Multi-strategy coupled virtual power plant dispatching system and method

Abstract

The present invention provides a multi-strategy coupled virtual power plant dispatching system and method, including: a data acquisition module, used to collect real-time operation data of each data source, and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station; a strategy determination module, used to perform multi-dimensional analysis of the power forecast demand based on the multi-dimensional optimization target, obtain the multi-dimensional dispatching strategy, and couple and coordinate the multi-dimensional dispatching strategy to determine the optimal balance point of the multi-dimensional optimization target; a dispatching management module, used to generate a dispatching plan based on the optimal balance point, and dispatch the distributed user-side energy storage power station based on the dispatching plan, and dynamically optimize the dispatching plan based on the execution result of the power dispatching. The dispatching effect and accuracy are guaranteed, and the multi-strategy coupling algorithm enables the virtual power plant to maximize the use of distributed user-side energy storage power station resources and optimize the overall operating efficiency while ensuring economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy dispatching, and in particular to a multi-strategy coupled virtual power plant dispatching system and method. Background Art

[0002] Multi-strategy coupled virtual power plant scheduling refers to the management and operation arrangements of virtual power plants by combining and synergizing multiple different scheduling strategies. Multi-strategy coupling means the comprehensive use of price-based strategies (such as time-of-use electricity price strategies), power balance-based strategies, resource optimization allocation-based strategies, and forecast-based strategies (such as forecasts of load and energy output).

[0003] However, the traditional multi-strategy coupled virtual power plant scheduling may have delays in information transmission between various parts, affecting the real-time adjustment and precise execution of the strategy. At the same time, sometimes it may only focus on the optimization of local strategies, but it is difficult to achieve the global optimal scheduling of the entire system. Some of the prediction models it relies on may not be accurate enough, resulting in deviations between strategy formulation and actual conditions, which greatly reduces the scheduling effect of virtual power plant scheduling.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides a multi-strategy coupled virtual power plant scheduling system and method. Summary of the invention

[0005] The present invention provides a multi-strategy coupled virtual power plant dispatching system and method, which is used to analyze the real-time operation data of each data source collected, so as to accurately and effectively determine the power forecast demand of the distributed user-side energy storage power station. Secondly, the power forecast demand is analyzed according to the multi-dimensional optimization target, so as to accurately and effectively determine the multi-dimensional dispatching strategy under the multi-dimensional optimization target, thereby providing data support for the effective dispatching of power plant electricity, and the obtained multi-dimensional dispatching strategy is coupled and coordinated to lock the optimal balance point of the multi-dimensional optimization target. Finally, a dispatching plan is generated according to the obtained optimal balance point to achieve effective retrieval of power plant electricity, and the generated dispatching plan is dynamically optimized to ensure the dispatching effect and accuracy. The multi-strategy coupling algorithm enables the virtual power plant to maximize the use of distributed user-side energy storage power station resources and optimize the overall operating efficiency while ensuring economic benefits. The intelligent dispatching strategy and collaborative dispatching mechanism ensure the efficient utilization of distributed energy storage resources and the stable operation of the system.

[0006] The present invention provides a multi-strategy coupled virtual power plant dispatching system, which is characterized by comprising:

[0007] The data acquisition module is used to collect real-time operation data from various data sources and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station;

[0008] A strategy determination module is used to perform multi-dimensional analysis on the power forecast demand based on the multi-dimensional optimization objectives, obtain the multi-dimensional dispatch strategy, and couple and coordinate the multi-dimensional dispatch strategy to determine the optimal balance point of the multi-dimensional optimization objectives;

[0009] The dispatching management module is used to generate a dispatching plan based on the optimal balance point, and to dispatch electric energy to the distributed user-side energy storage power station based on the dispatching plan, and to dynamically optimize the dispatching plan based on the execution result of the electric energy dispatching.

[0010] Preferably, a multi-strategy coupled virtual power plant dispatching system, a data acquisition module, comprises:

[0011] The data source determination unit is used to:

[0012] Traverse the virtual power plant dispatch process and obtain the business objectives of each link based on the results of the link traversal;

[0013] Determine business needs based on business objectives, and determine the real-time and effective data source corresponding to the virtual power plant scheduling based on business needs;

[0014] A channel building unit is used to extract basic configuration information of the program programming interface of each real-time valid data source, and build a channel between the management terminal and each real-time valid data source based on the basic configuration information according to the preset database driver and connection parameters;

[0015] The data collection unit is used to query the data in each real-time valid data source based on the constructed path according to the preset database driver, and obtain the real-time operation data to be collected based on the data query result.

[0016] Preferably, a multi-strategy coupled virtual power plant dispatching system, a data acquisition unit, comprises:

[0017] The data acquisition subunit is used to collect the real-time operation data to be collected and transmit the collected real-time operation data to the management terminal;

[0018] A data classification subunit, for extracting the time span of the real-time operation data based on the management terminal, and dividing the real-time operation data into short-term data and long-term data according to the time span based on a division standard;

[0019] The data storage subunit is used to classify and store short-term data and long-term data in a preset storage area based on time series.

[0020] Preferably, a multi-strategy coupled virtual power plant dispatching system, a data acquisition module, comprises:

[0021] Data parsing unit, used for:

[0022] Acquire the real-time operation data, and divide the real-time operation data into categories based on the data source;

[0023] Based on the spatiotemporal characteristics, the category splitting results are aligned with the starting points, and the primary observation data and the secondary observation data are determined from the multi-category real-time operation data based on the starting point alignment results;

[0024] Determine the relative change relationship between the data value of the primary observation data and the data value of the secondary observation data, and determine the weights of different categories of real-time operation data based on the relative change relationship;

[0025] Prediction unit, used to:

[0026] Obtain the terminal identity of the distributed user-side energy storage power station, and determine the corresponding real-time operation data set of each data source based on the terminal identity;

[0027] The real-time operating data of each data source of the distributed user-side energy storage power station and the corresponding weights are respectively input into the preset power demand forecasting model for analysis, and the power forecast demand of the distributed user-side energy storage power station is obtained based on the analysis results.

[0028] Preferably, a multi-strategy coupled virtual power plant dispatching system, a strategy determination module, comprises:

[0029] A policy determination unit, configured to:

[0030] Obtain multi-dimensional optimization targets for distributed user-side energy storage power stations, and determine multi-dimensional prediction factors corresponding to each dimensional optimization target;

[0031] Determining a first weight of a multi-dimensional prediction factor based on the attribute parameters of the optimization target of each dimension, and constructing a distributed analysis model based on the multi-dimensional prediction factor and the first weight;

[0032] Based on the distributed analysis model, the power forecast demand is analyzed respectively to obtain N dispatch strategies corresponding to each dimensional optimization target, and the difference parameters of the N dispatch strategies under the same dimensional optimization target are quantitatively analyzed to determine the sensitive parameters of the N dispatch strategies relative to the multi-dimensional prediction factors.

[0033] Based on the values ​​of sensitive parameters, the best scheduling strategy is selected from the N scheduling strategies corresponding to each dimensional optimization target, and the best scheduling strategies corresponding to the multi-dimensional optimization targets are summarized to obtain a multi-dimensional scheduling strategy;

[0034] Coupling coordination unit for:

[0035] Obtain the dispatching business of distributed user-side energy storage power stations and build a multi-level decision-making framework based on the dispatching business;

[0036] Integrate and deploy multi-dimensional scheduling strategies in each level of decision framework, and determine the constraints of each level of decision framework based on the integrated deployment results and scheduling business;

[0037] Determine the second weight of the multi-dimensional scheduling strategy in each level of the decision framework based on the scheduling business;

[0038] The optimal balance point determination unit is used to comprehensively analyze the multi-dimensional scheduling strategy of each level decision framework based on the constraints and the second weight, obtain the global optimal solution under each level decision framework, and obtain the optimal balance point of the multi-dimensional optimization objectives under different scheduling services based on the global optimal solution.

[0039] Preferably, a multi-strategy coupled virtual power plant dispatching system, a strategy determination unit, comprises:

[0040] A strategy acquisition subunit is used to acquire the obtained multi-dimensional scheduling strategy and, at the same time, to acquire the basic information of the distributed user-side energy storage power station;

[0041] The performance determination subunit is used to analyze the basic information of the distributed user-side energy storage power station, determine the equipment safe operation range of the distributed user-side energy storage power station, and modify the parameters of the multi-dimensional scheduling strategy based on the equipment safe operation range to obtain the target multi-dimensional scheduling strategy;

[0042] Display subunits for:

[0043] The target multi-dimensional scheduling strategy is transmitted to the management terminal for display, and based on the display result, the user is given the right to manually modify the target multi-dimensional scheduling strategy based on the management terminal;

[0044] Based on the activation results, the modification request submitted by the management terminal is monitored in real time, and the target multi-dimensional scheduling strategy is modified based on the modification request and then locked and packaged.

[0045] Preferably, a multi-strategy coupled virtual power plant dispatching system, a dispatching management module, includes:

[0046] A plan generation unit, used to read the optimal balance point, determine the power supply standard of each user-side energy storage power station according to the optimal balance point, and generate a scheduling plan according to the power supply standard;

[0047] A dispatch control unit, used to generate a distributed electric energy dispatch instruction based on the dispatch plan, and perform electric energy dispatch on the distributed user-side energy storage power station according to the distributed electric energy dispatch instruction;

[0048] The monitoring unit is used to monitor the electric energy dispatch execution data of each user-side energy storage power station in real time, determine the execution result of each user-side energy storage power station according to the execution data, and dynamically optimize the dispatch plan according to the execution result.

[0049] Preferably, a multi-strategy coupled virtual power plant dispatching system, monitoring unit, comprises:

[0050] The result acquisition subunit is used to obtain the actual execution result of the electric energy dispatch of the distributed user-side energy storage power station, and to match the actual execution result with the standard effect in the dispatch plan bit by bit;

[0051] Dynamic optimization subunit for:

[0052] Determine the difference parameter between the actual execution result and the standard effect based on the bitwise matching result, and lock the abnormal user-side energy storage power station based on the difference parameter;

[0053] Based on the difference parameters, the dispatch parameters of the abnormal user-side energy storage power station in the dispatch plan are corrected, and based on the correction results and the correlation between the distributed user-side energy storage power stations, the dispatch parameters of each user-side energy storage power station are synchronously corrected to complete the dynamic optimization of the dispatch plan.

[0054] Preferably, a multi-strategy coupled virtual power plant dispatching system, monitoring unit, comprises:

[0055] The result analysis subunit is used to:

[0056] Obtain the execution results of each user-side energy storage power station, and analyze the execution results to obtain the power output of each user-side energy storage power station at different time points;

[0057] Obtaining first billing standards of different user-side energy storage power stations at different time points, and obtaining the amount of electric energy sold by each user-side energy storage power station at different time points based on the first billing standards and the electric energy output;

[0058] At the same time, based on the execution result, the electric energy source of each user-side energy storage power station is determined, and based on the second billing standard of the electric energy source, the electric energy cost amount of each user-side energy storage power station is determined;

[0059] The economic benefit determination subunit is used to determine the economic benefit ratio of electric energy based on the electric energy sales amount and the electric energy cost amount, and feed back the economic benefit ratio as a feedback parameter to the management terminal to improve the dispatching plan.

[0060] The present invention provides a multi-strategy coupled virtual power plant scheduling method, comprising:

[0061] Step 1: Collect the real-time operation data of each data source, and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station;

[0062] Step 2: Based on the multi-dimensional optimization objectives, the power forecast demand is analyzed in multiple dimensions to obtain a multi-dimensional dispatching strategy, and the multi-dimensional dispatching strategy is coupled and coordinated to determine the optimal balance point of the multi-dimensional optimization objectives;

[0063] Step 3: Generate a dispatch plan based on the optimal balance point, dispatch electric energy to the distributed user-side energy storage power station based on the dispatch plan, and dynamically optimize the dispatch plan based on the execution result of the electric energy dispatch.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. By analyzing the real-time operating data collected from various data sources, the power forecast demand of the distributed user-side energy storage power station can be accurately and effectively determined. Secondly, the power forecast demand is analyzed according to the multi-dimensional optimization target, and the multi-dimensional scheduling strategy under the multi-dimensional optimization target is accurately and effectively determined, thereby providing data support for the effective scheduling of power plant electricity. The obtained multi-dimensional scheduling strategy is coupled and coordinated to lock the optimal balance point of the multi-dimensional optimization target. Finally, a scheduling plan is generated according to the obtained optimal balance point to achieve effective retrieval of power plant electricity, and the generated scheduling plan is dynamically optimized to ensure scheduling effect and accuracy. The multi-strategy coupling algorithm enables the virtual power plant to maximize the use of distributed user-side energy storage power station resources and optimize the overall operating efficiency while ensuring economic benefits. The intelligent scheduling strategy and collaborative scheduling mechanism ensure the efficient utilization of distributed energy storage resources and the stable operation of the system.

[0066] 2. By traversing the virtual power plant dispatching process, the business needs of each link can be accurately and effectively determined, so as to lock the real-time and effective data source corresponding to the virtual power plant dispatching according to the business needs. Secondly, the basic configuration information of the program programming interface of each real-time and effective data source is extracted, and the channel between the management terminal and each real-time and effective data source is constructed according to the basic configuration information, so as to facilitate data collection through the constructed channel, which provides convenience for data collection. Finally, the data in each real-time and effective data source is queried to realize the accurate and effective collection of the required data, which provides reliable data support for determining the power forecast demand and ensures the accuracy and reliability of the virtual power plant dispatching.

[0067] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0070] Figure 1 It is a structural diagram of a multi-strategy coupled virtual power plant dispatching system in an embodiment of the present invention;

[0071] Figure 2 It is a structural diagram of a data acquisition module in a multi-strategy coupled virtual power plant dispatching system in an embodiment of the present invention;

[0072] Figure 3 The present invention is a flowchart of a multi-strategy coupled virtual power plant scheduling system method in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0074] Embodiment 1:

[0075] This embodiment provides a multi-strategy coupled virtual power plant dispatching system, such as Figure 1 As shown, including:

[0076] The data acquisition module is used to collect real-time operation data from various data sources and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station;

[0077] A strategy determination module is used to perform multi-dimensional analysis on the power forecast demand based on the multi-dimensional optimization objectives, obtain the multi-dimensional dispatch strategy, and couple and coordinate the multi-dimensional dispatch strategy to determine the optimal balance point of the multi-dimensional optimization objectives;

[0078] The dispatching management module is used to generate a dispatching plan based on the optimal balance point, and to dispatch electric energy to the distributed user-side energy storage power station based on the dispatching plan, and to dynamically optimize the dispatching plan based on the execution result of the electric energy dispatching.

[0079] In this embodiment, the data source refers to databases corresponding to different data types, including energy markets, meteorological services, equipment sensors, distributed user-side energy storage power stations, etc.

[0080] In this embodiment, the real-time operation data refers to the specific data of the current operation status contained in different data sources.

[0081] In this embodiment, the predicted power demand refers to the power required by the distributed user-side energy storage power station obtained after analyzing the real-time operation data.

[0082] In this embodiment, the multi-dimensional optimization objectives are known in advance, including different aspects such as electricity price, system operation stability and operation efficiency.

[0083] In this embodiment, the multi-dimensional scheduling strategy refers to the corresponding scheduling strategy determined by the multi-dimensional optimization target, which corresponds to the multi-dimensional optimization target.

[0084] In this embodiment, coupling and coordinating the multi-dimensional scheduling strategies refers to comprehensively analyzing the obtained multi-dimensional scheduling strategies to determine the global optimal solution between the multi-dimensional scheduling strategies, so as to ensure the safe and effective scheduling of the power plant's electricity while ensuring the achievement of the multi-dimensional optimization goals, wherein the optimal balance point is the global optimal solution, that is, the specific situation of energy allocation to the distributed user-side energy storage power station obtained in the end.

[0085] The working principle and beneficial effects of the above technical solution are: by analyzing the real-time operating data collected from each data source, the power forecast demand of the distributed user-side energy storage power station can be accurately and effectively determined. Secondly, the power forecast demand is analyzed according to the multi-dimensional optimization target, and the multi-dimensional scheduling strategy under the multi-dimensional optimization target is accurately and effectively determined, thereby providing data support for the effective scheduling of power plant electricity, and the obtained multi-dimensional scheduling strategy is coupled and coordinated to lock the optimal balance point of the multi-dimensional optimization target. Finally, a scheduling plan is generated according to the obtained optimal balance point to achieve effective retrieval of power plant electricity, and the generated scheduling plan is dynamically optimized to ensure scheduling effect and accuracy. The multi-strategy coupling algorithm enables the virtual power plant to maximize the use of distributed user-side energy storage power station resources and optimize the overall operating efficiency, thereby ensuring the maximization of economic benefits. The intelligent scheduling strategy and collaborative scheduling mechanism ensure the efficient utilization of distributed energy storage resources and the stable operation of the system.

[0086] Embodiment 2:

[0087] Based on Example 1, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, such as Figure 2 As shown, the data acquisition module includes:

[0088] The data source determination unit is used to:

[0089] Traverse the virtual power plant dispatch process and obtain the business objectives of each link based on the results of the link traversal;

[0090] Determine business needs based on business objectives, and determine the real-time and effective data source corresponding to the virtual power plant scheduling based on business needs;

[0091] A channel building unit is used to extract basic configuration information of the program programming interface of each real-time valid data source, and build a channel between the management terminal and each real-time valid data source based on the basic configuration information according to the preset database driver and connection parameters;

[0092] The data collection unit is used to query the data in each real-time valid data source based on the constructed path according to the preset database driver, and obtain the real-time operation data to be collected based on the data query result.

[0093] In this embodiment, the virtual power plant scheduling process is known in advance and is used to characterize all steps required to perform virtual power plant scheduling.

[0094] In this embodiment, link traversal refers to determining the operating requirements and conditions of each link in the virtual power plant scheduling process.

[0095] In this embodiment, the business objectives refer to the business that needs to be performed and the goals that need to be achieved in each link.

[0096] In this embodiment, the real-time effective data source refers to the data type corresponding to the virtual power plant scheduling, including energy data and meteorological data.

[0097] In this embodiment, the basic configuration information of the program programming interface refers to the type of the program programming interface and the communication requirements (data format), wherein the program programming interface is an API interface.

[0098] In this embodiment, it is assumed that the database driver is set in advance and is used to query and traverse the data in the real-time valid data source.

[0099] In this embodiment, the connection parameters refer to specific configuration parameters required for connection between the management terminal and the program programming interface, including connection mode and the like.

[0100] The working principle and beneficial effects of the above technical solution are: by traversing the links of the virtual power plant dispatching process, the business needs of each link can be accurately and effectively determined, so as to lock the real-time and effective data source corresponding to the virtual power plant dispatching according to the business needs; secondly, the basic configuration information of the program programming interface of each real-time and effective data source is extracted, and the management terminal and the channel with each real-time and effective data source are constructed according to the basic configuration information, so as to facilitate data collection through the constructed channel, which provides convenience for data collection; finally, the data in each real-time and effective data source is queried to realize the accurate and effective collection of the required data, which provides reliable data support for determining the power forecast demand and ensures the accuracy and reliability of the virtual power plant dispatching.

[0101] Embodiment 3:

[0102] Based on Example 2, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, a data acquisition unit, including:

[0103] The data acquisition subunit is used to collect the real-time operation data to be collected and transmit the collected real-time operation data to the management terminal;

[0104] A data classification subunit, for extracting the time span of the real-time operation data based on the management terminal, and dividing the real-time operation data into short-term data and long-term data according to the time span based on a division standard;

[0105] The data storage subunit is used to classify and store short-term data and long-term data in a preset storage area based on time series.

[0106] In this embodiment, the time span refers to the total time length of the real-time running data from the beginning to the end.

[0107] In this embodiment, the division standard is set in advance, that is, the specific requirements and strategies for dividing the real-time operation data into short-term data and long-term data.

[0108] In this embodiment, short-term data and long-term data refer to parameters that can characterize the operation of distributed user-side energy storage power stations. For example, the operation time is long-term data, and the load condition at a certain time point is short-term data.

[0109] In this embodiment, the time series refers to the time order.

[0110] In this embodiment, the preset storage area is set in advance and is used to store the collected real-time operation data.

[0111] The working principle and beneficial effects of the above technical solution are: by transmitting the real-time operation data to the management terminal, it is convenient for the management terminal to perform corresponding processing and analysis operations on the real-time operation data, and to obtain the time span of the real-time operation data through the management terminal, so as to realize the division of the real-time operation data into short-term data and long-term data according to the time span, and store them, so as to facilitate the subsequent determination of the power forecast demand of the distributed user-side energy storage power station, thereby realizing the management of the virtual power plant scheduling.

[0112] Embodiment 4:

[0113] Based on Example 1, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, a data acquisition module, including:

[0114] Data parsing unit, used for:

[0115] Acquire the real-time operation data, and divide the real-time operation data into categories based on the data source;

[0116] Based on the spatiotemporal characteristics, the category splitting results are aligned with the starting points, and the primary observation data and the secondary observation data are determined from the multi-category real-time operation data based on the starting point alignment results;

[0117] Determine the relative change relationship between the data value of the primary observation data and the data value of the secondary observation data, and determine the weights of different categories of real-time operation data based on the relative change relationship;

[0118] Prediction unit, used to:

[0119] Obtain the terminal identity of the distributed user-side energy storage power station, and determine the corresponding real-time operation data set of each data source based on the terminal identity;

[0120] The real-time operating data of each data source of the distributed user-side energy storage power station and the corresponding weights are respectively input into the preset power demand forecasting model for analysis, and the power forecast demand of the distributed user-side energy storage power station is obtained based on the analysis results.

[0121] In this embodiment, the data source refers to the source of the data, which is a reference for classifying real-time operation data, such as energy markets, meteorological services, equipment sensors, distributed user-side energy storage power stations, etc.

[0122] In this embodiment, the spatiotemporal features refer to the same space and time.

[0123] In this embodiment, starting point alignment refers to aligning data generated at the same time and space.

[0124] In this embodiment, the primary observation data and the secondary observation data refer to the main data and the secondary data in the real-time operation data respectively, and the purpose is to determine the importance of the corresponding category of real-time operation data according to the relationship between the value changes.

[0125] In this embodiment, the preset power demand prediction model is configured in advance and is used to predict the power demand of the distributed user-side energy storage power station.

[0126] The working principle and beneficial effects of the above technical solution are: by splitting the real-time operation data into categories and analyzing the real-time operation data of each category obtained after the split, the weights of different categories of real-time operation data can be accurately and effectively determined. Secondly, the terminal identity of the distributed user-side energy storage power station is extracted to effectively bind the distributed user-side energy storage power station with the corresponding real-time operation data set according to the terminal identity. Finally, the real-time operation data of each data source of the distributed user-side energy storage power station and the corresponding weights are respectively input into the preset power demand forecasting model for analysis, so as to effectively determine the power forecast demand of the distributed user-side energy storage power station, thereby providing a reliable basis for power dispatching.

[0127] Embodiment 5:

[0128] Based on Example 1, this embodiment provides a multi-strategy coupled virtual power plant scheduling system, a strategy determination module, including:

[0129] A policy determination unit, configured to:

[0130] Obtain multi-dimensional optimization targets for distributed user-side energy storage power stations, and determine multi-dimensional prediction factors corresponding to each dimensional optimization target;

[0131] Determining a first weight of a multi-dimensional prediction factor based on the attribute parameters of the optimization target of each dimension, and constructing a distributed analysis model based on the multi-dimensional prediction factor and the first weight;

[0132] Based on the distributed analysis model, the power forecast demand is analyzed respectively to obtain N dispatch strategies corresponding to each dimensional optimization target, and the difference parameters of the N dispatch strategies under the same dimensional optimization target are quantitatively analyzed to determine the sensitive parameters of the N dispatch strategies relative to the multi-dimensional prediction factors.

[0133] Based on the values ​​of sensitive parameters, the best scheduling strategy is selected from the N scheduling strategies corresponding to each dimensional optimization target, and the best scheduling strategies corresponding to the multi-dimensional optimization targets are summarized to obtain a multi-dimensional scheduling strategy;

[0134] Coupling coordination unit for:

[0135] Obtain the dispatching business of distributed user-side energy storage power stations and build a multi-level decision-making framework based on the dispatching business;

[0136] Integrate and deploy multi-dimensional scheduling strategies in each level of decision framework, and determine the constraints of each level of decision framework based on the integrated deployment results and scheduling business;

[0137] Determine the second weight of the multi-dimensional scheduling strategy in each level of the decision framework based on the scheduling business;

[0138] The optimal balance point determination unit is used to comprehensively analyze the multi-dimensional scheduling strategy of each level decision framework based on the constraints and the second weight, obtain the global optimal solution under each level decision framework, and obtain the optimal balance point of the multi-dimensional optimization objectives under different scheduling services based on the global optimal solution.

[0139] In this embodiment, the multi-dimensional prediction factors refer to the various different categories of influencing factors that need to be considered under each dimensional optimization goal. For example, when the optimization goal is stable operation, the factors that need to be considered include the equipment performance of the distributed user-side energy storage power station and the load conditions at different times.

[0140] In this embodiment, the attribute parameter refers to the result or purpose that the optimization target of each dimension needs to achieve in the end.

[0141] In this embodiment, the first weight refers to the importance of prediction factors of different dimensions when performing analysis.

[0142] In this embodiment, the distributed analysis model refers to an analysis model corresponding to a multi-dimensional optimization objective.

[0143] In this embodiment, the quantitative analysis of difference parameters refers to determining the differences after comparing and analyzing N scheduling strategies under the same dimensional optimization objective.

[0144] In this embodiment, the sensitive parameter refers to the response plan or data of the scheduling strategy for the multi-dimensional prediction factors.

[0145] In this embodiment, the best scheduling strategy refers to selecting a scheduling strategy with the best scheduling effect from N scheduling strategies corresponding to the optimization target of each dimension.

[0146] In this embodiment, the dispatching service refers to the dispatching items that the distributed user-side energy storage power station needs to carry out, including the dispatching of electric energy.

[0147] In this embodiment, the multi-level decision-making framework includes long-term strategic decisions, daily operation optimization, and emergency response adjustments.

[0148] In this embodiment, the number of integration steps refers to deploying the multi-dimensional scheduling strategies in the decision framework of the corresponding level, so as to determine the decision information under the current decision target through the multi-dimensional scheduling strategies.

[0149] In this embodiment, the constraint condition refers to the limiting information corresponding to each level of the decision framework, such as the items and parameters that need to be optimized in daily optimization.

[0150] In this embodiment, the second weight refers to the corresponding importance of the multi-dimensional scheduling strategy in different levels of decision-making frameworks.

[0151] In this embodiment, the global optimal solution refers to the result obtained after comprehensive analysis of the multi-dimensional scheduling strategies in each tomographic decision framework.

[0152] In this embodiment, the optimal balance point refers to a scheduling solution that is finally determined and can simultaneously meet multi-dimensional optimization objectives under different scheduling services.

[0153] The working principle and beneficial effects of the above technical solution are: by determining the multi-dimensional optimization objectives of the distributed user-side energy storage power station and the multi-dimensional prediction factors corresponding to each dimensional optimization objective, the distributed analysis model can be accurately and effectively constructed according to the multi-dimensional optimization objectives and the multi-dimensional prediction factors. Secondly, the power forecast demand is analyzed through the constructed distributed analysis model to accurately and effectively determine the multi-dimensional scheduling strategy corresponding to the multi-dimensional optimization objectives. Finally, a multi-level decision-making framework is constructed according to the scheduling business of the distributed user-side energy storage power station, and the multi-dimensional scheduling strategy is deployed in different levels of decision-making frameworks respectively, and the global optimal solution under each level decision framework is determined according to the deployment results, and finally the optimal balance point of the multi-dimensional optimization objectives under different scheduling businesses is effectively determined, thereby improving the comprehensiveness and reliability of multi-strategy considerations and ensuring the effect of virtual power plant scheduling.

[0154] Embodiment 6:

[0155] Based on Example 1, this embodiment provides a multi-strategy coupled virtual power plant scheduling system, and the strategy determination unit includes:

[0156] A strategy acquisition subunit is used to acquire the obtained multi-dimensional scheduling strategy and, at the same time, to acquire the basic information of the distributed user-side energy storage power station;

[0157] The performance determination subunit is used to analyze the basic information of the distributed user-side energy storage power station, determine the equipment safe operation range of the distributed user-side energy storage power station, and modify the parameters of the multi-dimensional scheduling strategy based on the equipment safe operation range to obtain the target multi-dimensional scheduling strategy;

[0158] Display subunits for:

[0159] The target multi-dimensional scheduling strategy is transmitted to the management terminal for display, and based on the display result, the user is given the right to manually modify the target multi-dimensional scheduling strategy based on the management terminal;

[0160] Based on the activation results, the modification request submitted by the management terminal is monitored in real time, and the target multi-dimensional scheduling strategy is modified based on the modification request and then locked and packaged.

[0161] In this embodiment, the basic information refers to the maximum load multiplication capacity and electric energy demand of the distributed user-side energy storage power station.

[0162] In this embodiment, the safe operation range of the equipment refers to the operating parameter range that the distributed user-side energy storage power station needs to meet after parsing the basic information of the distributed user-side energy storage power station, that is, the equipment can operate safely and effectively within this range.

[0163] In this embodiment, the target multi-dimensional scheduling strategy refers to the final scheduling strategy obtained by correcting the multi-dimensional scheduling strategy through the equipment safe operation range.

[0164] In this embodiment, the manual modification permission refers to opening the user's permission to adjust the target multi-dimensional scheduling strategy in the management terminal, in order to facilitate adaptive adjustment of the target multi-dimensional scheduling strategy according to actual conditions.

[0165] The working principle and beneficial effects of the above technical solution are: by determining and parsing the basic information of the distributed user-side energy storage power station, the safe operation range of the equipment of the distributed user-side energy storage power station is effectively determined. Secondly, the multi-dimensional scheduling strategy is corrected according to the determined safe operation range of the equipment of the distributed user-side energy storage power station to ensure that the corresponding scheduling operations are performed within the safe operation range of the equipment of the distributed user-side energy storage power station. At the same time, the user is enabled to manually modify the target multi-dimensional scheduling strategy based on the management terminal, so as to adjust the target multi-dimensional scheduling strategy from multiple aspects, thereby ensuring the stable and safe operation of the distributed user-side energy storage power station.

[0166] Embodiment 7:

[0167] Based on Example 1, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, a dispatching management module, including:

[0168] A plan generation unit, used to read the optimal balance point, determine the power supply standard of each user-side energy storage power station according to the optimal balance point, and generate a scheduling plan according to the power supply standard;

[0169] A dispatch control unit, used to generate a distributed electric energy dispatch instruction based on the dispatch plan, and perform electric energy dispatch on the distributed user-side energy storage power station according to the distributed electric energy dispatch instruction;

[0170] The monitoring unit is used to monitor the electric energy dispatch execution data of each user-side energy storage power station in real time, determine the execution result of each user-side energy storage power station according to the execution data, and dynamically optimize the dispatch plan according to the execution result.

[0171] In this embodiment, the electric energy supply standard refers to the power supply adjustment amount of each user-side energy storage power station at the optimal balance point.

[0172] In this embodiment, the distributed electric energy dispatching instruction is generated according to the dispatching plan and is used to dispatch electric energy to the distributed user-side energy storage power station.

[0173] In this embodiment, the electric energy dispatch execution data refers to the specific execution status of dispatching the electric energy of each user-side energy storage power station.

[0174] The working principle and beneficial effects of the above technical solution are: by reading the optimal balance point, the electric power supply standard of each user-side energy storage power station can be effectively determined according to the optimal balance point, and the scheduling plan can be effectively determined according to the electric power supply standard. Secondly, distributed electric power scheduling instructions are generated according to the scheduling plan, and electric power scheduling of distributed user-side energy storage power stations is realized according to the distributed electric power scheduling instructions, thereby ensuring the reliability of electric power scheduling. Finally, the electric power scheduling process is monitored in real time, and the scheduling plan is dynamically optimized according to the monitoring results to ensure the final scheduling effect.

[0175] Embodiment 8:

[0176] Based on Example 7, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, a monitoring unit, including:

[0177] The result acquisition subunit is used to obtain the actual execution result of the electric energy dispatch of the distributed user-side energy storage power station, and to match the actual execution result with the standard effect in the dispatch plan bit by bit;

[0178] Dynamic optimization subunit for:

[0179] Determine the difference parameter between the actual execution result and the standard effect based on the bitwise matching result, and lock the abnormal user-side energy storage power station based on the difference parameter;

[0180] Based on the difference parameters, the dispatch parameters of the abnormal user-side energy storage power station in the dispatch plan are corrected, and based on the correction results and the correlation between the distributed user-side energy storage power stations, the dispatch parameters of each user-side energy storage power station are synchronously corrected to complete the dynamic optimization of the dispatch plan.

[0181] In this embodiment, the standard effect is known in advance.

[0182] In this embodiment, bitwise matching refers to matching the actual execution result with the standard effect in the scheduling plan in a one-to-one correspondence.

[0183] In this embodiment, the difference parameter refers to the specific difference between the actual execution result and the standard effect.

[0184] In this embodiment, an abnormal user-side energy storage power station refers to a user-side energy storage power station whose actual execution result does not meet the standard effect.

[0185] The working principle and beneficial effects of the above technical solution are: by matching the actual execution results with the standard effects in the dispatch plan bit by bit, the abnormal user-side energy storage power station is locked, and secondly, the dispatch parameters of the abnormal user-side energy storage power station are corrected according to the bit-by-bit matching results, thereby ensuring the accuracy and reliability of the dispatch plan, thereby improving the effect of electric energy dispatch of distributed user-side energy storage power stations.

[0186] Embodiment 9:

[0187] Based on Example 7, this embodiment provides a multi-strategy coupled virtual power plant dispatching system, a monitoring unit, including:

[0188] The result analysis subunit is used to:

[0189] Obtain the execution results of each user-side energy storage power station, and analyze the execution results to obtain the power output of each user-side energy storage power station at different time points;

[0190] Obtaining first billing standards of different user-side energy storage power stations at different time points, and obtaining the amount of electric energy sold by each user-side energy storage power station at different time points based on the first billing standards and the electric energy output;

[0191] At the same time, based on the execution result, the electric energy source of each user-side energy storage power station is determined, and based on the second billing standard of the electric energy source, the electric energy cost amount of each user-side energy storage power station is determined;

[0192] The economic benefit determination subunit is used to determine the economic benefit ratio of electric energy based on the electric energy sales amount and the electric energy cost amount, and feed back the economic benefit ratio as a feedback parameter to the management terminal to improve the dispatching plan.

[0193] In this embodiment, the first billing standard refers to the specific billing conditions of different user-side energy storage power stations at different time points.

[0194] In this embodiment, the source of electric energy refers to the electric energy that each user-side energy storage power station needs to dispatch from other user-side energy storage power stations.

[0195] In this embodiment, the second charging standard refers to the specific charging situation corresponding to the electric energy source.

[0196] In this embodiment, the economic benefit ratio refers to the economic benefits generated by each user-side energy storage power station.

[0197] The working principle and beneficial effect of the above technical solution are: by determining the amount of electricity sold and the amount of electricity cost of each user-side energy storage power station at different time points, the economic benefit ratio of each user-side energy storage power station can be accurately and effectively determined, thereby facilitating the improvement of the dispatching plan according to the economic benefit ratio, thereby ensuring the maximization of economic benefits.

[0198] Embodiment 10:

[0199] This embodiment provides a multi-strategy coupled virtual power plant scheduling method, such as Figure 3 As shown, including:

[0200] Step 1: Collect the real-time operation data of each data source, and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station;

[0201] Step 2: Based on the multi-dimensional optimization objectives, the power forecast demand is analyzed in multiple dimensions to obtain a multi-dimensional dispatching strategy, and the multi-dimensional dispatching strategy is coupled and coordinated to determine the optimal balance point of the multi-dimensional optimization objectives;

[0202] Step 3: Generate a dispatch plan based on the optimal balance point, dispatch electric energy to the distributed user-side energy storage power station based on the dispatch plan, and dynamically optimize the dispatch plan based on the execution result of the electric energy dispatch.

[0203] The working principle and beneficial effects of the above technical solution are: by analyzing the real-time operating data collected from each data source, the power forecast demand of the distributed user-side energy storage power station can be accurately and effectively determined. Secondly, the power forecast demand is analyzed according to the multi-dimensional optimization target, and the multi-dimensional scheduling strategy under the multi-dimensional optimization target is accurately and effectively determined, thereby providing data support for the effective scheduling of power plant electricity, and the obtained multi-dimensional scheduling strategy is coupled and coordinated to lock the optimal balance point of the multi-dimensional optimization target. Finally, a scheduling plan is generated according to the obtained optimal balance point to achieve effective retrieval of power plant electricity, and the generated scheduling plan is dynamically optimized to ensure scheduling effect and accuracy. The multi-strategy coupling algorithm enables the virtual power plant to maximize the use of distributed user-side energy storage power station resources and optimize the overall operating efficiency while ensuring economic benefits. The intelligent scheduling strategy and collaborative scheduling mechanism ensure the efficient utilization of distributed energy storage resources and the stable operation of the system.

[0204] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-strategy coupled virtual power plant dispatching system, characterized in that: include: The data acquisition module is used to collect real-time operation data from various data sources and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station; A strategy determination module is used to perform multi-dimensional analysis on the power forecast demand based on the multi-dimensional optimization objectives, obtain the multi-dimensional dispatch strategy, and couple and coordinate the multi-dimensional dispatch strategy to determine the optimal balance point of the multi-dimensional optimization objectives; A dispatch management module, which is used to generate a dispatch plan based on the optimal balance point, and to dispatch electric energy to the distributed user-side energy storage power station based on the dispatch plan, and to dynamically optimize the dispatch plan based on the execution result of the electric energy dispatch; The data acquisition module includes: Data parsing unit, used for: Acquire the real-time operation data, and divide the real-time operation data into categories based on the data source; Based on the spatiotemporal characteristics, the category splitting results are aligned with the starting points, and the primary observation data and the secondary observation data are determined from the multi-category real-time operation data based on the starting point alignment results; Determine the relative change relationship between the data value of the primary observation data and the data value of the secondary observation data, and determine the weights of different categories of real-time operation data based on the relative change relationship; Prediction unit, used to: Obtain the terminal identity of the distributed user-side energy storage power station, and determine the corresponding real-time operation data set of each data source based on the terminal identity; The real-time operation data of each data source of the distributed user-side energy storage power station and the corresponding weights are respectively input into the preset power demand forecasting model for analysis, and the power forecast demand of the distributed user-side energy storage power station is obtained based on the analysis results; The strategy determination module includes: A policy determination unit, configured to: Obtain multi-dimensional optimization targets for distributed user-side energy storage power stations, and determine multi-dimensional prediction factors corresponding to each dimensional optimization target; Determining a first weight of a multi-dimensional prediction factor based on the attribute parameters of the optimization target of each dimension, and constructing a distributed analysis model based on the multi-dimensional prediction factor and the first weight; Based on the distributed analysis model, the power forecast demand is analyzed respectively to obtain N dispatch strategies corresponding to each dimensional optimization target, and the difference parameters of the N dispatch strategies under the same dimensional optimization target are quantitatively analyzed to determine the sensitive parameters of the N dispatch strategies relative to the multi-dimensional prediction factors. Based on the values ​​of sensitive parameters, the best scheduling strategy is selected from the N scheduling strategies corresponding to each dimensional optimization target, and the best scheduling strategies corresponding to the multi-dimensional optimization targets are summarized to obtain a multi-dimensional scheduling strategy; Coupling coordination unit for: Obtain the dispatching business of distributed user-side energy storage power stations and build a multi-level decision-making framework based on the dispatching business; Integrate and deploy multi-dimensional scheduling strategies in each level of decision framework, and determine the constraints of each level of decision framework based on the integrated deployment results and scheduling business; Determine the second weight of the multi-dimensional scheduling strategy in each level of the decision framework based on the scheduling business; The optimal balance point determination unit is used to comprehensively analyze the multi-dimensional scheduling strategy of each level decision framework based on the constraints and the second weight, obtain the global optimal solution under each level decision framework, and obtain the optimal balance point of the multi-dimensional optimization objectives under different scheduling services based on the global optimal solution.

2. A multi-strategy coupled virtual power plant dispatching system according to claim 1, characterized in that: Data acquisition module, including: The data source determination unit is used to: Traverse the virtual power plant dispatch process and obtain the business objectives of each link based on the results of the link traversal; Determine business needs based on business objectives, and determine the real-time and effective data source corresponding to the virtual power plant scheduling based on business needs; A channel building unit is used to extract basic configuration information of the program programming interface of each real-time valid data source, and build a channel between the management terminal and each real-time valid data source based on the basic configuration information according to the preset database driver and connection parameters; The data collection unit is used to query the data in each real-time valid data source based on the constructed path according to the preset database driver, and obtain the real-time operation data to be collected based on the data query result.

3. A multi-strategy coupled virtual power plant dispatching system according to claim 2, characterized in that: Data acquisition unit, including: The data acquisition subunit is used to collect the real-time operation data to be collected and transmit the collected real-time operation data to the management terminal; A data classification subunit, for extracting the time span of the real-time operation data based on the management terminal, and dividing the real-time operation data into short-term data and long-term data according to the time span based on a division standard; The data storage subunit is used to classify and store short-term data and long-term data in a preset storage area based on time series.

4. The multi-strategy coupled virtual power plant dispatching system according to claim 1, characterized in that: A policy determination unit, comprising: A strategy acquisition subunit is used to acquire the obtained multi-dimensional scheduling strategy and, at the same time, to acquire the basic information of the distributed user-side energy storage power station; The performance determination subunit is used to analyze the basic information of the distributed user-side energy storage power station, determine the equipment safe operation range of the distributed user-side energy storage power station, and modify the parameters of the multi-dimensional scheduling strategy based on the equipment safe operation range to obtain the target multi-dimensional scheduling strategy; Display subunits for: The target multi-dimensional scheduling strategy is transmitted to the management terminal for display, and based on the display result, the user is given the right to manually modify the target multi-dimensional scheduling strategy based on the management terminal; Based on the activation results, the modification request submitted by the management terminal is monitored in real time, and the target multi-dimensional scheduling strategy is modified based on the modification request and then locked and packaged.

5. The multi-strategy coupled virtual power plant dispatching system according to claim 1, characterized in that: Scheduling management module, including: A plan generation unit, used to read the optimal balance point, determine the power supply standard of each user-side energy storage power station according to the optimal balance point, and generate a scheduling plan according to the power supply standard; A dispatch control unit, used to generate a distributed power dispatch instruction based on the dispatch plan, and perform power dispatch on the distributed user-side energy storage power station according to the distributed power dispatch instruction; The monitoring unit is used to monitor the electric energy dispatch execution data of each user-side energy storage power station in real time, determine the execution result of each user-side energy storage power station according to the execution data, and dynamically optimize the dispatch plan according to the execution result.

6. A multi-strategy coupled virtual power plant dispatching system according to claim 5, characterized in that: Monitoring unit, including: The result acquisition subunit is used to obtain the actual execution result of the electric energy dispatch of the distributed user-side energy storage power station, and to match the actual execution result with the standard effect in the dispatch plan bit by bit; Dynamic optimization subunit for: Determine the difference parameter between the actual execution result and the standard effect based on the bitwise matching result, and lock the abnormal user-side energy storage power station based on the difference parameter; Based on the difference parameters, the dispatch parameters of the abnormal user-side energy storage power station in the dispatch plan are corrected, and based on the correction results and the correlation between the distributed user-side energy storage power stations, the dispatch parameters of each user-side energy storage power station are synchronously corrected to complete the dynamic optimization of the dispatch plan.

7. A multi-strategy coupled virtual power plant dispatching system according to claim 5, characterized in that: Monitoring unit, including: The result analysis subunit is used to: Obtain the execution results of each user-side energy storage power station, and analyze the execution results to obtain the power output of each user-side energy storage power station at different time points; Obtaining first billing standards of different user-side energy storage power stations at different time points, and obtaining the amount of electric energy sold by each user-side energy storage power station at different time points based on the first billing standards and the electric energy output; At the same time, based on the execution result, the electric energy source of each user-side energy storage power station is determined, and based on the second billing standard of the electric energy source, the electric energy cost amount of each user-side energy storage power station is determined; The economic benefit determination subunit is used to determine the economic benefit ratio of electric energy based on the electric energy sales amount and the electric energy cost amount, and feed back the economic benefit ratio as a feedback parameter to the management terminal to improve the dispatching plan.

8. The multi-strategy coupled virtual power plant scheduling method of the multi-strategy coupled virtual power plant scheduling system according to claim 1, characterized in that: include: Step 1: Collect the real-time operation data of each data source, and analyze the real-time operation data to obtain the power forecast demand of the distributed user-side energy storage power station; Step 2: Based on the multi-dimensional optimization objectives, the power forecast demand is analyzed in multiple dimensions to obtain a multi-dimensional dispatching strategy, and the multi-dimensional dispatching strategy is coupled and coordinated to determine the optimal balance point of the multi-dimensional optimization objectives; Step 3: Generate a dispatch plan based on the optimal balance point, dispatch electric energy to the distributed user-side energy storage power station based on the dispatch plan, and dynamically optimize the dispatch plan based on the execution result of the electric energy dispatch.

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