Electric power spot market real-time scheduling method considering participation of virtual power plant

Abstract

The invention discloses an electric power spot market real-time scheduling method considering participation of a virtual power plant, and relates to the technical field related to electric power scheduling management, a plurality of distributed energy field areas are divided into independent modules, and the independent modules are accessed to a virtual power plant resource pool to form a plurality of independent resource modules. And the distributed computing nodes are configured to be connected with the dispatching center. And executing a rolling prediction mechanism, and transmitting a local resource prediction result to a power grid dispatching center. And collecting electricity demand data in real time, and formulating a global optimization target in combination with a local resource prediction result. Based on a distributed optimization strategy, global optimization target decomposition is carried out, sub-optimization targets are executed, a result is transmitted to a power grid dispatching center, and a real-time dispatching strategy is formulated. And packaging into a scheduling instruction according to the real-time scheduling strategy, and carrying out resource scheduling management. The technical problems that in the prior art, multiple distributed energy sources cannot be effectively integrated through an electric power spot market real-time scheduling method, so that electric power resources are wasted, and the operation efficiency of a power grid is reduced are solved.

Description

Technical Field

[0001] The present application relates to the technical field related to power dispatching management, and specifically to a real-time dispatching method for the power spot market taking into account the participation of virtual power plants. Background Art

[0002] With the rapid development of renewable energy, a large number of distributed energy sources such as wind power, photovoltaic power, and small hydropower stations are connected to the power system. These distributed energy sources are small in scale, widely distributed, and highly intermittent, which brings new challenges to the dispatching and management of the power grid. The traditional power dispatching model is difficult to fully utilize the advantages of distributed energy, resulting in waste of resources and reduced grid operation efficiency. Therefore, there is an urgent need for a real-time dispatching method for the power spot market that participates in the dispatching of distributed energy to solve the problems of waste of resources and reduced grid operation efficiency, thereby improving the operation efficiency and reliability of the power system.

[0003] Therefore, in the existing technology, the real-time dispatching method of the electricity spot market cannot effectively integrate multiple distributed energy sources, resulting in technical problems such as waste of electricity resources and reduced grid operation efficiency. Summary of the invention

[0004] This application provides a real-time dispatching method for the power spot market that takes into account the participation of virtual power plants, which solves the technical problem that the real-time dispatching method for the power spot market in the prior art cannot effectively integrate multiple distributed energy sources, resulting in waste of power resources and reduced grid operation efficiency. It achieves the technical effect of effective integration and real-time dispatching of distributed energy sources, improves the operating efficiency and reliability of the power system, and reduces the waste of power resources.

[0005] The present application provides a real-time dispatching method for the electricity spot market taking into account the participation of virtual power plants, the method comprising: dividing a plurality of distributed energy sites in a target area into independent modules, and connecting to a virtual power plant resource pool through the Internet of Things to form a plurality of independent resource modules; configuring corresponding distributed computing nodes based on the plurality of independent resource modules, and establishing a connection with a power grid dispatching center through a unified data interface and communication protocol; within a fixed period, the distributed computing nodes execute a rolling prediction mechanism, and transmit the local resource prediction results of the corresponding independent resource modules to the power grid dispatching center; collecting the electricity demand data of the target area to the power grid dispatching center in real time, and formulating a global optimization target in combination with the local resource prediction results; based on a distributed optimization strategy, after decomposing the global optimization target, the plurality of independent resource modules execute sub-optimization targets, and transmit the obtained multiple sub-target optimization results to the power grid dispatching center to formulate a real-time dispatching strategy; according to the real-time dispatching strategy, the power grid dispatching center encapsulates the dispatching instructions into dispatching instructions, and sends them to the plurality of independent resource modules for resource dispatching management.

[0006] In the implementation method, based on the distributed optimization strategy, after the global optimization target is decomposed, the multiple independent resource modules execute the sub-optimization targets, including: the power grid dispatching center decomposes the global optimization target to obtain multiple initial sub-optimization targets; the multiple initial sub-optimization targets are sent to the multiple independent resource modules, local target optimization is performed, and multiple initial sub-target optimization results are obtained and transmitted back to the power grid dispatching center; the power grid dispatching center performs multi-objective collaborative optimization based on the received multiple initial sub-target optimization results, sends the adjusted sub-optimization targets back to each module, and generates a real-time dispatching strategy.

[0007] In the implementation method, the power grid dispatching center decomposes the global optimization goal to obtain multiple sub-optimization goals, including: the power grid dispatching center is embedded with a distributed optimization model; the global optimization goal is preliminarily decomposed through the distributed optimization model, and initial sub-optimization goals are set, and the initial sub-optimization goals include preliminary input requirements and preliminary output requirements of each independent resource module.

[0008] In the implementation method, constructing the distributed optimization model includes: defining a global objective function according to the global optimization goal; formulating local objective functions respectively based on the multiple independent resource modules, and the local objective functions are consistent with the direction of the global optimization goal; setting initial parameter constraints respectively according to the resource characteristics of the multiple independent resource modules, and combining the global objective function and the local objective function to generate the distributed optimization model.

[0009] In the implementation method, the multiple initial sub-optimization targets are sent to the multiple independent resource modules, local target optimization is performed, and multiple initial sub-target optimization results are obtained, including: after receiving the multiple initial sub-optimization targets, the multiple independent resource modules perform local target optimization according to the preset feedback time limit, and generate corresponding initial sub-target optimization results, and the local target optimization includes power generation optimization, energy storage optimization and load response optimization; when any independent resource module fails to feed back the optimization result within the preset feedback time limit, the fault-tolerant mechanism is immediately started, and the predicted value is used to replace the initial sub-target optimization result of the module, and the sub-optimization targets of other modules are dynamically adjusted.

[0010] In the implementation method, within a fixed period, the distributed computing nodes execute a rolling prediction mechanism and transmit the local resource prediction results of the corresponding independent resource modules to the power grid dispatching center, including: the power grid dispatching center sets a fixed period of rolling prediction and synchronizes the prediction start time of all distributed computing nodes; each distributed computing node collects real-time meteorological forecast data from the corresponding independent resource module, and inputs the corresponding operation prediction model to generate a short-term resource output prediction result; the short-term resource output prediction result is added with a timestamp and module identifier to generate a local resource prediction result, and is transmitted to the power grid dispatching center.

[0011] In the implementation method, the power grid dispatching center monitors the operating status and execution effect of each independent resource module in real time to obtain actual dispatching execution data; compares the deviation between the actual dispatching execution data and the preset dispatching strategy, performs closed-loop feedback control according to the deviation comparison result, and dynamically adjusts the real-time dispatching strategy.

[0012] In the implementation method, closed-loop feedback control is performed according to the deviation comparison result, and the real-time scheduling strategy is dynamically adjusted, including: comparing and judging the deviation between the actual scheduling execution data and the preset scheduling strategy, determining the deviation size and deviation type, and the deviation type includes short-term fluctuations and continuous deviations; adjusting the real-time scheduling strategy according to the deviation size and deviation type, generating new scheduling instructions, and issuing them to each resource module in real time; after executing the new scheduling instructions, the power grid dispatching center continuously monitors and verifies the adjustment effect. If the deviation is reduced or disappears, it indicates that the adjustment is effective; otherwise, the strategy continues to be adjusted to form a closed-loop control.

[0013] The present application proposes a real-time dispatching method for the electric power spot market that considers the participation of virtual power plants. The method divides multiple distributed energy fields in the target area into independent modules, and connects to the virtual power plant resource pool through the Internet of Things to form multiple independent resource modules; and configures corresponding distributed computing nodes to establish a connection with the power grid dispatching center; in a fixed period, the rolling prediction mechanism is executed, and the local resource prediction results are transmitted to the power grid dispatching center; the power demand data of the target area is collected in real time, and the global optimization goal is formulated in combination with the local resource prediction results; based on the distributed optimization strategy, after the global optimization goal is decomposed, the multiple independent resource modules execute the sub-optimization goals, and the obtained multiple sub-goal optimization results are transmitted to the power grid dispatching center to formulate a real-time dispatching strategy; according to the real-time dispatching strategy, it is encapsulated into a dispatching instruction to perform resource dispatching management. The technical problem that the real-time dispatching method of the electric power spot market in the prior art cannot effectively integrate multiple distributed energy sources, resulting in waste of electric power resources and reduced power grid operation efficiency is solved. The technical effect of effectively integrating and real-time dispatching distributed energy sources is achieved, the operation efficiency and reliability of the power system are improved, and the waste of electric power resources is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A flow chart of a real-time dispatching method for a power spot market taking into account the participation of a virtual power plant provided in an embodiment of the present application;

[0016] Figure 2 A flow chart of a real-time dispatching strategy for a real-time dispatching method for a spot electricity market taking into account the participation of a virtual power plant provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The embodiment of the present application provides a real-time dispatching method for the electric power spot market taking into account the participation of virtual power plants, such as Figure 1 As shown, the method includes:

[0021] Divide multiple distributed energy sites in the target area into independent modules, and connect them to the virtual power plant resource pool through the Internet of Things to form multiple independent resource modules; configure corresponding distributed computing nodes based on the multiple independent resource modules, and establish a connection with the power grid dispatching center through a unified data interface and communication protocol; within a fixed period, the distributed computing nodes execute a rolling prediction mechanism and transmit the local resource prediction results of the corresponding independent resource modules to the power grid dispatching center;

[0022] The multiple distributed energy sites in the target area are divided into independent modules according to the power generation category, such as wind farms, photovoltaic power stations, small hydropower stations, etc., and the independent modules are connected to the virtual power plant resource pool through the Internet of Things to form multiple independent resource modules in the virtual power plant resource pool. The virtual power plant resource pool is a virtual power production entity that integrates multiple distributed energy sources and is uniformly managed and dispatched to achieve centralized management. This facilitates the power spot market to conduct power dispatch transactions and delivery in a short time. In the virtual power production entity, the power operation data of each independent module, such as meteorological forecast data, power generation data, etc., are exchanged through the Internet of Things, so that the independent resource module can obtain the operation status of the distributed energy site and use it for subsequent analysis. Subsequently, based on multiple independent resource modules, a corresponding distributed computing node is configured for each independent resource module, and a unified data interface and communication protocol are used to establish a connection with the power grid dispatch center. The distributed computing node is located inside the independent resource module and is responsible for local data processing, prediction and communication computing equipment. Furthermore, within a fixed period, the distributed computing node executes a rolling prediction mechanism to predict the energy output within a period of time in the future. The fixed period is a fixed period of rolling prediction set by the power grid dispatching center according to the real-time dispatching requirements of the power system. The shorter the fixed period, the higher the corresponding rolling prediction frequency and the higher the data update frequency. The local resource prediction results corresponding to the independent resource module are transmitted to the power grid dispatching center. The local resource prediction results include the independent resource module and the corresponding prediction time interval identifier and the short-term resource output prediction results.

[0023] The method provided in the embodiment of the present application also includes: the power grid dispatching center sets a fixed period of rolling forecast and synchronizes the forecast start time of all distributed computing nodes; each distributed computing node collects real-time meteorological forecast data from the corresponding independent resource module, and inputs the corresponding operation forecast model to generate a short-term resource output forecast result; adds a timestamp and module identifier to the short-term resource output forecast result to generate a local resource forecast result, and transmits it to the power grid dispatching center.

[0024] In a fixed period, the distributed computing node executes a rolling prediction mechanism and transmits the local resource prediction results of the corresponding independent resource module to the power grid dispatching center, including: the power grid dispatching center usually sets a fixed period of rolling prediction according to the real-time dispatching requirements of the power system. For example, once every 5 minutes, 15 minutes or 30 minutes, and the dispatching center sends a synchronization signal to all distributed computing nodes through the communication network to ensure that all nodes start prediction at the same time point. Subsequently, each distributed computing node collects real-time meteorological forecast data from the corresponding independent resource module, and the meteorological forecast data is the meteorological forecast data obtained by prediction in the next fixed period. The meteorological forecast data can be extracted through the meteorological station prediction data, and the meteorological forecast data is the associated meteorological data of the power generation equipment corresponding to each independent resource module. The exemplary photovoltaic power station corresponding meteorological forecast data includes solar radiation intensity, temperature, cloud cover, etc. The real-time meteorological forecast data is input into the corresponding operation prediction model to generate a short-term resource output prediction result. Among them, the operation prediction model corresponds to the independent resource module one by one, and the operation prediction model is constructed based on the neural network model.

[0025] When constructing the operation prediction model, the training data is obtained by acquiring the meteorological data in the historical operation process and the corresponding short-term power generation data, and the short-term power generation data is the power generation data within the preset time interval under the corresponding meteorological data. The neural network model is supervised and trained by the training data until the short-term resource output result of the model output meets the preset accuracy requirements, and the model training is completed to obtain the operation prediction model. Finally, the short-term resource output prediction result is added with a timestamp and a module identifier, that is, the short-term resource output prediction result is marked with a prediction time interval and an independent resource module identifier, and a local resource prediction result is generated and transmitted to the power grid dispatching center.

[0026] The power demand data of the target area is collected in real time to the power grid dispatching center, and the global optimization goal is formulated in combination with the local resource prediction results; based on the distributed optimization strategy, after the global optimization goal is decomposed, the sub-optimization goals are executed by the multiple independent resource modules, and the obtained multiple sub-goal optimization results are transmitted to the power grid dispatching center to formulate a real-time dispatching strategy; according to the real-time dispatching strategy, the power grid dispatching center encapsulates it into a dispatching instruction and sends it to the multiple independent resource modules to perform resource dispatching management.

[0027] The power demand data of the target area is collected in real time to the power grid dispatching center. The target area is the area where the power spot market needs to be dispatched in real time. The power demand data is the data information of the power consumption and demand of users in different time periods. Combined with the local resource prediction results, a global optimization goal is formulated. The global optimization goal is the power dispatching goal that the power grid dispatching needs to complete. Based on the distributed optimization strategy, after the global optimization goal is decomposed, the sub-optimization goals are executed by the multiple independent resource modules, and the obtained multiple sub-goal optimization results are transmitted to the power grid dispatching center to formulate a real-time dispatching strategy. Finally, according to the real-time dispatching strategy, the power grid dispatching center encapsulates the dispatching instructions into dispatching instructions, sends the dispatching instructions to the multiple independent resource modules, and completes the resource dispatching management of the distributed energy field through the independent resource modules. The technical problem that the real-time dispatching method of the power spot market in the prior art cannot effectively integrate multiple distributed energy sources, resulting in waste of power resources and reduced power grid operation efficiency is solved. The technical effect of effectively integrating and real-time dispatching of distributed energy sources is achieved, the operating efficiency and reliability of the power system are improved, and the waste of power resources is reduced.

[0028] like Figure 2 As shown, the method provided in the embodiment of the present application also includes: the power grid dispatching center decomposes the global optimization target to obtain multiple initial sub-optimization targets; sends the multiple initial sub-optimization targets to the multiple independent resource modules, performs local target optimization, obtains multiple initial sub-target optimization results and transmits them back to the power grid dispatching center; the power grid dispatching center performs multi-objective collaborative optimization based on the received multiple initial sub-target optimization results, sends the adjusted sub-optimization targets back to each module, and generates a real-time dispatching strategy.

[0029] Based on the distributed optimization strategy, after the global optimization target is decomposed, the multiple independent resource modules execute the sub-optimization targets, including: the power grid dispatching center decomposes the global optimization target to obtain multiple initial sub-optimization targets. The multiple initial sub-optimization targets are sent to the multiple independent resource modules, and the multiple independent resource modules perform local target optimization, randomly adjust the initial sub-optimization targets in the initial parameter constraints, obtain the multiple power generation costs and corresponding power generation and power generation adjustment amounts that meet the preset cost requirements output by the local objective function, and transmit the obtained multiple initial sub-target optimization results back to the power grid dispatching center. The power grid dispatching center performs multi-objective collaborative optimization based on the received multiple initial sub-target optimization results, that is, multiple initial sub-target optimization results are combined into multiple objectives, and the minimum positive value of the global optimization target output and the initial sub-target optimization results corresponding to the independent resource module are obtained. Finally, the adjusted sub-optimization targets are sent back to each module to generate a real-time dispatching strategy.

[0030] The method provided in the embodiment of the present application also includes: the power grid dispatching center is embedded with a distributed optimization model; the global optimization goal is preliminarily decomposed through the distributed optimization model, and an initial sub-optimization goal is set, and the initial sub-optimization goal includes a preliminary input requirement and a preliminary output requirement of each independent resource module.

[0031] The power grid dispatching center decomposes the global optimization target to obtain multiple sub-optimization targets, including: the power grid dispatching center is embedded with a distributed optimization model. The power grid dispatching center is embedded with a distributed optimization model. The global optimization target is preliminarily decomposed through the distributed optimization model, and the initial sub-optimization target is set. The initial sub-optimization target is an adjustment parameter in the local objective function in the distributed optimization model, including: power generation, power generation adjustment, etc. The initial sub-optimization target includes the preliminary input demand and preliminary output demand of each independent resource module. The preliminary input demand is the initial power generation and power generation adjustment demand, and the preliminary output demand is the final power generation and power generation adjustment demand of the output.

[0032] The method provided in an embodiment of the present application also includes: defining a global objective function according to the global optimization goal; formulating local objective functions respectively based on the multiple independent resource modules, and the local objective functions are consistent with the direction of the global optimization goal; setting initial parameter constraints respectively according to the resource characteristics of the multiple independent resource modules, and combining the global objective function and the local objective function to generate the distributed optimization model.

[0033] Constructing the distributed optimization model includes: according to the global optimization goal, the global optimization goal is the optimization goal of minimizing power generation cost that needs to be achieved at the power system level, defining a global objective function, the global objective function is the ratio of the product of the normalized sum of the power generation of each independent resource module minus the normalized difference of the power demand data and the average unit power generation cost to the sum of the power generation costs of each independent resource module. Based on the multiple independent resource modules, local objective functions are formulated respectively, the local objective function is consistent with the direction of the global optimization goal, the local objective function is a calculation function of power generation and power generation cost, and the exemplary power generation cost = unit power generation cost * (power generation - power generation adjustment amount), and the specific local objective function and the global optimization objective function can also be set accordingly according to the actual independent resource modules by professional and technical personnel. Furthermore, initial parameter constraints are set respectively according to the resource characteristics of the multiple independent resource modules, wherein the initial parameter constraints are constraints on the calculation parameters in the global objective function and the local objective function, such as the power generation constraint interval and power generation adjustment amount of the independent resource module, so that each module has a clear target range in local optimization, and the distributed optimization model is generated by combining the global objective function and the local objective function.

[0034] The method provided in the embodiment of the present application also includes: after receiving multiple initial sub-optimization targets, the multiple independent resource modules perform local target optimization according to a preset feedback time limit to generate corresponding initial sub-target optimization results, and the local target optimization includes power generation optimization, energy storage optimization and load response optimization; when any independent resource module fails to feed back the optimization result within the preset feedback time limit, the fault-tolerant mechanism is immediately started, the predicted value is used to replace the initial sub-target optimization result of the module, and the sub-optimization targets of other modules are dynamically adjusted.

[0035] After receiving multiple initial sub-optimization targets, the multiple independent resource modules perform local target optimization according to the preset feedback time limit. The local target optimization is to generate the corresponding initial sub-target optimization results according to the optimal local target optimization cost. The local target optimization includes power generation optimization, energy storage optimization and load response optimization. The preset feedback time limit is a preset optimization result feedback time limit. When any independent resource module fails to feedback the optimization result within the preset feedback time limit, the power grid dispatching center needs to carry out the next stage of power dispatching. Due to the lack of feedback data, the fault-tolerant mechanism is immediately activated, and the predicted value is used instead of the initial sub-target optimization result of the module, and the sub-optimization targets of other modules are dynamically adjusted.

[0036] The method provided in the embodiment of the present application also includes: the power grid dispatching center monitors the operating status and execution effect of each independent resource module in real time to obtain actual dispatching execution data; compares the deviation between the actual dispatching execution data and the preset dispatching strategy, performs closed-loop feedback control according to the deviation comparison result, and dynamically adjusts the real-time dispatching strategy.

[0037] The power grid dispatch center monitors the operating status and execution effect of each independent resource module in real time, and obtains actual dispatch execution data, including key parameters such as actual power generation, energy storage status, and load response. The actual dispatch execution data is compared with the preset dispatch strategy for deviation, and closed-loop feedback control is performed according to the deviation comparison result to dynamically adjust the real-time dispatch strategy.

[0038] The method provided in the embodiment of the present application also includes: comparing and judging the deviation between the actual scheduling execution data and the preset scheduling strategy, determining the deviation size and deviation type, and the deviation type includes short-term fluctuations and continuous deviations; adjusting the real-time scheduling strategy according to the deviation size and deviation type, generating new scheduling instructions, and issuing them to each resource module in real time; after executing the new scheduling instructions, the power grid dispatching center continuously monitors and verifies the adjustment effect. If the deviation is reduced or disappears, it indicates that the adjustment is effective; otherwise, continue to adjust the strategy to form a closed-loop control.

[0039] According to the deviation comparison result, closed-loop feedback control is performed to dynamically adjust the real-time scheduling strategy, including: comparing the actual operation data of each resource module with the planned value in the preset scheduling strategy, determining the difference between the actual value and the planned value and the corresponding data deviation type. The deviation type includes short-term fluctuations and persistent deviations. According to the deviation size and deviation type, for each module with deviation, the specific cause of the deviation is analyzed, such as equipment failure, environmental changes, etc., and according to the specific cause of the deviation, it is determined whether the deviation is an irreversible cause, such as environmental changes, equipment failure, control unresponsiveness, etc. The real-time scheduling strategy is adjusted according to the deviation size to compensate the deviation generated by the deviation module to other modules, generate a new scheduling instruction, and send it to each resource module in real time. After executing the new scheduling instruction, the power grid dispatching center continuously monitors and verifies the adjustment effect. If the deviation is reduced or disappears, it indicates that the adjustment is effective; otherwise, the strategy continues to be adjusted to form a closed-loop control.

[0040] The technical solution provided by the embodiment of the present invention is to divide multiple distributed energy fields in the target area into independent modules, and connect to the virtual power plant resource pool through the Internet of Things to form multiple independent resource modules; based on the multiple independent resource modules, corresponding distributed computing nodes are configured respectively, and a connection is established with the power grid dispatching center through a unified data interface and communication protocol; within a fixed period, the distributed computing node executes a rolling prediction mechanism, and transmits the local resource prediction results of the corresponding independent resource module to the power grid dispatching center; collects the power demand data of the target area to the power grid dispatching center in real time, and formulates a global optimization goal in combination with the local resource prediction results; based on the distributed optimization strategy, after decomposing the global optimization goal, the multiple independent resource modules execute the sub-optimization goals, and transmit the obtained multiple sub-goal optimization results to the power grid dispatching center to formulate a real-time dispatching strategy; according to the real-time dispatching strategy, the power grid dispatching center encapsulates the dispatching instructions into dispatching instructions and sends them to the multiple independent resource modules for resource dispatching management. It solves the technical problem that the real-time dispatching method of the power spot market in the prior art cannot effectively integrate multiple distributed energy sources, resulting in waste of power resources and reduced power grid operation efficiency. It achieves the technical effect of effective integration and real-time dispatch of distributed energy, improves the operating efficiency and reliability of the power system, and reduces the waste of power resources.

[0041] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A real-time dispatching method for the electricity spot market considering the participation of virtual power plants, characterized in that: The method comprises: Divide multiple distributed energy sites in the target area into independent modules and connect them to the virtual power plant resource pool through the Internet of Things to form multiple independent resource modules; Based on the multiple independent resource modules, corresponding distributed computing nodes are configured respectively, and a connection is established with a power grid dispatching center through a unified data interface and communication protocol; In a fixed period, the distributed computing nodes execute a rolling prediction mechanism and transmit the local resource prediction results of the corresponding independent resource modules to the power grid dispatching center; Collect the power demand data of the target area in real time to the power grid dispatching center, and formulate a global optimization goal in combination with the local resource prediction results; Based on the distributed optimization strategy, after decomposing the global optimization target, the multiple independent resource modules execute the sub-optimization targets, and transmit the obtained multiple sub-target optimization results to the power grid dispatching center to formulate a real-time dispatching strategy; According to the real-time dispatching strategy, the power grid dispatching center encapsulates it into a dispatching instruction and sends it to the multiple independent resource modules for resource dispatching management.

2. A real-time dispatching method for the electric power spot market considering the participation of virtual power plants as claimed in claim 1, characterized in that: Based on the distributed optimization strategy, after decomposing the global optimization target, the multiple independent resource modules execute the sub-optimization targets, including: The power grid dispatching center decomposes the global optimization objective to obtain a plurality of initial sub-optimization objectives; Sending the multiple initial sub-optimization targets to the multiple independent resource modules, performing local target optimization, obtaining multiple initial sub-target optimization results and transmitting them back to the power grid dispatching center; The power grid dispatching center performs multi-objective collaborative optimization according to the received multiple initial sub-objective optimization results, sends the adjusted sub-optimization objectives back to each module, and generates a real-time dispatching strategy.

3. A real-time dispatching method for the electric power spot market considering the participation of virtual power plants as claimed in claim 2, characterized in that: The power grid dispatching center decomposes the global optimization objective to obtain multiple sub-optimization objectives, including: The power grid dispatching center is embedded with a distributed optimization model; The global optimization target is preliminarily decomposed through the distributed optimization model to set initial sub-optimization targets, which include preliminary input requirements and preliminary output requirements of each independent resource module.

4. A real-time dispatching method for the electric power spot market considering the participation of virtual power plants as claimed in claim 3, characterized in that: Constructing the distributed optimization model includes: According to the global optimization goal, a global objective function is defined; Based on the multiple independent resource modules, local objective functions are formulated respectively, and the local objective functions are consistent with the direction of the global optimization objective; According to the resource characteristics of the multiple independent resource modules, initial parameter constraints are set respectively, and the distributed optimization model is generated by combining the global objective function and the local objective function.

5. A real-time dispatching method for the electric power spot market considering the participation of virtual power plants as claimed in claim 2, characterized in that: The multiple initial sub-optimization objectives are sent to the multiple independent resource modules, local objective optimization is performed, and multiple initial sub-objective optimization results are obtained, including: After receiving the multiple initial sub-optimization targets, the multiple independent resource modules perform local target optimization according to the preset feedback time limit to generate corresponding initial sub-target optimization results, wherein the local target optimization includes power generation optimization, energy storage optimization and load response optimization; When any independent resource module fails to send back the optimization result within the preset feedback time limit, the fault-tolerance mechanism is immediately activated, the predicted value is used to replace the initial sub-goal optimization result of the module, and the sub-optimization goals of other modules are dynamically adjusted.

6. A real-time dispatching method for electric power spot market considering the participation of virtual power plants as claimed in claim 1, characterized in that: In a fixed period, the distributed computing node executes a rolling prediction mechanism and transmits the local resource prediction results of the corresponding independent resource module to the power grid dispatching center, including: The power grid dispatching center sets a fixed period of rolling prediction and synchronizes the prediction start time of all distributed computing nodes; Each distributed computing node collects real-time meteorological forecast data from the corresponding independent resource module and inputs it into the corresponding operation forecast model to generate short-term resource output forecast results; A timestamp and a module identifier are added to the short-term resource output prediction result to generate a local resource prediction result, and the result is transmitted to the power grid dispatching center.

7. A real-time dispatching method for electric power spot market considering the participation of virtual power plants as claimed in claim 1, characterized in that: The method further comprises: The power grid dispatching center monitors the operating status and execution effect of each independent resource module in real time and obtains actual dispatching execution data; The actual scheduling execution data is compared with the preset scheduling strategy for deviation, and closed-loop feedback control is performed according to the deviation comparison result to dynamically adjust the real-time scheduling strategy.

8. A real-time dispatching method for the electric power spot market considering the participation of virtual power plants as claimed in claim 7, characterized in that: Closed-loop feedback control is performed according to the deviation comparison result to dynamically adjust the real-time scheduling strategy, including: Compare and judge the deviation between the actual scheduling execution data and the preset scheduling strategy to determine the deviation size and deviation type, wherein the deviation type includes short-term fluctuation and persistent deviation; According to the deviation size and deviation type, the real-time scheduling strategy is adjusted, a new scheduling instruction is generated, and the new scheduling instruction is issued to each resource module in real time; After executing the new dispatching instruction, the power grid dispatching center continuously monitors and verifies the adjustment effect. If the deviation is reduced or disappears, it indicates that the adjustment is effective; otherwise, the strategy continues to be adjusted to form a closed-loop control.

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