power output.

[0096] It should be noted that the renewable energy in this application mainly involves wind power, and high penetration rate mainly refers to scenarios where the wind power penetration rate is greater than or equal to 30%.

[0097] The technical solution provided in this application determines the fluctuation range of wind power output under a preset confidence level through a probability distribution model of wind power output. This range is then used to determine the expected value of the wind power output volatility. Reducing wind curtailment is the primary objective. A weighted combination function for evaluating the value of the energy storage system is constructed, considering the investment cost, operating cost, and power system stability benefits of the energy storage system. This function serves as the objective function. Constraints are imposed on charging / discharging power, remaining capacity, power system operation, rated power, and rated capacity. These constraints are combined to construct an optimal configuration model, which solves for the energy storage power and capacity configuration scheme. This approach balances multiple factors to ensure the scientific nature of the value assessment, maximizes the comprehensive benefits of the energy storage system, and allows for the rational configuration of the energy storage system. Specifically, the rated power constraint and the rated capacity constraint... The objective function is determined based on the expected value of wind power output volatility. The weighting coefficient of the wind curtailment rate is based on the fluctuation range. By considering the fluctuation range and the expected value of volatility, a precise basis can be provided for the power and capacity configuration of the energy storage system, effectively addressing the uncertainty of wind power output, thereby reducing the wind curtailment rate, improving wind power utilization, and reducing energy waste. Application scenarios are divided based on wind power penetration, load characteristics, and electricity pricing mechanisms. An optimized configuration model is used to simulate each application scenario and calculate operational performance indicators. Adjusting the energy storage power and capacity configuration scheme based on these indicators enables value assessment and targeted configuration optimization of the energy storage system in different scenarios. This improves the applicability and economy of the energy storage system under diverse operating conditions, achieving optimized configuration and efficient utilization of the energy storage system in the wind power environment.

[0098] To more clearly describe the technical solutions provided in the embodiments of this application, for example, a certain region has vigorously developed the wind power and photovoltaic industries in recent years, and the renewable energy penetration rate has approached 30%, but the problem of wind and solar curtailment is quite serious. Therefore, the method provided in the embodiments of this application is selected to optimize the configuration of the energy storage system.

[0099] I. Data Collection and Processing

[0100] Wind power and solar power output data for the region over the past three years were collected, with a time granularity of 15 minutes. Corresponding meteorological data, including wind speed, solar irradiance, and temperature, were also collected. The wind power output data sequence is denoted as follows: (The total number of time points over three years is approximately 26,280), and the photovoltaic power output data sequence is denoted as... .

[0101] During data processing, it was discovered that wind power data contained missing values ​​at time points 1000-1010. Wind power output at any time , Wind power output at any time According to the linear interpolation formula ,calculate Wind power output at any time This process is repeated to fill in all missing values, ensuring the data is complete and accurate, and providing a reliable foundation for subsequent analysis.

[0102] II. Quantifying the Expected Value of Renewable Energy Output Volatility

[0103] (I) Establishment of the probability distribution model

[0104] The processed wind power data were determined to follow a Weibull distribution using the maximum likelihood estimation method. The likelihood function was calculated as follows:

[0105] Taking the logarithm yields the log-likelihood function. .

[0106] right and By taking the partial derivatives and setting them to zero, and through iterative calculations, the shape parameters are finally determined. Scale parameters Thus, the probability density function of the Weibull distribution of wind power output in this region is obtained. This yields the probability distribution model.

[0107] (II) Application of Confidence Method

[0108] The fluctuation range of wind power output is calculated with a 95% confidence level. The probability density function of the Weibull distribution of wind power output is integrated. The lower limit of wind power output was obtained by using the Gaussian integral method. Upper limit of wind power output .

[0109] Known average wind power output According to the formula Calculate the expected value of wind power output volatility. The expected value of wind power output volatility calculated for this region. (The fluctuation range is 300kW, accounting for more than 60% of the average of 180kW), which belongs to the high volatility scenario. (For high volatility scenarios).

[0110] III. Optimize Energy Storage Power / Capacity Configuration

[0111] (I) Construction of the objective function

[0112] Set the objective function This is the objective function under medium volatility scenarios; under high volatility scenarios, the risk of wind curtailment is high, and it is necessary to prioritize reducing the wind curtailment rate. Therefore, the weighting coefficient of the wind curtailment rate in the objective function is adjusted. .

[0113] After adjustments in high-volatility scenarios: (Increase by 0.1), and adjust other weighting coefficients accordingly. (Investment cost), c=0.15 d=0.1 (stability benefit).

[0114] The final objective function is:

[0115] ;

[0116] Among them, wind curtailment rate According to the regional power grid plan, Take a fixed value .

[0117] Among them, the investment cost of energy storage systems (Unit power investment cost) Yuan / kW, unit capacity investment cost (yuan / kWh), operating cost (Unit charging cost) Yuan / kWh, unit discharge cost Yuan / kWh).

[0118] Stability benefits of power systems The weighting coefficients for improving voltage and frequency deviations are set according to the power system parameters of the region. , .

[0119] (ii) Setting of constraints

[0120] Regarding charging and discharging power constraints, the charging power of the energy storage system is set. Discharge power Remaining capacity constraints set the state of charge. Power system operation constraint setting node voltage ( (Rated voltage), system frequency .

[0121] In addition, the following references apply to rated power constraints and rated capacity constraints:

[0122] 1. Minimum power threshold ( )

[0123] .

[0124] Among them, the range of wind power output fluctuations in this region at a 95% confidence level: the lower limit of wind power output Upper limit of wind power output ;

[0125] The calculation yields: .

[0126] Meaning: The rated power of the energy storage system must be at least 300kW to cover the maximum fluctuation range and avoid the inability to absorb instantaneous excess wind power due to insufficient power (for example, when wind power suddenly increases from 50kW to 350kW, 300kW of energy storage power is required for fast charging).

[0127] 2. Minimum capacity threshold ( )

[0128] (The duration of the fluctuation is obtained from historical data statistics).

[0129] Historical data statistics for this region show that under high volatility scenarios ( The average duration of this state is 4 hours.

[0130] The calculation yields: .

[0131] Among these, it is necessary to ensure the rated power and rated capacity .

[0132] (III) Solving the Optimization Algorithm

[0133] The particle swarm optimization algorithm is used, with 50 particles, a maximum of 100 iterations, and inertia weights. The learning factor decreases linearly from 0.9 to 0.4. .

[0134] Through iterative calculations, the optimal energy storage capacity (rated power) was finally obtained. Rated capacity It is exactly equal to the minimum power threshold and the minimum capacity threshold, which satisfies the constraints. At this time, the objective function value is minimized, and the optimal configuration is achieved under the balance of multiple factors.

[0135] IV. Multi-scenario Analysis

[0136] (I) Scene Division

[0137] Divide into three scenarios:

[0138] Scenario 1: High Renewable Energy Penetration Rate Peak-valley difference (peak-valley load ratio) ( ), time-of-use electricity pricing scenarios;

[0139] Scenario 2: Medium Renewable Energy Penetration Rate Stable load (load fluctuation coefficient) ), fixed electricity price scenario;

[0140] Scenario 3: Low renewable energy penetration rate (This is applicable to scenarios with large load fluctuations and tiered electricity pricing.)

[0141] (II) Scenario Simulation and Evaluation

[0142] By substituting historical data and set conditions into the model, in Scenario 1, the energy storage system charges during off-peak hours (low electricity price) and discharges during peak hours (high electricity price), reducing the wind curtailment rate from 25% to 12%, and generating an additional revenue of 500,000 yuan per year through the peak-valley electricity price difference; in Scenario 2, the energy storage system is mainly used to smooth the output fluctuations of renewable energy, reducing the wind curtailment rate to 8%; in Scenario 3, the energy storage system has a significant effect on improving the stability of the power system, with voltage and frequency deviations controlled within the standard range, and the wind curtailment rate reduced to 5%.

[0143] As can be seen from the above embodiments, the method provided in this application can effectively quantify the wind power output fluctuation rate, optimize the configuration of the energy storage system, and significantly reduce the wind curtailment rate in different scenarios, thereby improving the stability of the power system and the economic benefits of the energy storage system.

[0144] Figure 2 This is a structural block diagram of a configuration device for multiple scenarios under renewable energy provided in an embodiment of this application, such as... Figure 2 As shown, the device includes:

[0145] The determination module 210 is used to determine the fluctuation range of wind power output under a preset information level based on the probability distribution model of wind power output, and to determine the expected value of wind power output fluctuation rate based on the fluctuation range;

[0146] The objective function determination module 220 is used to construct a weighted combination function for evaluating the value of the energy storage system, with the primary objective of reducing wind curtailment rate, and combining the investment cost, operating cost, and stability benefits of the power system, and use this function as the objective function; wherein, the weight coefficient of wind curtailment rate in the objective function is determined based on the expected value;

[0147] The model building module 230 is used to build an optimization configuration model based on the constraints of charging and discharging power, remaining capacity, power system operation, rated power, and rated capacity, and in combination with the objective function; wherein the rated power constraint and the rated capacity constraint are determined based on the fluctuation range.

[0148] Solving module 240 is used to solve the optimization configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system;

[0149] The adjustment module 250 is used to divide application scenarios based on wind power penetration rate, load characteristics, and electricity pricing mechanism, simulate each application scenario using the optimized configuration model, calculate the operation performance index, and adjust the energy storage power and capacity configuration scheme based on the operation performance index.

[0150] In an alternative embodiment, the expected value of the wind power output volatility is determined based on the following formula:

[0151] ;

[0152] in, and These are the lower limit and the upper limit of the wind power output, respectively; The expected value; This represents the average value of the wind power output;

[0153] in, ,in, Indicates time The wind power output; N is the number of time points corresponding to the total duration of wind power output.

[0154] In an optional embodiment, the objective function is:

[0155] ;

[0156] in, Wind curtailment rate; The investment cost of the energy storage system; The operating cost of the energy storage system; For the stability benefits of the power system; , , , These are the corresponding weighting coefficients;

[0157] in, ;in, The unit power investment cost, The rated power of the energy storage system is [missing information]. The unit capacity investment cost The rated capacity of the energy storage system;

[0158] in, ;

[0159] in, and These are the weighting coefficients for voltage frequency and frequency deviation improvement, respectively. and The time intervals before and after the energy storage system is put into operation are respectively: Voltage deviation, and The time intervals before and after the energy storage system is put into operation are respectively: Frequency deviation;

[0160] The charging and discharging power constraint is:

[0161] ;

[0162] ;

[0163] in, and At time respectively The charging power and discharging power of the energy storage system , , , These are the lower limit of charging power, the upper limit of charging power, the lower limit of discharging power, and the upper limit of discharging power of the energy storage system, respectively.

[0164] The remaining capacity constraint is:

[0165] ;

[0166] in, For at any time The state of charge of the energy storage system and These are the lower limit of the state of charge and the upper limit of the state of charge, respectively.

[0167] The power system operating constraints are:

[0168] ;

[0169] ;

[0170] in, For a moment node voltage, For a moment The system frequency; , , , These are the lower limit of node voltage, the upper limit of node voltage, the lower limit of system frequency, and the upper limit of system frequency.

[0171] In an optional embodiment, the weighting coefficients of the wind curtailment rate in the objective function are determined based on the expected value of the wind power output volatility, including:

[0172] If the expected value is less than the first preset volatility threshold, the weighting coefficient of the wind curtailment rate is the first preset weighting coefficient;

[0173] If the expected value is between the first preset volatility threshold and the second preset volatility threshold, the weighting coefficient of the wind curtailment rate is the second preset weighting coefficient;

[0174] If the expected value is greater than the second preset volatility threshold, the weighting coefficient of the wind curtailment rate is the third preset weighting coefficient; wherein, the second preset volatility threshold is greater than the first preset volatility threshold, and the third preset weighting coefficient is greater than the second preset weighting coefficient; the second preset weighting coefficient is greater than the first preset weighting coefficient.

[0175] Accordingly, the rated power constraint and the rated capacity constraint are determined based on the fluctuation range, including:

[0176] Based on the fluctuation range, a minimum power threshold is determined, and the rated power of the energy storage system is determined to be no less than the minimum power threshold.

[0177] Based on the fluctuation range and the duration of the fluctuation range, a minimum capacity threshold is determined, and the rated capacity of the energy storage system is determined to be no less than the minimum capacity threshold.

[0178] In an optional embodiment, solving the optimized configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system includes:

[0179] The particle swarm optimization algorithm is used to solve the optimization configuration model to obtain the configuration scheme of rated power and rated capacity of the energy storage system.

[0180] In the process of solving the optimization configuration model using the particle swarm optimization algorithm, the initial particle positions are based on the minimum power threshold and the minimum capacity threshold. If the fluctuation rate of the wind power output is greater than the first preset fluctuation rate threshold, the updated particle positions are shifted by increasing the first preset ratio based on the benchmark. If the fluctuation rate of the wind power output is greater than the second preset fluctuation rate threshold, the updated particle positions are shifted by increasing the second preset ratio based on the benchmark. The second preset ratio is greater than the first preset ratio.

[0181] In one optional embodiment, the wind power penetration rate includes high, medium, and low wind power penetration rates; the load characteristics include high load peak-valley difference and stable load; therefore, the electricity pricing mechanism includes time-of-use pricing mechanism and fixed pricing mechanism; wherein, when the peak-valley load ratio is greater than 2, the load characteristic is high load peak-valley difference; when the load fluctuation coefficient is less than or equal to 0.1, the load characteristic is stable load.

[0182] The application scenario is determined by selecting any one of the following from the wind power penetration rate, the load characteristics, and the sub-items included in the electricity pricing mechanism and combining them.

[0183] In one optional embodiment, the operational performance indicators include wind curtailment rate, economic benefits, and power system stability benefits;

[0184] The adjustment of the energy storage power and capacity configuration scheme based on the operational performance indicators includes:

[0185] If the wind curtailment rate does not meet the first preset requirement, increase the rated power or rated capacity of the energy storage system.

[0186] If the economic benefits do not meet the second preset requirements, increase the rated capacity of the energy storage system;

[0187] If the stability benefits of the power system do not meet the third preset requirement, increase the rated power or rated capacity of the energy storage system.

[0188] like Figure 3 As shown in the figure, this application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0189] Memory 113 is used to store computer programs;

[0190] In one embodiment of this application, when the processor 111 executes a program stored in the memory 113, it implements the method provided in any of the foregoing method embodiments, including:

[0191] The fluctuation range of wind power output under a preset confidence level is determined based on the probability distribution model of wind power output, and the expected value of wind power output volatility is determined based on the fluctuation range.

[0192] With the primary objective of reducing wind curtailment, a weighted combination function for evaluating the value of energy storage systems is constructed by combining the investment cost, operating cost, and stability benefits of the power system, and this function serves as the objective function; wherein, the weighting coefficient of wind curtailment in the objective function is determined based on the expected value.

[0193] An optimization configuration model is constructed using charging and discharging power constraints, remaining capacity constraints, power system operation constraints, rated power constraints, and rated capacity constraints as constraints, combined with the objective function; wherein, the rated power constraints and the rated capacity constraints are determined based on the fluctuation range.

[0194] Solving the optimization configuration model yields the energy storage power and capacity configuration scheme of the energy storage system;

[0195] Application scenarios are divided based on wind power penetration rate, load characteristics, and electricity pricing mechanism. The optimized configuration model is used to simulate each application scenario and calculate the operation performance index. The energy storage power and capacity configuration scheme is adjusted based on the operation performance index.

[0196] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.

[0197] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0199] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A configuration method for multiple scenarios under renewable energy, characterized in that, include: The fluctuation range of wind power output under a preset confidence level is determined based on the probability distribution model of wind power output, and the expected value of wind power output volatility is determined based on the fluctuation range. With the primary objective of reducing wind curtailment, a weighted combination function for evaluating the value of energy storage systems is constructed by combining the investment cost, operating cost, and stability benefits of the power system, and this function serves as the objective function; wherein, the weighting coefficient of wind curtailment in the objective function is determined based on the expected value. An optimization configuration model is constructed using charging and discharging power constraints, remaining capacity constraints, power system operation constraints, rated power constraints, and rated capacity constraints as constraints, combined with the objective function; wherein, the rated power constraints and the rated capacity constraints are determined based on the fluctuation range. Solving the optimization configuration model yields the energy storage power and capacity configuration scheme of the energy storage system; Application scenarios are divided based on wind power penetration rate, load characteristics, and electricity pricing mechanism. The optimized configuration model is used to simulate each application scenario and calculate the operation performance index. The energy storage power and capacity configuration scheme is adjusted based on the operation performance index. The weighting coefficients of the wind curtailment rate in the objective function are determined based on the expected value, including: If the expected value is less than the first preset volatility threshold, the weighting coefficient of the wind curtailment rate is the first preset weighting coefficient; If the expected value is between the first preset volatility threshold and the second preset volatility threshold, the weighting coefficient of the wind curtailment rate is the second preset weighting coefficient; If the expected value is greater than the second preset volatility threshold, the weighting coefficient of the wind curtailment rate is the third preset weighting coefficient; wherein, the second preset volatility threshold is greater than the first preset volatility threshold, and the third preset weighting coefficient is greater than the second preset weighting coefficient; the second preset weighting coefficient is greater than the first preset weighting coefficient. Accordingly, the rated power constraint and the rated capacity constraint are determined based on the fluctuation range, including: Based on the fluctuation range, a minimum power threshold is determined, and the rated power of the energy storage system is determined to be no less than the minimum power threshold. Based on the fluctuation range and the duration of the fluctuation range, a minimum capacity threshold is determined, and the rated capacity of the energy storage system is determined to be no less than the minimum capacity threshold.

2. The method according to claim 1, characterized in that, The expected value of the wind power output volatility is determined based on the following formula: ； in, and These are the lower limit and the upper limit of the wind power output, respectively; The expected value; This represents the average value of the wind power output; in, ,in, Indicates time The wind power output; N is the number of time points corresponding to the total duration of wind power output.

3. The method according to claim 1, characterized in that, The objective function is: ； in, Wind curtailment rate; The investment cost of the energy storage system; The operating cost of the energy storage system; For the stability benefits of the power system; , , , These are the corresponding weighting coefficients; in, ;in, The unit power investment cost The rated power of the energy storage system is [missing information]. The unit capacity investment cost The rated capacity of the energy storage system; in, ; in, and These are the weighting coefficients for voltage frequency and frequency deviation improvement, respectively. and The time intervals before and after the energy storage system is put into operation are respectively: Voltage deviation, and The time intervals before and after the energy storage system is put into operation are respectively: Frequency deviation; The charging and discharging power constraint is: ； ； in, and At time respectively The charging power and discharging power of the energy storage system , , , These are the lower limit of charging power, the upper limit of charging power, the lower limit of discharging power, and the upper limit of discharging power of the energy storage system, respectively. The remaining capacity constraint is: ； in, For at any time The state of charge of the energy storage system and These are the lower limit of the state of charge and the upper limit of the state of charge, respectively. The power system operating constraints are: ； ； in, For a moment node voltage, For a moment The system frequency; , , , These are the lower limit of node voltage, the upper limit of node voltage, the lower limit of system frequency, and the upper limit of system frequency.

4. The method according to claim 1, characterized in that, Solving the optimized configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system includes: The particle swarm optimization algorithm is used to solve the optimization configuration model to obtain the configuration scheme of rated power and rated capacity of the energy storage system. In the process of solving the optimized configuration model using the particle swarm optimization algorithm, the initial particle positions are based on the minimum power threshold and the minimum capacity threshold. If the fluctuation rate of wind power output is greater than the first preset fluctuation rate threshold, the particle update position is shifted by increasing the first preset ratio based on the benchmark. If the fluctuation rate of wind power output is greater than the second preset fluctuation rate threshold, the particle update position is shifted by increasing the second preset ratio based on the benchmark. The second preset ratio is greater than the first preset ratio.

5. The method according to claim 1, characterized in that, The wind power penetration rate includes high, medium and low wind power penetration rates; the load characteristics include high load peak-valley difference and stable load; therefore, the electricity pricing mechanism includes time-of-use pricing mechanism and fixed pricing mechanism; wherein, when the peak-valley load ratio is greater than 2, the load characteristic is high load peak-valley difference; when the load fluctuation coefficient is less than or equal to 0.1, the load characteristic is stable load. The application scenario is determined by selecting any one of the following from the wind power penetration rate, the load characteristics, and the sub-items included in the electricity pricing mechanism and combining them.

6. The method according to claim 1, characterized in that, The operational performance indicators include wind curtailment rate, economic benefits, and power system stability benefits. The adjustment of the energy storage power and capacity configuration scheme based on the operational performance indicators includes: If the wind curtailment rate does not meet the first preset requirement, increase the rated power or rated capacity of the energy storage system. If the economic benefits do not meet the second preset requirements, increase the rated capacity of the energy storage system; If the stability benefits of the power system do not meet the third preset requirement, increase the rated power or rated capacity of the energy storage system.

7. A configuration device for multiple scenarios under renewable energy, characterized in that, For performing the method according to any one of claims 1-6, comprising: The determination module is used to determine the fluctuation range of wind power output under a preset confidence level based on the probability distribution model of wind power output, and to determine the expected value of wind power output volatility based on the fluctuation range; The objective function determination module is used to construct a weighted combination function for evaluating the value of the energy storage system, with the primary objective of reducing wind curtailment rate, and combining the investment cost, operating cost, and stability benefits of the power system, and use this function as the objective function; wherein, the weight coefficient of wind curtailment rate in the objective function is determined based on the expected value; The model building module is used to construct an optimization configuration model based on the constraints of charging and discharging power, remaining capacity, power system operation, rated power, and rated capacity, and in combination with the objective function; wherein the rated power constraint and the rated capacity constraint are determined based on the fluctuation range. The solution module is used to solve the optimization configuration model to obtain the energy storage power and capacity configuration scheme of the energy storage system; The adjustment module is used to divide application scenarios based on wind power penetration rate, load characteristics, and electricity pricing mechanism, simulate each application scenario using the optimized configuration model, calculate the operation performance index, and adjust the energy storage power and capacity configuration scheme based on the operation performance index.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-6.

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