Optical storage combined system credible capacity calculation method based on load duration curve

Abstract

The invention relates to an optical storage combined system credible capacity calculation method based on a load duration curve, and aims to solve the problems of complex capacity credibility evaluation and low calculation efficiency of an optical storage system in the prior art. Firstly, a load duration curve is constructed based on historical load data, and a net load duration curve is generated in combination with a photovoltaic power generation output curve and energy storage operation parameters. A conditional value-at-risk theory is introduced, an energy storage scheduling optimization model with credible capacity maximization as a target is constructed, and the credible capacity of the optical storage system is finally defined and calculated by optimizing an energy storage charging and discharging strategy, adjusting a net load duration curve and quantifying peak load reduction. According to the method, data requirements and calculation complexity are remarkably reduced, and the method is suitable for rapid evaluation of resource planning and system design. The method not only can effectively reduce peak load and improve the reliability and stability of power grid operation, but also can provide a scientific basis for power grid resource allocation, and assists in realizing the targets of new energy consumption and carbon emission reduction.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic and energy storage optimization scheduling, and in particular relates to a method for calculating the credible capacity of a photovoltaic and energy storage combined system based on a load duration curve. Background Art

[0002] With the rapid growth of global energy demand and the continuous advancement of renewable energy technologies, solar photovoltaic power generation has become one of the most promising forms of renewable energy due to its cleanliness, sustainability, and affordability. However, the output of solar photovoltaic power generation is intermittent and fluctuating, significantly affected by weather conditions and the diurnal cycle, posing a serious challenge to the resource adequacy and reliability of the power system. To enhance the contribution of solar power generation to the power system, energy storage systems such as batteries are becoming a key technology option. By storing excess energy during low-load periods and releasing it during high-load periods, energy storage systems can significantly mitigate the volatility of photovoltaic power generation and improve its availability during peak load periods.

[0003] In combined solar and energy storage systems (PV-storage systems), effectively assessing their contribution to the resource adequacy of power systems is a hot topic of current research. This contribution is often characterized by credible capacity. Credible capacity refers to a resource's ability to effectively reduce the net load on the system during peak load periods and is a key indicator of resource reliability and economic value. However, traditional credible capacity calculation methods are often based on complex probabilistic models. These methods require extensive input data and computing resources, making them difficult to meet the rapid estimation requirements of practical projects. Summary of the Invention

[0004] The purpose of the present invention is to address the problem in the existing technology that it is difficult to accurately assess the reliability of the capacity of a photovoltaic and energy-storage power station. By constructing a load duration curve (LDC), introducing conditional value at risk (CVaR), and building an energy storage scheduling optimization model, the optimization goal is to maximize the net load reduction during peak load periods. The optimal scheduling scheme of the photovoltaic and energy-storage power station is solved, and the net load duration curve is calculated based on the optimized scheduling results. The net load reduction during peak periods is calculated. On this basis, the reliable capacity of the photovoltaic and energy-storage combined system is obtained, the capacity contribution of the photovoltaic and energy-storage combined system is quantified, and the application demand of photovoltaic and energy-storage power stations in actual power planning is improved.

[0005] To achieve the above objectives, the present invention provides a method for calculating the credible capacity of a photovoltaic-storage combined system based on a load duration curve, comprising the following steps: Step 1: Construct a load duration curve based on historical load data; Step 2: Based on the existing photovoltaic and energy storage capacity, obtain the output curve of the photovoltaic unit and the operating parameters of the energy storage unit; and generate the net load duration curve based on the historical load data; Step 3: Introduce conditional value at risk and construct an energy storage scheduling optimization model. The optimization goal of the model is to maximize the net load reduction during peak load periods. Step 4: Solve the energy storage scheduling optimization model to obtain the optimal solution, i.e., the optimized scheduling scheme for the solar-storage combined system. Recalculate the net load duration curve based on the optimized scheduling scheme and calculate the net load reduction during peak hours. Step 5: Based on the load duration curves obtained in steps 1 and 4, calculate the reliable capacity of the photovoltaic and energy storage system.

[0006] Furthermore, the step 1 specifically includes the following sub-steps: Step 101: Data collection and preprocessing; Obtain load time series data from historical data, with a data recording interval of 1 hour; Group the annual load data by season to analyze the load characteristics of a specific period and calculate the annual load statistics, including maximum load, minimum load, average load and load standard deviation; Step 102: sorting the pre-processed load data and constructing a load duration curve; Sort the preprocessed load data in descending order and associate the load level with its duration; The load duration curve is defined such that the horizontal axis is the cumulative duration of the load level from the maximum to the minimum, and the vertical axis is the sorted load level, and the load duration curve is drawn.

[0007] Furthermore, in step 101, the annual load time series data is obtained, with the data recording time interval being 1 hour, covering 8760 hours in a year, and the annual load statistics are calculated, including: Maximum load: ; Minimum load: ; Average load: ; Load standard deviation: ; Where: For the System load in hours; 、 are the maximum and minimum loads of the system respectively; is the average load of the system; is the standard deviation of the system load.

[0008] Furthermore, in step 102, the load data of 8760 hours throughout the year are rearranged in descending order to obtain a full-year load sorting queue. ; Where: After sorting Hourly load value; is the sort position index; The highest load in the whole year is ranked first in the queue N The time period corresponding to the load value is defined as the peak load period. N Indicates the amount of peak load.

[0009] Preferably, step 2 specifically includes the following sub-steps: Step 201: Based on the photovoltaic storage capacity and historical load data, obtain the output curve of the photovoltaic unit and the operating parameters of the energy storage unit, and calculate the net load at hour h , ; Where: For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 Respectively Hours of energy storage discharge and charging power; Step 202: Sort by largest to smallest order to generate a net load duration curve. ; Where, Represents the sorted net load.

[0010] Preferably, step 3 specifically includes the following sub-steps: Step 301: Introducing conditional value at risk; In the operation and scheduling of the photovoltaic and energy storage combined system, the conditional value at risk is used to transform the peak load risk into an optimization goal. By reducing the peak load, the expected value of the peak load exceeding the load threshold is minimized. During peak load periods, the optimization goal of energy storage scheduling is to minimize the excess net load value during these periods, thereby maximizing the capacity contribution of the solar-storage system. Step 302: Constructing an objective function of an energy storage scheduling optimization model; By building an energy storage scheduling optimization model, the operation of the photovoltaic and storage combined system is optimized and scheduled to maximize the net load reduction of the photovoltaic and storage combined system during peak load periods; Step 303: Determine the constraints of the energy storage scheduling optimization model, including energy storage power constraints, energy storage capacity constraints, and peak period net load maximum constraints. Preferably, in step 302, the objective function of the energy storage scheduling optimization model is: ; ; ; Where: For the Hourly net load value; is the reference net load level outside the peak period; is the number of hours during the peak load period; The portion exceeding the reference net load during the peak load period; For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 Respectively Hours of energy storage discharge and charging power; For the Energy level of the hourly energy storage system; is the charging and discharging efficiency of the energy storage system.

[0011] Preferably, in step 303, the constraint conditions specifically include: Energy storage power constraints: ; ; Where, 、 Respectively Hours of energy storage discharge and charging power; is the rated power of the energy storage system; Energy storage capacity constraints: ; Where, For the Energy level of the hourly energy storage system; is the total capacity of energy storage; Peak hour constraints: ; Where, For the Hourly net load value; The maximum net load value that the system can accept.

[0012] Preferably, the step 4 specifically includes: Step 401: Solve the energy storage scheduling optimization model to obtain the optimal solution , represents the optimal photovoltaic power generation in hour h; They represent the optimal energy storage discharging and charging power in hour h respectively; According to the optimal solution Calculate the net load after optimized scheduling , and sort them in descending order to generate an updated net load duration curve, ; Where, represents the net load after updating the sorting, l It is the sorting position index of the net load sequence after optimized scheduling; Step 402: Calculate the load reduction of the solar-storage combined system during the peak load period based on the net load duration curve obtained in step 401 and the load duration curve in step 1; ; ; ; ; Where: is the average value of the original load during the peak period; After sorting Hourly load value; The average value of net load after optimizing dispatch for peak hours; Indicates the net load after updating the sorting; The reduction amount of the solar-storage system during peak load period; It is the percentage of reduction relative to the original peak load.

[0013] Preferably, in step 5, the credible capacity of the photovoltaic-storage combined system refers to the power generation capacity that can be equivalently replaced by the photovoltaic-storage combined system during peak load periods; The load reduction of the photovoltaic and energy storage system during the peak load period obtained in step 4 is used as the reliable capacity of the photovoltaic and energy storage system. ; Where: is the reliable capacity of the optical storage system, It is the reduction amount of the solar storage system during peak load period.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) Provide a fast and efficient method for calculating trusted capacity; This paper proposes a fast and efficient method for calculating credible capacity based on load duration and net load duration curves, combined with a conditional value-at-risk (CVAR) energy storage optimization scheduling model. Compared to traditional, complex probabilistic and statistical methods, this method significantly reduces data requirements and computational complexity, making it suitable for rapidly assessing the contribution of solar-to-storage systems during resource planning and preliminary design stages.

[0015] (2) The peak-shaving and valley-filling effects are significant, helping the power grid to operate stably; This method significantly increases the contribution of a combined solar-energy storage system during peak load periods by reducing peak loads and smoothing the net load curve. The results provide a scientific basis for grid planning and resource allocation, helping to optimize power system resource adequacy, reduce reliance on traditional fossil fuel generators, and improve grid stability and reliability.

[0016] (3) Innovation in risk quantification mechanisms; For the first time, the conditional value at risk (CVaR) was introduced into energy storage scheduling optimization, and a risk-aware "peak load excess minimization" objective function was established, achieving a fusion decision-making process of risk control and capacity optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 Schematic diagram of the flow of a method for calculating the reliable capacity of a photovoltaic and storage combined system according to an embodiment of the present invention.

[0019] Figure 2 This is the load duration curve of the embodiment of the present invention and the net load duration curve obtained in step 2.

[0020] Figure 3 This is a comparison diagram of the net load duration curve before and after the photovoltaic storage optimization scheduling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Example 1: like Figure 1 As shown in FIG, the reliable capacity calculation method of the photovoltaic and energy storage combined system based on the load duration curve includes the following steps: Step 1: Construct a load duration curve based on historical load data.

[0022] Step 101: Data collection and preprocessing; The annual load time series data is obtained from the historical planning data. The data recording interval is 1 hour, covering 8760 hours in a year.

[0023] The annual load data is grouped by season to analyze the load characteristics of a specific period and calculate basic statistics of the annual load. The statistics are as follows: ; ; ; ; Where: For the System load in hours; 、 are the maximum and minimum loads of the system respectively; is the average load of the system; is the standard deviation of the system load.

[0024] Step 102: sorting and constructing load duration curves; The load duration curve is an important tool for describing system load variations. It sorts annual load data from highest to lowest and correlates load levels with their duration, laying the foundation for defining peak load periods and evaluating system resources.

[0025] Based on the pre-processed load data, the load data for 8760 hours throughout the year are rearranged in descending order: ; Where: After sorting Hourly load value; The sort position index.

[0026] After sorting, each The value represents the load level, and the corresponding The position represents the cumulative number of hours that the load lasts. The horizontal axis of the load duration curve is defined as the cumulative duration of the load level from the maximum to the minimum, and the vertical axis is the sorted load level.

[0027] Select the first few hours with the highest load from the load duration curve and record the corresponding load value and time period. Define the part of the year with the highest load as the peak load period, i.e. the first few hours with the highest load. The period is defined as follows: ; Where: is the peak load hours.

[0028] Step 2: Based on the existing solar-storage capacity, obtain the PV output curve and energy storage operating parameters. Generate a net load duration curve based on historical load data.

[0029] Net load refers to the remaining load that needs to be met by traditional power generation resources after taking into account the output of photovoltaic power generation and energy storage systems.

[0030] According to the photovoltaic storage capacity and historical load data, the photovoltaic output curve and the operating parameters of the energy storage are obtained. , calculate the net load as follows: ; Where: For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 For the Hours of energy storage discharge and charging power.

[0031] The calculated net load data Sort by largest to smallest to generate a net load duration curve: ; In the formula, for each period , Represents the sorted net load.

[0032] According to the net load duration curve, the peak load period is selected and the peak load reduction is calculated as follows: ; Where: The amount of peak load reduction; is the original load after sorting; is the net load after sorting; Hours during peak hours.

[0033] Step 3: Introduce conditional value at risk and construct an energy storage scheduling optimization model with the goal of maximizing the reliable capacity of solar energy storage.

[0034] Step 301: Introducing conditional value at risk (CVaR) into energy storage optimization; Conditional Value at Risk (CVaR) is a method for measuring risk in extreme situations, often used in optimization problems to minimize the expected value at the tail of a distribution. In energy storage scheduling, CVaR is used to transform peak load risk into an optimization objective, minimizing the expected value of exceeding a certain load threshold by reducing peak load.

[0035] In this model, during peak load periods, energy storage dispatch is optimized to minimize the excess net load value during these periods, thereby maximizing the credible capacity of the solar-storage system.

[0036] Step 302: Constructing the objective function By scheduling the charge and discharge of energy storage, the net load reduction of the solar-storage combined system during peak load periods is maximized, thereby maximizing the system's reliable capacity. The objective function is as follows: ; ; ; Where: For the Hourly net load value; is the reference net load level outside the peak period; is the number of hours during the peak load period; The portion exceeding the reference net load during the peak load period; For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 For the Hours of energy storage discharge and charging power; For the Energy level of the hourly energy storage system; is the charging and discharging efficiency of the energy storage system.

[0037] Step 303: Constraints The following formula is used as the constraint condition: Energy storage power constraints: ; ; Where: is the rated power of the energy storage system.

[0038] Energy storage capacity constraints: ; Where: is the total capacity of energy storage.

[0039] Peak hour constraints: ; Where: The maximum net load value that the system can accept.

[0040] Step 4: Based on the optimized dispatching plan obtained in step 3, readjust the net load duration curve and calculate the net load reduction during the peak period.

[0041] Update the net load sequence and use the optimized data hour by hour for 8760 hours of the year. 、 Update Payload Sequence .

[0042] The adjusted payload sequence It is necessary to sort them in descending order to generate an updated net load duration curve.

[0043] ; In the formula, for each period , Indicates the net load after update sorting.

[0044] Based on the adjusted net load curve and the original load curve, the curtailment contribution of the solar-storage system during peak hours is quantified.

[0045] ; ; ; ; Where: is the average value of the original load during the peak period; is the original load value after sorting; is the average value of net load adjusted for peak hours; Indicates the net load after update sorting. The reduction amount of the solar-storage system during peak load period; It is the percentage of reduction relative to the original peak load.

[0046] Step 5: Based on the curves obtained in steps 1 and 4, calculate the reliable capacity of the photovoltaic-storage combined system.

[0047] Credible capacity refers to the equivalent generation capacity that the PV-storage system can replace during peak load periods. It represents the ability of the PV-storage system to reduce peak load periods. The expression is as follows: ; Where: is the credible capacity of the optical storage system.

[0048] Calculation example: In order to verify the effectiveness of the present invention, the maximum load is set to 1000MW, the photovoltaic installed capacity is 300MW, the energy storage installed capacity is 100MW, the charging and discharging efficiency is 90%, and the confidence level is 0.95. Based on the historical load data of 8760 hours a year, the loads are sorted from large to small to generate a load duration curve, as shown in Figure 2. Figure 2 The net load is calculated and sorted to generate a net load duration curve, as shown in Figure 2 Then, with the goal of maximizing the credible capacity of the solar-storage system, an energy storage scheduling optimization model is constructed. Using this optimization scheme, the adjusted net load curve is recalculated, as shown in Figure 3 As shown in Table 1, the net load reduction during the peak period is calculated, and the PV storage system's trusted capacity is the reduction.

[0049] Table 1

[0050] Over time, load demand gradually decreased, while PV output and energy storage power significantly influenced the extent of net load reduction. The adjusted net load demand was significantly lower than the original load demand, demonstrating the significant effectiveness of the synergistic effect of PV and energy storage in peak load reduction. During peak hours, the maximum reduction reached 400 MW, optimizing power system dispatch and alleviating peak load pressure. Furthermore, the load reduction trend was closely correlated with PV output and energy storage power, highlighting the key role of clean energy and energy storage technologies in balancing grid load.

[0051] The TopN elastic peak period definition method provided in the embodiment dynamically identifies key periods through load sorting queues, which is more adaptable to the differences in load characteristics in different seasons / regions than the fixed period division method.

[0052] By calculating the reduction amount through a hyperbola comparison mechanism (original and optimized net load curves), the present invention controls the error of credible capacity assessment within ±3%, which is smaller than the traditional method (±5% error).

[0053] Example 2: The reliable capacity calculation system of the photovoltaic and storage combined system based on the load duration curve includes: Load duration curve module: used to process and sort historical load data and generate load duration curves, providing a basic load profile reference for reliable capacity analysis of solar-storage systems.

[0054] Energy storage optimization scheduling module: Based on the net load situation and system operation objectives, the conditional value at risk (CVaR) theory is used to build an optimization model to generate the optimal charging and discharging scheduling plan for the energy storage system.

[0055] Peak-Hour Net Load Reduction Calculation Module: This module identifies and quantifies the net load reduction achieved by the combined solar-energy storage system during peak load periods, building on net load calculation and dispatch optimization. Its output directly serves as the basis for quantifying trusted capacity and is a key component in assessing the value of solar-energy storage systems.

[0056] Trusted Capacity Calculation Module: Based on the output of the peak-hour net load reduction calculation module, the module quantifies the amount of traditional power generation capacity that the PV-storage system can effectively replace during peak load periods. The output can be directly used in application scenarios such as resource planning and power market capacity declaration.

[0057] The load duration curve module includes the following submodules: A1. Data collection and preprocessing submodule: 1) Functional description: Collect and clean the annual load time series data of the power system.

[0058] 2) Input data: 8760 hours of system load data, with a time granularity of 1 hour.

[0059] 3) Processing content: ① Detection and elimination of outliers; ② Grouping by season or month; ③ Statistical key indicators: maximum load, minimum load, average load, and load standard deviation.

[0060] A2. Sorting and duration mapping submodule: 1) Functional description: Sort the loads from large to small and map them to duration.

[0061] 2) Processing logic: ① Sort the load values ​​for the 8,760 hours of the year; ② Establish a correspondence between load levels and their duration; ③ The horizontal axis is the cumulative duration hours, and the vertical axis is the load value.

[0062] 3) Output data: load-duration data pair (t, L(t)), where t is the duration and L is the corresponding load level.

[0063] A3. Peak Hour Identification Submodule: 1) Functional Description: Identify system peak load periods based on the sorting results and set target time periods for subsequent trusted capacity reduction analysis.

[0064] 2) Processing logic: ① Extract the load periods corresponding to the top p% of the load sorting queue; ② Establish a correspondence between load levels and their durations.

[0065] 3) Output data: peak load period indicator set (time period index, load threshold, etc.).

[0066] A4. Curve Generation and Visualization Submodule 1) Function description: Draw a load duration curve graph for users to intuitively analyze load change trends.

[0067] 2) Processing logic: ① Generate a curve using the sorting results; ② Support superimposed display of raw load, net load and other comparative information; 3) Output data: load duration curve and export data.

[0068] The energy storage optimization scheduling module includes the following submodules: B1. Model parameter configuration submodule: 1) Functional Description: Receives user-entered energy storage and photovoltaic system parameters and defines scheduling boundaries and constraints.

[0069] 2) Input parameters: ① Energy storage system capacity, rated charge and discharge power, and charge and discharge efficiency; ② Initial energy state of energy storage; ③ PV output curve; ④ Net load time series; ⑤ Historical load data.

[0070] 3) Output data: Optimize the model initialization parameter set.

[0071] B2.CvaR risk target modeling submodule: 1) Functional description: The conditional value at risk theory is introduced to transform peak load risk into an optimization objective.

[0072] 2) Modeling logic: ① Objective function: Minimize the expected value of net load exceeding the reference threshold during peak hours; ② Optimization goal: Maximize the ability of the solar-storage system to reduce peak net load; 3) Mathematical form: ; Where: is the reference net load level outside the peak period; is the number of hours during the peak load period; It is the portion that exceeds the reference net load during the peak load period.

[0073] B3. Constraint definition submodule: 1) Function description: Set the operation boundary constraints during the optimization process.

[0074] 2) Constraint type: ① Energy storage power constraints: ; ; Where, 、 Respectively Hours of energy storage discharge and charging power; is the rated power of the energy storage system; ②Energy storage capacity constraints: ; Where, For the Energy level of the hourly energy storage system; is the total capacity of energy storage; ③ Peak period constraints: ; Where, For the Hourly net load value; The maximum net load value that the system can accept.

[0075] B4. Scheduling optimization solution submodule: 1) Functional description: Solve the above model using mixed integer programming to obtain the optimal scheduling strategy.

[0076] 2) Solution objective: Maximize net load reduction during peak hours; 3) Output: ① Optimal charging and discharging power per hour; ② Optimal photovoltaic power generation per hour; ③ Optimized net load time series.

[0077] B5. Optimization result evaluation and interaction submodule: 1) Function description: Analyze and optimize scheduling effects and provide parameter adjustment suggestions.

[0078] 2) Output: ① The smoothing effect of energy storage scheduling on the net load curve; ② Peak load reduction.

[0079] The peak hour net load reduction calculation module includes the following submodules: C1. Data synchronization submodule: 1) Function description: Determine the annual peak load period of the system based on the original load duration curve.

[0080] 2) Processing logic: ① Sort the annual load data from largest to smallest; ② Extract the load periods corresponding to the top p% of the load sorting queue; ③ Output the time index and load value corresponding to these hours.

[0081] 3) Input results: original load data for the whole year, and top p% value.

[0082] 4) Output result: sorting position index of the net load sequence after optimized scheduling.

[0083] C2. Net load curve comparison submodule: 1) Function description: Compare the changes in net load during peak hours before and after scheduling.

[0084] 2) Calculation method: ; ; ; ; Where: is the average value of the original load during the peak period; After sorting Hourly load value; The average value of net load after optimizing dispatch for peak hours; Indicates the net load after updating the sorting; The reduction amount of the solar-storage system during peak load period; It is the percentage of reduction relative to the original peak load.

[0085] 3) Output results: ① The reduction amount during the peak load period; ② The percentage of the reduction amount relative to the original peak load.

[0086] C3. Chart and analysis report generation submodule: 1) Functional description: Visual display and automatic analysis output of calculation results.

[0087] 2) Chart type: Original vs. adjusted peak net load duration curves.

[0088] The trusted capacity calculation module includes the following submodules: D1. Input data integration submodule: 1) Function description: Summarizes the calculation output data of the previous module.

[0089] 2) Input data: ① Original peak load mean; ② Average peak net load after optimized scheduling; ③ Total net load reduction.

[0090] 3) Output data: All variables required for trusted capacity calculation.

[0091] D2. Trusted Capacity Quantification Calculation Submodule: The credible capacity is calculated based on the peak load reduction, which is defined as follows: ; Where: is the reliable capacity of the optical storage system, It is the reduction amount of the solar storage system during peak load period.

[0092] D3. Trusted Capacity Result Output and Reporting Submodule: 1) Function description: Export trusted capacity calculation results and visualization materials.

[0093] 2) Output data: trusted capacity value.

Claims

1. A method for calculating the reliable capacity of a photovoltaic and energy storage system based on a load duration curve, characterized in that: The following steps are involved: Step 1: Construct a load duration curve based on historical load data; Step 2: Based on the existing photovoltaic and energy storage capacity, obtain the output curve of the photovoltaic unit and the operating parameters of the energy storage unit; and generate the net load duration curve based on the historical load data; Step 3: Introduce conditional value at risk and construct an energy storage scheduling optimization model. The optimization goal of the model is to maximize the net load reduction during peak load periods. Step 4: Solve the energy storage scheduling optimization model to obtain the optimal solution, i.e., the optimized scheduling scheme for the solar-storage combined system. Recalculate the net load duration curve based on the optimized scheduling scheme and calculate the net load reduction during peak hours. Step 5: Based on the load duration curves obtained in steps 1 and 4, calculate the reliable capacity of the photovoltaic and energy storage system.

2. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 1 is characterized in that: The step 1 specifically includes the following sub-steps: Step 101: Data collection and preprocessing; Obtain load time series data from historical data, with a data recording interval of 1 hour; Group the annual load data by season to analyze the load characteristics of a specific period and calculate the annual load statistics, including maximum load, minimum load, average load and load standard deviation; Step 102: sorting the pre-processed load data and constructing a load duration curve; Sort the preprocessed load data in descending order and associate the load level with its duration; The load duration curve is defined such that the horizontal axis is the cumulative duration of the load level from the maximum to the minimum, and the vertical axis is the sorted load level, and the load duration curve is drawn.

3. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 2 is characterized in that: In step 101, the load time series data for the whole year is obtained, with the data recording interval being 1 hour, covering 8760 hours in a year, and the statistics of the load for the whole year are calculated, including: Maximum load: ； Minimum load: ； Average load: ； Load standard deviation: ； Where: For the System load in hours; 、 are the maximum and minimum loads of the system respectively; is the average load of the system; is the standard deviation of the system load.

4. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 3 is characterized in that: In step 102, the load data of 8760 hours in a year are rearranged in descending order to obtain a load sorting queue for the whole year. ； Where: After sorting Hourly load value; is the sort position index; The highest load in the whole year is ranked first in the queue N The time period corresponding to the load value is defined as the peak load period. N Indicates the amount of peak load.

5. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 4 is characterized in that: The step 2 specifically includes the following sub-steps: Step 201: Based on the photovoltaic storage capacity and historical load data, obtain the output curve of the photovoltaic unit and the operating parameters of the energy storage unit, and calculate the net load at hour h , ； Where: For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 Respectively Hours of energy storage discharge and charging power; Step 202: Sort by largest to smallest order to generate a net load duration curve. ； Where, Represents the sorted net load.

6. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 5 is characterized in that: The step 3 specifically includes the following sub-steps: Step 301: Introducing conditional value at risk; In the operation and scheduling of the photovoltaic and energy storage combined system, the conditional value at risk is used to transform the peak load risk into an optimization goal. By reducing the peak load, the expected value of the peak load exceeding the load threshold is minimized. During peak load periods, the optimization goal of energy storage scheduling is to minimize the excess net load value during these periods, thereby maximizing the capacity contribution of the solar-storage system. Step 302: Constructing an objective function of an energy storage scheduling optimization model; By building an energy storage scheduling optimization model, the operation of the photovoltaic and storage combined system is optimized and scheduled to maximize the net load reduction of the photovoltaic and storage combined system during peak load periods; Step 303: Determine the constraints of the energy storage scheduling optimization model, including energy storage power constraints, energy storage capacity constraints, and peak period net load maximum constraints.

7. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 6 is characterized in that: In step 302, the objective function of the energy storage scheduling optimization model is: ； ； ； Where: For the Hourly net load value; is the reference net load level outside the peak period; is the number of hours during the peak load period; The portion exceeding the reference net load during the peak load period; For the Hourly historical load data; For the Hours of photovoltaic power generation; 、 Respectively Hours of energy storage discharge and charging power; For the Energy level of the hourly energy storage system; is the charging and discharging efficiency of the energy storage system.

8. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 7 is characterized in that: In step 303, the constraint conditions specifically include: Energy storage power constraints: ； ； Where, 、 Respectively Hours of energy storage discharge and charging power; is the rated power of the energy storage system; Energy storage capacity constraints: ； Where, For the Energy level of the hourly energy storage system; is the total capacity of energy storage; Peak hour constraints: ； Where, For the Hourly net load value; The maximum net load value that the system can accept.

9. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 8 is characterized in that: The step 4 specifically includes: Step 401: Solve the energy storage scheduling optimization model to obtain the optimal solution , represents the optimal photovoltaic power generation in hour h; They represent the optimal energy storage discharging and charging power in hour h respectively; According to the optimal solution Calculate the net load after optimized scheduling , and sort them in descending order to generate an updated net load duration curve, ； Where, represents the net load after updating the sorting, l It is the sorting position index of the net load sequence after optimized scheduling; Step 402: Calculate the load reduction of the solar-storage combined system during the peak load period based on the net load duration curve obtained in step 401 and the load duration curve in step 1; ； ； ； ； Where: is the average value of the original load during the peak period; After sorting Hourly load value; The average value of net load after optimizing dispatch for peak hours; Indicates the net load after updating the sorting; The reduction amount of the solar-storage system during peak load period; It is the percentage of reduction relative to the original peak load.

10. The method for calculating the reliable capacity of a photovoltaic-storage combined system based on a load duration curve according to claim 9, characterized in that: In step 5, the credible capacity of the photovoltaic-storage combined system refers to the power generation capacity that can be equivalently replaced by the photovoltaic-storage combined system during peak load periods; The load reduction of the photovoltaic and energy storage system during the peak load period obtained in step 4 is used as the reliable capacity of the photovoltaic and energy storage system. ； Where: is the reliable capacity of the optical storage system, It is the reduction amount of the solar storage system during peak load period.

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