Virtual power plant optimal scheduling method considering power consumption of power plant and pump station

Abstract

The invention discloses a virtual power plant optimal scheduling method considering power consumption of a power plant and a pump station, and belongs to the field of power system optimal scheduling. The method comprises the following steps: establishing a distributed pump station model containing photovoltaic power generation, energy storage equipment and a water pump; establishing a power plant model containing large wind power generation and hydraulic power generation; the power plant model and the distributed pump station model are connected with a power trading market through a power distribution network, and a power system interaction model based on distributed renewable energy sources is established; according to the electric power system interaction model, analyzing the condition of combined operation of power generation and power utilization facilities, and establishing a target function of a virtual power plant economic dispatching model by taking profit maximization of combined operation in a dispatching period as a target; and according to the objective functions of the power system interaction model and the virtual power plant economic dispatching model, establishing a mixed integer programming model of the virtual power plant economic dispatching, and solving by using a solver to obtain a final dispatching scheme.

Description

Technical Field

[0001] The invention belongs to the field of power system optimization and dispatching, and in particular relates to an optimal dispatching method for a virtual power plant taking into account the power consumption of a power plant and a pump station. Background Art

[0002] As an effective means of aggregating flexible resources, virtual power plants (VPPs) can achieve energy aggregation, storage, supply and consumption without changing the grid connection mode and geographical location of flexible resources with the help of advanced communication, metering, control and other technologies. They can effectively connect flexible resources with the power system, realize resource integration and allocation, and gradually participate in the operation of distributed energy in the electricity wholesale market as an aggregation entity. They are an important way for smart grids to achieve interactivity and intelligence on the energy supply and demand side.

[0003] At this stage, countries around the world rely on their energy policy strategies to vigorously develop renewable energy to cope with global warming, reduce reliance on fossil fuel electricity, improve the security of energy supply, and promote industry and development in regions where renewable energy technologies are applied. Introducing renewable energy into an increasingly competitive electricity market requires new technologies and operating systems to cope with the new technical and economic challenges brought about by the optimal integration of existing resources. Smart grids, virtual power plants, and digital transformation are key to this integration. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an optimal scheduling method for a virtual power plant taking into account the power consumption of power plants and pumping stations, thereby solving the problems in the prior art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations comprises the following steps:

[0007] Based on the power balance constraints of the pumping station, the constraints of the energy storage equipment and the constraints between the pumping station and the distribution network, a distributed pumping station model containing photovoltaic power generation, energy storage equipment and water pumps is established.

[0008] With wind farm and hydroelectric power plant output constraints, a power plant model including large-scale wind power and hydroelectric power is established;

[0009] The power plant model and the distributed pump station model are connected to the power trading market through the distribution network, and a power system interaction model based on distributed renewable energy is established based on the power balance constraints of the entire system and the power trading market constraints;

[0010] According to the interactive model of the power system based on distributed renewable energy, the situation of the joint operation of power generation and power consumption facilities is analyzed, and the objective function of the virtual power plant economic dispatch model is established with the goal of maximizing the profit of the joint operation within the dispatch cycle;

[0011] According to the objective function of the power system interaction model based on distributed renewable energy and the virtual power plant economic dispatch model, a mixed integer programming model for virtual power plant economic dispatch is established, and the model is solved by a solver to obtain a final dispatch plan.

[0012] Furthermore, the power balance constraint of the pump station is:

[0013]

[0014] In the formula, The surplus photovoltaic power generation per hour for each pumping station, Each pump station replenishes electricity every hour. The photovoltaic power generation per hour for each pumping station, The discharge and charge capacity of the energy storage equipment at each pumping station per hour, The amount of electricity consumed by each pump station pump per hour;

[0015] The constraints of the energy storage device are:

[0016]

[0017]

[0018] Where: P ESEmax It is the maximum power of single charge and discharge of energy storage equipment. are binary variables, representing the charging and discharging state parameters of the energy storage device in period i. When , it means that the energy storage device of the jth pump station is in the charging state in the i-th time period. When η represents that the energy storage device of the jth pump station is in the discharging state in the i-th time period; cha , η dis is the charging and discharging efficiency of the energy storage device, is the capacity of the energy storage equipment of the jth pumping station at time i;

[0019] The constraints between the pump station and the distribution network are:

[0020]

[0021] In the formula, Respectively represent the transaction status between each pumping station and the distribution network every hour.

[0022] Furthermore, the output constraints of the wind farm and the hydropower plant are:

[0023]

[0024] Where: is the hourly power generation of large wind power equipment, The output power of each large hydropower station per hour, the upper limits of wind power and hydropower output are and

[0025] Furthermore, the power balance constraint of the entire system is:

[0026]

[0027] Where: Indicates the amount of electricity purchased from the electricity market per hour, is the hourly power generation of large wind power equipment, The hourly power generation of each large hydroelectric power station, The amount of electricity sold to the electricity market every hour;

[0028] The power market transaction constraints are:

[0029]

[0030] In the formula, and They represent the maximum value of electricity purchased and sold in the i-th period, and They represent the state variables of buying and selling from the electricity market in the i-th period, and They represent the discharge and charge amounts of the energy storage equipment of the jth pump station in the i-th period, and They represent the amount of electricity purchased and sold from the electricity market in the i-th period, and are the maximum hourly power generation of large wind power plants and hydropower plants, respectively. It represents the power generation of the photovoltaic power generation equipment of the jth pumping station in the i-th time period.

[0031] Furthermore, the objective function of the virtual power plant economic dispatch model is:

[0032]

[0033] Where: TC is the total profit of the virtual power plant, T is the dispatch period, which is set to 8760h in this embodiment, and For real-time prices of transactions with the trading market, is the amount of electricity sold to the electricity market every hour, is the amount of electricity purchased from the electricity market per hour, C W , C H is the cost of large wind power generation and hydropower generation, C distribute is the cost of the PV surplus electricity exported from the distributed pumping station.

[0034] Furthermore, the cost of large-scale wind power generation and hydropower generation is:

[0035]

[0036] Where: f W 、f H are the electricity costs of wind power and hydropower respectively.

[0037] An optimal dispatching system for a virtual power plant considering the power consumption of power plants and pumping stations, comprising:

[0038] Pump station model building module: Based on the power balance constraints of the pump station, the constraints of the energy storage equipment and the constraints between the pump station and the distribution network, a distributed pump station model containing photovoltaic power generation, energy storage equipment and water pumps is established;

[0039] Power plant model building module: Based on the output constraints of wind farms and hydropower plants, a power plant model including large-scale wind power generation and hydropower generation is established;

[0040] Interaction model building module: connecting the power plant model and the distributed pump station model with the power trading market through the distribution network, and establishing an interactive model of the power system based on distributed renewable energy with the power balance constraint of the whole system and the power trading market constraint;

[0041] Objective function building module: according to the interactive model of the power system based on distributed renewable energy, the joint operation of power generation and power consumption facilities is analyzed, and the objective function of the virtual power plant economic dispatch model is established with the goal of maximizing the profit of the joint operation within the dispatch cycle;

[0042] And, the model solving module: according to the objective function of the power system interaction model based on distributed renewable energy and the virtual power plant economic dispatch model, a mixed integer programming model of the virtual power plant economic dispatch is established, and the solver is used to solve it to obtain the final dispatch plan.

[0043] A computer storage medium stores a readable program, which, when running, can execute the above-mentioned optimal scheduling method for a virtual power plant taking into account the power consumption of power plants and pumping stations.

[0044] An electronic device, characterized in that it comprises: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0045] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned optimal scheduling method for a virtual power plant that takes into account the power consumption of power plants and pumping stations.

[0046] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to perform operations corresponding to the above-mentioned optimal scheduling method for a virtual power plant that takes into account the power consumption of power plants and pumping stations.

[0047] Beneficial effects of the present invention:

[0048] 1. The present invention not only integrates common large-scale distributed renewable energy power generation technologies, but also incorporates small-scale photovoltaic self-consumption facilities, achieving synergy and complementarity between renewable energy sources of different scales and types. By combining large-scale wind power and hydropower generation with distributed and flexible photovoltaic power generation, it better adapts to energy demands in different periods and scenarios, and improves the stability and reliability of energy supply.

[0049] 2. The present invention optimizes scheduling so that power generation can better meet electricity demand, greatly reduces dependence on the power grid, and significantly reduces the cost of purchasing electricity from the power grid. This is of great significance for coping with power price fluctuations and high electricity costs of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 is the monthly power generation of the hydropower plant in Example 2;

[0052] Figure 2 is the monthly power generation of the wind farm in Example 2;

[0053] Figure 3 is the total energy generated by the photovoltaic power station in Example 2;

[0054] Figure 4 is the power consumption (load) of the pump station in Example 2;

[0055] Figure 5 The power generation data of various power plants divided by price in Example 2;

[0056] Figure 6 It is the scheduling result for July in Example 2;

[0057] Figure 7 It is a virtual power plant management flow chart. DETAILED DESCRIPTION

[0058] 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.

[0059] Example 1

[0060] An optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations comprises the following steps:

[0061] S1, based on the power balance constraints of the pumping station, the constraints of the energy storage equipment and the constraints between the pumping station and the distribution network, a distributed pumping station model containing photovoltaic power generation, energy storage equipment and water pumps is established;

[0062] 1) The power balance constraint of the pumping station is:

[0063]

[0064] In the formula, The surplus photovoltaic power generation per hour for each pumping station (i.e. the amount of electricity after the photovoltaic power generation meets the local water pump demand and the energy storage device is fully charged), Each pump station replenishes electricity every hour (the electricity replenished because photovoltaic power generation is not enough to support local water pumps and energy storage equipment). The photovoltaic power generation per hour for each pumping station, The discharge and charge capacity of the energy storage equipment at each pumping station per hour,

[0065] The power consumed by each pump station pump per hour, this formula means that the power of j (27) pump stations distributed per hour (8760h) in a year is always balanced.

[0066] 2) The constraints of energy storage equipment are:

[0067]

[0068] Where: P ESEmax It is the maximum power of single charge and discharge of energy storage equipment. are binary variables, representing the charging and discharging state parameters of the energy storage device in period i. When , it means that the energy storage device of the jth pump station is in the charging state in the i-th time period. When , it means that the energy storage device of the jth pump station is in the discharging state in the i-th time period. In order to prevent charging and discharging from being performed simultaneously, it is necessary to constrain the charging and discharging states:

[0069]

[0070] The capacity constraint of the energy storage device is:

[0071]

[0072] Where: η cha , η dis is the charging and discharging efficiency of the energy storage device, is the capacity of the energy storage equipment of the jth pumping station at the i-th time.

[0073] 3) The constraints between the pump station and the distribution network are:

[0074]

[0075] Where: Respectively represent the transaction status between each pump station and the distribution network every hour. In order to prevent the occurrence of two-way exchanges with the distribution network at the same time, the transaction status of each pump station is constrained as follows:

[0076]

[0077] S2, with the output constraints of wind farms and hydropower plants, establish a power plant model including large-scale wind power generation and hydropower generation;

[0078] The output constraints of wind farms and hydropower plants are:

[0079]

[0080] Where: is the hourly power generation of large wind power equipment, The output power of each large hydropower station per hour, the upper limits of wind power and hydropower output are and

[0081] S3, connects the power plant model established in S2 and the distributed pump station model established in S1 to the power trading market through the distribution network, and establishes a power system interaction model based on distributed renewable energy with the power balance constraints of the entire system and the power trading market constraints, such as Figure 7 As shown;

[0082] 1) The power balance constraint of the entire system is:

[0083]

[0084] Where: Indicates the amount of electricity purchased from the electricity market per hour, is the hourly power generation of large wind power equipment, The hourly power generation of each large hydroelectric power station, is the amount of electricity sold to the electricity market every hour. This formula shows that the power between the distribution networks in the entire virtual power plant is balanced every hour (8760h) in a year.

[0085] 2) The power market transaction constraints are:

[0086]

[0087] In the formula, represents the maximum amount of electricity purchased in the i-th period, and They represent the state variables of buying and selling from the electricity market in the i-th period, represents the discharge amount of the energy storage device of the jth pump station in the i-th period, and They represent the amount of electricity purchased and sold from the electricity market in the i-th period respectively.

[0088] This formula represents the amount of electricity that needs to be purchased to meet the working needs of the pump station when all power generation equipment has no power output, that is, the maximum amount of electricity purchased;

[0089]

[0090] In the formula, represents the maximum amount of electricity sold in the i-th period, represents the charge amount of the energy storage device of the jth pump station in the i-th period, and are the maximum hourly power generation of large wind power plants and hydropower plants, respectively. It represents the power generation of the photovoltaic power generation equipment of the jth pumping station in the i-th time period.

[0091] This formula shows the amount of electricity that can be sold when all pumping stations are not working (varies with the seasons) and the energy storage equipment is fully charged.

[0092] Because distributed photovoltaic power generation equipment must give priority to local pumping stations and energy storage equipment before selling excess electricity to the electricity market, in order to prevent the purchase and sale of electricity at the same time, the state variables of the purchase and sale of electricity are constrained:

[0093]

[0094] In the formula, and They represent the status of buying and selling from the electricity market in the i-th period respectively.

[0095] S4, based on the power system interaction model established in S3, analyzes the joint operation of power generation and power consumption facilities, takes maximizing the profit of joint operation within the dispatch cycle as the goal, and establishes the objective function of the virtual power plant economic dispatch model;

[0096] The objective function of the virtual power plant economic dispatch model is established to maximize the profit of the joint operation of power generation and power consumption facilities within the dispatch cycle, including the electricity sales revenue, electricity purchase cost and the power generation cost of distributed renewable energy; the objective function of the virtual power plant economic dispatch model is:

[0097]

[0098] Where: TC is the total profit of the virtual power plant, T is the dispatch period, which is set to 8760h in this embodiment, and For real-time prices of transactions with the trading market, is the amount of electricity sold to the electricity market every hour, is the amount of electricity purchased from the electricity market per hour, C W , C H is the cost of large wind power generation and hydropower generation, C distribute is the cost of the PV surplus electricity exported from the distributed pumping station.

[0099] The costs of purchasing from distributed small-scale power generation equipment units are as follows:

[0100]

[0101] Where: f PV is the cost per kWh of the surplus photovoltaic power generation in the distribution unit, Surplus photovoltaic power generation.

[0102] The costs of large-scale wind power and hydroelectric power are as follows:

[0103]

[0104] Where: f W 、f H are the electricity costs of wind power and hydropower respectively.

[0105] S5, based on the objective functions of the power system interaction model in S3 and the virtual power plant economic dispatch model in S4, a mixed integer programming model for the economic dispatch of the virtual power plant is established, and the solver is used to solve it to obtain the final dispatch plan.

[0106] When constructing a mixed integer programming model for the economic dispatch of virtual power plants, the various types of equipment in the distributed renewable energy power plants covered by the power system interaction model constructed in S3 are comprehensively considered, including economic management factors (including different energy generation costs, equipment maintenance costs, power trading market benefits, etc.) and technical management factors (equipment operating parameters, power limits, energy storage equipment characteristics, etc.).

[0107] In the Matlab environment, the cplex solver is selected to convert the model into the format required by the solver, and then the solution is performed after setting appropriate solution parameters (solution time limit, convergence accuracy, etc.) to obtain the final scheduling plan.

[0108] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program, which, when running, can execute the above-mentioned optimal scheduling method for a virtual power plant that takes into account the power consumption of power plants and pumping stations.

[0109] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0110] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned optimal scheduling method for a virtual power plant that takes into account the power consumption of power plants and pumping stations.

[0111] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to perform operations corresponding to the above-mentioned optimal scheduling method for a virtual power plant considering the power consumption of power plants and pumping stations.

[0112] Example 2

[0113] In this embodiment, a certain region is taken as an example to verify the effectiveness of the scheduling method proposed in Embodiment 1;

[0114] The area is mainly agricultural and requires large-scale use of water pumps to irrigate farmland. There is a wind farm in the area consisting of 9 wind turbines with a total installed capacity of 30MW. The hourly power generation data is as follows Figure 2 There are 6 hydropower plants, with installed capacity as shown in Table 1, and hourly power generation data as shown in Figure 1 There are also distributed pumping stations, each of which has a sub-consumption photovoltaic system with a total installed capacity of 15.5MW. The annual hourly power generation data is as follows: Figure 3 The load is the power consumption of the pump station, as shown in Figure 4 The electricity cost of renewable energy equipment is shown in Table 2.

[0115] Table 1 Installed capacity of hydropower stations

[0116]

[0117] Table 2 Cost per kilowatt-hour of renewable energy technologies

[0118]

[0119]

[0120] Assume that there are 6 prices (P1, P2…P6) for electricity connected to the power grid in the agricultural irrigation area. The distribution of the 6 prices on different days and dates is as follows: Figure 5 It can be seen that there is no perfect time match between power generation and power demand. Basically, power generation is highest during P6. The pumping station is first met by the on-site photovoltaic power station, and then by the system generator. When power generation cannot fully meet demand, additional energy is purchased from the power trading market.

[0121] Figure 6 The hourly results achieved to meet the demand of all pumping stations during one week in July (the month with the highest demand of the year) are shown. Hydroelectric power generation remains almost constant in this VPP solution, as the hydroelectric power plant relies on water flowing into the irrigation community through the water supply channel, and the pumping stations are fully operational during this month. In contrast, the variability of wind power generation is significant, resulting in some situations where demand is fully covered, but also in some periods where the contribution to the energy system is lower. When wind power generation is at its maximum, there is no need to purchase electricity from the grid, and the excess is exported to the grid; on the contrary, if wind power generation decreases, the system usually needs to purchase energy from the grid to meet demand. Finally, according to the requirements, the self-consumption of electricity from the photovoltaic power plant is achieved as much as possible. However, the energy consumption pattern of each pumping station is different, and the on-site photovoltaic power generation does not perfectly match the load distribution of each pumping station. Therefore, some pumping stations are almost not connected to the pumps during the day when there is a lot of photovoltaic power generation, so the self-consumption rate is very low, while other pumping stations have a self-consumption rate of around 50%. In the winter months, there is almost no demand and the solar power generation is exported to the grid; therefore, the system benefits increase due to the sale of surplus power.

[0122] In addition, the planned generation percentages of wind and hydroelectric power are very high (98.98% and 99.53%, respectively), but in certain hours it is more profitable to buy energy from the electricity market than to generate electricity from renewable energy plants. The generation capacity of on-site photovoltaic power plants reaches 100%, because these plants generate electricity as long as there are solar resources. This information illustrates the usefulness of the optimal dispatch model in maximizing the profit of the integrated VPP operation.

[0123] Table 3 Virtual power plant dispatch results

[0124]

[0125]

[0126] The main results obtained in terms of energy and costs are given in Table 3. The power plant meets 94.6% of the annual demand of the pumping station, significantly reducing the dependence on the grid and contributing to the sustainability of the system. The remaining 5.4% of the demand is purchased from the electricity market, either because of insufficient internal generation in the VPP or because it is more profitable to purchase energy.

[0127] Example 3

[0128] In this embodiment, a virtual power plant optimal scheduling system considering the power consumption of power plants and pumping stations is proposed, which specifically includes:

[0129] Pump station model building module: Based on the power balance constraints of the pump station, the constraints of the energy storage equipment and the constraints between the pump station and the distribution network, a distributed pump station model containing photovoltaic power generation, energy storage equipment and water pumps is established;

[0130] Power plant model building module: Based on the output constraints of wind farms and hydropower plants, a power plant model including large-scale wind power generation and hydropower generation is established;

[0131] Interaction model building module: connecting the power plant model and the distributed pump station model with the power trading market through the distribution network, and establishing an interactive model of the power system based on distributed renewable energy with the power balance constraint of the whole system and the power trading market constraint;

[0132] Objective function building module: according to the interactive model of the power system based on distributed renewable energy, the joint operation of power generation and power consumption facilities is analyzed, and the objective function of the virtual power plant economic dispatch model is established with the goal of maximizing the profit of the joint operation within the dispatch cycle;

[0133] And, the model solving module: according to the objective function of the power system interaction model based on distributed renewable energy and the virtual power plant economic dispatch model, a mixed integer programming model of the virtual power plant economic dispatch is established, and the solver is used to solve it to obtain the final dispatch plan.

[0134] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0135] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations, characterized in that: The following steps are involved: Based on the power balance constraints of the pumping station, the constraints of the energy storage equipment and the constraints between the pumping station and the distribution network, a distributed pumping station model containing photovoltaic power generation, energy storage equipment and water pumps is established. With wind farm and hydroelectric power plant output constraints, a power plant model including large-scale wind power and hydroelectric power is established; The power plant model and the distributed pump station model are connected to the power trading market through the distribution network, and a power system interaction model based on distributed renewable energy is established based on the power balance constraints of the entire system and the power trading market constraints; According to the interactive model of the power system based on distributed renewable energy, the situation of the joint operation of power generation and power consumption facilities is analyzed, and the objective function of the virtual power plant economic dispatch model is established with the goal of maximizing the profit of the joint operation within the dispatch cycle; According to the objective function of the power system interaction model based on distributed renewable energy and the virtual power plant economic dispatch model, a mixed integer programming model for virtual power plant economic dispatch is established, and the model is solved by a solver to obtain a final dispatch plan.

2. The optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations according to claim 1, characterized in that: The power balance constraint of the pump station is: In the formula, The surplus photovoltaic power generation per hour for each pumping station, Each pump station replenishes electricity every hour. The photovoltaic power generation per hour for each pumping station, The discharge and charge capacity of the energy storage equipment at each pumping station per hour, The amount of electricity consumed by each pump station pump per hour; The constraints of the energy storage device are: Where: P ESEmax It is the maximum power of single charge and discharge of energy storage equipment. are binary variables, representing the charging and discharging state parameters of the energy storage device in period i. When , it means that the energy storage device of the jth pump station is in the charging state in the i-th time period. When indicates that the energy storage device of the jth pump station is in the discharging state in the i-th time period; η cha , η dis is the charging and discharging efficiency of the energy storage device, is the capacity of the energy storage equipment of the jth pumping station at time i; The constraints between the pump station and the distribution network are: In the formula, Respectively represent the transaction status between each pumping station and the distribution network every hour.

3. The optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations according to claim 2, characterized in that: The output constraints of the wind farm and hydropower plant are: Where: is the hourly power generation of large wind power equipment, The output power of each large hydropower station per hour, the upper limits of wind power and hydropower output are and 4. The optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations according to claim 3, characterized in that: The power balance constraint of the entire system is: Where: Indicates the amount of electricity purchased from the electricity market per hour, is the hourly power generation of large wind power equipment, The hourly power generation of each large hydroelectric power station, The amount of electricity sold to the electricity market every hour; The power market transaction constraints are: In the formula, and They represent the maximum value of electricity purchased and sold in the i-th period, and They represent the state variables of buying and selling from the electricity market in the i-th period, and They represent the discharge and charge amounts of the energy storage equipment of the jth pump station in the i-th period, and They represent the amount of electricity purchased and sold from the electricity market in the i-th period, and are the maximum hourly power generation of large wind power plants and hydropower plants, respectively. It represents the power generation of the photovoltaic power generation equipment of the jth pumping station in the i-th time period.

5. The optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations according to claim 4, characterized in that: The objective function of the virtual power plant economic dispatch model is: Where: TC is the total profit of the virtual power plant, T is the dispatch period, which is set to 8760h in this embodiment, and For real-time prices of transactions with the trading market, is the amount of electricity sold to the electricity market every hour, is the amount of electricity purchased from the electricity market per hour, C W , C H is the cost of large wind power generation and hydropower generation, C distribute is the cost of the PV surplus electricity exported from the distributed pumping station.

6. The optimal scheduling method for a virtual power plant considering power consumption of power plants and pumping stations according to claim 5, characterized in that: The costs of large-scale wind power generation and hydropower generation are as follows: Where: f W 、f H are the electricity costs of wind power and hydropower respectively.

7. An optimal scheduling system for a virtual power plant taking into account the power consumption of power plants and pumping stations, characterized in that: include: Pump station model building module: Based on the power balance constraints of the pump station, the constraints of the energy storage equipment and the constraints between the pump station and the distribution network, a distributed pump station model containing photovoltaic power generation, energy storage equipment and water pumps is established; Power plant model building module: Based on the output constraints of wind farms and hydropower plants, a power plant model including large-scale wind power generation and hydropower generation is established; Interaction model building module: connecting the power plant model and the distributed pump station model with the power trading market through the distribution network, and establishing an interactive model of the power system based on distributed renewable energy with the power balance constraint of the whole system and the power trading market constraint; Objective function building module: according to the interactive model of the power system based on distributed renewable energy, the joint operation of power generation and power consumption facilities is analyzed, and the objective function of the virtual power plant economic dispatch model is established with the goal of maximizing the profit of the joint operation within the dispatch cycle; And, the model solving module: according to the objective function of the power system interaction model based on distributed renewable energy and the virtual power plant economic dispatch model, a mixed integer programming model of the virtual power plant economic dispatch is established, and the solver is used to solve it to obtain the final dispatch plan.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, it is able to execute the optimal scheduling method for a virtual power plant taking into account the power consumption of power plants and pumping stations as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to an optimal scheduling method for a virtual power plant that takes into account the power consumption of power plants and pumping stations as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that The computer instructions instruct the computing device to perform operations corresponding to a method for optimal scheduling of a virtual power plant taking into account the power consumption of power plants and pumping stations as described in any one of claims 1-6.

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