Main network energy storage power station planning system and method based on wind-solar space-time correlation

Abstract

The invention relates to the technical field of image recognition and processing, and discloses a main network energy storage power station planning system and method based on wind-solar space-time correlation, and the system comprises a data collection and preprocessing module which carries out the cleaning and preprocessing of a data collection and preprocessing module; the wind and light space-time characteristic analysis module is used for researching space-time distribution characteristics and reverse peak regulation characteristics of wind power and photovoltaic output and correlation between the space-time distribution characteristics and the reverse peak regulation characteristics; a modeling and simulation module; the scene generation and screening module is used for generating a typical daily operation scene; and the energy storage technology evaluation module is used for comprehensively evaluating the technical performance of different types of stored energy. According to the method, the spatial-temporal distribution characteristics, the reverse peak regulation characteristics and the correlation of wind power and photovoltaic output are deeply researched, the output spatial-temporal correlation model suitable for large-scale wind and light is constructed, the level of accepting and utilizing renewable energy sources by a power grid is greatly improved, and the effect of remarkably improving the absorption capacity of the power grid to the renewable energy sources is achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a main grid energy storage power station planning system and method based on the spatiotemporal correlation of wind and solar power. Background Art

[0002] As the global demand for clean energy continues to grow, wind power and photovoltaic power generation are increasingly used in the energy sector due to their green and sustainable characteristics. However, the output of wind power and photovoltaic power generation is obviously random, volatile and intermittent, which poses a huge challenge to the stable operation of the power system and the quality of power.

[0003] In order to effectively absorb large-scale wind power and photovoltaic power, energy storage technology has gradually become a key means to solve this problem. Energy storage power stations can store electricity when there is excess wind and solar power output, and release electricity when there is insufficient power, thereby smoothing power fluctuations, participating in peak and frequency regulation, and improving the stability of the power grid.

[0004] Traditional power planning methods often fail to fully consider these characteristics and correlations, resulting in inaccurate predictions of wind and solar power output and difficulty in accurately grasping their changing patterns, which in turn affects the planning and configuration of energy storage power stations and is unable to effectively adapt to the volatility and uncertainty of wind and solar energy. This results in the grid's insufficient ability to respond to sudden changes in wind and solar power output and its inability to fully utilize wind and solar energy, resulting in a large amount of waste of renewable energy and a low level of acceptance and utilization by the grid. Summary of the invention

[0005] In order to make up for the above shortcomings, the present invention provides a main grid energy storage power station planning system and method based on the spatiotemporal correlation of wind and solar energy, aiming to improve the poor adaptability and flexibility of the planning method, the inability to cope with the complex changes of wind and solar energy, and the waste of renewable energy and unstable grid operation caused by unreasonable planning.

[0006] In a first aspect, the present invention provides the following technical solution: a main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power, comprising:

[0007] A data acquisition and preprocessing module, which is used to collect wind power and photovoltaic output data, power grid operation data, and clean and preprocess them;

[0008] A wind-solar spatiotemporal characteristics analysis module, which is used to study the spatiotemporal distribution characteristics, anti-peak regulation characteristics and correlation between wind power and photovoltaic output;

[0009] A modeling and simulation module, which is used to construct a spatiotemporal correlation model of output suitable for large-scale wind and solar power, and to perform simulation analysis;

[0010] A peak-shaving impact assessment module, which is used to quantitatively analyze the impact of large-scale wind and solar access on power grid peak-shaving;

[0011] A scenario generation and screening module, which is used to generate typical daily operation scenarios and screen and optimize scenario requirements;

[0012] An energy storage technology evaluation module is used to comprehensively evaluate the technical performance, economic cost and applicable scenarios of different types of energy storage.

[0013] Preferably, the data acquisition and preprocessing module includes a data collection unit, a data screening unit, a data correction unit and a data format conversion unit. The data collection unit is used to collect wind power and photovoltaic output data and basic information of power grid operation data from different locations through a variety of sensors and monitoring equipment. The basic information of power grid operation data includes voltage information, current information, power information, frequency information, equipment status information, fault information and power grid topology information. The data screening unit is used to screen out valid and critical data and remove redundant and irrelevant data. The data correction unit is used to correct deviations and errors in the data. The data format conversion unit is used to convert the collected data into a unified format for processing.

[0014] Preferably, the wind-solar spatiotemporal characteristics analysis module includes a spatiotemporal distribution characteristics analysis unit, an anti-peak shaving characteristics analysis unit, a seasonal characteristics analysis unit and a daily characteristics analysis unit. The spatiotemporal distribution characteristics analysis unit is used to apply time series analysis and geographic information system technology to deeply study the distribution patterns of wind power and photovoltaic output at different time and space scales. The anti-peak shaving characteristics analysis unit is used to analyze the degree of mismatch between wind power and photovoltaic output and the grid load curve in time and its negative impact on peak shaving. The seasonal characteristics analysis unit is used to study the changing characteristics and patterns of wind and solar output in different seasons. The daily characteristics analysis unit is used to analyze the fluctuation and trend of wind and solar output within a day.

[0015] Preferably, the modeling and simulation module includes a modeling unit, a simulation parameter setting unit, a simulation result output unit and a simulation error analysis unit. The modeling unit is used to construct an electric vehicle model that can accurately reflect the spatiotemporal correlation of wind and solar power output based on historical data and physical principles. The simulation parameter setting unit is used to set the simulation time step and boundary condition parameters. The simulation result output unit is used to output the grid operation status and power balance results obtained by simulation. The simulation error analysis unit is used to analyze the error between the simulation results and the actual data and evaluate the accuracy of the model.

[0016] Preferably, the peak-shaving impact assessment module includes a peak-shaving demand assessment unit, a peak-shaving capacity assessment unit, a peak-shaving cost assessment unit and a peak-shaving risk assessment unit. The peak-shaving demand assessment unit is used to assess the scale of peak-shaving demand of the power grid under different scenarios. The peak-shaving capacity assessment unit is used to assess the peak-shaving capacity of existing power grids and planned power grids. The peak-shaving cost assessment unit is used to calculate the equipment investment and operating expenses in the peak-shaving process. The peak-shaving risk assessment unit is used to analyze the peak-shaving risks caused by the uncertainty of wind and solar power.

[0017] Preferably, the scenario generation and screening module includes a scenario generation unit, a scenario classification unit, a scenario screening unit, a distributed energy storage unit and a scenario optimization unit. The scenario generation unit is used to comprehensively consider multiple factors including historical data, weather forecasts, and power grid planning to generate a large number of executable scenarios. The scenario classification unit is used to classify the generated scenarios, and the scenarios include typical scenarios and extreme scenarios. The scenario screening unit is used to select representative and important scenarios from a large number of scenarios according to preset screening conditions. The scenario optimization unit is used to further optimize the screened scenarios. The distributed energy storage unit is used to store vehicle models, electric vehicle grouping indicators, electric vehicle charging and discharging behavior models, and electric vehicle subgroups based on charging and discharging prices, to establish electric vehicle travel models, charging station models, and screening and classification models for electric vehicle network participants.

[0018] Preferably, the energy storage technology evaluation module includes a technical performance evaluation unit, a cost-benefit evaluation unit, an environmental impact evaluation unit and a market maturity evaluation unit. The technical performance evaluation unit is used to evaluate the energy density, power density, response time, and cycle life technical indicators of different energy storage technologies. The cost-benefit evaluation unit is used to analyze the investment cost, operating cost and expected benefits of different energy storage technologies. The environmental impact evaluation unit is used to evaluate the impact of energy storage technology on the environment throughout its life cycle. The market maturity evaluation unit is used to evaluate the application degree and development trend of different energy storage technologies in the market.

[0019] In a second aspect, the present invention provides the following technical solution, a main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power, comprising the following steps:

[0020] S1. Data collection and preprocessing: Using the data collection and preprocessing module, the basic information of wind power and photovoltaic power output and grid operation data is collected through the data collection unit, and the redundant and irrelevant data are eliminated through the data screening unit, and the deviation errors are corrected by the data correction unit, and then the format is unified through the data format conversion unit;

[0021] S2. Analysis of wind and solar spatiotemporal characteristics: With the help of wind and solar spatiotemporal characteristics analysis module, the spatiotemporal distribution characteristics analysis unit is used to study the output law of different spatiotemporal scales, the anti-peak regulation characteristics analysis unit is used to evaluate the mismatch effect with the load curve, the seasonal characteristics analysis unit is used to explore the changes in different seasons, and the daily characteristics analysis unit is used to analyze the intraday fluctuation trend;

[0022] S3, Modeling and simulation: Relying on the modeling and simulation module, based on historical data and physical principles, the modeling unit builds a model that accurately reflects the temporal and spatial correlation of wind and solar power output, sets parameters through the simulation parameter setting unit, obtains the operating status and power balance results through the simulation result output unit, and evaluates the accuracy of the electric vehicle model through the simulation error analysis unit;

[0023] S4. Peak-shaving impact assessment: Calculate the scale of peak-shaving demand under different scenarios through the peak-shaving demand assessment unit, evaluate the peak-shaving capacity of existing and planned power grids with the help of the peak-shaving capacity assessment unit, calculate various costs with the peak-shaving cost assessment unit, and analyze the peak-shaving risks caused by the uncertainty of wind and solar power with the help of the peak-shaving risk assessment unit;

[0024] S5, scenario generation and screening: Using the scenario generation and screening module, the scenario generation unit generates a large number of operation scenarios based on multiple factors, which are classified by the scenario classification unit, and the representative and important scenarios are selected by the scenario screening unit, and then optimized by the scenario optimization unit;

[0025] S6. Energy storage technology evaluation: With the help of the energy storage technology evaluation module, the energy density index is evaluated through the technical performance evaluation unit, the investment and operating costs and benefits are analyzed using the cost-benefit evaluation unit, the environmental impact of the entire life cycle is evaluated with the help of the environmental impact assessment unit, and the market maturity assessment unit determines the market application and development trends. Considering the demand for distributed renewable energy access, with the goal of reducing network losses, reducing electric vehicle user costs and shaving peaks and filling valleys, a distributed renewable energy and electric vehicle coordinated scheduling model is established.

[0026] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power is implemented.

[0027] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power is implemented.

[0028] The present invention has the following beneficial effects:

[0029] 1. The present invention can accurately grasp the changing rules of wind power and photovoltaic power output through in-depth research on their spatiotemporal distribution characteristics, anti-peak characteristics and correlation, and construct a spatiotemporal correlation model for large-scale wind and photovoltaic output, providing a reliable basis for the planning of energy storage power stations. The planning system and method can effectively adapt to the volatility and uncertainty of wind and photovoltaic energy, greatly improve the level of acceptance and utilization of renewable energy by the power grid, and significantly enhance the power grid's ability to absorb renewable energy.

[0030] 2. The present invention constructs a coordinated scheduling model for distributed renewable energy and electric vehicles, considers factors such as vehicle type, access system location and adjustable time period, establishes electric vehicle grouping indicators and charging and discharging behavior models, achieves peak shaving and valley filling, balances the peak-valley difference of electricity supply and demand, reduces network losses, reduces losses in the energy transmission process, significantly improves energy utilization efficiency, and reduces energy waste.

[0031] 3. The present invention helps to accelerate the popularization of renewable energy in the power sector, increase its share in the energy structure, promote the transformation and upgrading of the energy structure, reduce dependence on traditional fossil energy, and achieve the effect of clean, low-carbon and sustainable development of energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a system architecture diagram of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed by the present invention;

[0033] Figure 2 This is a data acquisition and preprocessing module architecture diagram of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed by the present invention;

[0034] Figure 3 This is the architecture diagram of the wind-solar spatiotemporal characteristics analysis module of the main grid energy storage power station planning system based on the wind-solar spatiotemporal correlation proposed by the present invention;

[0035] Figure 4 This is a modeling and simulation module architecture diagram of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed by the present invention;

[0036] Figure 5 This is the architecture diagram of the peak load impact assessment module of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed by the present invention;

[0037] Figure 6 This is a diagram of the scenario generation and screening module architecture of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed in the present invention;

[0038] Figure 7 This is the architecture diagram of the energy storage technology evaluation module of the main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power proposed by the present invention;

[0039] Figure 8 This is a flow chart of the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power proposed in the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.

[0041] Embodiment 1

[0042] Reference Figure 1-Figure 7 In the first embodiment of the present invention, the present invention provides a main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power, including:

[0043] Data acquisition and preprocessing module: The data acquisition and preprocessing module is used to collect wind power and photovoltaic output data, power grid operation data, and clean and preprocess them;

[0044] Wind and solar spatiotemporal characteristics analysis module: The wind and solar spatiotemporal characteristics analysis module is used to study the spatiotemporal distribution characteristics, anti-peak characteristics and correlation between wind power and photovoltaic output;

[0045] Modeling and simulation module: The modeling and simulation module is used to build a spatiotemporal correlation model for large-scale wind and solar output and conduct simulation analysis;

[0046] Peak load impact assessment module: The peak load impact assessment module is used to quantitatively analyze the impact of large-scale wind and solar access on grid peak load;

[0047] Scenario generation and screening module: The scenario generation and screening module is used to generate typical daily operation scenarios and screen and optimize scenario requirements;

[0048] Energy storage technology evaluation module, the energy storage technology evaluation module is used to comprehensively evaluate the technical performance, economic cost and applicable scenarios of different types of energy storage.

[0049] The data acquisition and preprocessing module includes a data collection unit, a data screening unit, a data correction unit and a data format conversion unit. The data collection unit is used to collect wind power and photovoltaic output data and basic information of power grid operation data from different locations through a variety of sensors and monitoring equipment. The basic information of power grid operation data includes voltage information, current information, power information, frequency information, equipment status information, fault information and power grid topology information. The data screening unit is used to screen out valid and critical data and remove redundant and irrelevant data. The data correction unit is used to correct deviations and errors in the data. The data format conversion unit is used to convert the collected data into a unified format for processing.

[0050] Specifically, the data collection unit ensures the integrity and diversity of the data, providing a rich source of data for subsequent analysis. It obtains voltage information through voltage sensors installed at wind farms, photovoltaic power stations and key nodes of the power grid, current information through current sensors, and power information through power monitoring equipment. These data are then transmitted to the data center in real time with the help of remote communication technology.

[0051] Data screening unit, which selects data of practical value for analysis and decision-making from massive amounts of raw data;

[0052] Data correction unit, which corrects the deviations and errors in the data, ensures the accuracy of the data, avoids the deviation of subsequent analysis results caused by data errors, and improves the reliability of decision-making;

[0053] The data format conversion unit converts data from different data sources and in different formats into a standard format that can be processed and analyzed by the system, thereby achieving smooth flow and processing of data within the system and improving data processing efficiency.

[0054] The wind and solar spatiotemporal characteristics analysis module includes a spatiotemporal distribution characteristics analysis unit, an anti-peak regulation characteristics analysis unit, a seasonal characteristics analysis unit and a daily characteristics analysis unit. The spatiotemporal distribution characteristics analysis unit is used to apply time series analysis and geographic information system technology to deeply study the distribution patterns of wind power and photovoltaic output at different time and space scales. The anti-peak regulation characteristics analysis unit is used to analyze the degree of mismatch between wind power and photovoltaic output and the grid load curve in time and its negative impact on peak regulation. The seasonal characteristics analysis unit is used to study the changing characteristics and patterns of wind and solar output in different seasons. The daily characteristics analysis unit is used to analyze the fluctuation and trend of wind and solar output within a day.

[0055] Specifically, the temporal and spatial distribution characteristics analysis unit uses time series analysis and geographic information system technology to comprehensively analyze the output distribution patterns of wind power and photovoltaic power in different time and space dimensions, which can clearly present the temporal and spatial variation patterns of power and provide precise guidance for power allocation and facility layout;

[0056] The anti-peak load characteristic analysis unit accurately quantifies the time mismatch between wind power and photovoltaic output and the grid load curve, and evaluates its adverse impact on peak load regulation. Grid operators plan peak load regulation resources in advance to ensure the stable operation of the grid.

[0057] Seasonal characteristics analysis unit, which deeply explores the changing characteristics and laws of wind and solar power output in different seasons, provides important reference for seasonal power dispatch and energy storage planning, and improves the ability of the power grid to cope with seasonal changes in supply and demand;

[0058] The daily characteristic analysis unit carefully analyzes the fluctuations and trends of wind and solar power output within a day, which helps to formulate real-time dispatch strategies within the day and optimize the immediate allocation of power resources.

[0059] The modeling and simulation module includes a modeling unit, a simulation parameter setting unit, a simulation result output unit and a simulation error analysis unit. The modeling unit is used to build an electric vehicle model that can accurately reflect the temporal and spatial correlation of wind and solar power output based on historical data and physical principles. The simulation parameter setting unit is used to set the simulation time step and boundary condition parameters. The simulation result output unit is used to output the grid operation status and power balance results obtained by simulation. The simulation error analysis unit is used to analyze the error between the simulation results and the actual data and evaluate the accuracy of the model.

[0060] Specifically, the modeling unit creates a model that can accurately reflect the temporal and spatial correlation of wind and solar power output based on rich historical data and relevant physical principles;

[0061] The simulation parameter setting unit reasonably sets the key parameters in the simulation process, such as time step, boundary conditions, etc., to ensure the accuracy and effectiveness of the simulation, make the simulation results closer to the actual situation, and provide valuable reference for analysis and decision-making;

[0062] The simulation result output unit outputs the grid operation status and power balance calculated during the simulation process in a clear and intuitive form, making it easy for researchers and decision makers to quickly obtain key information and make accurate judgments and decisions;

[0063] The simulation error analysis unit compares the simulation results with the actual measurement data and deeply analyzes the errors between the two, thereby evaluating the accuracy and reliability of the model, helping to discover deficiencies in the model and providing a basis for improving and optimizing the model.

[0064] The peak-shaving impact assessment module includes a peak-shaving demand assessment unit, a peak-shaving capacity assessment unit, a peak-shaving cost assessment unit and a peak-shaving risk assessment unit. The peak-shaving demand assessment unit is used to assess the peak-shaving demand scale of the power grid under different scenarios. The peak-shaving capacity assessment unit is used to assess the peak-shaving capacity of the existing power grid and the planned power grid. The peak-shaving cost assessment unit is used to calculate the equipment investment and operating expenses in the peak-shaving process. The peak-shaving risk assessment unit is used to analyze the peak-shaving risks caused by the uncertainty of wind and solar power.

[0065] Specifically, the peak-shaving demand assessment unit accurately measures the scale of peak-shaving demand required by the power grid in various scenarios, provides clear peak-shaving targets for power grid planning and operation, and helps to rationally allocate peak-shaving resources;

[0066] The peak load capacity assessment unit conducts a comprehensive assessment of the peak load capacity of the existing power grid and the planned power grid, clarifies the peak load potential of the power grid at different stages, and provides a basis for power grid upgrade and optimization;

[0067] The peak-shaving cost assessment unit calculates in detail the equipment investment, operating expenses and other costs involved in the peak-shaving process, helping decision makers control costs and improve the economic efficiency of power grid operation while meeting peak-shaving needs;

[0068] The peak load risk assessment unit conducts in-depth analysis of the peak load risk caused by the uncertainty of wind and solar power output, formulates strategies to deal with risks in advance, enhances the grid's ability to respond to emergencies, and ensures stable operation of the grid.

[0069] The scenario generation and screening module includes a scenario generation unit, a scenario classification unit, a scenario screening unit, a distributed energy storage unit and a scenario optimization unit. The scenario generation unit is used to comprehensively consider multiple factors including historical data, weather forecasts, and power grid planning to generate a large number of executable scenarios. The scenario classification unit is used to classify the generated scenarios, including typical scenarios and extreme scenarios. The scenario screening unit is used to select representative and important scenarios from a large number of scenarios according to preset screening conditions. The scenario optimization unit is used to further optimize the screened scenarios. The distributed energy storage unit is used to store vehicle models, electric vehicle grouping indicators, electric vehicle charging and discharging behavior models, and electric vehicle subgroups based on charging and discharging prices, to establish electric vehicle travel models, charging station models, and screening and classification models for electric vehicle network participants.

[0070] Specifically, the scenario generation unit comprehensively considers multiple key factors, such as historical data, weather forecasts, and power grid planning, to create a large number of feasible operation scenarios, provide rich scenario options for subsequent analysis and decision-making, and fully consider various possible situations;

[0071] The scenario classification unit scientifically classifies the numerous scenarios generated into different types such as typical scenarios and extreme scenarios, so as to facilitate targeted analysis and processing of the scenarios and improve work efficiency and decision-making accuracy;

[0072] The scenario screening unit carefully selects the scenarios with significant representativeness and importance from a large number of scenarios based on the preset strict screening conditions, reduces unnecessary analysis workload, focuses on key scenarios, and improves the pertinence and effectiveness of decision-making;

[0073] The scenario optimization unit further optimizes and improves the selected scenarios to make them more consistent with actual operation conditions and planning requirements, and to improve the practicality and accuracy of the scenarios;

[0074] Distributed energy storage units store various models and indicators related to electric vehicles, build relevant models of electric vehicle travel, charging stations and network participants, provide data support and model basis for studying the coordinated interaction between electric vehicles and power grids, and promote the effective use of distributed energy.

[0075] The energy storage technology evaluation module includes a technical performance evaluation unit, a cost-benefit evaluation unit, an environmental impact assessment unit and a market maturity assessment unit. The technical performance evaluation unit is used to evaluate the energy density, power density, response time, and cycle life technical indicators of different energy storage technologies. The cost-benefit evaluation unit is used to analyze the investment cost, operating cost and expected benefits of different energy storage technologies. The environmental impact assessment unit is used to evaluate the impact of energy storage technology on the environment throughout its life cycle. The market maturity assessment unit is used to evaluate the application level and development trend of different energy storage technologies in the market.

[0076] Specifically, the technical performance evaluation unit carefully evaluates the performance of different energy storage technologies in terms of key technical indicators such as energy density, power density, response time, and cycle life, clarifies the advantages and disadvantages of various energy storage technologies in terms of performance, and provides a basis for technology selection;

[0077] Cost-benefit evaluation unit, which comprehensively analyzes the investment cost, operating cost and expected benefits of different energy storage technologies, helping decision makers weigh the feasibility and profitability of different energy storage technologies at the economic level;

[0078] Environmental impact assessment unit, which evaluates the impact of energy storage technology on the environment throughout its life cycle, including resource consumption, pollutant emissions, etc., promotes the selection of more environmentally friendly energy storage technology, and promotes sustainable development;

[0079] The market maturity assessment unit evaluates the application level, development trend and market acceptance of different energy storage technologies in the market, provides a reference for predicting the market prospects and development potential of energy storage technologies, and reduces the risks of technology application.

[0080] Embodiment 2:

[0081] Reference Figure 8 In a second embodiment of the present invention, the present invention provides a main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power, comprising the following steps:

[0082] S1. Data collection and preprocessing: Using the data collection and preprocessing module, the basic information of wind power and photovoltaic power output and grid operation data is collected through the data collection unit, and the redundant and irrelevant data are eliminated through the data screening unit, and the deviation errors are corrected by the data correction unit, and then the format is unified through the data format conversion unit;

[0083] S2. Analysis of wind and solar spatiotemporal characteristics: With the help of wind and solar spatiotemporal characteristics analysis module, the spatiotemporal distribution characteristics analysis unit is used to study the output law of different spatiotemporal scales, the anti-peak regulation characteristics analysis unit is used to evaluate the mismatch effect with the load curve, the seasonal characteristics analysis unit is used to explore the changes in different seasons, and the daily characteristics analysis unit is used to analyze the intraday fluctuation trend;

[0084] S3, Modeling and simulation: Relying on the modeling and simulation module, based on historical data and physical principles, the modeling unit builds a model that accurately reflects the temporal and spatial correlation of wind and solar power output, sets parameters through the simulation parameter setting unit, obtains the operating status and power balance results through the simulation result output unit, and evaluates the accuracy of the electric vehicle model through the simulation error analysis unit;

[0085] S4. Peak-shaving impact assessment: Calculate the scale of peak-shaving demand under different scenarios through the peak-shaving demand assessment unit, evaluate the peak-shaving capacity of existing and planned power grids with the help of the peak-shaving capacity assessment unit, calculate various costs with the peak-shaving cost assessment unit, and analyze the peak-shaving risks caused by the uncertainty of wind and solar power with the help of the peak-shaving risk assessment unit;

[0086] S5, scenario generation and screening: Using the scenario generation and screening module, the scenario generation unit generates a large number of operation scenarios based on multiple factors, which are classified by the scenario classification unit, and the representative and important scenarios are selected by the scenario screening unit, and then optimized by the scenario optimization unit;

[0087] S6. Energy storage technology evaluation: With the help of the energy storage technology evaluation module, the energy density index is evaluated through the technical performance evaluation unit, the investment and operating costs and benefits are analyzed using the cost-benefit evaluation unit, the environmental impact of the entire life cycle is evaluated with the help of the environmental impact assessment unit, and the market maturity assessment unit determines the market application and development trends. Considering the demand for distributed renewable energy access, with the goal of reducing network losses, reducing electric vehicle user costs and shaving peaks and filling valleys, a distributed renewable energy and electric vehicle coordinated scheduling model is established.

[0088] Embodiment 3

[0089] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power in the above embodiment.

[0090] Embodiment 4

[0091] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a terminal, which includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power in the above embodiment.

[0092] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0093] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power, characterized by: include: A data acquisition and preprocessing module, which is used to collect wind power and photovoltaic output data, power grid operation data, and clean and preprocess them; A wind-solar spatiotemporal characteristics analysis module, which is used to study the spatiotemporal distribution characteristics, anti-peak regulation characteristics and correlation between wind power and photovoltaic output; A modeling and simulation module, which is used to construct a spatiotemporal correlation model of output suitable for large-scale wind and solar power, and to perform simulation analysis; A peak-shaving impact assessment module, which is used to quantitatively analyze the impact of large-scale wind and solar access on power grid peak-shaving; A scenario generation and screening module, which is used to generate typical daily operation scenarios and screen and optimize scenario requirements; An energy storage technology evaluation module is used to comprehensively evaluate the technical performance, economic cost and applicable scenarios of different types of energy storage.

2. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The data acquisition and preprocessing module includes a data collection unit, a data screening unit, a data correction unit and a data format conversion unit. The data collection unit is used to collect wind power and photovoltaic output data and basic information of power grid operation data from different locations through a variety of sensors and monitoring equipment. The basic information of power grid operation data includes voltage information, current information, power information, frequency information, equipment status information, fault information and power grid topology information. The data screening unit is used to screen out valid and critical data and remove redundant and irrelevant data. The data correction unit is used to correct deviations and errors in the data. The data format conversion unit is used to convert the collected data into a unified format for processing.

3. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The wind-solar spatiotemporal characteristic analysis module includes a spatiotemporal distribution characteristic analysis unit, an anti-peak regulation characteristic analysis unit, a seasonal characteristic analysis unit and a daily characteristic analysis unit. The spatiotemporal distribution characteristic analysis unit is used to use time series analysis and geographic information system technology to deeply study the distribution law of wind power and photovoltaic output at different time and space scales. The anti-peak regulation characteristic analysis unit is used to analyze the degree of mismatch between wind power and photovoltaic output and the grid load curve in time and its negative impact on peak regulation. The seasonal characteristic analysis unit is used to study the changing characteristics and laws of wind and solar output in different seasons. The daily characteristic analysis unit is used to analyze the fluctuation and trend of wind and solar output within a day.

4. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The modeling and simulation module includes a modeling unit, a simulation parameter setting unit, a simulation result output unit and a simulation error analysis unit. The modeling unit is used to construct an electric vehicle model that can accurately reflect the temporal and spatial correlation of wind and solar power output based on historical data and physical principles. The simulation parameter setting unit is used to set the simulation time step and boundary condition parameters. The simulation result output unit is used to output the grid operation status and power balance results obtained by simulation. The simulation error analysis unit is used to analyze the error between the simulation results and the actual data and evaluate the accuracy of the model.

5. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The peak-shaving impact assessment module includes a peak-shaving demand assessment unit, a peak-shaving capacity assessment unit, a peak-shaving cost assessment unit and a peak-shaving risk assessment unit. The peak-shaving demand assessment unit is used to assess the peak-shaving demand scale of the power grid under different scenarios. The peak-shaving capacity assessment unit is used to assess the peak-shaving capacity of the existing power grid and the planned power grid. The peak-shaving cost assessment unit is used to calculate the equipment investment and operating expenses in the peak-shaving process. The peak-shaving risk assessment unit is used to analyze the peak-shaving risks caused by the uncertainty of wind and solar power.

6. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The scenario generation and screening module includes a scenario generation unit, a scenario classification unit, a scenario screening unit, a distributed energy storage unit and a scenario optimization unit. The scenario generation unit is used to comprehensively consider multiple factors including historical data, weather forecasts, and power grid planning to generate a large number of executable scenarios. The scenario classification unit is used to classify the generated scenarios, and the scenarios include typical scenarios and extreme scenarios. The scenario screening unit is used to select representative and important scenarios from a large number of scenarios according to preset screening conditions. The scenario optimization unit is used to further optimize the screened scenarios. The distributed energy storage unit is used to store vehicle models, electric vehicle grouping indicators, electric vehicle charging and discharging behavior models, and electric vehicle subgroups based on charging and discharging prices, to establish electric vehicle travel models, charging station models, and screening and classification models for electric vehicle network participants.

7. The main grid energy storage power station planning system based on wind-solar spatiotemporal correlation according to claim 1 is characterized in that: The energy storage technology evaluation module includes a technical performance evaluation unit, a cost-benefit evaluation unit, an environmental impact evaluation unit and a market maturity evaluation unit. The technical performance evaluation unit is used to evaluate the energy density, power density, response time, and cycle life technical indicators of different energy storage technologies; the cost-benefit evaluation unit is used to analyze the investment cost, operating cost and expected benefits of different energy storage technologies; the environmental impact evaluation unit is used to evaluate the impact of energy storage technology on the environment throughout its life cycle; and the market maturity evaluation unit is used to evaluate the application degree and development trend of different energy storage technologies in the market.

8. A main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power, characterized in that: The main grid energy storage power station planning system based on the spatiotemporal correlation of wind and solar power as described in any one of claims 1 to 7 comprises the following steps: S1. Data collection and preprocessing: Using the data collection and preprocessing module, the basic information of wind power and photovoltaic power output and grid operation data is collected through the data collection unit, and the redundant and irrelevant data are eliminated through the data screening unit, and the deviation errors are corrected by the data correction unit, and then the format is unified through the data format conversion unit; S2. Analysis of wind and solar spatiotemporal characteristics: With the help of wind and solar spatiotemporal characteristics analysis module, the spatiotemporal distribution characteristics analysis unit is used to study the output law of different spatiotemporal scales, the anti-peak regulation characteristics analysis unit is used to evaluate the mismatch effect with the load curve, the seasonal characteristics analysis unit is used to explore the changes in different seasons, and the daily characteristics analysis unit is used to analyze the intraday fluctuation trend; S3, Modeling and simulation: Relying on the modeling and simulation module, based on historical data and physical principles, the modeling unit builds a model that accurately reflects the temporal and spatial correlation of wind and solar power output, sets parameters through the simulation parameter setting unit, obtains the operating status and power balance results through the simulation result output unit, and evaluates the accuracy of the electric vehicle model through the simulation error analysis unit; S4. Peak-shaving impact assessment: Calculate the scale of peak-shaving demand under different scenarios through the peak-shaving demand assessment unit, evaluate the peak-shaving capacity of existing and planned power grids with the help of the peak-shaving capacity assessment unit, calculate various costs with the peak-shaving cost assessment unit, and analyze the peak-shaving risks caused by the uncertainty of wind and solar power with the help of the peak-shaving risk assessment unit; S5, scenario generation and screening: Using the scenario generation and screening module, the scenario generation unit generates a large number of operation scenarios based on multiple factors, which are classified by the scenario classification unit, and the representative and important scenarios are selected by the scenario screening unit, and then optimized by the scenario optimization unit; S6. Energy storage technology evaluation: With the help of the energy storage technology evaluation module, the energy density index is evaluated through the technical performance evaluation unit, the investment and operating costs and benefits are analyzed using the cost-effectiveness evaluation unit, the environmental impact of the entire life cycle is evaluated with the help of the environmental impact assessment unit, and the market maturity assessment unit determines the market application and development trends. Considering the demand for distributed renewable energy access, with the goal of reducing network losses, reducing electric vehicle user costs and shaving peaks and filling valleys, a distributed renewable energy and electric vehicle coordinated scheduling model is established.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power as described in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the main grid energy storage power station planning method based on the spatiotemporal correlation of wind and solar power as claimed in claim 8 is implemented.

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