Virtual power plant cooperative control scheduling method and system

Abstract

The invention is suitable for the technical field of power grid control, and provides a virtual power plant cooperative control scheduling method and system, and the method comprises the following steps: constructing a multi-dimensional resource library, each resource node in the multi-dimensional resource library corresponding to real-time adjustable power, power adjustment cost, response delay time, a carbon emission coefficient and a resource availability coefficient; establishing a global optimization model at the cloud, wherein the global optimization model fuses economy, environmental benefits and system stability; solving the global optimization model by adopting an improved non-dominated sorting genetic algorithm to obtain an optimal scheduling strategy, and issuing the optimal scheduling strategy to a control center and an edge terminal; and determining a real-time state of the resource node based on the edge terminal, and adjusting the optimal scheduling strategy according to the real-time state. According to the method, the response precision and the system stability of the virtual power plant are remarkably improved by constructing the global optimization model and dynamic cooperative control and adopting the improved non-dominated sorting genetic algorithm for solving.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and specifically relates to a virtual power plant collaborative control and dispatching method and system. Background Technique

[0002] With the large-scale access of renewable energy sources (such as wind power and photovoltaic power) to the power grid, the volatility and uncertainty of the power system have increased significantly, and the traditional centralized power generation dispatching mode is difficult to adapt to the flexible characteristics of distributed energy sources. As a new type of energy aggregation management mode, the virtual power plant realizes the collaborative optimization dispatching of resources by integrating distributed power sources, energy storage systems, controllable loads and other resources, and has become a key technology to improve the flexibility and reliability of the power grid. However, the existing virtual power plant dispatching technologies face the following challenges: there are inherent contradictions among economy, environmental benefits and system stability, and it is difficult for traditional single-objective optimization methods to take them into account; moreover, multi-objective optimization problems involve high-dimensional decision variables and complex constraints, and traditional algorithms are prone to fall into local optima and have low computational efficiency. Therefore, it is necessary to provide a virtual power plant collaborative control and dispatching method and system to solve the above problems. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a virtual power plant collaborative control and dispatching method and system to solve the problems existing in the above background technology.

[0004] The present invention is implemented as follows. A virtual power plant collaborative control and dispatching method, the method includes the following steps: Construct a multi-dimensional resource library, and each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power regulation cost, response delay time, carbon emission coefficient and resource availability coefficient; Aggregate all resource nodes in the control center to form a resource pool of the virtual power plant; Establish a global optimization model in the cloud, and the global optimization model integrates economy, environmental benefits and system stability; Use an improved non-dominated sorting genetic algorithm to solve the global optimization model, obtain the optimal dispatching strategy, and send the optimal dispatching strategy to the control center and the edge terminal; Based on the edge terminal, determine the real-time state of the resource node, and adjust the optimal dispatching strategy according to the real-time state.

[0005] As a further solution of the present invention: the step of constructing the multi-dimensional resource library specifically includes: Collect the power generation impact data of each resource node, and determine the real-time adjustable power according to the power generation impact data; Determine the power regulation cost, response delay time and carbon emission coefficient of each resource node according to the resource node type and historical data; Determine the predicted power, rated power, and power generation equipment status of the resource nodes, and calculate the resource availability factor; Integrate the real-time adjustable power, power regulation cost, response delay time, carbon emission factor, and resource availability factor of all resource nodes to construct a multi-dimensional resource library.

[0006] As a further solution of the present invention: The step of establishing a global optimization model in the cloud specifically includes: Determine the total regulation power of the virtual power plant according to the load demand of the power grid and the grid interaction power; Determine the economic objective according to the power regulation cost, determine the environmental protection objective according to the carbon emission factor, and determine the stability objective according to the response delay time and the resource availability factor; Construct a global optimization model according to the total regulation power, economic objective, environmental protection objective, and stability objective.

[0007] As a further solution of the present invention: The step of using the improved non-dominated sorting genetic algorithm to solve the global optimization model to obtain the optimal scheduling strategy specifically includes: Perform chromosome coding, where each gene position corresponds to the regulation power ΔPi of a resource node, and directly map the chromosome to the values of the three objective functions of economy, environmental protection, and stability; Initialize the population according to a predetermined quantity, and use stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated based on the perturbation of the neighborhood of the historical optimal solution, C% of the individuals are generated by greedy heuristics, and A + B + C = 100; Determine the dominance relationship of each individual, and divide the population into multiple non-dominated levels according to the dominance relationship. The first level is the solution that is not dominated by any individual at all, the second level is the solution that is only dominated by the first level, and so on; For the individuals within each level, calculate the normalized distance of the three objective values respectively; in each objective direction, sort the individuals according to the normalized value, calculate the distance between adjacent individuals in the objective space, and determine the crowding degree; Perform genetic iterative operations on the population according to the non-dominated level and the crowding degree, and output the optimal scheduling strategy.

[0008] As a further solution of the present invention: The step of adjusting the optimal scheduling strategy according to the real-time state specifically includes: When the resource availability factor in the real-time state is lower than the availability threshold, reduce the regulation power of the resource node proportionally; When the response delay time is higher than the delay threshold, issue an instruction in advance, and the advance time is determined according to the response delay time and the safety margin.

[0009] Another object of the present invention is to provide a virtual power plant collaborative control and dispatching system, which includes: A resource library construction module for constructing a multi-dimensional resource library, where each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient; A resource node aggregation module for aggregating all resource nodes in the control center to form a resource pool of the virtual power plant; An optimization model establishment module for establishing a global optimization model in the cloud, where the global optimization model integrates economy, environmental benefits, and system stability; An optimal dispatching strategy module for solving the global optimization model by using an improved non-dominated sorting genetic algorithm to obtain an optimal dispatching strategy, and sending the optimal dispatching strategy to the control center and the edge terminal; An edge terminal adjustment module for determining the real-time state of the resource node based on the edge terminal and adjusting the optimal dispatching strategy according to the real-time state.

[0010] As a further solution of the present invention: the resource library construction module includes: An adjustable power unit for collecting power generation impact data of each resource node and determining real-time adjustable power according to the power generation impact data; A historical data analysis unit for determining the power adjustment cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data; A resource availability unit for determining the predicted power, rated power, and power generation equipment status of the resource node, and calculating the resource availability coefficient; A resource library construction unit for integrating the real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient of all resource nodes to construct a multi-dimensional resource library.

[0011] As a further solution of the present invention: the optimization model establishment module includes: A total adjustment power unit for determining the total adjustment power of the virtual power plant according to the load demand of the power grid and the power grid interaction power; A multi-dimensional target determination unit for determining an economic target according to the power adjustment cost, an environmental protection target according to the carbon emission coefficient, and a stability target according to the response delay time and the resource availability coefficient; A global optimization model unit for constructing a global optimization model according to the total adjustment power, economic target, environmental protection target, and stability target.

[0012] As a further solution of the present invention: the optimal dispatching strategy module includes: Chromosome coding unit, used for chromosome coding, where each gene locus corresponds to the regulation power ΔPi of a resource node, and the chromosome is directly mapped to the objective function values of economy, environmental protection, and stability; Population initialization unit, used for initializing the population according to a predetermined quantity, adopting stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated based on neighborhood perturbation of the historical optimal solution, C% of the individuals are generated by greedy heuristic, and A + B + C = 100; Non-dominated ranking unit, used to determine the dominance relationship of each individual, and divide the population into multiple non-dominated ranks according to the dominance relationship. The first rank is the solution that is not dominated by any individual, the second rank is the solution that is only dominated by the first rank, and so on; Crowding degree determination unit, used to calculate the normalized distance of the three objective values for each individual within each rank; in each objective direction, sort the individuals according to the normalized value, calculate the distance between adjacent individuals in the objective space, and determine the crowding degree; Iterative output unit, used to perform genetic iterative operations on the population according to the non-dominated rank and crowding degree, and output the optimal scheduling strategy.

[0013] As a further solution of the present invention: the edge terminal adjustment module includes: Regulation power adjustment unit, used to reduce the regulation power of the resource node proportionally when the resource availability coefficient in the real-time state is lower than the availability threshold; Instruction issuing advance unit, used to issue instructions in advance when the response delay time is higher than the delay threshold, and the advance time is determined according to the response delay time and the safety margin.

[0014] Compared with the prior art, the beneficial effects of the present invention are: The present invention constructs a global optimization model and dynamic cooperative control that integrates economy, environmental benefits, and system stability, and uses an improved non-dominated sorting genetic algorithm to solve, determines the optimal scheduling strategy, has high solution efficiency, significantly improves the response accuracy and system stability of the virtual power plant, and provides an optimization strategy for decision-makers to make multi-objective trade-offs. In addition, the edge terminal will also adaptively adjust the optimal scheduling strategy according to the real-time state to ensure that the scheduling instructions match the actual operating state, and the effect is better. Description of the drawings

[0015] Figure 1 It is a flowchart of a virtual power plant cooperative control scheduling method.

[0016] Figure 2 It is a flowchart of constructing a multi-dimensional resource library in a virtual power plant cooperative control scheduling method.

[0017] Figure 3It is a flowchart for establishing a global optimization model in the cloud in a virtual power plant collaborative control and scheduling method.

[0018] Figure 4 It is a flowchart for obtaining the optimal scheduling strategy in a virtual power plant collaborative control and scheduling method.

[0019] Figure 5 It is a flowchart for adjusting the optimal scheduling strategy in a virtual power plant collaborative control and scheduling method.

[0020] Figure 6 It is a schematic structural diagram of a virtual power plant collaborative control and scheduling system. Detailed implementation manners

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0023] As Figure 1 shown, an embodiment of the present invention provides a virtual power plant collaborative control and scheduling method, and the method includes the following steps: S100, constructing a multi-dimensional resource library, where each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power regulation cost, response delay time, carbon emission coefficient, and resource availability coefficient; S200, aggregating all resource nodes in the control center to form a resource pool of the virtual power plant; S300, establishing a global optimization model in the cloud, and the global optimization model integrates economy, environmental benefits, and system stability; S400, using an improved non-dominated sorting genetic algorithm to solve the global optimization model, obtaining an optimal scheduling strategy, and sending the optimal scheduling strategy to the control center and the edge terminal; S500, determining the real-time status of the resource node based on the edge terminal, and adjusting the optimal scheduling strategy according to the real-time status.

[0024] It should be noted that traditional scheduling methods usually convert multi-objectives into single-objectives (such as weighted summation), and weights need to be set manually, which is highly subjective and difficult to balance multi-objective conflicts. The current static scheduling strategy cannot respond to resource status changes in real time (such as equipment failures, communication delays), resulting in a large deviation between the scheduling instructions and the actual execution. Moreover, traditional multi-objective optimization algorithms (such as ordinary NSGA-II) are prone to falling into local optima when dealing with high-dimensional problems, and the calculation takes a long time. The embodiments of the present invention aim to solve the above problems.

[0025] In the embodiment of the present invention, first, a multi-dimensional resource library needs to be constructed. The multi-dimensional resource library contains a large number of resource nodes, and each resource node corresponds to real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient. Among them, the real-time adjustable power is determined by the device itself and real-time environmental factors. For example, the real-time adjustable power of photovoltaic power generation is jointly determined by light intensity, photovoltaic panel area, photoelectric conversion efficiency, and inverter capacity; the adjustable power of energy storage devices is limited by their current state of charge (SOC) and charge and discharge rate. The power adjustment cost comprehensively considers electricity price cost, equipment aging cost, and operation and maintenance cost. For example, the adjustment cost of energy storage devices includes electricity price cost and battery life loss cost caused by charge and discharge; the cost of interruptible load includes user compensation cost and equipment start-stop loss. The resource availability coefficient is mainly determined by the device state. Then, all resource nodes are aggregated in the control center to form a resource pool of the virtual power plant. Here, classification aggregation can be performed: Resources are divided into a fast response pool and a slow response pool according to the response delay characteristics, which is convenient for preferentially calling fast response resources during scheduling. Geographic aggregation can also be performed: Resource nodes in the same region are aggregated into a sub-resource pool to reduce communication delay and scheduling complexity. In the embodiment of the present invention, an edge terminal is deployed for each resource node, and all edge terminals can be regulated by the control center. The edge terminal itself has the ability of data processing and analysis, and both the edge terminal and the control center are communicatively connected to the cloud. In the embodiment of the present invention, a global optimization model is established in the cloud. The global optimization model integrates economic efficiency, environmental benefits, and system stability, can minimize the scheduling cost, minimize the carbon emissions, preferentially call resources with low carbon emissions, and ensure the stable operation of the power grid. Preferably, the improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the global optimization model to obtain the optimal scheduling strategy, which improves the global search ability and convergence speed of the algorithm, avoids falling into local optimum, and then the optimal scheduling strategy is sent to the control center and the edge terminal. In addition, the edge terminal will also determine the real-time state of the resource node, including the availability coefficient and response delay, and adjust the optimal scheduling strategy according to the real-time state to ensure that the scheduling instruction matches the actual operation state. Through the construction of a global optimization model and dynamic cooperative control that integrates economic efficiency, environmental benefits, and system stability, and the use of the improved non-dominated sorting genetic algorithm for solution, the embodiment of the present invention significantly improves the response accuracy and system stability of the virtual power plant, provides an optimization strategy for multi-objective trade-off for decision-makers, and is particularly applicable to modern power grid scenarios with a high proportion of renewable energy.

[0026] As Figure 2 shown, as a preferred embodiment of the present invention, the steps of constructing the multi-dimensional resource library specifically include: S101, collecting the power generation impact data of each resource node, and determining the real-time adjustable power according to the power generation impact data; S102. Determine the power regulation cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data; S103. Determine the predicted power, rated power, and power generation equipment status of the resource node, and calculate the resource availability factor; S104. Integrate the real-time adjustable power, power regulation cost, response delay time, carbon emission coefficient, and resource availability factor of all resource nodes to construct a multi-dimensional resource library.

[0027] In the embodiment of the present invention, the power generation impact data of each resource node will be collected. It is easy to understand that the power generation impact data of each resource type is different. Taking photovoltaic power generation as an example, the power generation impact data includes the photovoltaic conversion efficiency η, the real-time light intensity I, the photovoltaic panel area S, the inverter rated power P1, and the photovoltaic panel rated power P2. Then, the adjustable power of photovoltaic power generation = η × I × S × min(1, P1 / P2). Then, determine the power regulation cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data. For example, the carbon emission coefficient of photovoltaic power generation is 50 kgCO2 / MWh, and the carbon emission coefficient of diesel power generation is 820 kgCO2 / MWh. Next, determine the predicted power P3, rated power P2, and power generation equipment status SC of the resource node, and calculate the resource availability factor. The resource availability factor = P3 / P2 × β × SC, where β is the status constraint coefficient. Finally, perform the integration, and the multi-dimensional resource library is constructed.

[0028] As Figure 3 shown, as a preferred embodiment of the present invention, the steps of establishing a global optimization model in the cloud specifically include: S301. Determine the total regulation power of the virtual power plant according to the load demand of the power grid and the grid interaction power; S302. Determine the economic objective according to the power regulation cost, determine the environmental protection objective according to the carbon emission coefficient, and determine the stability objective according to the response delay time and the resource availability factor; S303. Construct a global optimization model according to the total regulation power, economic objective, environmental protection objective, and stability objective.

[0029] In the embodiments of the present invention, it is necessary to determine the total regulation power PV of the virtual power plant according to the load demand PL of the power grid and the current grid interaction power PG, and PV = PL - PG. Then, the economic objective G1 is determined according to the power regulation cost, and G1 = Σ(Ci×ΔPi), where Ci is the power regulation cost of the i-th resource node, and ΔPi is the regulation power of the i-th resource node; and the environmental protection objective G2 is determined according to the carbon emission coefficient, and G2 = Σ(Ei×ΔPi), where Ei is the carbon emission coefficient; and the stability objective G3 is determined according to the response delay time and the resource availability coefficient, and G3 = Σ(Ti×|ΔPi|)+Σ(Ai×(1 - Ui)), where Ti is the response delay time, Ai is the availability penalty coefficient, which is a fixed value, and Ui is the resource availability coefficient (0 to 1). In this way, a global optimization model can be constructed.

[0030] As Figure 4 shown, as a preferred embodiment of the present invention, the steps of using the improved non-dominated sorting genetic algorithm to solve the global optimization model and obtain the optimal scheduling strategy specifically include: S401, perform chromosome encoding, each gene bit corresponds to the regulation power ΔPi of a resource node, and directly map the chromosome to the function values of the three objectives of economy, environmental protection, and stability; S402, initialize the population according to a predetermined quantity, and adopt stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated based on the neighborhood perturbation of the historical optimal solution, C% of the individuals are generated by the greedy heuristic, and A + B + C = 100; S403, determine the dominance relationship of each individual, and divide the population into multiple non-dominated levels according to the dominance relationship. The first level is the solution that is not dominated by any individual at all, the second level is the solution that is only dominated by the first level, and so on; S404, for the individuals within each level, calculate the normalized distance of the three objective values respectively; in each objective direction, sort the individuals according to the normalized value, calculate the distance between adjacent individuals in the objective space, and determine the crowding degree; S405, perform genetic iterative operations on the population according to the non-dominated level and the crowding degree, and output the optimal scheduling strategy.

[0031] In the embodiments of the present invention, first, chromosome coding is performed. Real - valued vector coding is adopted, and each gene locus corresponds to the adjustment power ΔPi of a resource node. The chromosome is directly mapped to the values of three objective functions, namely economy, environmental protection, and stability. Then, population initialization is carried out according to a predetermined number (for example, 200). Stratified sampling is used: 50% of the individuals are generated by Latin hypercube sampling to ensure uniform coverage of the objective space, 30% of the individuals are generated by perturbing the neighborhood of the historical optimal solution, and 20% of the individuals are generated by a greedy heuristic, preferentially selecting nodes with low cost and low carbon emissions. Next, the dominance relationship of each individual is determined. For each individual in the population, compare its three objective values of economy, environmental protection, and stability. If individual a is not inferior to individual b in all objectives and is better than individual b in at least one objective, then a dominates b. The population is divided into multiple non - dominated levels according to the dominance relationship. The first level is the solutions that are not dominated by any individual, the second level is the solutions that are only dominated by the first level, and so on. Then, for the individuals within each level, the normalized distances of the three objective values are calculated respectively. The objective values need to be mapped to the interval [0, 1] to avoid the influence of dimensional differences. In each objective direction, the individuals are sorted according to the normalized values, and the distances between adjacent individuals in the objective space are calculated to determine the crowding degree. It should be noted that a scheduling strategy is called a non - dominated solution if it is not inferior to other strategies in the three objectives of economy, environmental benefits, and system stability and is better than other strategies in at least one objective.

[0032] Then, genetic iterative operations on the population need to be carried out according to the non - dominated levels and crowding degrees to output the optimal scheduling strategy. Specifically: Randomly select several individuals from the current population, compare their non - dominated levels and crowding degrees, and select the better individuals to enter the next generation. Then, crossover operations are performed on the selected parent individuals to generate offspring individuals. Through the crossover method of simulated binary coding, local search ability is realized in real - number coding. Mutation operations are performed on the offspring individuals, and the gene values (the adjustment power ΔPi of the resource nodes) are randomly adjusted with a certain probability to enhance the population diversity. The parent population and the offspring population are combined to form a temporary population. The non - dominated sorting and crowding degree calculation are performed on the temporary population again, and the optimal N individuals (N is the preset population size) are selected to form the next - generation population. Stop when the preset maximum number of iterations is reached. Then, extract the individuals with non - dominated level 1 from the final population to form the Pareto - front solution set (the set of non - dominated solutions). Then, according to the actual requirements (such as paying more attention to economy or environmental protection), select the solution that best meets the preference from the Pareto front as the optimal scheduling strategy.

[0033] As Figure 5 shown, as a preferred embodiment of the present invention, the step of adjusting the optimal scheduling strategy according to the real - time state specifically includes: S501. When the resource availability coefficient in the real-time state is lower than the availability threshold, proportionally reduce the regulation power of the resource node. S502. When the response delay time is higher than the delay threshold, issue instructions in advance, and the advance time is determined according to the response delay time and the safety margin.

[0034] In the embodiment of the present invention, if the resource availability coefficient is lower than the threshold (such as 0.6), proportionally reduce its regulation power. For example, if the original scheduled PV reduction is 1 MW and the availability drops to 0.5, the actual reduction is 0.5 MW. In addition, when the response delay time is higher than the delay threshold, it indicates a large delay, and instructions need to be issued in advance. The advance time = response delay time + safety margin, and the safety margin is a fixed value, such as 2 seconds.

[0035] As Figure 6 shown, the embodiment of the present invention also provides a virtual power plant collaborative control and scheduling system, and the system includes: A resource library construction module 100, configured to construct a multi-dimensional resource library, and each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power regulation cost, response delay time, carbon emission coefficient, and resource availability coefficient; A resource node aggregation module 200, configured to aggregate all resource nodes in the control center to form a resource pool of the virtual power plant; An optimization model establishment module 300, configured to establish a global optimization model in the cloud, and the global optimization model integrates economy, environmental benefits, and system stability; An optimal scheduling strategy module 400, configured to solve the global optimization model by using an improved non-dominated sorting genetic algorithm to obtain an optimal scheduling strategy, and send the optimal scheduling strategy to the control center and the edge terminal; An edge terminal adjustment module 500, configured to determine the real-time state of the resource node based on the edge terminal, and adjust the optimal scheduling strategy according to the real-time state.

[0036] As a preferred embodiment of the present invention, the resource library construction module 100 includes: An adjustable power unit, configured to collect power generation influence data of each resource node, and determine the real-time adjustable power according to the power generation influence data; A historical data analysis unit, configured to determine the power regulation cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data; A resource availability unit, configured to determine the predicted power, rated power, and power generation equipment state of the resource node, and calculate the resource availability coefficient; A resource library construction unit, which is used to integrate the real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient of all resource nodes to construct a multi-dimensional resource library.

[0037] As a preferred embodiment of the present invention, the optimization model establishment module 300 includes: A total adjustment power unit, which is used to determine the total adjustment power of the virtual power plant according to the load demand of the power grid and the grid interaction power. A multi-dimensional target determination unit, which is used to determine the economic target according to the power adjustment cost, determine the environmental protection target according to the carbon emission coefficient, and determine the stability target according to the response delay time and the resource availability coefficient. A global optimization model unit, which is used to construct a global optimization model according to the total adjustment power, economic target, environmental protection target, and stability target.

[0038] As a preferred embodiment of the present invention, the optimal scheduling strategy module 400 includes: A chromosome coding unit, which is used to perform chromosome coding. Each gene position corresponds to the adjustment power ΔPi of a resource node, and the chromosome is directly mapped to the function values of three objective functions of economy, environmental protection, and stability. A population initialization unit, which is used to initialize the population according to a predetermined quantity and adopt stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated based on the neighborhood perturbation of the historical optimal solution, and C% of the individuals are generated by the greedy heuristic method, where A + B + C = 100. A non-dominated level unit, which is used to determine the domination relationship of each individual, and divide the population into multiple non-dominated levels according to the domination relationship. The first level is the solution that is not dominated by any individual at all, the second level is the solution that is only dominated by the first level, and so on. A crowding degree determination unit, which is used to calculate the normalized distance of the three objective values for each individual within each level; in each objective direction, the individuals are sorted according to the normalized value, and the distance between adjacent individuals in the objective space is calculated to determine the crowding degree. An iterative output unit, which is used to perform genetic iterative operations on the population according to the non-dominated level and the crowding degree, and output the optimal scheduling strategy.

[0039] As a preferred embodiment of the present invention, the edge terminal adjustment module 500 includes: An adjustment power adjustment unit, which is used to proportionally reduce the adjustment power of the resource node when the resource availability coefficient in the real-time state is lower than the availability threshold. An instruction issuing advance unit, which is used to issue an instruction in advance when the response delay time is higher than the delay threshold, and the advance time is determined according to the response delay time and the safety margin.

[0040] The above only describes in detail the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0041] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0042] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0043] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of the embodiments. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only to be regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. A collaborative control and scheduling method for a virtual power plant, characterized in that, The method includes the following steps: Construct a multi-dimensional resource library, where each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient; Aggregate all resource nodes in the control center to form a resource pool of the virtual power plant; Establish a global optimization model in the cloud, and the global optimization model integrates economy, environmental benefits, and system stability; Use an improved non-dominated sorting genetic algorithm to solve the global optimization model to obtain an optimal scheduling strategy, and send the optimal scheduling strategy to the control center and the edge terminal; Determine the real-time status of the resource node based on the edge terminal, and adjust the optimal scheduling strategy according to the real-time status.

2. The virtual power plant collaborative control and scheduling method according to claim 1, wherein The step of constructing the multi-dimensional resource library specifically includes: Collect the power generation impact data of each resource node, and determine the real-time adjustable power according to the power generation impact data; Determine the power adjustment cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data; Determine the predicted power, rated power, and power generation equipment status of the resource node, and calculate the resource availability coefficient; Integrate the real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient of all resource nodes to construct a multi-dimensional resource library.

3. The virtual power plant collaborative control and scheduling method according to claim 1, wherein The step of establishing the global optimization model in the cloud specifically includes: Determine the total adjustment power of the virtual power plant according to the load demand of the power grid and the grid interaction power; Determine the economic objective according to the power adjustment cost, determine the environmental protection objective according to the carbon emission coefficient, and determine the stability objective according to the response delay time and resource availability coefficient; Construct a global optimization model according to the total adjustment power, economic objective, environmental protection objective, and stability objective.

4. The virtual power plant collaborative control and scheduling method according to claim 3, wherein The step of using the improved non-dominated sorting genetic algorithm to solve the global optimization model to obtain the optimal scheduling strategy specifically includes: Perform chromosome coding, where each gene position corresponds to the adjustment power ΔPi of a resource node, and directly map the chromosome to the function values of three objective functions of economy, environmental protection, and stability; Initialize the population according to a predetermined quantity, and use stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated by perturbing the neighborhood of the historical optimal solution, and C% of the individuals are generated by greedy heuristics, where A + B + C = 100; Determine the dominance relationship of each individual, and divide the population into multiple non-dominated levels according to the dominance relationship. The first level is the solution that is not dominated by any individual, the second level is the solution that is only dominated by the first level, and so on; For the individuals within each level, calculate the normalized distance of the three objective values respectively; in each objective direction, sort the individuals according to the normalized value, calculate the distance between adjacent individuals in the objective space, and determine the crowding degree; Perform genetic iteration operations on the population according to the non-dominated level and crowding degree, and output the optimal scheduling strategy.

5. The virtual power plant collaborative control and scheduling method according to claim 1, wherein The step of adjusting the optimal scheduling strategy according to the real-time status specifically includes: When the resource availability coefficient in the real-time status is lower than the availability threshold, proportionally reduce the adjustment power of the resource node; When the response delay time is higher than the delay threshold, issue an instruction in advance, and the advance time is determined according to the response delay time and the safety margin.

6. A virtual power plant collaborative control and dispatching system, characterized in that, The system includes: A resource library construction module for constructing a multi-dimensional resource library, where each resource node in the multi-dimensional resource library corresponds to real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient; A resource node aggregation module for aggregating all resource nodes in the control center to form a resource pool of a virtual power plant; An optimization model establishment module for establishing a global optimization model in the cloud, where the global optimization model integrates economy, environmental benefits, and system stability; An optimal scheduling strategy module for solving the global optimization model using an improved non-dominated sorting genetic algorithm to obtain an optimal scheduling strategy and sending the optimal scheduling strategy to the control center and edge terminals; An edge terminal adjustment module for determining the real-time status of resource nodes based on edge terminals and adjusting the optimal scheduling strategy according to the real-time status.

7. The virtual power plant collaborative control and dispatching system according to claim 6, wherein The resource library construction module includes: An adjustable power unit for collecting power generation impact data of each resource node and determining real-time adjustable power according to the power generation impact data; A historical data analysis unit for determining the power adjustment cost, response delay time, and carbon emission coefficient of each resource node according to the resource node type and historical data; A resource availability unit for determining the predicted power, rated power, and power generation equipment status of resource nodes and calculating the resource availability coefficient; A resource library construction unit for integrating the real-time adjustable power, power adjustment cost, response delay time, carbon emission coefficient, and resource availability coefficient of all resource nodes to construct a multi-dimensional resource library.

8. The virtual power plant collaborative control and dispatching system according to claim 6, characterized in that, The optimization model establishment module includes: A total adjustment power unit for determining the total adjustment power of the virtual power plant according to the load demand of the power grid and the grid interaction power; A multi-dimensional objective determination unit for determining an economic objective according to the power adjustment cost, an environmental protection objective according to the carbon emission coefficient, and a stability objective according to the response delay time and the resource availability coefficient; A global optimization model unit for constructing a global optimization model according to the total adjustment power, economic objective, environmental protection objective, and stability objective.

9. The virtual power plant collaborative control and dispatching system according to claim 8, characterized in that The optimal scheduling strategy module includes: A chromosome coding unit for performing chromosome coding, where each gene locus corresponds to the adjustment power ΔPi of a resource node, and directly mapping the chromosome to the values of three objective functions of economy, environmental protection, and stability; A population initialization unit for initializing the population according to a predetermined quantity, using stratified sampling: A% of the individuals are generated by Latin hypercube sampling, B% of the individuals are generated by perturbing the neighborhood of the historical optimal solution, and C% of the individuals are generated by a greedy heuristic, where A + B + C = 100; A non-dominated level unit for determining the dominance relationship of each individual and dividing the population into multiple non-dominated levels according to the dominance relationship. The first level is the solution that is not dominated by any individual at all, the second level is the solution that is only dominated by the first level, and so on; Crowdedness determination unit, which is used to calculate the normalized distances of three target values for each individual within each level; in each target direction, sort the individuals according to the normalized values, calculate the distances between adjacent individuals in the target space, and determine the crowdedness; Iterative output unit, which is used to perform genetic iterative operations on the population according to the non-dominated levels and crowdedness, and output the optimal scheduling strategy.

10. The virtual power plant collaborative control and scheduling system according to claim 6, wherein The edge terminal adjustment module includes: Regulation power adjustment unit, which is used to proportionally reduce the regulation power of the resource node when the resource availability coefficient in the real-time state is lower than the availability threshold; Instruction issuing advance unit, which is used to issue instructions in advance when the response delay time is higher than the delay threshold, and the advance time is determined according to the response delay time and the safety margin.

Images