Virtual power plant regulation and control method and system for realizing new energy consumption

Abstract

The invention relates to a virtual power plant regulation and control method and system for realizing new energy consumption. The method comprises the steps of performing statistics according to a wind and light power generation module to obtain predicted power generation amount E1 in each time period; according to an electric vehicle scheduling module, predicting the actual charging demand electric quantity QS1 of the electric vehicle in each time period; predicting actual load demand electric quantity QS2 in each time period according to a load prediction module; according to the energy storage module, calculating the storable electric quantity C and the standby residual capacity CS of each time period in real time; and according to the virtual power plant global scheduling module, the electric quantity difference value E difference required by each time period is calculated according to the predicted electric quantity E1 of each time period, the actual charging demand electric quantity QS1, the actual load demand electric quantity QS2 and the storable electric quantity C, and according to the electric quantity difference value E difference, a scheduling power generation instruction is issued to the heat power unit module for time-phased power generation. The distributed new energy consumption can be realized, and the stability and economy of grid-connected scheduling operation of the distributed new energy equipment can be improved at the same time.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant control technology, and in particular to a virtual power plant control method and system for realizing new energy consumption. Background Art

[0002] Against the backdrop of energy shortages, severe environmental pollution and global climate change, a large number of distributed power sources such as wind power and photovoltaic power have emerged and are widely and randomly connected to the power grid. Renewable energy generation (such as wind power stations and photovoltaic power stations) has the characteristics of intermittent or randomness and the existence of prediction errors. Large-scale grid connection must take into account the impact of uncertainty, which increases the difficulty of stable operation of the grid-connected dispatching system. With the rise of electric vehicles, electric vehicles have the dual attributes of mobile energy storage and load. Their use of electricity is also random. The direct access of large-scale electric vehicle loads will increase the complexity of coordinated operation, and their charging time is highly overlapped with the time of daily electricity consumption. The random multi-peak charging load coincides with the peak of residents' daily electricity load, exacerbating the peak-to-valley difference of the load, and bringing huge challenges to the stable dispatching operation of the grid-connected dispatching system; most of the existing virtual power plant dispatching methods do not consider the dispatching factors of electric vehicles. For example, the invention patent CN115659666A discloses a virtual power plant wind and solar joint optimization dispatching method considering comprehensive demand response, which only considers the aggregation of multiple energy sources and does not consider the impact of the access of active loads such as electric vehicles on the dispatching system.

[0003] In recent years, virtual power plants can achieve the aggregation and coordinated optimization of energy storage systems, distributable generators, controllable loads and electric vehicles through advanced information and communication technologies and software systems. This can not only alleviate the peak load pressure of electric vehicle charging, but also smooth out the fluctuation of wind and solar power, ensure the quality of power and improve the economic efficiency of power generation. However, the existing research considers relatively single factors, cannot achieve comprehensive dispatch, and has not proposed an economical and effective dispatch method. In addition, with the widespread access of renewable energy and various flexible loads on the user side of virtual power plants, the line flow of the distribution system often exceeds the limit, resulting in line blockage and power accidents. In view of this, how to develop a virtual power plant control method and system that can realize the consumption of new energy, so as to improve the economic efficiency while improving the stable scheduling of grid-connected equipment, is an urgent problem to be solved. Summary of the invention

[0004] In response to the above-mentioned technical problems to be solved, the present invention provides a virtual power plant control method and system for realizing new energy consumption, which improves the stability of grid-connected scheduling and operation of distributed new energy equipment while realizing distributed new energy consumption.

[0005] The present invention provides a virtual power plant control method for realizing new energy consumption, the method comprising: Step S1: The wind and solar power generation module obtains the predicted power generation E1 for each period by statistics; Step S2: The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; Step S3: The load forecasting module forecasts the actual load demand QS2 in each period; Step S4: The energy storage module counts the storable capacity C and the spare remaining capacity CS in real time in each time period; Step S5: The virtual power plant global dispatch module calculates the required power difference E for each period based on the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; Step S6: The thermal unit module generates electricity in different time periods according to the dispatched power generation instructions.

[0006] Furthermore, in step S1, the wind-solar power generation module obtains the predicted power generation E1 for each time period by statistics, which specifically includes: The wind-solar power generation module loads a preset power generation prediction model, takes the historical operation data of the dispatching area, the historical meteorological data of the previous day and the historical day, and the meteorological forecast data of the previous day and the day as input, predicts and outputs the power generation E0 of each time period of the day, and then combines the prediction deviation value and the first margin value of the power generation prediction model to calculate and obtain the predicted power generation E1 of each time period; preferably, one hour is one time period; Furthermore, in step S2, the electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period, specifically including: The electric vehicle scheduling module receives the electricity consumption plan uploaded by the car user, and counts the power demand Q1, the storable power C1 and the dischargeable power W1 in each period of the day; Based on the historical operation data of the charging stations in the scheduling area, a charging load curve for cars in the charging station other than users who upload electricity consumption plans is constructed, and a charging load calculation model is loaded to predict the demand power Q2 for each time period of the day based on the charging load curve. Then, based on the demand power Q1 and the demand power Q2, combined with the second margin value, the actual demand power QS1 for each time period of the day is predicted.

[0007] Furthermore, in step S3, the load forecasting module forecasts the actual load demand QS2 in each time period, specifically including: The load forecasting module constructs a power load curve of other loads in the scheduling area except the charging station based on the historical operation data of the scheduling area, and loads a power load calculation model to predict the power demand Q3 in each time period based on the power load curve and the historical load changes in each time period, and combines the third margin value to predict the actual power demand QS2 in each time period of the day.

[0008] Furthermore, in step S4, the energy storage module respectively counts the storable power C and the spare remaining capacity CS in each time period in real time, specifically including: In the energy storage module, the initial capacity of the energy storage device is divided into the storable capacity C2 and the spare capacity C3; at the initial scheduling moment, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3; During the dispatching process, if the consumption is completed, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3; If the amount of electricity stored in the energy storage device that exceeds half of the initial capacity is C4, then the storable amount C is equal to -C4, and the spare remaining capacity CS is equal to C3-C4; If the amount of electricity stored in the energy storage device is less than half of the initial capacity, which is C5, then the storable amount C is equal to C5, and the spare remaining capacity CS is equal to C3.

[0009] Furthermore, in step S5, the difference E of the power required for each time period is calculated. 差 , including: The virtual power plant global dispatch module obtains the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period, calculates the difference E2 between the sum of the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period and the predicted power generation E1, and then uses the N equal parts of the sum of the storable power C1 and the spare capacity C3 as the margin in each time period to comprehensively calculate the actual power difference E1. 差 , the E 差 That is, the amount of electricity that the thermal unit module needs to generate in each time period.

[0010] Furthermore, the virtual power plant global dispatch module also includes a power flow monitoring module, which obtains the actual difference E of the power in each period. 差 , predict the power generation E1 and the power load and charging load in each time period, calculate the active power and reactive power of each node side in the dispatching area network, and when it is calculated that the voltage phase angle or amplitude of the node side exceeds the first preset threshold, further determine the intervention method of the node side: If it is determined that the power supply needs to be increased, the required power value EZ and the sum of the dischargeable power W1 and the power stored in the energy storage module Cc are calculated. 储, and judge the EZ and E 储 Size between: When EZ is less than or equal to E 储 When EZ is greater than E, the virtual power plant global dispatch module sends an electric energy dispatch instruction to the electric vehicle dispatch module and the energy storage module to release the increased power value EZ, among which the energy storage module is called for discharge first; when EZ is greater than E 储 When EZ and E are calculated 储 The difference between 1差 The virtual power plant global dispatch module calculates the difference E 1差 Send dispatching power generation instructions to thermal unit modules; If it is determined that there is excess electricity, the remaining electricity Ey on the node side is calculated, and the remaining electricity Ey is stored in the nearest energy storage module, and the storable electricity C1, the storable electricity C of the energy storage module and the spare capacity C3 are detected and calculated in real time. If it is detected that the storable electricity C in the current period is 0, and the capacity occupancy rate of the sum of the storable electricity C1 and the spare capacity C3 reaches the second preset threshold, the actual electricity difference E is calculated in the next period. 差 At E 差 50% of the sum of the storable power C1 and the spare capacity C3 is deducted from the total as the power generation of the thermal unit module, and the 50% of the sum of the storable power C1 and the spare capacity C3 is controlled by the electric vehicle dispatching module and the energy storage module to discharge and supply.

[0011] Furthermore, in step S6, the thermal unit module generates electricity in time periods according to the dispatched power generation instruction, which specifically includes: The virtual power plant global dispatch module calculates the actual difference E of the electricity in each period. 差 and the difference E 1差 The dispatching power generation instruction is sent to the thermal unit module, and the thermal unit module generates power in time periods according to the dispatching power generation instruction.

[0012] Furthermore, the virtual power plant global dispatching module also obtains the charging load peaks and troughs corresponding to each time period based on the charging load curve constructed by the electric vehicle dispatching module, and then transfers 50% of the charging demand electricity of the sum of the storable capacity C1 and the backup capacity C3 to the corresponding trough period during the charging load peak period, and adjusts the charging electricity price during the charging load peak period accordingly, guiding the electric vehicles to charge during the corresponding trough period.

[0013] The present invention also includes a virtual power plant control system for realizing new energy consumption, the virtual power plant control system comprising: a virtual power plant global dispatching module, a wind and solar power generation module, an electric vehicle dispatching module, a load prediction module, an energy storage module and a thermal unit module; The wind and solar power generation module obtains the predicted power generation E1 for each time period by statistics, wherein one hour is one time period; The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; The load forecasting module predicts the actual load demand QS2 in each time period; The energy storage module respectively counts the storable amount C and the spare remaining capacity CS in each time period in real time; The virtual power plant global dispatch module calculates the required power difference E in each period according to the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each period. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; The thermal power unit module generates electricity in different time periods according to the dispatched power generation instructions.

[0014] The beneficial effects of the present invention are: 1. When predicting the power generation of new energy modules, the prediction is realized by combining the prediction deviation value and the first margin value of the power generation prediction model, taking into account the fluctuation of the power generation prediction model and the system fluctuation, so as to obtain a more accurate prediction result of power generation; 2. On the basis of the existing daily forecast and consumption, a time-segment forecast is proposed to achieve more refined real-time regulation, and a cyclical consumption method is adopted in which reserve is made based on margin and then consumption is carried out when the storage threshold is reached. Compared with the daily forecast and consumption process, it can have a shorter consumption cycle and more accurate consumption results, and at the same time, the margin is maintained to increase the adjustability of the system and improve the ability to respond to emergencies; 3. During the dispatching process, the node voltage is monitored in real time, and different control strategies are adopted to intervene according to the specific power supply situation on the node side, eliminate line congestion, and improve the stability of power grid operation; 4. Scheduling based on electricity consumption plans uploaded by users increases user participation. The charging and discharging behavior of electric vehicles is regulated through demand response technology, which reduces the peak-to-valley difference in load while absorbing the grid-connected power generation of renewable energy, thereby increasing the dispatching income of virtual power plants. 5. It can be directly transformed on the basis of the existing scheduling, and only the scheduled equipment needs to be re-planned, that is, the scheduling process is optimized on the basis of software, saving transformation costs and improving economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A step diagram of a virtual power plant control method for realizing new energy consumption according to the present invention; Figure 2 This is a structural diagram of the virtual power plant control system for realizing new energy consumption according to the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0017] like Figure 1 As shown, the present invention provides a virtual power plant control method for realizing new energy consumption, the method comprising: Step S1: The wind and solar power generation module obtains the predicted power generation E1 for each time period by statistics, wherein one hour is one time period; Step S2: The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; Step S3: The load forecasting module forecasts the actual load demand QS2 in each period; Step S4: The energy storage module counts the storable capacity C and the spare remaining capacity CS in real time in each time period; Step S5: The virtual power plant global dispatch module calculates the required power difference E for each period based on the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; Step S6: The thermal unit module generates electricity in different time periods according to the dispatched power generation instructions.

[0018] Furthermore, in step S1, the wind-solar power generation module obtains the predicted power generation E1 for each time period by statistics, which specifically includes: The wind-solar power generation module loads a preset power generation prediction model, takes the historical operation data of the dispatching area, the historical meteorological data of the previous day and the historical day, and the meteorological forecast data of the previous day and the day as input, predicts and outputs the power generation E0 of each time period of the day, and then combines the prediction deviation value and the first margin value of the power generation prediction model to calculate and obtain the predicted power generation E1 of each time period; Right now ;in, is the prediction deviation value of the power generation prediction model, and Y1 is the first margin value; Specifically, the power generation prediction model adopts a fuzzy neural network model, and the historical meteorological data of the previous day can be only the meteorological data of the previous day of the previous year, or the meteorological data corresponding to the maximum probability value calculated based on the probability density statistics of the meteorological data of the previous day of the previous year; the historical meteorological data of the current day is obtained in the same way; The prediction deviation value of the power generation prediction model is the mean square value of the historical daily prediction deviation values, and learning and updating are performed after the daily prediction deviation value is calculated every day; during the learning and updating process, the values ​​of the daily prediction deviation values ​​that far exceed the threshold value need to be eliminated. The first margin value is the power generation margin value determined based on the proportion of the historical annual power generation of the wind and solar power generation module in the total annual power generation.

[0019] Furthermore, in step S2, the electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period, specifically including: The electric vehicle scheduling module receives the electricity consumption plan uploaded by the car user, and counts the power demand Q1, the storable power C1 and the dischargeable power W1 in each period of the day; Based on the historical operation data of the charging station in the dispatching area, a charging load curve of the cars in the charging station other than the users who uploaded the electricity consumption plan is constructed, and a charging load calculation model is loaded to predict the demand power Q2 for each time period of the day based on the charging load curve, and then the actual demand power QS1 for each time period of the day is predicted based on the demand power Q1 and the demand power Q2, combined with the second margin value Y2; Right now ;The second margin value Y2 is the power margin value estimated by the electric vehicle scheduling module based on the electric vehicle sales data of the previous quarter; Furthermore, in step S3, the load forecasting module forecasts the actual load demand QS2 in each time period, specifically including: The load forecasting module constructs the power load curve of other loads in the dispatching area except the charging station according to the historical operation data of the dispatching area, and loads the power load calculation model to predict the power demand Q3 of each time period according to the power load curve and the historical load changes of each time period, and predicts the actual power demand QS2 of each time period of the day in combination with the third margin value Y3; Right now ; The third margin value Y3 is the power margin value determined by the load forecasting module based on the historical load changes on the day; Furthermore, in step S4, the energy storage module respectively counts the storable power C and the spare remaining capacity CS in each time period in real time, specifically including: In the energy storage module, the initial capacity of the energy storage device is divided into storable capacity C2 and spare capacity C3; at the initial scheduling moment, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3.

[0020] During the dispatching process, if the consumption is completed, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3; If the amount of electricity stored in the energy storage device that exceeds half of the initial capacity is C4, then the storable amount C is equal to -C4, and the spare remaining capacity CS is equal to C3-C4; If the amount of electricity stored in the energy storage device is less than half of the initial capacity, which is C5, then the storable amount C is equal to C5, and the spare remaining capacity CS is equal to C3.

[0021] Among them, the spare capacity is rotated in the energy storage device according to a preset cycle.

[0022] Furthermore, in step S5, the difference E of the power required for each time period is calculated. 差 , including: The virtual power plant global dispatch module obtains the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period, and calculates the difference E2 between the sum of the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period and the predicted power generation E1, that is, ; Then, in each time period, the sum of the storable capacity C1 and the backup capacity C3 is divided into N equal parts as the margin, that is, ; Where N is an integer greater than or equal to 2; Comprehensively calculate the actual difference in power E 差 , ; The E 差 That is, the amount of electricity that the thermal unit module needs to generate in each time period. To set, more power needs to be generated.

[0023] Furthermore, the virtual power plant global dispatch module also includes a power flow monitoring module, which obtains the actual difference E of the power in each period. 差 , predict the power generation E1 and the power load and charging load in each time period, calculate the active power and reactive power of each node side in the dispatching area network, and when it is calculated that the voltage phase angle or amplitude of the node side exceeds the first preset threshold, further determine the intervention method of the node side: If it is determined that the power supply needs to be increased, the required power value EZ and the sum of the dischargeable power W1 and the power stored in the energy storage module Cc are calculated. 储 , and judge the EZ and E 储 Size between: When EZ is less than or equal to E 储 When EZ is greater than E, the virtual power plant global dispatch module sends an electric energy dispatch instruction to the electric vehicle dispatch module and the energy storage module to release the increased power value EZ, among which the energy storage module is called for discharge first; when EZ is greater than E 储 When EZ and E are calculated 储 The difference between 1差 The virtual power plant global dispatch module calculates the difference E 1差Send dispatching power generation instructions to thermal unit modules; Specifically, when EZ is less than or equal to E 储 When the amount of electricity stored in the electric vehicles and energy storage modules in the dispatching system can be temporarily supplied, the global dispatching module of the virtual power plant will give priority to issuing dispatching instructions to the energy storage module for discharge. When the amount of electricity stored in the energy storage module is less than the third set threshold, the electric energy dispatching instruction will be sent to the electric vehicle dispatching module. With the help of the smart charging pile, the user who uploaded the electricity plan will be controlled to discharge the electric vehicle. If it is determined that there is excess electricity, the remaining electricity Ey on the node side is calculated, and the remaining electricity Ey is stored in the nearest energy storage module, and the storable electricity C1, the storable electricity C of the energy storage module and the spare capacity C3 are detected and calculated in real time. If it is detected that the storable electricity C in the current period is 0, and the capacity occupancy rate of the sum of the storable electricity C1 and the spare capacity C3 reaches the second preset threshold, the actual electricity difference E is calculated in the next period. 差 At E 差 50% of the sum of the storable power C1 and the spare capacity C3 is deducted from the total as the power generation of the thermal unit module, and the 50% of the sum of the storable power C1 and the spare capacity C3 is controlled by the electric vehicle dispatching module and the energy storage module to discharge and supply.

[0024] Specifically, after the consumption is completed and the dispatch begins, Generate electricity for the excess power, and store the excess power in the electric vehicle and the backup energy storage device until it is detected that the storable power C in the current period is 0, and the capacity occupancy rate of the sum of the storable power C1 and the backup capacity C3 reaches the second preset threshold, and the actual power difference E is calculated in the next period 差 At E 差 The power generation of the thermal unit module is taken as 50% of the sum of the storable power C1 and the backup capacity C3. At this time, the excess power of the electric vehicle and the backup energy storage equipment is consumed to realize a consumption cycle. This cyclic reserve and then consumption consumption method can have a shorter consumption cycle and more accurate consumption results than the daily predicted consumption process. If the appropriate N value is set, multiple consumptions can be achieved within the day; and at the same time, a margin is retained to deal with emergencies, such as emergency dispatch when wind and solar power generation fails, which does not affect the power supply.

[0025] Furthermore, in step S6, the thermal unit module generates electricity in time periods according to the dispatched power generation instruction, which specifically includes: The virtual power plant global dispatch module calculates the actual difference E of the electricity in each period. 差 and the difference E 1差 The dispatching power generation instruction is sent to the thermal unit module, and the thermal unit module generates power in time periods according to the dispatching power generation instruction.

[0026] Furthermore, the virtual power plant global dispatching module also obtains the charging load peaks and troughs corresponding to each time period based on the charging load curve constructed by the electric vehicle dispatching module, and then transfers 50% of the charging demand electricity of the sum of the storable capacity C1 and the backup capacity C3 to the corresponding trough period during the charging load peak period, and adjusts the charging electricity price during the charging load peak period accordingly, guiding the electric vehicles to charge during the corresponding trough period.

[0027] Furthermore, if there is a need to temporarily increase the power supply, the electric vehicle scheduling module can also set preferential electricity prices to guide users outside the uploaded power consumption plan to control electric vehicles for grid-connected power supply, and encourage electric vehicles to participate in discharge demand response when there is a temporary power generation shortage by setting compensation prices.

[0028] like Figure 2 As shown, the present invention also includes a virtual power plant control system for realizing new energy consumption, and the virtual power plant control system includes: a virtual power plant global scheduling module, a wind and solar power generation module, an electric vehicle scheduling module, a load prediction module, an energy storage module and a thermal unit module; The wind and solar power generation module obtains the predicted power generation E1 for each time period by statistics, wherein one hour is one time period; The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; The load forecasting module predicts the actual load demand QS2 in each time period; The energy storage module respectively counts the storable amount C and the spare remaining capacity CS in each time period in real time; The virtual power plant global dispatch module calculates the required power difference E in each period according to the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each period. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; The thermal power unit module generates electricity in different time periods according to the dispatched power generation instructions.

[0029] The electric vehicle is connected to the grid based on the intelligent charging pile equipment and is charged or discharged under the control of the electric vehicle scheduling module.

[0030] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a ROM, a RAM, etc.

[0031] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A virtual power plant control method for realizing new energy consumption, characterized in that: include: Step S1: The wind and solar power generation module obtains the predicted power generation E1 for each time period by statistics; Step S2: The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; Step S3: The load forecasting module forecasts the actual load demand QS2 in each period; Step S4: The energy storage module counts the storable capacity C and the spare remaining capacity CS in real time in each time period; Step S5: The virtual power plant global dispatch module calculates the required power difference E for each period based on the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; Step S6: The thermal unit module generates electricity in different time periods according to the dispatched power generation instructions.

2. A virtual power plant control method for realizing new energy consumption as claimed in claim 1, characterized in that: In step S1, the wind and solar power generation module obtains the predicted power generation E1 for each time period by statistics, which specifically includes: The wind-solar power generation module loads a preset power generation prediction model, takes the historical operation data of the scheduling area, the historical meteorological data of the previous day and the historical day, and the meteorological forecast data of the previous day and the day as input, predicts and outputs the power generation E0 of each time period of the day, and then combines the prediction deviation value and the first margin value of the power generation prediction model to calculate and obtain the predicted power generation E1 of each time period.

3. A virtual power plant control method for realizing new energy consumption as claimed in claim 1, characterized in that: In step S2, the electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period, specifically including: The electric vehicle scheduling module receives the electricity consumption plan uploaded by the car user, and counts the power demand Q1, the storable power C1 and the dischargeable power W1 in each period of the day; Based on the historical operation data of the charging stations in the scheduling area, a charging load curve for cars in the charging station other than users who upload electricity consumption plans is constructed, and a charging load calculation model is loaded to predict the demand power Q2 for each time period of the day based on the charging load curve. Then, based on the demand power Q1 and the demand power Q2, combined with the second margin value, the actual demand power QS1 for each time period of the day is predicted.

4. A virtual power plant control method for realizing new energy consumption as claimed in claim 1, characterized in that: In step S3, the load forecasting module forecasts the actual load demand QS2 in each time period, specifically including: The load forecasting module constructs a power load curve of other loads in the scheduling area except the charging station based on the historical operation data of the scheduling area, and loads a power load calculation model to predict the power demand Q3 in each time period based on the power load curve and the historical load changes in each time period, and combines the third margin value to predict the actual power demand QS2 in each time period of the day.

5. A virtual power plant control method for realizing new energy consumption as claimed in claim 1, characterized in that: In step S4, the energy storage module respectively counts the storable power C and the spare remaining capacity CS in each time period in real time, specifically including: In the energy storage module, the initial capacity of the energy storage device is divided into the storable capacity C2 and the spare capacity C3; at the initial scheduling moment, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3; During the dispatching process, if the consumption is completed, the storable capacity C is equal to the storable capacity C2, and the spare remaining capacity CS is equal to the spare capacity C3; If the amount of electricity stored in the energy storage device that exceeds half of the initial capacity is C4, then the storable amount C is equal to -C4, and the spare remaining capacity CS is equal to C3-C4; If the amount of electricity stored in the energy storage device is less than half of the initial capacity, which is C5, then the storable amount C is equal to C5, and the spare remaining capacity CS is equal to C3.

6. A virtual power plant control method for realizing new energy consumption as described in any one of claims 1 to 5, characterized in that: In step S5, the difference E of the amount of electricity required for each time period is calculated. 差 , specifically including: The virtual power plant global dispatch module obtains the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period, calculates the difference E2 between the sum of the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each time period and the predicted power generation E1, and then uses the N equal parts of the sum of the storable power C1 and the spare capacity C3 as the margin in each time period to comprehensively calculate the actual power difference E1. 差 , the E 差 That is, the amount of electricity that the thermal unit module needs to generate in each time period.

7. A virtual power plant control method for realizing new energy consumption as claimed in claim 6, characterized in that: The virtual power plant global dispatch module also includes a power flow monitoring module, which obtains the actual difference E of the power in each time period. 差 , predict the power generation E1 and the power load and charging load in each time period, calculate the active power and reactive power of each node side in the dispatching area network, and when it is calculated that the voltage phase angle or amplitude of the node side exceeds the first preset threshold, further determine the intervention method of the node side: If it is determined that the power supply needs to be increased, the required power value EZ and the sum of the dischargeable power W1 and the power stored in the energy storage module Cc are calculated. 储 , and judge the EZ and E 储 Size between: When EZ is less than or equal to E 储 When EZ is greater than E, the virtual power plant global dispatch module sends an electric energy dispatch instruction to the electric vehicle dispatch module and the energy storage module to release the increased power value EZ, among which the energy storage module is called for discharge first; when EZ is greater than E 储 When EZ and E are calculated 储 The difference between 1差 The virtual power plant global dispatch module calculates the difference E 1差 Send dispatching power generation instructions to thermal unit modules; If it is determined that there is excess electricity, the remaining electricity Ey on the node side is calculated, and the remaining electricity Ey is stored in the nearest energy storage module, and the storable electricity C1, the storable electricity C of the energy storage module and the spare capacity C3 are detected and calculated in real time. If it is detected that the storable electricity C in the current period is 0, and the capacity occupancy rate of the sum of the storable electricity C1 and the spare capacity C3 reaches the second preset threshold, the actual electricity difference E is calculated in the next period. 差 At E 差 50% of the sum of the storable power C1 and the spare capacity C3 is deducted from the total as the power generation of the thermal unit module, and the 50% of the sum of the storable power C1 and the spare capacity C3 is controlled by the electric vehicle dispatching module and the energy storage module to discharge and supply.

8. A virtual power plant control method for realizing new energy consumption as claimed in claim 7, characterized in that: In step S6, the thermal unit module generates electricity in different time periods according to the dispatched power generation instruction, which specifically includes: The virtual power plant global dispatch module calculates the actual difference E of the electricity in each period. 差 and the difference E 1差 The dispatching power generation instruction is sent to the thermal unit module, and the thermal unit module generates power in time periods according to the dispatching power generation instruction.

9. A virtual power plant control method for realizing new energy consumption as claimed in claim 8, characterized in that: The virtual power plant global dispatching module also obtains the charging load peaks and troughs corresponding to each time period based on the charging load curve constructed by the electric vehicle dispatching module, and then transfers 50% of the charging demand electricity of the sum of the storable capacity C1 and the backup capacity C3 to the corresponding trough period during the charging load peak period, and adjusts the charging electricity price during the charging load peak period accordingly to guide electric vehicles to charge during the corresponding trough period.

10. A virtual power plant control system for realizing new energy consumption, characterized in that: The virtual power plant control system includes: a virtual power plant global dispatching module, a wind and solar power generation module, an electric vehicle dispatching module, a load forecasting module, an energy storage module and a thermal unit module; The wind and solar power generation module statistically obtains the predicted power generation E1 for each time period; The electric vehicle scheduling module predicts the actual charging power demand QS1 of the electric vehicle in each time period; The load forecasting module predicts the actual load demand QS2 in each time period; The energy storage module respectively counts the storable amount C and the spare remaining capacity CS in each time period in real time; The virtual power plant global dispatch module calculates the required power difference E in each period according to the predicted power generation E1, the actual charging power demand QS1, the actual load power demand QS2 and the storable power C in each period. 差 , and according to the power difference E 差 Send dispatching power generation instructions to thermal unit modules; The thermal unit module generates electricity in different time periods according to the dispatched power generation instructions.

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