Multi-target planning method and system for source network load storage cooperation based on quick response

Abstract

The invention discloses a fast response-based multi-target planning method for source network load storage cooperation, which belongs to the field of offshore photovoltaic technology, and comprises the steps of acquiring previous operation data, processing the data, constructing a source network load storage data set, setting constraint conditions according to equipment operation conditions, and establishing a source network load storage data set; performing multi-objective optimization solution on the source network load storage data set by applying a genetic algorithm to obtain a multi-objective optimization result; through the source network load storage multi-objective optimization system, historical data can be subjected to multi-objective optimization and stored in a database in a classified mode, existing data can be analyzed and processed, a new optimization operation scheme is obtained and stored in the database, the database is continuously improved, database scheme matching response is accelerated, and the optimization efficiency is improved. The system operation speed is effectively accelerated, so that the operation characteristics of the system are improved, the power supply reliability is improved, the energy waste is reduced, the sustainable development of the power system is promoted, and the problems of the existing source network load storage operation response delay and the like are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of source-grid-load-storage operation planning, and specifically relates to a multi-objective planning method and system based on rapid response source-grid-load-storage collaboration. Background Art

[0002] With the rapid development of new energy, the grid dispatching mode is changing from the traditional "source follows load" to the "source, grid, load and storage multi-coordinated dispatching control" mode. This change is aimed at coping with the uncertainty and volatility of new energy, improving the flexibility and stability of the grid, and ensuring the effective absorption of new energy. The rapid development of new energy and DC transmission technology has made the characteristics of the grid increasingly complex, and the traditional dispatching mode has been unable to meet the new requirements. Therefore, new technologies and solutions are needed to adapt to these changes and ensure the safe and efficient operation of the grid.

[0003] In order to achieve rapid response and multi-objective optimization, it is necessary to build a comprehensive management platform for source, grid, load and storage resources, which can realize unified management and dispatch of various energy resources, improve resource utilization efficiency and the intelligent level of power grid operation. In the multi-objective planning of source, grid, load and storage, big data technology is used to process and analyze massive data, and cloud computing technology is used for unified management and service of resources. The application of these technologies helps to improve the response speed of the system and the quality of decision-making. Summary of the invention

[0004] The purpose of the present invention is to address the problem of untimely response of the existing integrated management platform, and propose a multi-objective planning method and system based on rapid response source-grid-load-storage collaboration. Through the multi-objective optimization system of source-grid-load-storage, historical data can be multi-objective optimized and classified and stored in a database. Existing data can also be analyzed and processed to obtain new optimized operation plans and store them in the database. By continuously improving the database, the database plan matching response is accelerated, which effectively accelerates the system operation rate, thereby improving the system's operating characteristics, improving power supply reliability, reducing energy waste, promoting the sustainable development of the power system, and solving the existing problems of source-grid-load-storage operation response delays.

[0005] In order to solve the above technical problems, the present invention provides a multi-objective planning method based on rapid response source-grid-load-storage coordination, including: a multi-objective planning method based on rapid response source-grid-load-storage coordination, characterized in that it includes the following steps:

[0006] S1. Obtain the previous operation data of the source side, grid side, load side and storage side, process the data and construct a source grid load storage data set;

[0007] S2. Set constraints according to equipment operating conditions, and use genetic algorithms to perform multi-objective optimization on the source-grid-load-storage data set to obtain multi-objective optimization results;

[0008] S3, performing feasibility analysis on the multi-objective optimization results, outputting a source-grid-load-storage control feasibility plan and storing it in a database;

[0009] S4. Input the data of the source side, grid side, load side and storage side in sequence, call the source-grid-load-storage control feasibility plan and execute the storage operation rules until the input data of the source side, grid side, load side and storage side all meet the storage operation rules, then output the current source-grid-load-storage control feasibility plan and execute the output plan.

[0010] Preferably, the past operation data of the source side, grid side, load side and storage side include all past operation data of power sources, transmission and distribution networks, loads and energy storage equipment, as well as wind and light data affecting power generation of photovoltaic equipment and wind turbines, and also include equipment performance data of source, grid, load and storage.

[0011] Preferably, the processing of data comprises:

[0012] Sa, divide the source data of the source network load storage into n source subsets according to the data range [a, b];

[0013] Sb, divide each source sub-set into n network sub-sets according to the network end data range [c, d] of the source network load storage;

[0014] Sc, on the basis of each grid set, the load end range [e, f] of the source grid load storage is divided into n different load sub-sets in the same way;

[0015] Sd, according to each load subset, it is divided into n different categories according to the storage end data range [g,h] to obtain the source grid load storage data set.

[0016] Preferably, the constraints in S2 include power balance constraints, wind and solar power abandonment constraints, energy storage balance constraints, and rotating reserve constraints; and the multi-objective optimization includes operation stability optimization and operation cost optimization.

[0017] Preferably, S2 includes: under constraints, optimizing configuration with the goal of best system operation stability, lowest system operation cost and maximum benefit, using genetic algorithm to perform multi-objective optimization solution, thereby obtaining a series of non-dominated solutions, and selecting from the non-dominated solutions.

[0018] Preferably, the selection from the non-dominated solutions includes: selecting a multi-objective optimization solution with the best benefit, stable operation, minimum wind and solar abandonment rate, and maximum utilization of renewable resources from the non-dominated solutions; wherein the specific selection process is as follows:

[0019] Sa, normalize the income, stability, wind and solar power abandonment rate and renewable resource utilization rate, where the lower the stability and wind and solar power abandonment rate, the better; the higher the income and renewable resource utilization rate, the better;

[0020] Sb, calculate the K value of the multi-objective optimization solution respectively, the specific calculation expression is as follows:

[0021] K=ω1θ1+ω2θ2+ω3θ3+ω4θ4;

[0022] Among them, ω1, ω2, ω3, and ω4 are the weights of income, operation stability, wind and solar abandonment rate, and renewable resource utilization rate, and they satisfy Its specific value is defined according to the requirements of the power grid; θ1, θ2, θ3, and θ4 are dimensionless values ​​of revenue, operational stability, wind and solar abandonment rate, and renewable resource utilization rate, respectively;

[0023] Sc. Set the selection threshold and select the multi-objective optimization solution with a K value greater than or equal to the selection threshold as the best optimization solution.

[0024] Preferably, in S3, the feasibility analysis of the multi-objective optimization results includes:

[0025] Verify the constraints of power supply and demand balance, and exclude planning schemes that do not meet the power constraints; verify the stability of the source-grid-load-storage system, and exclude planning schemes that do not meet the system stability; verify the feasibility of emergency response, and exclude planning schemes that do not meet the feasibility of emergency response; verify the cost feasibility, and exclude planning schemes that do not meet the cost feasibility; sort the best optimization schemes that meet all constraints according to the weighted matrix of the verification conditions, select the best multi-objective optimization scheme and store it in the database.

[0026] Preferably, the storage operation rules include:

[0027] Sa. Input source grid load storage data and collect source grid load storage operation and performance data; read source data, search in the database with the x% range of source data, determine whether there is a multi-objective optimization solution for the source data, if not, update the database, and then perform multi-objective optimization solution until there is a multi-objective optimization solution and store it in the database; x% is defined according to the accuracy requirements;

[0028] Sb. Search the database within the range of x% of the network data to determine whether there is a multi-objective optimization solution for the network data. If there is no multi-objective optimization solution, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database;

[0029] Sc. Search the database within the range of x% of the load end data to determine whether there is a multi-objective optimization solution for the load end data. If there is no multi-objective optimization solution, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database;

[0030] Sd. Search in the database within the range of x% of the storage-end data to determine whether there is a multi-objective optimization solution for the storage-end data. If the number of multi-objective optimization solutions is 0, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database; if the number of multi-objective optimization solutions is 1, output the system operation; if there are 2 or more multi-objective optimization solutions within the final search range, calculate the variance of the source grid load storage data respectively, and select the solution with the smallest variance as the best solution for output operation.

[0031] The multi-objective planning system based on rapid response source-grid-load-storage coordination is applicable to the multi-objective planning method based on rapid response source-grid-load-storage coordination mentioned above, including:

[0032] The data acquisition module is used to collect relevant operating data of the target device during the operation of the source grid load storage device;

[0033] The data processing module is used to process the data, classify the data in an orderly manner, and classify the data into different types according to the data characteristics;

[0034] The program planning module takes system operation stability and operation benefits as the goals, combines constraints, uses genetic algorithms to solve multiple objectives and conduct feasibility analysis, outputs source-grid-load-storage control feasibility plans and stores them in the database;

[0035] The system calls the module, inputs source grid load storage data, calls the source grid load storage control feasibility plan and executes the storage operation rules until the optimal plan is output and operated.

[0036] Beneficial effects of the present invention:

[0037] 1. This solution can perform multi-objective optimization on historical data and store them in a classified manner in a database through a multi-objective optimization system of source, grid, load and storage. It can also analyze and process existing data to obtain new optimized operation plans and store them in the database. By continuously improving the database, the matching response of the database plan is accelerated, which effectively accelerates the system operation rate, thereby improving the system's operating characteristics, improving power supply reliability, reducing energy waste, promoting the sustainable development of the power system, and solving existing problems such as delayed response of source, grid, load and storage operations.

[0038] 2. Through feasibility analysis, this plan has obtained the best solution and stored it in the database. This method can significantly improve the operating characteristics of the system, reduce the impact of load fluctuations on the stability of the power system, and effectively improve the absorption of new energy.

[0039] 3. This solution adds a solution matching and data update system, and the matching similarity can reach more than 98%. With the continuous improvement and updating of the database solution, the matching rate will be faster and the system operation will be further stabilized, thereby achieving rapid response of the system.

[0040] 4. This solution can classify and process source, grid, load and storage data to achieve rapid data matching, thereby enhancing the response rate of the multi-objective optimization solution, solving the operation lag problem where planning and scheduling lag behind load fluctuations, ensuring high precision and high efficiency of source, grid, load and storage coordinated operation, and effectively overcoming the shortcomings of control lag in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0042] Figure 1 is a flow chart of the present invention;

[0043] Figure 2 A flow chart of source-grid-load-storage data processing provided by an embodiment of the present invention;

[0044] Figure 3 A flow chart of a multi-objective optimization algorithm provided for an embodiment of the present invention;

[0045] Figure 4 A flow chart of source-grid-load-storage multi-objective optimization scheme matching provided for an embodiment of the present invention;

[0046] Figure 5 It is a structural schematic diagram of the electronic device of the present invention;

[0047] Figure 6 A schematic diagram of the structure of a multi-objective planning system for source-grid-load-storage operation based on rapid response provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] Example 1: Figure 1 As shown, a multi-objective planning method and system based on rapid response source-grid-load-storage coordination includes: collecting the previous operation data of the source-grid-load-storage and processing the data. Constructing a comprehensive data set that satisfies the source-grid-load-storage according to the processed data; setting constraint boundaries according to the equipment operating conditions, and performing multi-objective optimization on the constructed source-grid-load-storage data set; performing feasibility analysis on the multi-objective optimization results, outputting a source-grid-load-storage control feasibility plan, and storing it in a database for rapid response coordinated control operation of the source-grid-load-storage; inputting source-grid-load-storage data and matching it with the database optimization plan. If there is a matching plan, it is output for operation. If there is no similar plan, the database is updated, the plan is re-matched, and then the operation is output.

[0050] Furthermore, the source, grid, load and storage operation data and weather equipment performance data include: all previous operation data of power supply, transmission and distribution network, load and energy storage equipment, as well as wind and light data affecting photovoltaic equipment power generation and wind turbines, as well as source, grid, load and storage equipment performance data.

[0051] Furthermore, the data is classified and processed. First, the source data of the source grid load storage is divided into n source subsets according to the data range [a, b]. Then, each source subset is divided into n network subsets according to the network data range [c, d] of the source grid load storage. On the basis of each network-end network subset, the source grid load storage is divided into n different load subsets in the same way according to the load end range [e, f]. Finally, the load subset of each load end is divided into n different categories according to the storage end data range [g, h]. Multi-objective optimization and feasibility analysis are carried out according to the n different categories. After the best solution is obtained, the final data is fused and stored in the database for rapid matching of subsequent data.

[0052] Furthermore, the equipment operating condition constraint boundaries include: power balance constraint, wind and solar curtailment constraint, energy storage balance constraint, and spinning reserve constraint. Multi-objective optimization includes operation stability optimization and operation cost optimization.

[0053] Furthermore, under various constraints, the system operation stability is the best, the system operation cost is the lowest, and the benefit is the maximum. The multi-objective optimization solution is mainly solved by using the genetic algorithm using Python's geatpy library, so as to obtain a series of non-dominated solutions and select from the non-dominated solutions. The selection principle based on a series of non-dominated solutions is: the best benefit, stable operation, the lowest wind and solar abandonment rate, and the optimization scheme that maximizes the use of renewable resources such as wind and solar. Among them, the specific selection process is as follows:

[0054] Sa, normalize the benefits, stability, wind and solar abandonment rate and renewable resource utilization rate; for stability (voltage and frequency fluctuation variance) and wind and solar abandonment rate, the lower the better, you can use:

[0055] where f max and f min are the maximum and minimum values ​​among all solutions respectively.

[0056] For benefits and renewable resource utilization, the higher the better, normalization can be used:

[0057] where f max and f min are the maximum and minimum values ​​among all solutions respectively.

[0058] Sb, calculate the K value of the multi-objective optimization solution respectively, the specific calculation expression is as follows:

[0059] K=ω1θ1+ω2θ2+ω3θ3+ω4θ4;

[0060] Among them, ω1, ω2, ω3, and ω4 are the weights of income, operation stability, wind and solar abandonment rate, and renewable resource utilization rate, and they satisfy Its specific value is defined according to the requirements of the power grid; θ1, θ2, θ3, and θ4 are dimensionless values ​​of revenue, operational stability, wind and solar abandonment rate, and renewable resource utilization rate, respectively;

[0061] Sc. Set the selection threshold and select the multi-objective optimization solution with a K value greater than or equal to the selection threshold as the best optimization solution.

[0062] It is understandable that there may be multiple optimal optimization solutions in the end, and further processing is being carried out on this situation.

[0063] like Figure 3 As shown, the specific method of genetic algorithm is as follows:

[0064] 1) Read source grid load and storage data.

[0065] 2) Initialize the population, using rated power and capacity as search variables.

[0066] 3) Call the fitness function to solve the fitness value of each individual.

[0067] 4) Sort the groups. Set the number of non-dominated groups n p Individuals with a value of 0 are placed in the initial layer, and all individuals dominate the set S p n p Subtract 1. Repeat the process until the entire population is stratified and the non-dominated layer F is obtained. n (n=1,2,…,k).

[0068]

[0069] is the objective function value of the jth target at point i.

[0070] 5) Sort the populations in each layer by the size of the fitness value, and the crowding degree of the boundary individuals in each layer is infinite.

[0071] 6) Produce offspring through selection, crossover, and mutation. Compare individuals with each other, and the one with the larger non-dominated order wins, and the one with the larger crowding distance of the same dominated order wins.

[0072] 7) Generate a new population. In the iteration, the elite retention strategy is adopted to form a new population R(t) from the parent population P(t) and the child population Q(t). Then, R(t) is stratified and added to the new population P(t+1) until the number of individuals in P(t+1) is N. p .

[0073] 8) Repeat the above process until the maximum number of iterations is reached.

[0074] Furthermore, for the feasibility verification of multi-objective optimization schemes: verify the power supply and demand balance constraints, and exclude planning schemes that do not meet the power constraints; verify the stability of the source-grid-load-storage system, and exclude planning schemes that do not meet the system stability; verify the feasibility of emergency response, and exclude planning schemes that do not meet the feasibility of emergency response; verify the cost feasibility, and exclude planning schemes that do not meet the cost feasibility; sort the best optimization schemes that meet all constraints according to the weighted matrix of the verification conditions, select the optimal scheme, and store it in the database.

[0075] Furthermore, the source grid load storage data is input, the source grid load storage operation and performance data is collected, the source end data is read, and the x% range of the source end data is searched in the database to see if there is a multi-objective optimization solution. If there is no multi-objective optimization solution, the database is updated, that is, the source grid load storage data of this time is input, multi-objective optimization is performed, feasibility analysis is performed, and the final optimization solution is obtained, stored in the database, and then re-matched; if there is, search within the x% range of the grid end data to see if there is a multi-objective optimization solution. If not, the database is updated and re-matched. If so, the next step is to search within the x% range of the load end data, and continue to determine whether there is a multi-objective optimization solution. If not, the database is updated and re-matched. If so, the last storage end data is searched within the x% range to determine whether there is a multi-objective optimization solution. If not, the database is updated and re-matched. If there are multiple optimization solutions within the search range, the best solution is selected, that is, the solution with the smallest variance with the source grid load storage data is output and operated. The x% range search can be defined according to the accuracy requirements of the power grid.

[0076] An electronic device, characterized in that it includes: a central processing unit, a storage medium, and a computer executable program that can be run on the central processing unit; a high-reading speed storage medium, on which the executable computer program, test files and other related contents are stored. It is characterized in that the program and the test file can be executed by the central processing unit to implement a multi-objective planning method for source-grid-load-storage coordination based on rapid response.

[0077] A multi-objective planning method based on rapid response of source-grid-load-storage collaboration consists of four modules: Data acquisition module: This module collects relevant operating data of the target equipment during the operation of the source-grid-load-storage equipment to form a comprehensive data set of source-grid-load-storage. The data set includes the operating data of the source-grid-load-storage equipment in the past, the wind-solar-light temperature data, and the equipment performance data. Data processing module: This module classifies the data in an orderly manner and classifies them into different types according to the data characteristics to facilitate subsequent data matching and control operation. Scheme planning module: With the system operation stability and operation benefit as the goal and the operation conditions as the boundary constraints, the genetic algorithm is used to solve the multi-objective problem, and the feasibility of a series of non-dominated solutions is verified. The solution with the best benefit, stable operation, the lowest wind-solar-light abandonment rate, and the maximum utilization of renewable resources such as wind and solar light is selected as the final optimization solution, and it is classified and stored in the database. System call module: Input the source-grid-load-storage data and match it with the database optimization solution. If there is a similar or close matching solution, the control output operation of the power system is carried out. If there is no similar solution, the database is updated, the solution is re-matched, and the operation is output again.

[0078] Example 2: Figure 1 As shown in the figure, the multi-objective planning method based on rapid response source-grid-load-storage coordination includes:

[0079] (1) Collect the previous operation data of the source, grid, load and storage, and process the data. Construct a comprehensive data set that satisfies the source, grid, load and storage requirements based on the processed data;

[0080] It is understandable that the purpose of collecting historical operation data of source, grid, load and storage and classifying the data is to quickly match the subsequent database optimization plan with the input data. The collection of source, grid, load and storage operation data includes: all previous operation data of power supply, transmission and distribution network, load, and energy storage equipment, as well as wind and light data that affect photovoltaic equipment power generation and wind turbines, and also includes the equipment performance data of source, grid, load and storage. The collection of operation data, wind photovoltaic and source, grid, load and storage equipment performance data is mainly for setting boundaries and constraints for subsequent multi-objective optimization.

[0081] Specifically, if Figure 2 As shown, the source-grid-load-storage data processing flow provided by the embodiment of the present application includes: processing data including data feature extraction and classification, firstly dividing the source-end data of the source-grid-load-storage into different subsets according to the data range, and then dividing the grid end of the source-grid-load-storage into different subsets according to each subset, and then dividing the load end of the source-grid-load-storage into different subsets in the same way on the basis of the grid end sub-set, and finally dividing the storage end data range into different categories according to the load end sub-set, performing multi-objective optimization and feasibility analysis according to different categories, and after obtaining the best solution, fusing the final data and storing it in the database for rapid matching of subsequent data.

[0082] Set constraint boundaries according to equipment operating conditions and perform multi-objective optimization on the constructed source-grid-load-storage data set;

[0083] Specifically, the constraint boundaries are set according to the collected wind and solar power output of the source, grid, load and storage, as well as the equipment performance data conditions, including: power balance constraint, wind and solar power abandonment constraint, energy storage balance constraint, and rotating reserve constraint. Its multi-objective optimization includes operation stability optimization and operation cost optimization.

[0084] Furthermore, under various constraints, the system operation stability is the best, the system operation cost is the lowest, and the benefit is the maximum. The multi-objective optimization solution is mainly solved by using the genetic algorithm using the geatpy library of Python, so as to obtain a series of non-dominated solutions and select from the non-dominated solutions. The selection principle based on a series of non-dominated solutions is: the best benefit, stable operation, the lowest wind and solar abandonment rate, and the optimization scheme of maximizing the use of renewable resources such as wind and solar.

[0085] (3) Conduct feasibility analysis on the multi-objective optimization results, output a feasible control plan for the source, grid, load and storage, and store it in the database to facilitate rapid response and coordinated control operation of the source, grid, load and storage.

[0086] (4) Input source grid load and storage data and match it with the database optimization plan. If there is a matching plan, it will be output and run. If there is no similar plan, the database will be updated, the plan will be re-matched, and then the output will be run.

[0087] like Figure 4 As shown, the specific process of operation plan data matching is as follows: input source grid load storage data, collect source grid load storage operation and performance data, read source data, and search in the database within the range of x% of the source data to see if there is a multi-objective optimization plan. If there is no multi-objective optimization solution, the database is updated, that is, the source grid load storage data is input, multi-objective optimization is performed, feasibility analysis is performed, the final optimization solution is obtained, stored in the database, and then re-matched; if there is, search within the x% range of the grid data to see if there is a multi-objective optimization solution. If not, update the database and re-match. If yes, continue to search within the x% range of the load data in the next step, and continue to judge whether there is a multi-objective optimization solution. If not, update the database and re-match. If yes, search within the x% range of the storage data in the last step, and continue to judge whether there is a multi-objective optimization solution. If the number of multi-objective optimization solutions is 0, update the database, and then perform multi-objective optimization solutions until there is a multi-objective optimization solution and store it in the database; if the number of multi-objective optimization solutions is 1, the output system runs; if there are 2 or more multi-objective optimization solutions within the final search range, the variance of the source grid load storage data is calculated respectively, and the solution with the smallest variance is selected as the best solution for output operation. That is, the solution with the smallest variance with the source grid load storage data is output and operated. The x% range search can be defined according to the accuracy requirements of the power grid.

[0088] Since the source, grid, load and storage data are classified in sequence according to the source side, grid side, load side and storage side, in order to facilitate matching with the database, it is necessary to input the source side, grid side, load side and storage side data in sequence for matching. This matching method has faster search speed and higher search accuracy.

[0089] The above scheme matching of source, grid, load and storage is performed with a search range of x%, and the final matching source, grid, load and storage similarity can reach more than 1-x%, which greatly enhances the feasibility of the matching scheme.

[0090] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device may include:

[0091] A memory, a processor, and a computer program stored in the memory and executable on the processor.

[0092] When the processor executes the program, the X-ray image defect detection method based on regional feature extraction provided in the above embodiment is implemented.

[0093] Furthermore, the electronic device further comprises:

[0094] Communication interface, used for communication between memory and processor.

[0095] Memory is used to store computer programs that can be run on the processor.

[0096] The memory may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0097] If the memory, processor and communication interface are implemented independently, the communication interface, memory and processor can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0098] Optionally, in a specific implementation, if the memory, processor and communication interface are integrated on a chip, the memory, processor and communication interface can communicate with each other through an internal interface.

[0099] The processor may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0100] Figure 6 A schematic diagram of the structure of a multi-objective planning system for source-grid-load-storage operation based on rapid response provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, a multi-objective planning system for source-grid-load-storage operation based on rapid response includes: data collection and processing equipment and multi-objective operation optimization equipment, operation plan matching equipment, data storage management equipment, and operation status display equipment.

[0101] The data collection and processing equipment collects the operation data, wind, light, temperature data and equipment performance data of the source, grid, load and storage equipment and classifies and processes the data; with the data set constructed by the data collection and processing equipment as input, the source, grid, load and storage prediction equipment uses GPU and CPU through program instructions to classify and process the input data, and then outputs the source, grid, load and storage data category; with the source, grid, load and storage data category as input, the multi-objective operation optimization equipment calls system resources through program instructions, calculates and processes the input data, and then outputs the source, grid, load and storage operation plan; the source, grid, load and storage operation planning results will be transmitted to the operation status display equipment and the data storage management equipment. Among them, the operation status display equipment visualizes the source, grid, load and storage operation planning results, including power generation capacity, energy storage operation status, power grid flow, etc. The operation status display equipment is controlled by the multi-objective operation optimization equipment and uses an LED liquid crystal display to complete the display. The data storage management equipment stores and manages the source, grid, load and storage prediction sequence and operation planning results, which can be read and queried at any time. The device is controlled by a computer program and the storage medium is a mechanical hard disk.

[0102] In summary, the embodiments of the present application have the following advantages:

[0103] It is able to classify and process source, grid, load and storage data, thereby achieving rapid data matching, enhancing the response rate of the multi-objective optimization scheme, solving the operational lag problem where planning and scheduling lag behind load fluctuations, ensuring high precision and high efficiency in the coordinated operation of source, grid, load and storage, and effectively overcoming the shortcomings of control lag in related technologies.

[0104] This application provides a multi-objective optimization method, and through feasibility analysis, obtains the best solution and stores it in the database. This method can significantly improve the operating characteristics of the system, reduce the impact of load fluctuations on the stability of the power system, and effectively improve the absorption of new energy.

[0105] This application also adds a solution matching and data update system, and the matching similarity can reach more than 98%. With the continuous improvement and updating of the database solution, the matching rate will be faster and the system operation will be further stabilized, thereby achieving rapid response of the system.

[0106] The system is a multi-objective optimization system of source, grid, load and storage. It can perform multi-objective optimization on historical data and store it in a database by category. It can also analyze and process existing data to obtain new optimized operation plans and store them in the database. By continuously improving the database, the database plan matching response is accelerated, which effectively accelerates the system operation rate, thereby improving the system's operating characteristics, improving power supply reliability, reducing energy waste, promoting the sustainable development of the power system, and solving existing problems such as source, grid, load and storage operation response delays.

[0107] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A multi-objective planning method based on rapid response source-grid-load-storage coordination, characterized by: The steps include: S1. Obtain the previous operation data of the source side, grid side, load side and storage side, process the data and construct a source grid load storage data set; S2. Set constraints according to equipment operating conditions, and use genetic algorithms to perform multi-objective optimization on the source-grid-load-storage data set to obtain multi-objective optimization results; S3, performing feasibility analysis on the multi-objective optimization results, outputting a source-grid-load-storage control feasibility plan and storing it in a database; S4. Input the data of the source side, grid side, load side and storage side in sequence, call the source-grid-load-storage control feasibility plan and execute the storage operation rules until the input data of the source side, grid side, load side and storage side all meet the storage operation rules, then output the current source-grid-load-storage control feasibility plan and execute the output plan.

2. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 1 is characterized by: The past operation data of the source side, grid side, load side and storage side include all past operation data of power sources, transmission and distribution networks, loads and energy storage equipment, as well as wind and light data that affect power generation of photovoltaic equipment and wind turbines, and also include equipment performance data of source, grid, load and storage.

3. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 1 is characterized in that: The processing of data includes: Sa, divide the source data of the source network load storage into n source subsets according to the data range [a, b]; Sb, divide each source sub-set into n network sub-sets according to the network end data range [c, d] of the source network load storage; Sc, on the basis of each grid set, the load end range [e, f] of the source grid load storage is divided into n different load sub-sets in the same way; Sd, according to each load subset, it is divided into n different categories according to the storage end data range [g,h] to obtain the source grid load storage data set.

4. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 1 is characterized by: The constraints in S2 include power balance constraints, wind and solar power abandonment constraints, energy storage balance constraints, and rotating reserve constraints; the multi-objective optimization includes operation stability optimization and operation cost optimization.

5. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 4 is characterized in that: The S2 includes: under the constraints, optimizing the configuration with the goal of best system operation stability, lowest system operation cost and maximum benefit, using genetic algorithm to solve multi-objective optimization, so as to obtain a series of non-dominated solutions, and selecting from the non-dominated solutions.

6. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 5 is characterized in that: The selection from the non-dominated solutions includes: selecting a multi-objective optimization solution with the best benefit, stable operation, minimum wind and solar abandonment rate, and maximum utilization of renewable resources from the non-dominated solutions; wherein the specific selection process is as follows: Sa, normalize the income, stability, wind and solar power abandonment rate and renewable resource utilization rate, where the lower the stability and wind and solar power abandonment rate, the better; the higher the income and renewable resource utilization rate, the better; Sb, calculate the K value of the multi-objective optimization solution respectively, the specific calculation expression is as follows: K=ω1θ1+ω2θ2+ω3θ3+ω4θ4; Among them, ω1, ω2, ω3, and ω4 are the weights of income, operation stability, wind and solar abandonment rate, and renewable resource utilization rate, and they satisfy Its specific value is defined according to the requirements of the power grid; θ1, θ2, θ3, and θ4 are dimensionless values ​​of revenue, operational stability, wind and solar abandonment rate, and renewable resource utilization rate, respectively; Sc. Set the selection threshold and select the multi-objective optimization solution with a K value greater than or equal to the selection threshold as the best optimization solution.

7. The multi-objective planning method for source-grid-load-storage coordination based on rapid response according to claim 5 is characterized in that: In S3, the feasibility analysis of the multi-objective optimization results includes: Verify the power supply and demand balance constraints and eliminate planning schemes that do not meet the power constraints; Verify the stability of the source-grid-load-storage system and eliminate planning schemes that do not meet the system stability requirements; Verify the feasibility of emergency response and eliminate planning schemes that do not meet the feasibility requirements of emergency response; Verify cost feasibility and eliminate planning schemes that do not meet cost feasibility requirements; The best optimization solutions that meet all constraints are sorted according to the verification condition weighted matrix, and the best multi-objective optimization solution is selected and stored in the database.

8. The multi-objective planning method based on rapid response source-grid-load-storage coordination according to claim 1 is characterized in that: The storage operation rules include: Sa. Input source grid load storage data and collect source grid load storage operation and performance data; read source data, search in the database with the x% range of source data, determine whether there is a multi-objective optimization solution for the source data, if not, update the database, and then perform multi-objective optimization solution until there is a multi-objective optimization solution and store it in the database; x% is defined according to the accuracy requirements; Sb. Search the database within the range of x% of the network data to determine whether there is a multi-objective optimization solution for the network data. If there is no multi-objective optimization solution, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database; Sc. Search the database within the range of x% of the load end data to determine whether there is a multi-objective optimization solution for the load end data. If there is no multi-objective optimization solution, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database; Sd. Search in the database within the range of x% of the storage-end data to determine whether there is a multi-objective optimization solution for the storage-end data. If the number of multi-objective optimization solutions is 0, update the database and perform multi-objective optimization again until a multi-objective optimization solution exists and is stored in the database; if the number of multi-objective optimization solutions is 1, output the system operation; if there are 2 or more multi-objective optimization solutions within the final search range, calculate the variance of the source grid load storage data respectively, and select the solution with the smallest variance as the best solution for output operation.

9. A multi-objective planning system based on rapid response source-grid-load-storage coordination, applicable to the multi-objective planning method based on rapid response source-grid-load-storage coordination as claimed in any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to collect relevant operating data of the target device during the operation of the source grid load storage device; The data processing module is used to process the data, classify the data in an orderly manner, and classify the data into different types according to the data characteristics; The scheme planning module takes system operation stability and operation benefits as the goal, combines constraints, uses genetic algorithms to solve multiple objectives and conduct feasibility analysis, outputs source-grid-load-storage control feasibility plans and stores them in the database; The system calls the module, inputs source grid load storage data, calls the source grid load storage control feasibility plan and executes the storage operation rules until the optimal plan is output and operated.

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