Source network load storage integration-based capacity configuration method and system

Abstract

The invention relates to the technical field of electric storage capacity configuration, in particular to a capacity configuration method and system based on source network load storage integration. The method comprises the following steps: acquiring power utilization data, power generation data and environment temperature, and analyzing and predicting a load demand by using an ARI MA model in combination with a linear regression model; preliminarily calculating and analyzing energy storage capacity configuration based on actual load requirements, and establishing a source network load storage capacity configuration optimization model in which the source network load storage capacity and the energy storage capacity are mutually restrained; introducing an environment temperature change factor, and redesigning an energy storage capacity configuration strategy; and evaluating the stability of the capacity configuration strategy by using a phase space analysis algorithm, and introducing the Lyapunov index into the source network load storage capacity configuration optimization model to determine the capacity configuration strategy. According to the capacity configuration method and system based on source-network load storage integration, an energy storage capacity configuration strategy is adjusted based on dynamic changes of actual load requirements and environmental factors, and energy storage capacity configuration is stably optimized by combining a Lyapunov index analysis system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric storage capacity configuration, and in particular to a capacity configuration method and system based on source-grid-load-storage integration. Background Art

[0002] The capacity configuration method based on the integration of source, grid, load and storage aims to dynamically adjust the energy storage capacity configuration strategy to cope with load demand fluctuations and environmental changes and ensure the stability and optimality of the energy storage system under variable load conditions. By combining the prediction of load demand based on factors such as ambient temperature and humidity and the analysis of the stability of energy storage capacity configuration based on the Lyapunov index, the optimal adjustment and real-time feedback mechanism of energy storage capacity configuration are controlled to achieve that under different load demands and environmental conditions, the energy storage capacity configuration can quickly and effectively respond to sudden load fluctuations.

[0003] Existing energy storage capacity configuration methods are usually difficult to cope with sudden changes in load demand and the influence of environmental factors. In addition, under conventional load demand, the energy storage capacity configuration strategy of capacity configuration remains unchanged. However, when the temperature and humidity of the environment change greatly, especially the peak electricity consumption in winter and summer, the load demand will suddenly change. The energy storage capacity configuration strategy needs to change accordingly. Only the preliminary calculation of the energy storage capacity configuration by the source-grid-load-storage capacity configuration optimization model cannot achieve the best and safest strategy for the source-grid-load-storage at the same time. Introducing the Lyapunov index into the source-grid-load-storage capacity configuration optimization model can well judge whether the preliminary calculated energy storage capacity configuration is stable. Therefore, a capacity configuration method and system based on source-grid-load-storage integration are provided. Summary of the invention

[0004] The purpose of the present invention is to provide a capacity configuration method and system based on source-grid-load-storage integration, so as to solve the problem raised in the above background technology that under conventional load demand, the energy storage capacity configuration strategy of capacity configuration remains unchanged, but when the temperature and humidity of the environment have large changes, especially the peak electricity consumption in winter and summer, it will cause a sudden change in load demand, and the energy storage capacity configuration strategy needs to change accordingly.

[0005] To achieve the above object, the present invention provides a capacity configuration method based on source-grid-load-storage integration, comprising:

[0006] S1. Obtain electricity consumption data, power generation data and environmental parameters, and use the ARIMA model combined with the linear regression model to analyze and predict load demand;

[0007] S2. Preliminary calculation and analysis of energy storage capacity configuration based on actual load demand, and establishment of a source-grid-load-storage capacity configuration optimization model between source-grid-load-storage and energy storage capacity, and preliminary calculation of energy storage capacity configuration;

[0008] S3. Use the phase space analysis algorithm to evaluate the stability of the capacity configuration strategy, and introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy;

[0009] S4. Based on the final capacity configuration strategy, feedback is given and the source-grid-load scheduling tripartite strategy is executed.

[0010] As a further improvement of the technical solution, in S1, power consumption data, power generation data and environmental parameters are obtained, and the load demand is predicted by using the ARIMA model combined with the linear regression model. The specific method steps are as follows:

[0011] S1.1, obtain electricity consumption data E(t), power generation data G(t), load curve L(t) and environmental parameters;

[0012] Where t is the time, and the environmental parameters include temperature T(t) and humidity H(t);

[0013] S1.2. Construct a power demand forecasting model based on the ARIMA model;

[0014] S1.3. Use the power demand forecasting model to analyze and calculate the predicted load demand;

[0015] S1.4. Use the linear regression model to correct the predicted load demand and calculate the actual load demand.

[0016] As a further improvement of the technical solution, in S1.2, a power demand forecasting model is constructed based on the ARIMA model, and the specific method steps are as follows:

[0017] S1.2.1. Perform time series analysis on the load curve L(t), and initialize the autoregressive term order p, the difference order d, and the sliding average term order q;

[0018] S1.2.2. Use the ARIMA model to build a power demand forecasting model:

[0019]

[0020] in, is the load demand forecast; μ is the long-term mean; p is the order of the autoregressive term; d is the number of differences; q is the order of the moving average term; φ p is the autoregressive parameter; θ q is the sliding average parameter; l d (tp) is the differential load data at time tp; ∈(tq) is the error term at time tq; ∈(t) is the error term at time t; P is the total order of autoregressive terms; Q is the total order of sliding average terms.

[0021] As a further improvement of the technical solution, in S1.4, a linear regression model is used to correct the predicted load demand and calculate the actual load demand. The specific method is as follows:

[0022]

[0023] in, is the actual load demand; α0 is the intercept term; α1 is the ARIMA prediction coefficient; α2 is the temperature coefficient; α3 is the humidity coefficient.

[0024] As a further improvement of the technical solution, in S2, the energy storage capacity configuration is preliminarily calculated and analyzed based on the actual load demand, and a source-grid-load-storage capacity configuration optimization model between the source-grid-load-storage and the energy storage capacity is established. The specific method steps are as follows:

[0025] S2.1. Combined with the renewable energy generation and the charging and discharging characteristics of the energy storage system, the initial estimate of energy storage capacity configuration is:

[0026]

[0027] Among them, C storage is the initial estimated energy storage capacity configuration; G(t) is the renewable energy power generation; t0 is the start time of the estimation; t1 is the end time of the estimation;

[0028] S2.2. The actual load demand, renewable energy generation and energy storage capacity configuration are comprehensively constructed into a source-grid-load-storage capacity configuration optimization model, and the optimal energy storage capacity configuration is calculated:

[0029] Source-grid-load-storage capacity configuration optimization model:

[0030]

[0031] Among them, P storage (t) is the charging power of the energy storage system at time t; η charge is the charging efficiency of the energy storage system; α4 is the load demand weight; α5 is the capacity configuration weight;

[0032] The particle swarm optimization algorithm is used to solve the source-grid-load-storage capacity configuration optimization model and obtain the optimal energy storage capacity configuration C storage,opt .

[0033] As a further improvement of the technical solution, in S3, the phase space analysis algorithm is used to evaluate the stability of the capacity configuration strategy, and the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method steps are as follows:

[0034] S3.1. Construct a two-dimensional phase space using the delayed coordinates of the actual load demand and the charging power of the energy storage system, and solve the Lyapunov exponent;

[0035] S3.2. Introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy.

[0036] As a further improvement of the technical solution, in S3.1, the actual load demand and the charging power of the energy storage system are used to construct a two-dimensional phase space through delayed coordinates, and the Lyapunov index is solved. The specific method steps are as follows:

[0037] S3.1.1. Construct a two-dimensional phase space using the delayed coordinates to combine the actual load demand and the energy storage system charging power:

[0038]

[0039] Among them, X(t) is the state vector of the actual load demand of the system combined with the charging power of the energy storage system;

[0040] S3.1.2. Based on the two-dimensional phase space and the system state vector, calculate the Lyapunov index:

[0041]

[0042] Among them, λ is the Lyapunov index; δX(0) is the initial difference of the two-dimensional phase space trajectory; δX(t) is the difference of the two-dimensional phase space trajectory at time t.

[0043] As a further improvement of the technical solution, in S3.2, the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method is as follows:

[0044]

[0045] Among them, β is the Lyapunov exponential weight; λ is the Lyapunov exponent;

[0046] The particle swarm optimization algorithm is used again to solve the source-grid-load-storage capacity configuration optimization model and obtain the final energy storage capacity configuration C storage,final .

[0047] As a further improvement of the technical solution, in S4, based on the final capacity configuration strategy, the source-grid-load scheduling three-party strategy is fed back and executed, as follows:

[0048] S4.1. Renewable energy generation dispatch: When the actual load demand is lower than the renewable energy generation, the renewable energy will be used directly;

[0049] S4.2. Grid dispatch: When actual load demand is higher than renewable energy generation, renewable energy is used first and additional power is obtained from the grid;

[0050] S4.3 Load management: When actual load demand is higher than renewable energy generation, adjust the electricity consumption of industrial and residential users.

[0051] On the other hand, the present invention provides a capacity configuration system based on the integration of source, grid, load and storage, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned capacity configuration method based on the integration of source, grid, load and storage.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. In the capacity configuration method and system based on the integration of source, grid, load and storage, the dynamic coupling analysis based on load demand and external factors such as ambient temperature and humidity can adjust the energy storage capacity configuration strategy in real time to ensure that the energy storage system can flexibly adapt to environmental changes and load demand fluctuations to ensure the continuity and stability of power supply.

[0054] 2. This capacity configuration method and system based on the integration of source, grid, load and storage introduces the Lyapunov index into the energy storage capacity configuration optimization model, and combines it with the particle swarm optimization algorithm for global optimal solution, so as to judge and ensure the stability of the capacity configuration of the energy storage system under variable load and environmental conditions, prevent system imbalance caused by unstable energy storage capacity, and ensure the safe and optimal configuration of energy storage capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1:

[0058] See also Figure 1 As shown, this embodiment provides a capacity configuration method based on source-grid-load-storage integration, including the following steps:

[0059] S1. Obtain electricity consumption data, power generation data and environmental parameters, and use the ARIMA model combined with the linear regression model to analyze and predict load demand;

[0060] In this embodiment S1, power consumption data, power generation data and environmental parameters are obtained, and the load demand is predicted by using the ARIMA model combined with the linear regression model. The specific method steps are as follows:

[0061] S1.1, obtain electricity consumption data E(t), power generation data G(t), load curve L(t) and environmental parameters;

[0062] Where t is the time, and the environmental parameters include temperature T(t) and humidity H(t);

[0063] S1.2. Construct a power demand forecasting model based on the ARIMA model;

[0064] S1.3. Use the power demand forecasting model to analyze and calculate the predicted load demand;

[0065] S1.4. Use the linear regression model to correct the predicted load demand and calculate the actual load demand.

[0066] In this embodiment S1.2, a power demand forecasting model is constructed based on the ARIMA model, and the specific method steps are as follows:

[0067] S1.2.1. Perform time series analysis on the load curve L(t), and initialize the autoregressive term order p, the difference order d, and the sliding average term order q;

[0068] S1.2.2. Use the ARIMA model to build a power demand forecasting model:

[0069]

[0070] in, is the load demand forecast; μ is the long-term mean; p is the order of the autoregressive term; d is the number of differences; q is the order of the moving average term; φ p is the autoregressive parameter; θ q is the sliding average parameter; L d (tp) is the differential load data at time tp; ∈(tq) is the error term at time tq; ∈(t) is the error term at time t; P is the total order of autoregressive terms; Q is the total order of sliding average terms.

[0071] In this embodiment, the ARIMA model is a time series prediction model, which is used to model and predict time series data with trends; the ARIMA model consists of three parts: autoregression, difference and moving average;

[0072] The ARIMA model is used to construct a power demand forecasting model as follows:

[0073] Electricity demand is essentially a time series data, and usually contains seasonal fluctuations, long-term trends and short-term fluctuations. The ARIMA model processes these characteristics through autoregression and sliding average, and can better capture the changing pattern of electricity demand; the electricity demand curve usually has long-term trends and cyclical changes. ARIMA uses differential technology to convert non-stationary data into stationary data, which is convenient for modeling and prediction; the ARIMA model is based on historical data for modeling and prediction, and can handle seasonal fluctuations and trend changes;

[0074] The electricity demand forecasting model is a mathematical model based on historical electricity demand data and environmental parameters; it is used to predict electricity demand in a certain period of time in the future or in the long term.

[0075] In this embodiment S1.4, the predicted load demand is corrected using a linear regression model to calculate the actual load demand. The specific method is as follows:

[0076]

[0077] in, is the actual load demand; α0 is the intercept term; α1 is the ARIMA prediction coefficient; α2 is the temperature coefficient; α3 is the humidity coefficient.

[0078] In this embodiment, the linear regression model is a statistical analysis method used to model the relationship between one or more independent variables and a dependent variable; the ARIMA model predicts load demand based on historical data, which takes into account the autoregression and sliding average of the time series, but may not be able to accurately capture dynamic environmental factors (such as temperature, humidity, etc.). The linear regression model can further modify the prediction results of the ARIMA model by introducing temperature and humidity factors, thereby obtaining a more accurate actual load demand; the ARIMA model provides a load forecast based on historical data, and by introducing a linear regression model to modify it, more environmental factors can be added to the existing time series forecast, thereby achieving a more accurate forecast.

[0079] S2. Preliminary calculation and analysis of energy storage capacity configuration based on actual load demand, and establishment of a source-grid-load-storage capacity configuration optimization model between source-grid-load-storage and energy storage capacity, and preliminary calculation of energy storage capacity configuration;

[0080] In this embodiment S2, the energy storage capacity configuration is preliminarily calculated and analyzed based on the actual load demand, and a source-grid-load-storage capacity configuration optimization model between the source-grid-load-storage and energy storage capacity is established. The specific method steps are as follows:

[0081] S2.1. Combined with the renewable energy generation and the charging and discharging characteristics of the energy storage system, the initial estimate of energy storage capacity configuration is:

[0082]

[0083] Among them, C storage is the initial estimated energy storage capacity configuration; G(t) is the renewable energy power generation; t0 is the start time of the estimation; t1 is the end time of the estimation;

[0084] S2.2. The actual load demand, renewable energy generation and energy storage capacity configuration are comprehensively constructed into a source-grid-load-storage capacity configuration optimization model, and the optimal energy storage capacity configuration is calculated:

[0085] Source-grid-load-storage capacity configuration optimization model:

[0086]

[0087] Among them, P storage (t) is the charging power of the energy storage system at time t; η charge is the charging efficiency of the energy storage system; α4 is the load demand weight; α5 is the capacity configuration weight;

[0088] The particle swarm optimization algorithm is used to solve the source-grid-load-storage capacity configuration optimization model and obtain the optimal energy storage capacity configuration C storage,opt .

[0089] In this embodiment, the source-grid-load-storage capacity configuration optimization model is a mathematical model for balancing the relationship between various parts of the power system, and aims to optimize the overall operating efficiency and stability of the power system by reasonably configuring the energy storage capacity. The source-grid-load-storage capacity configuration optimization model combines the actual load demand, the power generation of renewable energy, the charging and discharging characteristics of the energy storage system, and the energy storage capacity configuration, and aims to optimize the energy storage capacity configuration according to the changes in power demand, the fluctuations in renewable energy generation, and the performance characteristics of the energy storage system;

[0090] The particle swarm optimization algorithm is an intelligent optimization algorithm that simulates the foraging behavior of bird flocks. It continuously searches for the optimal solution to the problem by simulating the movement of particles in the solution space. The particle swarm optimization algorithm can effectively conduct a global search in the entire solution space by simulating the collaborative search of multiple particles to avoid falling into the local optimal solution. It is particularly important when optimizing the configuration of energy storage capacity, because the optimization problem involves multiple variables and constraints, and there may be multiple local optimal solutions. Particle swarm optimization can find a solution that is closer to the global optimal solution.

[0091] S3. Use the phase space analysis algorithm to evaluate the stability of the capacity configuration strategy, and introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy;

[0092] In this embodiment S3, the phase space analysis algorithm is used to evaluate the stability of the capacity configuration strategy, and the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method steps are as follows:

[0093] S3.1. Construct a two-dimensional phase space using the delayed coordinates of the actual load demand and the charging power of the energy storage system, and solve the Lyapunov exponent;

[0094] S3.2. Introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy.

[0095] In this embodiment, the phase space analysis algorithm is a method for analyzing the dynamic characteristics of a system by converting time series data into trajectories in a multidimensional space. The system state changes over time, and the phase space analysis reveals the inherent laws of these dynamic changes by constructing a phase space. The phase space analysis algorithm usually uses a delayed coordinate embedding method to convert time series data into a multidimensional state space.

[0096] The Lyapunov index is an important indicator to measure the sensitivity of a dynamic system to initial conditions. It reflects the stability and chaotic characteristics of the system; if the Lyapunov index is positive, it means that the system is chaotic, and any small change in the initial conditions will cause a huge change in the system state; if the Lyapunov index is negative, it means that the system is stable, and the system state will gradually tend to equilibrium or stable state; if the Lyapunov index is zero, it means that the behavior of the system is periodic; the Lyapunov index is used to evaluate the stability of the source-grid-load-storage capacity configuration strategy; by calculating the index, it can be determined whether the energy storage capacity configuration strategy will lead to instability or chaotic behavior due to changes in load demand or energy storage system charging and discharging strategies;

[0097] The purpose of introducing the Lyapunov index into the source-grid-load-storage capacity configuration optimization model is to ensure the reliability and security of the capacity configuration strategy through stability analysis, as follows:

[0098] By calculating the Lyapunov index, we can determine whether the current capacity allocation strategy will cause system instability. If the Lyapunov index is negative, it means that the current strategy is stable and can continue to be executed. If the Lyapunov index is positive, it may mean that the capacity allocation strategy is potentially unstable and needs to be adjusted.

[0099] When the Lyapunov index indicates that the current capacity allocation strategy is unstable, the Lyapunov index can be used as a constraint to further adjust the capacity allocation strategy to ensure its stability and security.

[0100] In this embodiment S3.1, the actual load demand and the charging power of the energy storage system are used to construct a two-dimensional phase space through delayed coordinates, and the Lyapunov index is solved. The specific method steps are as follows:

[0101] S3.1.1. Construct a two-dimensional phase space using the delayed coordinates to combine the actual load demand and the energy storage system charging power:

[0102]

[0103] Among them, X(t) is the state vector of the actual load demand of the system combined with the charging power of the energy storage system;

[0104] S3.1.2. Based on the two-dimensional phase space and the system state vector, calculate the Lyapunov index:

[0105]

[0106] Among them, λ is the Lyapunov index; δX(0) is the initial difference of the two-dimensional phase space trajectory; δX(t) is the difference of the two-dimensional phase space trajectory at time t.

[0107] In this embodiment S3.2, the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method is as follows:

[0108]

[0109] Among them, β is the Lyapunov exponential weight; λ is the Lyapunov exponent;

[0110] The particle swarm optimization algorithm is used again to solve the source-grid-load-storage capacity configuration optimization model and obtain the final energy storage capacity configuration C storage,final .

[0111] S4: Based on the final capacity configuration strategy, feedback is given and the source-grid-load scheduling tripartite strategy is executed;

[0112] In this embodiment S4, based on the final capacity configuration strategy, the source-grid-load scheduling three-party strategy is fed back and executed, as follows:

[0113] S4.1. Renewable energy generation dispatch: When the actual load demand is lower than the renewable energy generation, the renewable energy will be used directly;

[0114] S4.2. Grid dispatch: When actual load demand is higher than renewable energy generation, renewable energy is used first and additional power is obtained from the grid;

[0115] S4.3 Load management: When actual load demand is higher than renewable energy generation, adjust the electricity consumption of industrial and residential users.

[0116] Embodiment 2:

[0117] This embodiment provides a capacity configuration system based on source-grid-load-storage integration, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of the above-mentioned capacity configuration method based on source-grid-load-storage integration.

[0118] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A capacity configuration method based on source-grid-load-storage integration, characterized in that: The following steps are involved: S1. Obtain electricity consumption data, power generation data and environmental parameters, and use the ARIMA model combined with the linear regression model to analyze and predict load demand; S2. Preliminary calculation and analysis of energy storage capacity configuration based on actual load demand, and establishment of a source-grid-load-storage capacity configuration optimization model between source-grid-load-storage and energy storage capacity, and preliminary calculation of energy storage capacity configuration; S3. Use the phase space analysis algorithm to evaluate the stability of the capacity configuration strategy, and introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy; S4. Based on the final capacity configuration strategy, feedback is given and the source-grid-load scheduling tripartite strategy is executed.

2. The capacity configuration method based on source-grid-load-storage integration according to claim 1 is characterized by: In S1, power consumption data, power generation data and environmental parameters are obtained, and the load demand is analyzed and predicted using the ARIMA model combined with the linear regression model. The specific method steps are as follows: S1.1, obtain electricity consumption data E(t), power generation data G(t), load curve L(t) and environmental parameters; Where t is the time, and the environmental parameters include temperature T(t) and humidity H(t); S1.

2. Construct a power demand forecasting model based on the ARIMA model; S1.

3. Use the power demand forecasting model to analyze and calculate the predicted load demand; S1.

4. Use the linear regression model to correct the predicted load demand and calculate the actual load demand.

3. The capacity configuration method based on source-grid-load-storage integration according to claim 2 is characterized in that: In S1.2, a power demand forecasting model is constructed based on the ARIMA model. The specific method steps are as follows: S1.2.

1. Perform time series analysis on the load curve L(t), and initialize the autoregressive term order p, the difference order d, and the sliding average term order q; S1.2.

2. Use the ARIMA model to build a power demand forecasting model: in, is the load demand forecast; μ is the long-term mean; p is the order of the autoregressive term; d is the number of differences; q is the order of the moving average term; φ p is the autoregressive parameter; θ q is the sliding average parameter; L d (tp) is the differential load data at time tp; ∈(tq) is the error term at time tq; ∈(t) is the error term at time t; P is the total order of autoregressive terms; Q is the total order of sliding average terms.

4. The capacity configuration method based on source-grid-load-storage integration according to claim 3 is characterized in that: In S1.4, the predicted load demand is corrected using a linear regression model to calculate the actual load demand. The specific method is as follows: in, is the actual load demand; α0 is the intercept term; α1 is the ARIMA prediction coefficient; α2 is the temperature coefficient; α3 is the humidity coefficient.

5. The capacity configuration method based on source-grid-load-storage integration according to claim 4 is characterized in that: In S2, the energy storage capacity configuration is preliminarily calculated and analyzed based on the actual load demand, and a source-grid-load-storage capacity configuration optimization model between the source-grid-load-storage and energy storage capacity is established. The specific method steps are as follows: S2.

1. Combined with the renewable energy generation and the charging and discharging characteristics of the energy storage system, the initial estimate of energy storage capacity configuration is: Among them, C storage is the initial estimated energy storage capacity configuration; G(t) is the renewable energy power generation; t0 is the start time of the estimation; t1 is the end time of the estimation; S2.

2. The actual load demand, renewable energy generation and energy storage capacity configuration are comprehensively constructed into a source-grid-load-storage capacity configuration optimization model, and the optimal energy storage capacity configuration is calculated: Source-grid-load-storage capacity configuration optimization model: Among them, P storage (t) is the charging power of the energy storage system at time t; η charge is the charging efficiency of the energy storage system; α4 is the load demand weight; α5 is the capacity configuration weight; The particle swarm optimization algorithm is used to solve the source-grid-load-storage capacity configuration optimization model and obtain the optimal energy storage capacity configuration C storage,opt .

6. The capacity configuration method based on source-grid-load-storage integration according to claim 5 is characterized in that: In S3, the phase space analysis algorithm is used to evaluate the stability of the capacity configuration strategy, and the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method steps are as follows: S3.

1. Construct a two-dimensional phase space using the delayed coordinates of the actual load demand and the charging power of the energy storage system, and solve the Lyapunov exponent; S3.

2. Introduce the Lyapunov index into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy.

7. The capacity configuration method based on source-grid-load-storage integration according to claim 6 is characterized in that: In S3.1, the actual load demand and the charging power of the energy storage system are used to construct a two-dimensional phase space through delayed coordinates, and the Lyapunov index is solved. The specific method steps are as follows: S3.1.

1. Construct a two-dimensional phase space using the delayed coordinates to combine the actual load demand and the energy storage system charging power: Among them, X(t) is the state vector of the actual load demand of the system combined with the charging power of the energy storage system; S3.1.

2. Based on the two-dimensional phase space and the system state vector, calculate the Lyapunov index: Among them, λ is the Lyapunov index; δX(0) is the initial difference of the two-dimensional phase space trajectory; δX(t) is the difference of the two-dimensional phase space trajectory at time t.

8. The capacity configuration method based on source-grid-load-storage integration according to claim 7 is characterized in that: In S3.2, the Lyapunov index is introduced into the source-grid-load-storage capacity configuration optimization model to determine the final capacity configuration strategy. The specific method is as follows: Among them, β is the Lyapunov exponential weight; λ is the Lyapunov exponent; The particle swarm optimization algorithm is used again to solve the source-grid-load-storage capacity configuration optimization model and obtain the final energy storage capacity configuration C storage,final .

9. The capacity configuration method based on source-grid-load-storage integration according to claim 8 is characterized in that: In S4, based on the final capacity configuration strategy, the source-grid-load scheduling three-party strategy is fed back and executed, as follows: S4.

1. Renewable energy generation dispatch: When the actual load demand is lower than the renewable energy generation, the renewable energy will be used directly; S4.

2. Grid dispatch: When actual load demand is higher than renewable energy generation, renewable energy is used first and additional power is obtained from the grid; S4.3 Load management: When actual load demand is higher than renewable energy generation, adjust the electricity consumption of industrial and residential users.

10. A capacity configuration system based on source-grid-load-storage integration, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the capacity configuration method based on source-grid-load-storage integration as described in any one of claims 1-9.

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