Virtual power plant dispatching system and method for power transmission

Abstract

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant dispatching system and method for power transmission. The system comprises a data acquisition module, a data storage module, a data analysis processing module, a load prediction module, a scheduling decision module, an execution control module, a monitoring evaluation module, distributed energy power generation equipment, solar power generation equipment, wind power generation equipment, a load terminal and an energy storage device. And the distributed energy power generation equipment, the solar power generation equipment, the wind power generation equipment, the load terminal and the energy storage device are in signal connection with the execution control module and are uniformly controlled by the execution control module. According to the technical scheme provided by the invention, a more perfect and accurate mathematical model and constraint conditions are established by taking the lowest cost as a target, so that the power demand for maximizing the economic benefit can be calculated more accurately, a better scheduling strategy is formulated, and higher economic benefit is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a virtual power plant dispatching system and method for power transmission. Background Art

[0002] China's energy structure is accelerating its transition to green. Virtual power plants have emerged as the times require, and they can participate in power market operations and power system operation regulation by controlling a variety of distributed resources, such as energy storage, controllable loads, and electric vehicles.

[0003] Virtual power plants not only serve as "positive power plants" to supply power to the system for peak load regulation, but also as "negative power plants" to increase load absorption and coordinate valley filling. This model fully taps the system's flexibility and demand-side resources, and improves the real-time balance and safe supply capabilities of the power system. Virtual power plants aggregate and optimize distributed resources through digital technology, control technology, Internet of Things technology, and communication technology.

[0004] The key technologies of virtual power plants include coordinated control technology, precise decision-making technology, automatic allocation of system instructions, and multi-resource aggregation optimization technology. Information and communication technology is also an important part of it, ensuring that virtual power plants can monitor and control distributed resources in real time. In terms of operating mechanism, virtual power plants have gone through three stages: invitation-based, market-based, and autonomous dispatching. At present, China's virtual power plants are in the transition stage from invitation-based to market-based, with demand response as the main profit model.

[0005] During the research and practice of this method, the inventors of the present invention found that the virtual power plant’s insufficient load forecasting resulted in the inability to adjust the scheduling in a timely manner, which weakened the efficiency and accuracy of resource allocation; at the same time, at the data monitoring and perception level, the lack of data collection and communication systems led to the widespread existence of data islands; at the data analysis level, the lack of reasonable configuration made it impossible to maximize economic benefits. Summary of the invention

[0006] The present invention provides a virtual power plant dispatching system and method for power transmission, which can accurately predict the load and realize timely response of the system, while maximizing economic benefits.

[0007] The present invention provides a virtual power plant dispatching system for power transmission, comprising: a data acquisition module, a data storage module, a data analysis and processing module, a load prediction module, a dispatching decision module, an execution control module, a monitoring and evaluation module, distributed energy generation equipment, a solar power generation equipment, a wind power generation equipment, a load terminal, and an energy storage device, wherein the distributed energy generation equipment, the solar power generation equipment, the wind power generation equipment, the load terminal, and the energy storage device are connected to the execution control module by signals and are uniformly controlled by the execution control module;

[0008] Data acquisition module: collects the operating parameters of the distributed energy generation equipment, the solar power generation equipment, the wind power generation equipment, the load terminal and the energy storage device, including power generation power, energy storage status, load demand, grid voltage and current;

[0009] Data storage module: storage system historical data;

[0010] Data analysis and processing module: analyzes and processes the data collected by the data acquisition module, establishes a function with the goal of minimizing the power supply cost, calculates the required power results under the goal of giving priority to the grid-connected operation of distributed energy, solar energy, and wind energy, and predicts the required power generation power according to the load prediction results of the load prediction module;

[0011] Load forecasting module: calls the historical data of the data storage module to establish a load forecasting model, continuously adjusts the load forecasting model according to the real-time data obtained by the data acquisition module, and generates load forecasting results;

[0012] Scheduling decision module: formulates optimized scheduling strategies based on the calculation results of the data analysis and processing module, and generates scheduling instructions based on the scheduling strategies;

[0013] Execution control module: responsible for executing the dispatching instructions generated by the dispatching decision module, controlling each of the distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the energy storage device, and realizing real-time control of the power system;

[0014] Monitoring and evaluation module: monitors the operating status of the power system in real time, evaluates the execution effect of the dispatching strategy formulated by the dispatching decision module, promptly discovers abnormal conditions in the system, and feeds back to the data analysis and processing module and the dispatching decision module to adjust and optimize the calculation power and dispatching strategy.

[0015] The present invention provides a method for using a virtual power plant dispatching system for power transmission, comprising the following steps:

[0016] S1: Collect the operating parameters of distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminals and energy storage devices through the data acquisition module;

[0017] S2: storing the collected data in the data storage module and sending it to the data analysis and processing module and the load forecasting module;

[0018] S3: The load forecasting module calls the historical data of the data storage module to establish a forecasting model, continuously adjusts the forecasting model according to the acquired real-time data, generates load forecasting results and sends them to the data analysis and processing module;

[0019] S4: The data analysis and processing module analyzes and processes the collected data, calculates the power generation of distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the charging and discharging power of energy storage devices with the goal of minimizing power supply costs, and uses intelligent algorithms to predict the power generation according to the load forecast results;

[0020] S5: The dispatch decision module formulates an optimized dispatch strategy according to the results of the data analysis and processing module and the cost characteristics and environmental protection requirements of different power generation resources, and generates a dispatch instruction according to the dispatch strategy;

[0021] S6: The execution control module executes the scheduling instruction generated by the scheduling decision module to control each of the distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the energy storage device to operate according to the calculated power;

[0022] S7: The monitoring and evaluation module monitors the operating status of the power system in real time, promptly discovers and reports abnormal conditions in the system to the data analysis and processing module, and evaluates the execution effect of the scheduling strategy formulated by the scheduling decision module, and feeds back to the scheduling decision module;

[0023] S8: The data analysis and processing module adjusts the constraints according to the abnormal report and recalculates the required operating power. The scheduling decision module adjusts and optimizes the scheduling strategy according to the recalculation results and execution effect feedback.

[0024] Preferably, the data analysis and processing module takes the minimum power supply cost as the decision-making goal and solves the optimal scheduling plan. Assuming the power supply cost is A, the total time considered is T, and within the time period t, the operating cost coefficient of the distributed energy generation equipment is K 1 , the operating cost coefficient of the solar power generation equipment is K 2 The wind power generation equipment operating cost coefficient is K 3 , the energy storage device operating cost coefficient is K s , the power generation power of the distributed energy generation equipment is The power generation capacity of the solar power generation equipment is The power generation capacity of the wind power generation equipment is The discharge power of the energy storage device in time period t is Charging power is The cost function of purchasing electricity is C(t); the objective function of minimizing the power supply cost is expressed as:

[0025]

[0026] Preferably, the electricity price changes according to a certain periodic pattern within a day, and is fitted into an electricity price function X(t). The cost of purchasing electricity C(t) is simply calculated by multiplying the electricity price function by the electricity purchased from the power grid. Assuming that the electricity purchased from the power grid in the time period t is q, the calculation formula of the cost function C(t) of purchasing electricity is obtained as follows:

[0027] C(t)=X(t)·q.

[0028] Preferably, the electricity purchased from the power grid is q, which is calculated by the difference between the planned output of the virtual power plant and the power load demand of the load terminal, and q≥0. Assuming that in the time period t, the planned output of the virtual power plant is D(t), and the power load demand of the load terminal is L(t), the calculation formula of the planned output of the virtual power plant D(t) is:

[0029]

[0030] Therefore, the calculation formula for the power q purchased by the power grid is:

[0031] q=(L(t)-D(t))·t.

[0032] Preferably, the data analysis and processing module pre-sets the minimum value of the power generation of the distributed energy generation equipment according to the maximum and minimum values ​​of the fluctuation range of the past operation data of the distributed energy generation equipment, solar power generation equipment, and wind power generation equipment stored in the data storage module after removing abnormal values. and maximum value The minimum value of the power generation capacity of the solar power generation device and maximum value The minimum value of the power generation capacity of the wind power generation equipment and maximum value The data analysis and processing module calculates the maximum charging power based on the energy storage device system design and available capacity. and maximum discharge power q min and q max Determined by the operation of the power grid and relevant national policies and regulations, the following constraints can be obtained:

[0033]

[0034] Preferably, the scheduling decision module generates a scheduling strategy based on the calculation results of the data analysis and processing module, and sends scheduling instructions according to the scheduling strategy. The scheduling instructions include the startup and shutdown time control and output adjustment of the distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment, and the charging and discharging control of the energy storage device, so as to control all equipment to meet the power requirements obtained from the calculation results within time t.

[0035] Preferably, during the execution of the dispatch instruction, the monitoring and evaluation module continuously monitors the load changes, power generation output, grid stability, and equipment operation status of the power grid, and sends the monitored data to the data analysis and processing module in a timely manner so as to adjust the constraints and re-analyze the data when an abnormality occurs.

[0036] Preferably, the load prediction module calls the distribution network load, controllable load parameters and historical data records of distribution operation stored in the database, and uses the neural network control algorithm to perform calculation learning to establish a load prediction model; the load prediction module uses time as an index to extract the power consumption feature clustering set and the index response parameters in the distribution network load, and performs operating load prediction on the index response parameters according to the load prediction model to obtain the predicted load, and the data analysis and processing module calculates the required power in advance according to the predicted load.

[0037] Preferably, the scheduling decision module first generates scheduling instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment based on the power generation power forecast obtained by the data analysis and processing module, and then controls the charging and discharging of the energy storage device according to the actual calculation results, and adjusts the scheduling instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment according to the actual calculations.

[0038] It can be seen from the above technical solutions that: 1. Since the embodiment of the present invention establishes a more complete and accurate mathematical model and constraint conditions with the goal of minimizing cost, it can more accurately calculate the power demand for maximizing economic benefits, thereby formulating a better scheduling strategy and obtaining higher economic benefits;

[0039] 2. Since the embodiment of the present invention introduces an intelligent monitoring and evaluation mechanism, it is possible to monitor the operating status of the power system in real time, discover problems in a timely manner and recalculate power demand, and adjust the dispatching strategy according to the operating conditions, thereby improving the stability and reliability of the system;

[0040] 3. Since the embodiments of the present invention use advanced data collection technology and high-precision sensors, the accuracy and real-time performance of data collection are improved, providing a reliable basis for accurate scheduling decisions;

[0041] 4. Since the embodiment of the present invention predicts the load through the load prediction module and calculates the required power in advance through the data analysis and processing module, the system can issue scheduling instructions to the power generation equipment in advance according to the calculation, so that the system can respond to electricity demand and improve the stability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of a virtual power plant dispatching method for power transmission of the present invention;

[0044] Figure 2 It is a framework diagram of a virtual power plant dispatching system for power transmission according to the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The embodiment of the present invention provides a virtual power plant dispatching system for power transmission, comprising: a data acquisition module, a data storage module, a data analysis and processing module, a load prediction module, a dispatching decision module, an execution control module, a monitoring and evaluation module, distributed energy generation equipment, a solar power generation equipment, a wind power generation equipment, a load terminal, and an energy storage device, wherein the distributed energy generation equipment, the solar power generation equipment, the wind power generation equipment, the load terminal, and the energy storage device are connected to the execution control module by signals and are uniformly controlled by the execution control module;

[0047] Data acquisition module: collects the operating parameters of distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminals and energy storage devices, including power generation power, energy storage status, load demand, grid voltage and current;

[0048] Data storage module: storage system historical data;

[0049] Data analysis and processing module: Analyze and process the data collected by the data acquisition module, establish a function with the goal of minimizing the power supply cost, calculate the required power results under the goal of giving priority to the grid-connected operation of distributed energy, solar energy, and wind energy, and predict the required power generation power based on the load prediction results of the load prediction module;

[0050] Load forecasting module: calls the historical data of the data storage module to establish a load forecasting model, continuously adjusts the load forecasting model according to the real-time data obtained by the data acquisition module, and generates load forecasting results;

[0051] Scheduling decision module: formulates optimized scheduling strategies based on the calculation results of the data analysis and processing module, and generates scheduling instructions based on the scheduling strategies;

[0052] Execution control module: responsible for executing the dispatch instructions generated by the dispatch decision module, controlling various distributed energy generation equipment, solar power generation equipment, wind power generation equipment and energy storage devices, and realizing real-time control of the power system;

[0053] Monitoring and evaluation module: monitors the operating status of the power system in real time, evaluates the execution effect of the dispatch strategy formulated by the dispatch decision module, promptly discovers abnormal conditions in the system, and feeds back to the data analysis and processing module and the dispatch decision module to adjust and optimize the calculation power and dispatch strategy.

[0054] The embodiment of the present invention also provides a corresponding method of use, which is described in detail below:

[0055] Example 1: Please refer to Figure 1 , a method for using a virtual power plant dispatching system for power transmission, comprising the following steps:

[0056] S1: Collect the operating parameters of distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminals and energy storage devices through the data acquisition module;

[0057] S2: storing the collected data in the data storage module and sending it to the data analysis and processing module and the load forecasting module;

[0058] S3: The load forecasting module calls the historical data of the data storage module to establish a forecasting model, continuously adjusts the forecasting model according to the acquired real-time data, generates load forecasting results and sends them to the data analysis and processing module;

[0059] S4: The data analysis and processing module analyzes and processes the collected data, calculates the power generation of distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the charging and discharging power of energy storage devices with the goal of minimizing power supply costs, and uses intelligent algorithms to predict the power generation according to the load forecast results;

[0060] S5: The dispatch decision module formulates an optimized dispatch strategy based on the results of the data analysis and processing module and the cost characteristics and environmental protection requirements of different power generation resources, and generates dispatch instructions based on the dispatch strategy;

[0061] S6: The execution control module executes the dispatching instructions generated by the dispatching decision module to control each distributed energy generation equipment, solar power generation equipment, wind power generation equipment and energy storage device to operate according to the calculated power;

[0062] S7: The monitoring and evaluation module monitors the operating status of the power system in real time, promptly discovers and reports abnormal conditions in the system to the data analysis and processing module, and evaluates the execution effect of the dispatch strategy formulated by the dispatch decision module, and feeds back to the dispatch decision module;

[0063] S8: The data analysis and processing module adjusts the constraints according to the abnormal report and recalculates the required operating power. The scheduling decision module adjusts and optimizes the scheduling strategy according to the recalculation results and execution effect feedback.

[0064] Embodiment 2: The data analysis and processing module calculates the optimal power generation power of each device and the charge and discharge power of the energy storage battery in a certain time period by establishing a mathematical model, takes the minimum power supply cost as the decision-making goal, obtains the maximum economic benefit, and thus solves the optimal scheduling plan; let the power supply cost be A, the total time considered be T, and let the operating cost coefficient of the distributed energy power generation equipment in the time period t be K 1 , the operating cost coefficient of the solar power generation equipment is K 2 The wind power generation equipment operating cost coefficient is K 3 , the energy storage device operating cost coefficient is K s , the power generation power of the distributed energy generation equipment is The power generation capacity of the solar power generation equipment is The power generation capacity of the wind power generation equipment is The discharge power of the energy storage device in time period t is Charging power is The cost function of purchasing electricity is C(t); the objective function of minimizing the power supply cost is expressed as:

[0065]

[0066] Assume that the electricity price changes in a certain periodic pattern within a day, and fit it to the electricity price function X(t). The cost of purchasing electricity C(t) is simply calculated by multiplying the electricity price function by the electricity purchased from the power grid. Assume that the electricity purchased from the power grid in the time period t is q, and the calculation formula of the cost function C(t) of purchasing electricity is obtained as follows:

[0067] C(t) = X(t)·q;

[0068] The power purchased from the power grid is q, which is calculated by the difference between the planned output of the virtual power plant and the power load demand of the load terminal, and q≥0. Assume that in the time period t, the planned output of the virtual power plant is D(t), and the power load demand of the load terminal is L(t). The calculation formula of the planned output of the virtual power plant D(t) is:

[0069]

[0070] Therefore, the calculation formula for the power q purchased by the power grid is:

[0071] q = (L(t) - D(t)) t;

[0072] The data analysis and processing module pre-sets the minimum value of the power generation of the distributed energy generation equipment, solar power generation equipment, and wind power generation equipment according to the maximum and minimum values ​​of the fluctuation range after removing abnormal values ​​from the past operation data of the distributed energy generation equipment, solar power generation equipment, and wind power generation equipment stored in the data storage module. and maximum value The minimum value of the power generation capacity of the solar power generation device and maximum value The minimum value of the power generation capacity of the wind power generation equipment and maximum value The data analysis and processing module calculates the maximum charging power based on the energy storage device system design and available capacity. and maximum discharge power q min and q max Determined by the operation of the power grid and relevant national policies and regulations, the following constraints can be obtained:

[0073]

[0074] The final calculation model is:

[0075]

[0076]

[0077] Example 3: Please refer to Figure 2 The scheduling decision module generates a scheduling strategy based on the calculation results of the data analysis and processing module, and sends scheduling instructions according to the scheduling strategy. The scheduling instructions include the start-up and shutdown time control and output adjustment of distributed energy generation equipment, solar power generation equipment, and wind power generation equipment, and the charging and discharging control of energy storage devices, so as to control all equipment to reach the power requirements obtained from the calculation results within time t;

[0078] During the execution of dispatch instructions, the monitoring and evaluation module continuously monitors the load changes, power generation output, grid stability, and equipment operation of the power grid, and sends the monitored data to the data analysis and processing module in a timely manner so that the constraints can be adjusted and the data can be re-analyzed when an abnormality occurs;

[0079] The load forecasting module calls the distribution network load, controllable load parameters and historical data records of distribution operation stored in the database, and uses the neural network control algorithm to perform calculation learning to establish a load forecasting model; the load forecasting module uses time as an index to extract the power consumption feature clustering set and the index response parameters in the distribution network load, and performs operating load forecasting on the index response parameters according to the load forecasting model to obtain the predicted load. The data analysis and processing module calculates the required power in advance according to the predicted load;

[0080] The dispatch decision module first generates dispatch instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment based on the power generation power forecast obtained by the data analysis and processing module, and then controls the charging and discharging of the energy storage device according to the actual calculation results, and adjusts the dispatch instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment according to the actual calculation.

[0081] It should be noted that, since the information interaction, execution process, etc. between the units in the above-mentioned devices and systems are based on the same concept as the embodiments of the method of the present invention, the specific contents can be found in the description of the embodiments of the method of the present invention and will not be repeated here.

[0082] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium.

[0083] The above is a detailed introduction to a virtual power plant dispatching system and method for power transmission provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A virtual power plant dispatching system for power transmission, characterized in that: include: Data acquisition module, data storage module, data analysis and processing module, load forecasting module, scheduling decision module, execution control module, monitoring and evaluation module, distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminal, energy storage device, the distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminal, energy storage device signals are connected to the execution control module and are uniformly controlled by the execution control module; Data acquisition module: collects the operating parameters of the distributed energy generation equipment, the solar power generation equipment, the wind power generation equipment, the load terminal and the energy storage device, including power generation power, energy storage status, load demand, grid voltage and current; Data storage module: storage system historical data; Data analysis and processing module: analyzes and processes the data collected by the data acquisition module, establishes a function with the goal of minimizing the power supply cost, calculates the required power results under the goal of giving priority to the grid-connected operation of distributed energy, solar energy, and wind energy, and predicts the required power generation power according to the load prediction results of the load prediction module; Load forecasting module: calls the historical data of the data storage module to establish a load forecasting model, continuously adjusts the load forecasting model according to the real-time data obtained by the data acquisition module, and generates load forecasting results; Scheduling decision module: formulates optimized scheduling strategies based on the calculation results of the data analysis and processing module, and generates scheduling instructions based on the scheduling strategies; Execution control module: responsible for executing the dispatching instructions generated by the dispatching decision module, controlling each of the distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the energy storage device, and realizing real-time control of the power system; Monitoring and evaluation module: monitors the operating status of the power system in real time, evaluates the execution effect of the dispatching strategy formulated by the dispatching decision module, promptly discovers abnormal conditions in the system, and feeds back to the data analysis and processing module and the dispatching decision module to adjust and optimize the calculation power and dispatching strategy.

2. The method for using the virtual power plant dispatching system for power transmission according to claim 1, characterized in that: The following steps are involved: S1: Collect the operating parameters of distributed energy generation equipment, solar power generation equipment, wind power generation equipment, load terminals and energy storage devices through the data acquisition module; S2: storing the collected data in the data storage module and sending it to the data analysis and processing module and the load forecasting module; S3: The load forecasting module calls the historical data of the data storage module to establish a forecasting model, continuously adjusts the forecasting model according to the acquired real-time data, generates load forecasting results and sends them to the data analysis and processing module; S4: The data analysis and processing module analyzes and processes the collected data, calculates the power generation of distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the charging and discharging power of energy storage devices with the goal of minimizing power supply costs, and uses intelligent algorithms to predict the power generation according to the load forecast results; S5: The dispatch decision module formulates an optimized dispatch strategy according to the results of the data analysis and processing module and the cost characteristics and environmental protection requirements of different power generation resources, and generates a dispatch instruction according to the dispatch strategy; S6: The execution control module executes the scheduling instruction generated by the scheduling decision module to control each of the distributed energy generation equipment, solar power generation equipment, wind power generation equipment and the energy storage device to operate according to the calculated power; S7: The monitoring and evaluation module monitors the operating status of the power system in real time, promptly discovers and reports abnormal conditions in the system to the data analysis and processing module, and evaluates the execution effect of the scheduling strategy formulated by the scheduling decision module, and feeds back to the scheduling decision module; S8: The data analysis and processing module adjusts the constraints according to the abnormal report and recalculates the required operating power. The scheduling decision module adjusts and optimizes the scheduling strategy according to the recalculation results and execution effect feedback.

3. The method for using the virtual power plant dispatching system for power transmission according to claim 1, characterized in that: The data analysis and processing module takes the minimum power supply cost as the decision-making goal and solves the optimal scheduling plan. Assume that the power supply cost is A, the total time considered is T, and within the time period t, the operating cost coefficient of the distributed energy generation equipment is K1, the operating cost coefficient of the solar power generation equipment is K2, the operating cost coefficient of the wind power generation equipment is K3, and the operating cost coefficient of the energy storage device is K s , the power generation power of the distributed energy generation equipment is The power generation capacity of the solar power generation equipment is The power generation capacity of the wind power generation equipment is The discharge power of the energy storage device in time period t is Charging power is The cost function of purchasing electricity is C(t); the objective function of minimizing the power supply cost is expressed as:

4. The method for using the virtual power plant dispatching system for power transmission according to claim 1 or 3, characterized in that: Assume that the electricity price changes in a certain periodic pattern within a day, and fit it to the electricity price function X(t). The cost of purchasing electricity C(t) is simply calculated by multiplying the electricity price function by the electricity purchased from the power grid. Assume that the electricity purchased from the power grid in the time period t is q, and the calculation formula of the cost function C(t) of purchasing electricity is obtained as follows: C(t)=X(t)·q.

5. The method for using the virtual power plant dispatching system for power transmission according to claim 1 or 3, characterized in that: The power purchased from the power grid is q, which is calculated by the difference between the planned output of the virtual power plant and the power load demand of the load terminal, and q≥0. Assume that in the time period t, the planned output of the virtual power plant is D(t), and the power load demand of the load terminal is L(t). The calculation formula of the planned output of the virtual power plant D(t) is: Therefore, the calculation formula for the power q purchased by the power grid is: q=(L(t)-D(t))·t.

6. The method for using the virtual power plant dispatching system for power transmission according to claim 1 or 3, characterized in that: The data analysis and processing module pre-sets the minimum value of the power generation of the distributed energy generation equipment according to the maximum and minimum values ​​of the fluctuation range of the distributed energy generation equipment, solar power generation equipment, and wind power generation equipment after removing abnormal values ​​from the past operation data stored in the data storage module. and maximum value The minimum value of the power generation capacity of the solar power generation device and maximum value The minimum value of the power generation capacity of the wind power generation equipment and maximum value The data analysis and processing module calculates the maximum charging power based on the energy storage device system design and available capacity. and maximum discharge power q min and q max Determined by the operation of the power grid and relevant national policies and regulations, the following constraints can be obtained:

7. The method for using the virtual power plant dispatching system for power transmission according to claim 1 or 3, characterized in that: The scheduling decision module generates a scheduling strategy according to the calculation results of the data analysis and processing module, and sends scheduling instructions according to the scheduling strategy. The scheduling instructions include the startup and shutdown time control and output adjustment of the distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment, and the charging and discharging control of the energy storage device, so as to control all equipment to meet the power requirements obtained from the calculation results within time t.

8. The method for using the virtual power plant dispatching system for power transmission according to claim 1, characterized in that: During the execution of the dispatch instructions, the monitoring and evaluation module continuously monitors the load changes, power generation output, grid stability, and equipment operation of the power grid, and sends the monitored data to the data analysis and processing module in a timely manner so as to adjust the constraints and re-analyze the data when an abnormality occurs.

9. The method for using the virtual power plant dispatching system for power transmission according to claim 1, characterized in that: The load prediction module calls the distribution network load, controllable load parameters and historical data records of distribution operation stored in the database, and uses the neural network control algorithm to perform calculation learning to establish a load prediction model; the load prediction module uses time as an index to extract the power consumption feature clustering set and the index response parameters in the distribution network load, and performs operating load prediction on the index response parameters according to the load prediction model to obtain the predicted load. The data analysis and processing module calculates the required power in advance according to the predicted load.

10. The method for using the virtual power plant dispatching system for power transmission according to claim 1, characterized in that: The scheduling decision module first generates scheduling instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment based on the power generation power forecast obtained by the data analysis and processing module, and then controls the charging and discharging of the energy storage device according to the actual calculation results, and adjusts the scheduling instructions for distributed energy power generation equipment, solar power generation equipment, and wind power generation equipment according to the actual calculations.

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