Operation method and device of virtual power plant and computer readable storage medium

Abstract

The invention provides an operation method and device for a virtual power plant and a computer readable storage medium, and the method comprises the steps: building a maximum profit function of a target virtual power plant, and enabling the maximum profit function to be used for calculating the maximum value of the difference between the electricity selling income and the operation cost of the virtual power plant; establishing energy balance constraint conditions among the virtual power plants according to the operation parameters of the virtual power plants; establishing constraint conditions of internal resource scheduling of each virtual power plant to obtain a plurality of operation constraint conditions; under the condition that the energy balance constraint condition and all the operation constraint conditions are met, a maximum value is obtained through calculation by adopting a profit maximum function, and the maximum profit of the target virtual power plant is obtained; and performing power dispatching among the target virtual power plant, other virtual power plants and a power grid according to the parameter of the profit maximization function corresponding to the maximum profit. The problem that profit maximization distributed energy dispatching is difficult to realize in practice in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of power dispatching technology, and in particular to a method, device, computer-readable storage medium and computer program product for operating a virtual power plant. Background Art

[0002] The existing energy system is transforming into a low-carbon, intelligent and distributed system, which has promoted the surge in distributed energy. Distributed energy can supply electricity and other auxiliary services, but its inherent uncertain supply and demand fluctuations also bring challenges to the normal operation of the power grid. Virtual power plants can aggregate and dispatch these distributed energy sources to participate in the energy market, thereby alleviating or reducing the challenges brought to the operation of the power grid. Existing virtual power plants usually focus on grouping different distributed energy sources to increase the diversity of the investment portfolio and reduce the operating risks of a single distributed energy source; different distributed energy groups bid in economic and financial aspects in the energy market.

[0003] Existing virtual power plant models only focus on the economic optimization of distributed energy without considering the technical limitations of the distribution system, making the scheduling of distributed energy difficult to implement in actual optimization models. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, computer-readable storage medium and computer program product for operating a virtual power plant, so as to at least solve the problem that the scheduling of distributed energy with maximum profit in the prior art is difficult to achieve in practice.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for operating a virtual power plant is provided, including: establishing a profit maximization function of a target virtual power plant, the profit maximization function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants; establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, the energy balance constraints being used to ensure the energy balance between the virtual power plants and the power grid; establishing constraints for the internal resource scheduling of the virtual power plants, and obtaining a plurality of operating constraints, the internal resources including renewable resource systems, energy storage systems, electric vehicles and loads; under the condition that the energy balance constraints and all the operating constraints are met, the profit maximization function is used to calculate the maximum value, and the maximum profit of the target virtual power plant is obtained; and according to the parameters of the profit maximization function corresponding to the maximum profit, power is dispatched between the target virtual power plant and other virtual power plants and the power grid.

[0006] Optionally, establishing a profit maximization function of the target virtual power plant includes: establishing a power sales revenue function of the target virtual power plant Where s∈Ω s is the sequence number and set of usage scenarios, ρ s is the probability of using scenario s, is the time-of-use electricity price, P k,s,h is the power used by the user in the usage scenario s, is the charging power of the electric vehicle, Δ t is the usage time; establishing the target operation cost function of the virtual power plant Among them, C g_run The operating cost of power generation, is the active power generated by power generation under usage scenario s, is the active power purchased from the virtual power plant, the cost of discharging the electric vehicle, is the power released under the electric vehicle usage scenario s; the profit maximum function C is established according to the electricity sales income function and the operating cost function MAX =C VPP_sell -C VPP_run , where C MAX is the maximum profit, C VPP_sell is the income obtained by the virtual power plant from selling electricity, C VPP_run is the operating cost of the virtual power plant.

[0007] Optionally, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, including: establishing the energy balance constraints Among them, ξ ∈Ω ξ is the sequence number and set of the virtual power plant, h∈Ω h is the sequence number and set of time, k∈Ω k is the sequence number and set of wind, solar, storage and load, ev∈Ω ev is the serial number and set of the electric vehicles, g∈Ω g is the sequence number and set of generators, P ξ,k,h is the power used by the virtual power plant, To supplement the electric vehicle with power, is the power imported by the virtual power plant from other virtual power plants per hour, is the power imported from the grid per hour by the virtual power plant, is the active power generated by the generator of the renewable resource system, The power released by the electric vehicle, is the power exported by the virtual power plant to other virtual power plants per hour, It is the power exported to the grid by the virtual power plant every hour.

[0008] Optionally, establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: establishing a first operating constraint corresponding to the electric vehicle h>o, where eν={1,2,…,N} is the number of electric vehicles, t ev is the charging time of the electric vehicle, σ h is the average value over time, μ h is the standard deviation of time; establish the second operation constraint condition corresponding to the electric vehicle in, is the power transferred from the generator to the electric vehicle, n is the number of the loads, is the power delivered from the virtual power plant to the electric vehicle, The maximum charging capacity of the electric vehicle is limited; the third operating constraint condition corresponding to the electric vehicle is established in, is the power output by the electric vehicle to the virtual power plant, is the maximum discharge capacity of the electric vehicle; and establishing a fourth operating constraint condition corresponding to the electric vehicle. in, is a binary state variable of the charging state; and establishes the fifth operation constraint condition corresponding to the electric vehicle. in, is a binary state variable of the discharge state; and establishes the sixth operation constraint condition corresponding to the electric vehicle. Among them, E eν,k,n,s,h is the current state of charge of the electric vehicle, E eν,k,n,s,h-1 is the state of charge of the electric vehicle in the previous cycle, is the charging efficiency of the electric vehicle, is the charging power of the electric vehicle, is the discharge efficiency of the electric vehicle, is the discharge efficiency of the electric vehicle; establishing the seventh operation constraint condition corresponding to the electric vehicle in, is the minimum storage energy limit of the electric vehicle, E eν,k,n,s,h is the current stored energy of the electric vehicle, The maximum storage energy limit of the electric vehicle; establish the eighth operation constraint condition corresponding to the electric vehicle in, For the arrival time of each of the electric vehicles, For the time when each electric vehicle leaves, P eν,k,n,s,h For each electric vehicle’s charging or discharging capacity, BC eν For each of the electric vehicles, the battery capacity is set; and a ninth operation constraint condition corresponding to the electric vehicle is established. or in, is the power transmitted from the electric vehicle to the virtual power plant, is the power transmitted from the virtual power plant to the electric vehicle, is the power transmitted from the generator to the electric vehicle.

[0009] Optionally, establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: establishing a tenth operating constraint corresponding to the renewable resource system and in, The minimum active power limit for the generators of the renewable resource system, The maximum active power limit for the generators of the renewable resource system, The minimum reactive power limit for the generators of the renewable resource system, The maximum reactive power limit for the generators of the renewable resource system.

[0010] Optionally, establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: establishing an eleventh operating constraint corresponding to line loss Among them, P l,s,h is the active power flow on the line, is the active power requirement of the line node, PL l,s,h is the active power loss of the line; establish the twelfth operating constraint condition corresponding to the line loss Among them, Q l,s,h is the reactive power flow on the line, is the reactive power requirement of the line node, QL l,s,h is the reactive power loss of the line.

[0011] Optionally, establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: establishing a thirteenth operating constraint corresponding to the virtual power plant and in, is the minimum active power limit of the virtual power plant, is the maximum active power limit of the virtual power plant, is the minimum reactive power limit of the virtual power plant, is the maximum reactive power limit of the virtual power plant.

[0012] According to another aspect of the present application, a virtual power plant operation device is provided, comprising: a first establishment unit, used to establish a profit maximization function of a target virtual power plant, the profit maximization function is used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, and the target virtual power plant is any one of a plurality of associated virtual power plants; a second establishment unit, used to establish energy balance constraints between each of the virtual power plants according to the operating parameters of each of the virtual power plants, the energy balance constraints are used to ensure the energy balance between each of the virtual power plants and the power grid; a third establishment unit, used to establish constraints for the internal resource scheduling of each of the virtual power plants, and obtain a plurality of operating constraints, the internal resources include a renewable resource system, an energy storage system, an electric vehicle and a load; a calculation unit, used to calculate the maximum value using the profit maximization function to obtain the maximum profit of the target virtual power plant when the energy balance constraint and all of the operating constraints are met; a processing unit, used to perform power scheduling between the target virtual power plant and other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, any one of the methods described above is implemented.

[0015] By applying the technical solution of the present application, the operation method of the above-mentioned virtual power plant establishes energy balance constraints. The virtual power plant first optimizes internal demand. If there is an energy shortage or surplus, energy is optimized among other virtual power plants. If the energy shortage or surplus among virtual power plants cannot be resolved, scheduling optimization is performed on the power grid to resolve the energy shortage or surplus problem. A profit maximization function is established so that the virtual power plant can obtain maximum benefits. The technical limitations of implementing distributed energy scheduling in practical applications are taken into account, and constraints on the internal resources of the virtual power plant are established. Finally, according to the parameters of the profit maximization function corresponding to the maximum profit, power scheduling is performed between the target virtual power plant and other virtual power plants and the power grid. On the basis of ensuring that the actual application is feasible, the profit of the target virtual power plant is maximized, which solves the problem that the scheduling of distributed energy with maximum profit in the prior art is difficult to implement in practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for operating a virtual power plant provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic diagram of a flow chart of a method for operating a virtual power plant provided according to an embodiment of the present application is shown;

[0018] Figure 3 A schematic diagram of the architecture of an optimization model provided according to an embodiment of the present application is shown;

[0019] Figure 4 A structural block diagram of an operating device of a virtual power plant provided according to an embodiment of the present application is shown.

[0020] The above drawings include the following reference numerals:

[0021] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the scheduling of distributed energy with maximum profit in the prior art is difficult to achieve in practice. To solve this technical problem, the embodiments of the present application provide a method, device, computer-readable storage medium and computer program product for operating a virtual power plant.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for operating a virtual power plant according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the operation method of the virtual power plant in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for operating a virtual power plant running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 is a flow chart of a method for operating a virtual power plant according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0031] Step S201, establishing a maximum profit function of a target virtual power plant, the maximum profit function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants;

[0032] Step S202, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0033] Step S203, establishing constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include renewable resource systems, energy storage systems, electric vehicles, and loads;

[0034] Step S204, when the energy balance constraint and all the operation constraints are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit of the target virtual power plant;

[0035] Step S205 , performing power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0036] The above-mentioned operation method of the virtual power plant establishes energy balance constraints. The virtual power plant first optimizes internal demand. If there is an energy shortage or surplus, energy is optimized among other virtual power plants. If the energy shortage or surplus among virtual power plants cannot be solved, the problem of energy shortage or surplus is solved by optimizing the dispatch to the power grid. A profit maximization function is established so that the virtual power plant can obtain maximum benefits. The technical limitations of distributed energy dispatch in practical applications are taken into account, and constraints on the internal resources of the virtual power plant are established. Finally, according to the parameters of the profit maximization function corresponding to the maximum profit, power dispatch is performed between the target virtual power plant and other virtual power plants and the power grid. On the basis of ensuring that the actual application is feasible, the profit of the target virtual power plant is maximized, which solves the problem that the dispatch of distributed energy with maximum profit in the existing technology is difficult to achieve in practice.

[0037] It should be noted that the above method is a method for power dispatching using a virtual power plant optimization model that considers a variety of renewable resources, energy storage systems and electric vehicles. The virtual power plant optimization model aims to maximize the profit of the virtual power plant by optimally dispatching various distributed resources. Virtual power plants and power grids, as well as virtual power plants and other virtual power plants can buy and sell electricity to each other. The model is mainly composed of two layers. The first-layer optimization model is mainly used to manage and optimize the transmission of electricity between virtual power plants and power grids. The second-layer optimization model is mainly used to manage and optimize the transmission of electricity between virtual power plants and between virtual power plants and various internal resources, and takes into account the technical implementation constraints between virtual power plants. The architecture of the constructed optimization model is as follows: Figure 3As shown. The first level is the power grid, which will only directly generate power transmission between virtual power plants, and will not be directly associated with various resources under the virtual power plant. The second level is each virtual power plant. The resources contained in different virtual power plants are not exactly the same. In this embodiment, a total of 3 virtual power plants are set up; the first virtual power plant is equipped with two photovoltaic power stations, a wind power station, two electric vehicle charging stations and two residential areas; the second virtual power plant is equipped with a photovoltaic power station, two wind power stations, a storage power station, an industrial plant, and two electric vehicle charging stations; the third virtual power plant is equipped with a photovoltaic power station, a wind power station, a storage power station, a residential area and an electric vehicle charging station; the virtual power plants can be directly associated with each other to transmit power; each virtual power plant is directly associated with various resources within it to transmit power. Such a two-layer optimization model can reduce the computational complexity of the optimization process. Moreover, the various resources within each virtual power plant only exchange information with the virtual power plant associated with itself, avoiding the leakage of various internal resource information in the virtual power plant to the power grid, and achieving a certain degree of privacy information protection.

[0038] In order to maximize the profit of the virtual power plant, in an optional implementation, the above step S201 includes:

[0039] Step S2011, establishing the electricity sales revenue function of the above-mentioned virtual power plant in, s∈Ω s is the sequence number and set of usage scenarios, ρ s is the probability of using scenario s, is the time-of-use electricity price, P k,s,h is the power used by the user in the above usage scenario, is the charging power of the electric vehicle, Δ t For usage time;

[0040] Step S2012: Establishing the operating cost function of the target virtual power plant Among them, C g_run The operating cost of power generation, is the active power generated by power generation under usage scenario s, is the active power purchased from the above virtual power plant, The cost of discharging the electric vehicle mentioned above is The power released in the above-mentioned electric vehicle usage scenario s;

[0041] Step S2013: Establish the maximum profit function C according to the electricity sales revenue function and the operating cost function. MAX =C VPP_sell -CVPP_run , where C MAX For the maximum profit mentioned above, C VPP_sell is the income from the sale of electricity by the virtual power plant, C VPP_run is the operating cost of the above virtual power plant.

[0042] In the above implementation, a second-level optimization model of a virtual power plant is proposed that takes into account a variety of renewable resources, energy storage systems, and electric vehicles. The second-level optimization model is each virtual power plant, each virtual power plant is interconnected, each virtual power plant is interconnected with the power grid, and each virtual power plant is interconnected with various resources within it. The second-level optimization model is to optimize the scheduling of various distributed energy sources, thereby ensuring the maximization of the profit of the virtual power plant. The maximum profit expression formula is: C MAX =C VPP_sell -C VPP_run (1), where C MAX represents the maximum profit, C VPP_sell represents the income of the virtual power plant from selling electricity, C VPP_run Represents the operating cost of the virtual power plant. The income obtained by the virtual power plant from selling electricity C VPP_sell The specific composition includes: in, s∈Ω s Indicates the sequence number and set of usage scenarios, ρ s represents the probability of a certain usage scenario. In a specific embodiment, the uncertainty scenarios of photovoltaic power generation and wind power generation are generated by using the Latin hypercube sampling method; λ h T represents the time-of-use electricity price, P k,s,h Indicates the power used by the user in a certain scenario. represents the charging power of the electric vehicle, Δ t Indicates the usage time. VPP_run The specific components of the operating costs of a virtual power plant include: Among them, C g_run represents the operating cost of power generation, Indicates the active power generated by a certain scenario. represents the active power purchased from the virtual power plant, represents the cost of discharging electric vehicles, Indicates the power released by an electric vehicle in a certain scenario.

[0043] In order to ensure energy balance between each virtual power plant and the power grid, in an optional implementation, the above step S202 includes:

[0044] Step S2021, establish the above energy balance constraint condition Among them, ξ ∈Ωξ is the sequence number and set of the above virtual power plants, h∈Ω h is the sequence number and set of time, k∈Ω k is the sequence number and set of wind, solar, storage and load, ev∈Ω ev is the serial number and set of the above electric vehicles, g∈Ω g is the sequence number and set of generators, P ξ,k,h is the power used by the above virtual power plant, To provide additional power to the electric vehicle, is the power imported by the above virtual power plant from other above virtual power plants per hour, is the power imported from the grid per hour by the virtual power plant above, is the active power generated by the generator of the above renewable resource system, The power released by the electric vehicles mentioned above, is the power exported by the above virtual power plant to other above virtual power plants per hour, It is the power exported to the grid by the above virtual power plant every hour.

[0045] In the above implementation, a first-level optimization model of a virtual power plant is proposed that takes into account a variety of renewable resources, energy storage systems, and electric vehicles. The first-level optimization model is the power grid, which is directly connected to each virtual power plant. The first-level optimization model is to ensure the energy balance of the system, which needs to balance the energy used by all loads, including renewable resources, energy storage systems, and electric vehicles within each virtual power plant, energy transmission between each virtual power plant, and between the virtual power plant and the external power grid. The energy balance relationship of the system can be expressed according to the following formula That is, the energy balance constraint condition is the premise for optimizing the scheduling of various distributed energy sources, and the energy balance constraint condition needs to be met.

[0046] In order to ensure that the power dispatching plan is feasible, in an optional implementation, the above step S203 includes:

[0047] Step S2031: Establish the first operating constraint condition corresponding to the electric vehicle h>o, where eν={1,2,…,N} is the number of electric vehicles mentioned above, t ev is the charging time of the above electric vehicle, σ h is the average value over time, μ h is the standard deviation of time;

[0048] Step S2032: Establish the second operation constraint condition corresponding to the electric vehicle in, is the power transmitted from the generator to the electric vehicle, n is the number of the loads, is the power delivered from the virtual power plant to the electric vehicle, The maximum charging capacity of the above electric vehicles is limited;

[0049] Step S2033: Establish the third operating constraint condition corresponding to the electric vehicle in, is the power output by the electric vehicle to the virtual power plant, is the maximum discharge capacity of the above electric vehicle;

[0050] Step S2034: Establish the fourth operating constraint condition corresponding to the electric vehicle in, is a binary state variable of the charging state;

[0051] Step S2035: Establish the fifth operation constraint condition corresponding to the electric vehicle in, is the binary state variable of the discharge state;

[0052] Step S2036: Establish the sixth operating constraint condition corresponding to the electric vehicle Among them, E eν,k,n,s,h is the current state of charge of the electric vehicle, E eν,k,n,s,h-1 is the state of charge of the electric vehicle in the previous cycle, is the charging efficiency of the above electric vehicle, is the charging power of the above electric vehicle, is the discharge efficiency of the above electric vehicle, is the discharge efficiency of the above electric vehicle;

[0053] Step S2037, establishing the seventh operation constraint condition corresponding to the electric vehicle in, is the minimum storage energy limit of the above electric vehicle, E eν,k,n,s,h For the current storage energy of the electric vehicle mentioned above, The maximum storage energy limit of the above electric vehicle;

[0054] Step S2038: Establishing the eighth operating constraint condition corresponding to the electric vehicle in, For the arrival time of each of the above electric vehicles, For the time when each of the above electric vehicles leaves, P eν,k,n,s,h For each electric vehicle’s charging or discharging capacity, BC eν The battery capacity of each of the above electric vehicles;

[0055] Step S2039: Establish the ninth operation constraint condition corresponding to the electric vehicle or in, is the power transmitted from the electric vehicle to the virtual power plant, is the power transmitted from the virtual power plant to the electric vehicle, is the power transmitted from the generator to the above electric vehicle.

[0056] In the above implementation, considering the restrictions of the actual situation of electric vehicles on power dispatch, various operation constraints corresponding to the above electric vehicles are established. For example, when commercial electric vehicles are charged in a parking lot, there is a certain correlation between the parking lot utilization rate and working hours, and the correlation can be represented by a normal distribution: Where eν = {1, 2, ..., N} represents the number of electric vehicles, t ev represents the charging time of electric vehicles, σ h represents the average value over time, μ h is the standard deviation of time. The charging and discharging of electric vehicles also need to meet the following related constraints: in, represents the power transferred from the generator to the electric vehicle, n represents the number of certain loads, represents the power delivered from the virtual power plant to the electric vehicle, represents the maximum charging capacity limit of electric vehicles. That is, formula (6) indicates that the power provided by generators and virtual power plants to electric vehicles should be less than the maximum charging capacity of various electric vehicles. in, represents the power output of electric vehicles to the virtual power plant, represents the maximum discharge capacity of the electric vehicle. That is, formula (7) indicates that the power output of the electric vehicle to the virtual power plant should be less than the maximum discharge capacity of the electric vehicle. in, Indicates the charging state, and its value is 0 or 1. That is, formula (8) indicates that the charging power of an electric vehicle should be less than the maximum charging capacity of the electric vehicle. in, Indicates the discharge state, and its value is 0 or 1. That is, formula (9) indicates that the discharge power of the electric vehicle should be less than the maximum discharge capacity of the electric vehicle. The above formula is a constraint on the charging and discharging of electric vehicles, that is, formula (10) indicates that the charging and discharging of electric vehicles cannot be performed at the same time. Among them, E eν,k,n,s,h Indicates the current state of charge of the electric vehicle, E eν,k,n,s,h-1Indicates the state of charge of the electric vehicle in the previous cycle. represents the charging efficiency of electric vehicles, represents the charging power of the electric vehicle, represents the discharge efficiency of electric vehicles, Indicates the discharge efficiency of the electric vehicle. That is, formula (11) indicates that the current state of charge of the electric vehicle is obtained based on the state of charge of the previous cycle and the current state of charge or discharge of the electric vehicle. in, represents the minimum storage energy limit of electric vehicles, E eν,k,n,s,h represents the current storage energy of the electric vehicle, Indicates the maximum storage energy limit of the electric vehicle. That is, formula (12) indicates that the current storage energy of the electric vehicle should be limited to the required minimum and maximum storage energy limit range of the electric vehicle.

[0057] in, represents the arrival time of each electric car, represents the time when each electric vehicle leaves, P eν,k,n,s,h Represents the charging or discharging capacity of each electric vehicle, BC eν represents the battery capacity of each electric vehicle. That is, formula (13) represents the specific charging state calculation method of the electric vehicle. or in, represents the power transferred from the electric vehicle to the virtual power plant, represents the power transferred from the virtual power plant to the electric vehicle, represents the power transmitted from the generator to the electric vehicle. That is, formula (14) represents the arrival and departure time of the electric vehicle and the energy transmission constraints between the generator and the virtual power plant. Whether before the arrival or after the departure of the electric vehicle, there is no energy transmission between the electric vehicle and the generator and the virtual power plant. Only when these operating constraints are met can the power dispatch of the electric vehicle be realized.

[0058] In order to ensure that the power dispatching plan is feasible, in an optional implementation, the above step S203 further includes:

[0059] Step S20311, establishing the eleventh operation constraint condition corresponding to the line loss Among them, P l,s,h is the active power flow on the line, is the active power requirement of the line node, PL l,s,h is the active power loss of the line;

[0060] Step S20312: Establishing the twelfth operation constraint condition corresponding to the line loss Among them, Q l,s,h is the reactive power flow on the line, is the reactive power requirement of the line node, QL l,s,h is the reactive power loss of the line.

[0061] In the above implementation, in addition to the above constraints on wind, solar, storage and electric vehicles, the power balance and line loss on the line should also be considered to further ensure that it is close to the actual situation. The balance of active power can be expressed as: Among them, P l,s,h Indicates the active power flow on the line. Indicates the active power requirement of the line node, PL l,s,h Indicates the active power loss of the line. Among them, Q l,s,h Indicates the reactive power flow on the line. Indicates the reactive power requirement of the line node, QL l,s,h Indicates the reactive power loss of the line.

[0062] In order to ensure that the power dispatching plan is feasible, in an optional implementation, the above step S203 further includes:

[0063] Step S20310: Establish the tenth operating constraint condition corresponding to the above renewable resource system and in, is the minimum active power limit of the generator of the above renewable resource system, is the maximum active power limit of the generator of the above renewable resource system, is the minimum reactive power limit of the generator of the above renewable resource system, The maximum reactive power limit of the generators in the above renewable resource systems.

[0064] In the above implementation, in order to further ensure that it is close to the actual situation and ensure that the power dispatching plan can be realized, it is also necessary to meet the operating constraints of the generator corresponding to the renewable resource system. The active power and reactive power of the generator are respectively limited by formulas (17) and (18): in, Indicates the minimum active power limit of the generator, Indicates the maximum active power limit of the generator, Indicates the minimum reactive power limit of the generator, Indicates the maximum reactive power limit of the generator.

[0065] In order to ensure that the power dispatching plan is feasible, in an optional implementation, the above step S203 further includes:

[0066] Step S20313: Establish the thirteenth operation constraint condition corresponding to the virtual power plant and in, is the minimum active power limit of the above virtual power plant, is the maximum active power limit of the above virtual power plant, is the minimum reactive power limit of the above virtual power plant, is the maximum reactive power limit of the above virtual power plant.

[0067] In the above implementation, in order to further ensure that it is close to the actual situation and ensure that the power dispatching plan can be realized, it is also necessary to meet the corresponding operating constraints of the virtual power plant. The active power and reactive power of the virtual power plant are respectively restricted by formulas (19) and (20): in, represents the minimum active power limit of the virtual power plant, represents the maximum active power limit of the virtual power plant, represents the minimum reactive power limit of the virtual power plant, Indicates the maximum reactive power limit of the virtual power plant.

[0068] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0069] The embodiment of the present application also provides an operating device for a virtual power plant. It should be noted that the operating device for a virtual power plant in the embodiment of the present application can be used to execute the operating method for a virtual power plant provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0070] The following is an introduction to the operating device of the virtual power plant provided in the embodiment of the present application.

[0071] Figure 4 is a structural block diagram of an operating device of a virtual power plant according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0072] A first establishing unit 10 is used to establish a maximum profit function of a target virtual power plant, wherein the maximum profit function is used to calculate the maximum value of the difference between the power sales income and the operating cost of the virtual power plant, and the target virtual power plant is any one of the multiple associated virtual power plants;

[0073] A second establishing unit 20 is used to establish energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, and the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0074] A third establishing unit 30 is used to establish constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include a renewable resource system, an energy storage system, an electric vehicle, and a load;

[0075] A calculation unit 40 is used to calculate a maximum value using the profit maximum function to obtain the maximum profit of the target virtual power plant when the energy balance constraint condition and all the operation constraints are met;

[0076] The processing unit 50 is used to perform power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0077] The operating device of the above-mentioned virtual power plant establishes energy balance constraints. The virtual power plant first optimizes internal demand. If there is an energy shortage or surplus, energy is optimized among other virtual power plants. If the energy shortage or surplus among virtual power plants cannot be solved, the problem of energy shortage or surplus is solved by dispatching optimization to the power grid. A profit maximization function is established so that the virtual power plant can obtain maximum benefits. The technical limitations of distributed energy dispatching in practical applications are taken into consideration, and constraints on the internal resources of the virtual power plant are established. Finally, according to the parameters of the profit maximization function corresponding to the maximum profit, power dispatch is carried out between the target virtual power plant and other virtual power plants and the power grid. On the basis of ensuring that the actual application is feasible, the profit of the target virtual power plant is maximized, which solves the problem that the dispatching of distributed energy with maximum profit in the existing technology is difficult to achieve in practice.

[0078] It should be noted that the above method is a method for power dispatching using a virtual power plant optimization model that considers a variety of renewable resources, energy storage systems and electric vehicles. The virtual power plant optimization model aims to maximize the profit of the virtual power plant by optimally dispatching various distributed resources. Virtual power plants and power grids, as well as virtual power plants and other virtual power plants can buy and sell electricity to each other. The model is mainly composed of two layers. The first-layer optimization model is mainly used to manage and optimize the transmission of electricity between virtual power plants and power grids. The second-layer optimization model is mainly used to manage and optimize the transmission of electricity between virtual power plants and between virtual power plants and various internal resources, and takes into account the technical implementation constraints between virtual power plants. The architecture of the constructed optimization model is as follows: Figure 3 As shown. The first level is the power grid, which will only directly generate power transmission between virtual power plants, and will not be directly associated with various resources under the virtual power plant. The second level is each virtual power plant. The resources contained in different virtual power plants are not exactly the same. In this embodiment, a total of 3 virtual power plants are set up; the first virtual power plant is equipped with two photovoltaic power stations, a wind power station, two electric vehicle charging stations and two residential areas; the second virtual power plant is equipped with a photovoltaic power station, two wind power stations, a storage power station, an industrial plant, and two electric vehicle charging stations; the third virtual power plant is equipped with a photovoltaic power station, a wind power station, a storage power station, a residential area and an electric vehicle charging station; the virtual power plants can be directly associated with each other to transmit power; each virtual power plant is directly associated with various resources within it to transmit power. Such a two-layer optimization model can reduce the computational complexity of the optimization process. Moreover, the various resources within each virtual power plant only exchange information with the virtual power plant associated with itself, avoiding the leakage of various internal resource information in the virtual power plant to the power grid, and achieving a certain degree of privacy information protection.

[0079] In order to ensure the maximization of the profit of the virtual power plant, in an optional implementation manner, the first establishing unit includes:

[0080] The first establishment module is used to establish the electricity sales revenue function of the above-mentioned virtual power plant in, s∈Ω s is the sequence number and set of usage scenarios, ρ s is the probability of using scenario s, is the time-of-use electricity price, P k,s,h is the power used by the user in the above usage scenario, is the charging power of the electric vehicle, Δ t For usage time;

[0081] The second establishment module is used to establish the operating cost function of the above-mentioned virtual power plant Among them, C g_run The operating cost of power generation, is the active power generated by power generation under usage scenario s, is the active power purchased from the above virtual power plant, The cost of discharging the electric vehicle mentioned above is The power released in the above-mentioned electric vehicle usage scenario s;

[0082] The third establishment module is used to establish the above profit maximum function C according to the above electricity sales income function and the above operating cost function MAX =C VPP_sell -C VPP_run , where C MAX For the maximum profit mentioned above, C VPP_sell is the income from the sale of electricity by the virtual power plant, C VPP_run is the operating cost of the above virtual power plant.

[0083] In the above implementation, a second-level optimization model of a virtual power plant is proposed that takes into account a variety of renewable resources, energy storage systems, and electric vehicles. The second-level optimization model is each virtual power plant, each virtual power plant is interconnected, each virtual power plant is interconnected with the power grid, and each virtual power plant is interconnected with various resources within it. The second-level optimization model is to optimize the scheduling of various distributed energy sources, thereby ensuring the maximization of the profit of the virtual power plant. The maximum profit expression formula is: C MAX =C VPP_sell -C VPP_run (1), where C MAX represents the maximum profit, C VPP_sell represents the income of the virtual power plant from selling electricity, C VPP_run Represents the operating cost of the virtual power plant. The income obtained by the virtual power plant from selling electricity C VPP_sell The specific composition includes: in, s∈Ω s Indicates the sequence number and set of usage scenarios, ρ s Indicates the probability of a certain usage scenario. In a specific embodiment, the uncertainty scenarios of photovoltaic power generation and wind power generation are generated by using the Latin hypercube sampling method; represents the time-of-use electricity price, P k,s,h Indicates the power used by the user in a certain scenario. represents the charging power of the electric vehicle, Δ t Indicates the usage time. VPP_run The specific components of the operating costs of a virtual power plant include: Among them, C g_run represents the operating cost of power generation, Indicates the active power generated by a certain scenario. represents the active power purchased from the virtual power plant, represents the cost of discharging electric vehicles, Indicates the power released by an electric vehicle in a certain scenario.

[0084] In order to ensure energy balance between each virtual power plant and the power grid, in an optional implementation, the second establishing unit includes:

[0085] The fourth establishment module is used to establish the above energy balance constraints Among them, ξ ∈Ω ξ is the sequence number and set of the above virtual power plants, h∈Ω h is the sequence number and set of time, k∈Ω k is the sequence number and set of wind, solar, storage and load, ev∈Ω ev is the serial number and set of the above electric vehicles, g∈Ω g is the sequence number and set of generators, P ξ,k,h is the power used by the above virtual power plant, To provide additional power to the electric vehicle, is the power imported by the above virtual power plant from other above virtual power plants per hour, is the power imported from the grid per hour by the virtual power plant above, is the active power generated by the generator of the above renewable resource system, The power released by the electric vehicles mentioned above, is the power exported by the above virtual power plant to other above virtual power plants per hour, It is the power exported to the grid by the above virtual power plant every hour.

[0086] In the above implementation, a first-level optimization model of a virtual power plant is proposed that takes into account a variety of renewable resources, energy storage systems, and electric vehicles. The first-level optimization model is the power grid, which is directly connected to each virtual power plant. The first-level optimization model is to ensure the energy balance of the system, which needs to balance the energy used by all loads, including renewable resources, energy storage systems, and electric vehicles within each virtual power plant, energy transmission between each virtual power plant, and between the virtual power plant and the external power grid. The energy balance relationship of the system can be expressed according to the following formula That is, the energy balance constraint condition is the premise for optimizing the scheduling of various distributed energy sources, and the energy balance constraint condition needs to be met.

[0087] In order to ensure that the power dispatching plan is feasible, in an optional implementation, the third establishing unit includes:

[0088] The fifth establishment module is used to establish the first operating constraint condition corresponding to the electric vehicle h>o, where eν={1,2,…,N} is the number of electric vehicles mentioned above, t ev is the charging time of the above electric vehicle, σ h is the average value over time, μ h is the standard deviation of time;

[0089] The sixth establishment module is used to establish the second operation constraint condition corresponding to the electric vehicle in, is the power transmitted from the generator to the electric vehicle, n is the number of the loads, is the power delivered from the virtual power plant to the electric vehicle, The maximum charging capacity of the above electric vehicles is limited;

[0090] The seventh establishment module is used to establish the third operation constraint condition corresponding to the electric vehicle in, is the power output by the electric vehicle to the virtual power plant, is the maximum discharge capacity of the above electric vehicle;

[0091] The eighth establishment module is used to establish the fourth operation constraint condition corresponding to the electric vehicle in, is a binary state variable of the charging state;

[0092] The ninth establishment module is used to establish the fifth operation constraint condition corresponding to the electric vehicle in, is the binary state variable of the discharge state;

[0093] The tenth establishment module is used to establish the sixth operation constraint condition corresponding to the electric vehicle Among them, E eν,k,n,s,h is the current state of charge of the electric vehicle, E eν,k,n,s,h-1 is the state of charge of the electric vehicle in the previous cycle, is the charging efficiency of the above electric vehicle, is the charging power of the above electric vehicle, is the discharge efficiency of the above electric vehicle, is the discharge efficiency of the above electric vehicle;

[0094] The eleventh establishment module is used to establish the seventh operation constraint condition corresponding to the electric vehicle in, is the minimum storage energy limit of the above electric vehicle, E eν,k,n,s,hFor the current storage energy of the electric vehicle mentioned above, The maximum storage energy limit of the above electric vehicle;

[0095] The twelfth establishment module is used to establish the eighth operation constraint condition corresponding to the electric vehicle in, For the arrival time of each of the above electric vehicles, For the time when each of the above electric vehicles leaves, P eν,k,n,s,h For each electric vehicle’s charging or discharging capacity, BC eν The battery capacity of each of the above electric vehicles;

[0096] The thirteenth establishment module is used to establish the ninth operation constraint condition corresponding to the electric vehicle or in, is the power transmitted from the electric vehicle to the virtual power plant, is the power transmitted from the virtual power plant to the electric vehicle, is the power transmitted from the generator to the above electric vehicle.

[0097] In the above implementation, considering the restrictions of the actual situation of electric vehicles on power dispatch, various operation constraints corresponding to the above electric vehicles are established. For example, when commercial electric vehicles are charged in a parking lot, there is a certain correlation between the parking lot utilization rate and working hours, and the correlation can be represented by a normal distribution: Where eν = {1, 2, ..., N} represents the number of electric vehicles, t ev represents the charging time of electric vehicles, σ h represents the average value over time, μ h is the standard deviation of time. The charging and discharging of electric vehicles also need to meet the following related constraints: in, represents the power transferred from the generator to the electric vehicle, n represents the number of certain loads, represents the power delivered from the virtual power plant to the electric vehicle, represents the maximum charging capacity limit of electric vehicles. That is, formula (6) indicates that the power provided by generators and virtual power plants to electric vehicles should be less than the maximum charging capacity of various electric vehicles. in, represents the power output of electric vehicles to the virtual power plant, represents the maximum discharge capacity of the electric vehicle. That is, formula (7) indicates that the power output of the electric vehicle to the virtual power plant should be less than the maximum discharge capacity of the electric vehicle. in, Indicates the charging state, and its value is 0 or 1. That is, formula (8) indicates that the charging power of an electric vehicle should be less than the maximum charging capacity of the electric vehicle. in, Indicates the discharge state, and its value is 0 or 1. That is, formula (9) indicates that the discharge power of the electric vehicle should be less than the maximum discharge capacity of the electric vehicle. The above formula is a constraint on the charging and discharging of electric vehicles, that is, formula (10) indicates that the charging and discharging of electric vehicles cannot be performed at the same time. Among them, E eν,k,n,s,h Indicates the current state of charge of the electric vehicle, E eν,k,n,s,h-1 Indicates the state of charge of the electric vehicle in the previous cycle. represents the charging efficiency of electric vehicles, represents the charging power of the electric vehicle, represents the discharge efficiency of electric vehicles, Indicates the discharge efficiency of the electric vehicle. That is, formula (11) indicates that the current state of charge of the electric vehicle is obtained based on the state of charge of the previous cycle and the current state of charge or discharge of the electric vehicle. in, represents the minimum storage energy limit of electric vehicles, E eν,k,n,s,h represents the current storage energy of the electric vehicle, Indicates the maximum storage energy limit of the electric vehicle. That is, formula (12) indicates that the current storage energy of the electric vehicle should be limited to the required minimum and maximum storage energy limit range of the electric vehicle.

[0098] in, represents the arrival time of each electric car, represents the time when each electric vehicle leaves, P eν,k,n,s,h Represents the charging or discharging capacity of each electric vehicle, BC eν represents the battery capacity of each electric vehicle. That is, formula (13) represents the specific charging state calculation method of the electric vehicle. or in, represents the power transferred from the electric vehicle to the virtual power plant, represents the power transferred from the virtual power plant to the electric vehicle, represents the power transmitted from the generator to the electric vehicle. That is, formula (14) represents the arrival and departure time of the electric vehicle and the energy transmission constraints between the generator and the virtual power plant. Whether before the arrival or after the departure of the electric vehicle, there is no energy transmission between the electric vehicle and the generator and the virtual power plant. Only when these operating constraints are met can the power dispatch of the electric vehicle be realized.

[0099] In order to ensure that the power dispatching plan is feasible, in an optional implementation manner, the third establishing unit further includes:

[0100] The fourteenth establishment module is used to establish the eleventh operation constraint condition corresponding to the line loss Among them, P l,s,h is the active power flow on the line, is the active power requirement of the line node, PL l,s,h is the active power loss of the line;

[0101] The fifteenth establishment module is used to establish the twelfth operation constraint condition corresponding to the line loss Among them, Q l,s,h is the reactive power flow on the line, is the reactive power requirement of the line node, QL l,s,h is the reactive power loss of the line.

[0102] In the above implementation, in addition to the above constraints on wind, solar, storage and electric vehicles, the power balance and line loss on the line should also be considered to further ensure that it is close to the actual situation. The balance of active power can be expressed as: Among them, P l,s,h Indicates the active power flow on the line. Indicates the active power requirement of the line node, PL l,s,h Indicates the active power loss of the line. Among them, Q l,s,h Indicates the reactive power flow on the line. Indicates the reactive power requirement of the line node, QL l,s,h Indicates the reactive power loss of the line.

[0103] In order to ensure that the power dispatching plan is feasible, in an optional implementation manner, the third establishing unit further includes:

[0104] The sixteenth establishment module is used to establish the tenth operation constraint condition corresponding to the above renewable resource system and in, is the minimum active power limit of the generator of the above renewable resource system, is the maximum active power limit of the generator of the above renewable resource system, is the minimum reactive power limit of the generator of the above renewable resource system, The maximum reactive power limit of the generators in the above renewable resource systems.

[0105] In the above implementation, in order to further ensure that it is close to the actual situation and ensure that the power dispatching plan can be realized, it is also necessary to meet the operating constraints of the generator corresponding to the renewable resource system. The active power and reactive power of the generator are respectively limited by formulas (17) and (18): in, Indicates the minimum active power limit of the generator, Indicates the maximum active power limit of the generator, Indicates the minimum reactive power limit of the generator, Indicates the maximum reactive power limit of the generator.

[0106] In order to ensure that the power dispatching plan is feasible, in an optional implementation manner, the third establishing unit further includes:

[0107] The seventeenth establishment module is used to establish the thirteenth operation constraint condition corresponding to the above virtual power plant and in, is the minimum active power limit of the above virtual power plant, is the maximum active power limit of the above virtual power plant, is the minimum reactive power limit of the above virtual power plant, is the maximum reactive power limit of the above virtual power plant.

[0108] In the above implementation, in order to further ensure that it is close to the actual situation and ensure that the power dispatching plan can be realized, it is also necessary to meet the corresponding operating constraints of the virtual power plant. The active power and reactive power of the virtual power plant are respectively restricted by formulas (19) and (20): in, represents the minimum active power limit of the virtual power plant, represents the maximum active power limit of the virtual power plant, represents the minimum reactive power limit of the virtual power plant, Indicates the maximum reactive power limit of the virtual power plant.

[0109] The operating device of the virtual power plant includes a processor and a memory. The first establishment unit, the second establishment unit, the third establishment unit, the calculation unit and the processing unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0110] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem that the scheduling of distributed energy with maximum profit in the prior art is difficult to achieve in practice is solved by adjusting kernel parameters.

[0111] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0112] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the operating method of the virtual power plant.

[0113] Specifically, the operation methods of virtual power plants include:

[0114] Step S201, establishing a maximum profit function of a target virtual power plant, the maximum profit function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants;

[0115] Step S202, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0116] Step S203, establishing constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include renewable resource systems, energy storage systems, electric vehicles, and loads;

[0117] Step S204, when the energy balance constraint and all the operation constraints are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit of the target virtual power plant;

[0118] Step S205 , performing power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0119] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the operating method of the virtual power plant when running.

[0120] Specifically, the operation methods of virtual power plants include:

[0121] Step S201, establishing a maximum profit function of a target virtual power plant, the maximum profit function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants;

[0122] Step S202, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0123] Step S203, establishing constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include renewable resource systems, energy storage systems, electric vehicles, and loads;

[0124] Step S204, when the energy balance constraint and all the operation constraints are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit of the target virtual power plant;

[0125] Step S205 , performing power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0126] An embodiment of the present invention provides a virtual power plant operation system, the virtual power plant operation system includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0127] Step S201, establishing a maximum profit function of a target virtual power plant, the maximum profit function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants;

[0128] Step S202, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0129] Step S203, establishing constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include renewable resource systems, energy storage systems, electric vehicles, and loads;

[0130] Step S204, when the energy balance constraint and all the operation constraints are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit of the target virtual power plant;

[0131] Step S205 , performing power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0132] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0133] Step S201, establishing a maximum profit function of a target virtual power plant, the maximum profit function being used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, the target virtual power plant being any one of a plurality of associated virtual power plants;

[0134] Step S202, establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid;

[0135] Step S203, establishing constraints for internal resource scheduling of each of the virtual power plants to obtain a plurality of operating constraints, wherein the internal resources include renewable resource systems, energy storage systems, electric vehicles, and loads;

[0136] Step S204, when the energy balance constraint and all the operation constraints are met, the maximum profit function is used to calculate the maximum value to obtain the maximum profit of the target virtual power plant;

[0137] Step S205 , performing power dispatching between the target virtual power plant, the other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

[0138] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0144] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0145] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0146] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0147] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0148] 1) The operation method of the virtual power plant of the present application establishes energy balance constraints. The virtual power plant first optimizes internal demand. If there is an energy shortage or surplus, energy is optimized among other virtual power plants. If the energy shortage or surplus among virtual power plants cannot be resolved, scheduling optimization is performed on the power grid to resolve the energy shortage or surplus problem. A profit maximization function is established so that the virtual power plant can obtain maximum benefits. The technical limitations of distributed energy scheduling in practical applications are taken into consideration. Constraints on the internal resources of the virtual power plant are established. Finally, according to the parameters of the profit maximization function corresponding to the maximum profit, power is dispatched between the target virtual power plant and other virtual power plants and the power grid. On the basis of ensuring that the actual application is feasible, the profit of the target virtual power plant is maximized, which solves the problem that the scheduling of distributed energy with maximum profit in the prior art is difficult to achieve in practice.

[0149] 2) The operating device of the virtual power plant of the present application establishes energy balance constraints. The virtual power plant first optimizes internal demand. If there is an energy shortage or surplus, energy is optimized among other virtual power plants. If the energy shortage or surplus among virtual power plants cannot be resolved, scheduling optimization is performed on the power grid to resolve the energy shortage or surplus problem. A profit maximization function is established so that the virtual power plant can obtain maximum benefits. The technical limitations of distributed energy scheduling in practical applications are taken into account, and constraints on the internal resources of the virtual power plant are established. Finally, according to the parameters of the profit maximization function corresponding to the maximum profit, power is dispatched between the target virtual power plant and other virtual power plants and the power grid. On the basis of ensuring that the actual application is feasible, the profit of the target virtual power plant is maximized, which solves the problem that the scheduling of distributed energy with maximum profit in the prior art is difficult to achieve in practice.

[0150] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for operating a virtual power plant, characterized in that: include: Establishing a profit maximization function of a target virtual power plant, wherein the profit maximization function is used to calculate the maximum value of the difference between the power sales revenue and the operating cost of the virtual power plant, wherein the target virtual power plant is any one of the multiple associated virtual power plants; Establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, wherein the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid; Establishing constraints for internal resource scheduling of each virtual power plant to obtain a plurality of operating constraints, wherein the internal resources include a renewable resource system, an energy storage system, an electric vehicle, and a load; Under the condition that the energy balance constraint condition and all the operation constraints are satisfied, the maximum value is calculated by using the profit maximization function to obtain the maximum profit of the target virtual power plant; Electricity dispatch is performed between the target virtual power plant, other virtual power plants and the power grid according to the parameters of the maximum profit function corresponding to the maximum profit.

2. The method according to claim 1, characterized in that Establishing the profit maximization function of the virtual power plant mentioned in the objective includes: Establish the electricity sales revenue function of the target virtual power plant in, s∈Ω s is the sequence number and set of usage scenarios, ρ s is the probability of using scenario s, is the time-of-use electricity price, P k,s,h is the power used by the user in the usage scenario s, is the charging power of the electric vehicle, Δ t For usage time; Establishing the target virtual power plant operating cost function Among them, C g_run The operating cost of power generation, is the active power generated by power generation in usage scenario s, is the active power purchased from the virtual power plant, the cost of discharging the electric vehicle, The power released in the electric vehicle usage scenario s; The profit maximum function C is established according to the electricity sales income function and the operating cost function MAX =C VPP_sell -C VPP_run , where C MAX is the maximum profit, C VPP_sell is the income obtained by the virtual power plant from selling electricity, C VPP_run is the operating cost of the virtual power plant.

3. The method according to claim 1, characterized in that Establishing energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, including: Establish the energy balance constraint Among them, ξ ∈Ω ξ is the sequence number and set of the virtual power plant, h∈Ω h is the sequence number and set of time, k∈Ω k is the sequence number and set of wind, solar, storage and load, ev∈Ω ev is the serial number and set of the electric vehicle, g∈Ω g is the sequence number and set of generators, P ξ,k,h is the power used by the virtual power plant, To supplement the electric vehicle with power, is the power imported by the virtual power plant from other virtual power plants per hour, is the power imported from the grid per hour by the virtual power plant, is the active power generated by the generator of the renewable resource system, The power released by the electric vehicle, is the power exported by the virtual power plant to other virtual power plants per hour, It is the power exported to the power grid by the virtual power plant every hour.

4. The method according to any one of claims 1 to 3, characterized in that Establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: Establishing a first operating constraint condition corresponding to the electric vehicle Wherein, eν={1,2,…,N} is the number of electric vehicles, t ev is the charging time of the electric vehicle, σ h is the average value over time, μ h is the standard deviation of time; Establishing a second operating constraint condition corresponding to the electric vehicle in, is the power transferred from the generator to the electric vehicle, n is the number of the loads, is the power delivered from the virtual power plant to the electric vehicle, The maximum charging capacity of the electric vehicle is limited; Establishing the third operating constraint condition corresponding to the electric vehicle in, is the power output by the electric vehicle to the virtual power plant, is the maximum discharge capacity of the electric vehicle; Establishing the fourth operating constraint condition corresponding to the electric vehicle in, is a binary state variable of the charging state; Establish the fifth operating constraint condition corresponding to the electric vehicle in, is the binary state variable of the discharge state; Establishing the sixth operating constraint condition corresponding to the electric vehicle Among them, E eν,k,n,s,h is the current state of charge of the electric vehicle, E eν,k,n,s,h-1 is the state of charge of the electric vehicle in the previous cycle, is the charging efficiency of the electric vehicle, is the charging power of the electric vehicle, is the discharge efficiency of the electric vehicle, is the discharge efficiency of the electric vehicle; Establish the seventh operating constraint condition corresponding to the electric vehicle in, is the minimum storage energy limit of the electric vehicle, E eν,k,n,s,h is the current stored energy of the electric vehicle, is the maximum storage energy limit of the electric vehicle; Establishing the eighth operating constraint condition corresponding to the electric vehicle in, For the arrival time of each of the electric vehicles, For the time when each electric vehicle leaves, P eν,k,n,s,h For each electric vehicle’s charging or discharging capacity, BC eν is the battery capacity of each of said electric vehicles; Establish the ninth operating constraint condition corresponding to the electric vehicle or in, is the power transmitted from the electric vehicle to the virtual power plant, is the power transmitted from the virtual power plant to the electric vehicle, is the power transmitted from the generator to the electric vehicle.

5. The method according to any one of claims 1 to 3, characterized in that Establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: Establishing the tenth operating constraint condition corresponding to the renewable resource system and in, The minimum active power limit for the generators of the renewable resource system, The maximum active power limit for the generators of the renewable resource system, The minimum reactive power limit for the generators of the renewable resource system, The maximum reactive power limit for the generators of the renewable resource system.

6. The method according to any one of claims 1 to 3, characterized in that Establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: Establish the eleventh operating constraint corresponding to line loss Among them, P l,s,h is the active power flow on the line, is the active power requirement of the line node, PL l,s,h is the active power loss of the line; Establish the twelfth operating constraint corresponding to line loss Among them, Q l,s,h is the reactive power flow on the line, is the reactive power requirement of the line node, QL l,s,h is the reactive power loss of the line.

7. The method according to any one of claims 1 to 3, characterized in that Establishing constraints for internal resource scheduling of each virtual power plant to obtain multiple operating constraints, including: Establishing the thirteenth operating constraint condition corresponding to the virtual power plant and in, is the minimum active power limit of the virtual power plant, is the maximum active power limit of the virtual power plant, is the minimum reactive power limit of the virtual power plant, is the maximum reactive power limit of the virtual power plant.

8. An operating device for a virtual power plant, characterized in that: include: A first establishing unit is used to establish a maximum profit function of a target virtual power plant, wherein the maximum profit function is used to calculate the maximum value of the difference between the power sales income and the operating cost of the virtual power plant, and the target virtual power plant is any one of the multiple associated virtual power plants; A second establishing unit is used to establish energy balance constraints between the virtual power plants according to the operating parameters of the virtual power plants, and the energy balance constraints are used to ensure energy balance between the virtual power plants and the power grid; A third establishing unit is used to establish constraints for internal resource scheduling of each virtual power plant to obtain a plurality of operating constraints, wherein the internal resources include a renewable resource system, an energy storage system, an electric vehicle, and a load; a calculation unit, configured to calculate a maximum value using the profit maximum function to obtain a maximum profit of the target virtual power plant under the condition that the energy balance constraint condition and all the operation constraints are satisfied; A processing unit is used to perform power dispatch between the target virtual power plant, other virtual power plants and the power grid according to the parameters of the profit maximization function corresponding to the maximum profit.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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