Distributed virtual power plant collaborative management method and system, medium and processor

Abstract

The invention provides a distributed virtual power plant collaborative management method, which comprises the following steps that a multi-virtual power plant collaborative management model is constructed, and the collaborative management model comprises an objective function and constraint conditions; using a Lagrange dual relaxation algorithm to relax the collaborative management model, and converting an original problem of the collaborative management model into a multi-virtual power plant distributed optimization problem; a distributed partially observable Markov decision process is adopted to reconstruct a day-ahead multi-period coordination optimization scheduling problem in the coordination management model into a real-time optimization scheduling problem; and solving the collaborative management model through a quantum genetic algorithm. The lowest cost of coordinated optimization scheduling of multiple virtual power plants is taken as a target function, and the optimization problem is solved by adopting a quantum genetic algorithm to improve the calculation efficiency, so that the technical problem of low regulation and control economic benefits of the virtual power plants in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant control technology, and in particular to a distributed virtual power plant collaborative management method, system, medium and processor. Background Art

[0002] Virtual power plant (VPP) is mainly composed of power generation system, energy storage equipment and communication system. It is a power coordination management system that realizes the aggregation and coordinated optimization of distributed energy (including clean energy, controllable load and energy storage system) through advanced information communication technology and software system, so as to participate in the power market and power grid operation as a special power plant. The virtual power plant is proposed to integrate various distributed energy sources, including distributed power sources, controllable loads and energy storage devices, so as to coordinate the contradictions between smart grid and distributed energy, and fully tap the value and benefits that distributed energy brings to the power grid and users.

[0003] In recent years, researchers have done a lot of research on optimizing a single VPP, involving four levels: configuration, planning, operation, and allocation. With the advancement of the power industry, the coexistence of multiple VPPs in the same regional distribution network will become an important development trend. The power resources between these VPPs have temporal and spatial complementary characteristics. Therefore, by coordinating the optimization of multiple VPPs in the same regional distribution network and considering the power mutual assistance between VPPs, the interests of VPPs can be balanced and optimized. In comparison, better economic benefits may be obtained than for each VPP to participate in the power market independently. However, there is currently no better method for the coordinated control of multiple VPPs, which makes the economic benefits of virtual power plant regulation in the existing technology relatively low.

[0004] In view of this, a distributed virtual power plant collaborative management method, system, medium and processor are needed. Summary of the invention

[0005] In view of the problem that there is no good collaborative control method for multiple VPPs in the prior art, the present invention provides a distributed virtual power plant collaborative management method, system, medium and processor, which can solve the technical problem of low economic efficiency of virtual power plant regulation in the prior art by constructing the objective function and constraint conditions for collaborative management of multiple virtual power plants through conversion and solution. The specific technical solution is as follows:

[0006] A distributed virtual power plant collaborative management method comprises the following steps:

[0007] Constructing a multi-virtual power plant collaborative management model, wherein the collaborative management model includes an objective function and constraint conditions;

[0008] The collaborative management model is relaxed by using the Lagrangian dual relaxation algorithm, and the original problem of the collaborative management model is transformed into a distributed optimization problem of multiple virtual power plants;

[0009] A distributed partially observable Markov decision process is used to reconstruct the multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem.

[0010] The collaborative management model is solved by a quantum genetic algorithm.

[0011] Furthermore, the construction of the objective function includes the following steps:

[0012] The cost function of each virtual power plant in the T time domain is calculated as follows:

[0013]

[0014] in,

[0015] Based on the cost function of each virtual power plant, the objective function is constructed as:

[0016]

[0017] In the above formula, N represents the number of virtual power plants; W VPP，n is the cost function of the nth virtual power plant; C grid,t Represents the income from purchasing and selling electricity to the power grid; C vpp,t Represents the income from purchasing and selling electricity from other VPPs; C cl,t represents the load shedding compensation cost; C om,t Indicates the equipment operating cost; k grid,s,t represents the unit price of electricity sold by the virtual power plant to the grid at time t; k grid,b,t represents the unit price of electricity purchased by the virtual power plant from the power grid at time t; k vpp,t represents the transaction electricity price between virtual power plants at time t; k cl represents the controllable load compensation cost; k om Indicates the equipment operating cost; P grid,s,t P represents the power sold by the virtual power plant to the grid at time t; grid,b,t P represents the power purchased by the virtual power plant from the grid at time t; vpp,s,t P represents the power sold by the virtual power plant to other virtual power plants at time t; vpp,b,t P represents the power purchased by the virtual power plant from other virtual power plants at time t; cl,i,t Indicates load interruption power; P om,i,t Indicates the power generated by the equipment; M cl Represents the number of controllable loads in the virtual power plant; M om Indicates the number of devices in the virtual power plant.

[0018] Furthermore, the transaction price k between virtual power plants vpp,t The calculation formula includes:

[0019]

[0020] in,

[0021] In the above formula, Q vpp-s,n.t Indicates the remaining power of the virtual power plant; Q vpp-q,n.t Indicates the power shortage of the virtual power plant.

[0022] Furthermore, the constraints include internal power flow constraints of the virtual power plant, internal safe operation constraints of the virtual power plant, transaction constraints of the virtual power plant and transaction balance constraints between virtual power plants.

[0023] Furthermore, the internal power flow constraint of the virtual power plant is:

[0024]

[0025] In the above formula, M represents the number of nodes in the virtual power plant; P i,t represents the active power injected by node i at time t; Q i,t represents the reactive power injected by node i at time t; V i,t Represents the voltage of node i; V j,t represents the voltage of node j; G ij represents the conductance between node i and node j; B ij represents the susceptance between node i and node j; δ ij,t Represents the phase difference between node i and node j at time t.

[0026] Furthermore, the internal safety operation constraints of the virtual power plant are as follows:

[0027]

[0028] In the above formula, Indicates the upper limit of the voltage amplitude at node i; Indicates the lower limit of the voltage amplitude at node i; Indicates the upper limit of branch ij current; I ij,t Represents the current in branch ij at time t.

[0029] Furthermore, the virtual power plant transaction constraints are as follows:

[0030]

[0031] In the above formula, Indicates the maximum interactive power between the virtual power plant and the power grid; represents the maximum interactive power between the virtual power plant and other virtual power plants; D s,t , D b,tRespectively represent the power selling status and power purchasing status of the virtual power plant, 1 means participating in the transaction, and 0 means not participating in the transaction;

[0032] The transaction balance constraints between virtual power plants are as follows:

[0033]

[0034] In the above formula, P vpp,s,n,t Represents the remaining power of the virtual power plant; P vpp,q,n,t Indicates the power shortage of the virtual power plant.

[0035] A distributed virtual power plant collaborative management system, used to implement the distributed virtual power plant collaborative management method as described above, comprising:

[0036] A construction module, which is used to construct a multi-virtual power plant collaborative management model, wherein the collaborative management model includes an objective function and constraint conditions;

[0037] A conversion module, which is used to relax the collaborative management model using a Lagrangian dual relaxation algorithm, and convert the original problem of the collaborative management model into a multi-virtual power plant distributed optimization problem;

[0038] The reconstruction module is used to reconstruct the multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem by using a distributed partially observable Markov decision process;

[0039] A solution module is used to solve the collaborative management model through a quantum genetic algorithm.

[0040] A 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 distributed virtual power plant collaborative management method as described above.

[0041] A processor is used to run a program, wherein the program executes the distributed virtual power plant collaborative management method as described above when running.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. It is able to construct the objective function and constraint conditions for the collaborative management of multiple virtual power plants, and solve the technical problem of low economic efficiency of virtual power plant regulation in the existing technology through conversion and solution.

[0044] 2. Taking the lowest cost of coordinated optimization dispatch of multiple virtual power plants as the objective function, a coordinated optimization dispatch model of multiple virtual power plants in multiple time periods is constructed, and a price function for electricity trading between virtual power plants based on supply and demand relationships is constructed to increase the enthusiasm of virtual power plants to participate in internal direct transactions. The original optimization problem is decoupled based on the Lagrangian dual relaxation method to form an optimization dispatch sub-problem of multiple virtual power plants. A real-time optimization dispatch model is constructed based on a distributed partially observable Markov decision process, and a quantum genetic algorithm is used to solve the optimization problem to improve computational efficiency. The technical problem of low economic benefits of virtual power plant regulation in the existing technology is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0046] Figure 1 A schematic diagram of a process flow of a distributed virtual power plant collaborative management method of the present invention;

[0047] Figure 2 It is a structural schematic diagram of a distributed virtual power plant collaborative management system of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0050] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] Embodiment 1

[0053] In this embodiment, the virtual power plant is composed of wind power (Wind Turbine, WT), micro gas turbine (Micro Turbine, MT), energy storage (Energy Storage System, ESS) and controllable load (Controllable Load, CL).

[0054] like Figure 1 The figure shows a flow chart of a distributed virtual power plant collaborative management method, which includes the following steps:

[0055] S1: Construct a collaborative management model of multiple virtual power plants, wherein the collaborative management model includes an objective function and constraints.

[0056] In a specific implementation, the objective function of constructing the multi-virtual power plant collaborative management model includes the following steps:

[0057] The cost function of each virtual power plant in the T time domain is calculated as follows:

[0058]

[0059] in,

[0060] Based on the cost function of each virtual power plant, the objective function is constructed as:

[0061]

[0062] In the above formula, N represents the number of virtual power plants; W VPP，n is the cost function of the nth virtual power plant; C grid,t Represents the income from purchasing and selling electricity to the power grid; C vpp,t Represents the income from purchasing and selling electricity from other VPPs; C cl,t represents the load shedding compensation cost; C om,t Indicates the equipment operating cost; k grid,s,t represents the unit price of electricity sold by the virtual power plant to the grid at time t; k grid,b,t represents the unit price of electricity purchased by the virtual power plant from the power grid at time t; k vpp,t represents the transaction electricity price between virtual power plants at time t; k cl represents the controllable load compensation cost; k om Indicates the equipment operating cost; P grid,s,tP represents the power sold by the virtual power plant to the grid at time t; grid,b,t P represents the power purchased by the virtual power plant from the grid at time t; vpp,s,t P represents the power sold by the virtual power plant to other virtual power plants at time t; vpp,b,t P represents the power purchased by the virtual power plant from other virtual power plants at time t; cl,i,t Indicates load interruption power; P om,i,t Indicates the power generated by the equipment; M cl Represents the number of controllable loads in the virtual power plant; M om Indicates the number of devices in the virtual power plant.

[0063] In the specific implementation, a virtual power plant electricity transaction price function based on the supply and demand relationship is constructed to improve the enthusiasm of virtual power plants to participate in internal direct transactions. The virtual power plant transaction price k vpp,t The calculation formula includes:

[0064]

[0065] In the above formula, Q vpp-s,n.t Indicates the remaining power of the virtual power plant; Q vpp-q,n.t Indicates the power shortage of the virtual power plant.

[0066] In a specific implementation, the constraints include internal power flow constraints of the virtual power plant, internal safe operation constraints of the virtual power plant, transaction constraints of the virtual power plant, and transaction balance constraints between virtual power plants.

[0067] Furthermore, the internal power flow constraint of the virtual power plant is:

[0068]

[0069] In the above formula, M represents the number of nodes in the virtual power plant; P i,t represents the active power injected by node i at time t; Q i,t represents the reactive power injected by node i at time t; V i,t Represents the voltage of node i; V j,t represents the voltage of node j; G ij represents the conductance between node i and node j; B ij represents the susceptance between node i and node j; δ ij,t Represents the phase difference between node i and node j at time t.

[0070] Furthermore, the internal safety operation constraints of the virtual power plant are as follows:

[0071]

[0072] In the above formula, Indicates the upper limit of the voltage amplitude at node i; Indicates the lower limit of the voltage amplitude at node i; Indicates the upper limit of branch ij current; I ij,t Represents the current in branch ij at time t.

[0073] Furthermore, the virtual power plant transaction constraints are as follows:

[0074]

[0075] In the above formula, Indicates the maximum interactive power between the virtual power plant and the power grid; represents the maximum interactive power between the virtual power plant and other virtual power plants; D s,t , D b,t Respectively represent the power selling status and power purchasing status of the virtual power plant, 1 means participating in the transaction, and 0 means not participating in the transaction;

[0076] The transaction balance constraints between virtual power plants are as follows:

[0077]

[0078] In the above formula, P vpp,s,n,t Represents the remaining power of the virtual power plant; P vpp,q,n,t Indicates the power shortage of the virtual power plant.

[0079] S2: Using the Lagrangian dual relaxation algorithm to relax the collaborative management model, the original problem of the collaborative management model is transformed into a multi-virtual power plant distributed optimization problem.

[0080] Lagrange dual relaxation is an optimization method that converts the difficult part of the constraint into a part of the objective function, thereby expanding the search range. This method is not only applicable to nonlinear programming problems, but also can solve integer programming problems.

[0081] In Lagrangian dual relaxation, the constraints that make the problem difficult are absorbed into the objective function, and the objective function remains linear. In this way, the transformed problem can be solved in polynomial time, or although it cannot be solved in polynomial time, it can be solved quickly due to its smaller scale. This method helps solve the original problem.

[0082] The basic principle of the Lagrange dual relaxation method is to transform the difficult part of the constraint into a part of the objective function and expand the scope of the search. It can decompose the coupled variables in the constraint and transform them into independent sub-problems, thus simplifying the original problem. For the problem after Lagrange dual relaxation, we need to do two things: solve the sub-problems and update the Lagrange multipliers. Sub-problems are generally not NP-hard and are usually small in scale and can be solved directly.

[0083] S3: A distributed partially observable Markov decision process is used to reconstruct the day-ahead multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem.

[0084] Decentralized Partial Observable Markov Decision Process (Dec-POMDP) ​​is a model that describes multi-agent decision problems. Each agent can only observe its own state and action, but cannot know the state and action of other agents. Therefore, each agent needs to infer the global state transition based on its own observations and the actions of other agents.

[0085] In Dec-POMDP, each agent has its own action space and state space, and can observe the state space of other agents. Each agent has a local Bayesian filter to infer the state of other agents, and can take actions to affect the global state transition.

[0086] When solving the Dec-POMDP problem, we can use a hybrid training mechanism, an exploration algorithm based on pseudo-counting, an AC framework, obtain information and actions of other intelligent agents during training, adopt opponent modeling, simulate the strategies of other intelligent agents, use meta-learning to adapt to non-stationary environments more quickly, average distribution, differential reward distribution, function distribution, and TVT algorithm based on episodic memory retrieval.

[0087] S4: Solving the collaborative management model through a quantum genetic algorithm.

[0088] Quantum Genetic Algorithm (QGA) is an optimization algorithm that combines quantum computing and genetic algorithms. It uses quantum bits (qubits) as information carriers and achieves more efficient search and optimization through the superposition and entanglement of quantum states.

[0089] In QGA, quantum bits are used to represent individuals (chromosomes) in genetic algorithms. Each quantum bit can be in a state of 0 or 1, or in a superposition state, that is, in a state of 0 and 1 at the same time. Through quantum gate operations, the evolution of chromosomes can be realized to achieve the purpose of finding the optimal solution.

[0090] In the technical solution of this embodiment, the lowest cost of coordinated optimization and dispatch of multiple virtual power plants is taken as the objective function, a coordinated optimization and dispatch model of multiple virtual power plants in multiple time periods (that is, a collaborative management model) is constructed, and an electricity price function for electricity trading between virtual power plants based on supply and demand relationships is constructed to increase the enthusiasm of virtual power plants to participate in internal direct transactions; the original optimization problem is decoupled based on the Lagrangian dual relaxation method to form an optimization dispatch sub-problem of multiple virtual power plants; a real-time optimization dispatch model is constructed based on a distributed partially observable Markov decision process, and a quantum genetic algorithm is used to solve the optimization problem to improve computational efficiency, thereby solving the technical problem of low economic efficiency of virtual power plant regulation in the prior art.

[0091] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0092] Embodiment 2

[0093] A distributed virtual power plant collaborative management system, used to implement the distributed virtual power plant collaborative management method as described above, comprising:

[0094] A construction module, which is used to construct a multi-virtual power plant collaborative management model, wherein the collaborative management model includes an objective function and constraint conditions;

[0095] A conversion module, which is used to relax the collaborative management model using a Lagrangian dual relaxation algorithm, and convert the original problem of the collaborative management model into a multi-virtual power plant distributed optimization problem;

[0096] The reconstruction module is used to reconstruct the multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem by using a distributed partially observable Markov decision process;

[0097] A solution module is used to solve the collaborative management model through a quantum genetic algorithm.

[0098] The distributed virtual power plant collaborative management system is used to execute Figure 1 The distributed virtual power plant collaborative management method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the distributed virtual power plant collaborative management system can refer to the contents described in the embodiment of the distributed virtual power plant collaborative management method, which will not be repeated here.

[0099] Embodiment 3

[0100] A computer-readable storage medium, the computer-readable storage medium including a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the distributed virtual power plant collaborative management method as described above. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0101] Embodiment 4

[0102] A processor is used to run a program, wherein the program executes the distributed virtual power plant collaborative management method as described above when running.

[0103] The present application provides a distributed virtual power plant collaborative management method, comprising the following steps: constructing a collaborative management model of multiple virtual power plants, the collaborative management model including an objective function and constraints; relaxing the collaborative management model using a Lagrangian dual relaxation algorithm, and converting the original problem of the collaborative management model into a distributed optimization problem of multiple virtual power plants; using a distributed partially observable Markov decision process to reconstruct the day-ahead multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem; and solving the collaborative management model using a quantum genetic algorithm. Taking the lowest cost of coordinated optimization and dispatch of multiple virtual power plants as the objective function, a coordinated optimization and dispatch model of multiple virtual power plants in multiple time periods (also known as a collaborative management model) is constructed, and an electricity price function for electricity trading between virtual power plants based on supply and demand relationships is constructed to increase the enthusiasm of virtual power plants to participate in internal direct transactions; the original optimization problem is decoupled based on the Lagrangian dual relaxation method to form an optimal dispatch sub-problem of multiple virtual power plants; a real-time optimization dispatch model is constructed based on a distributed partially observable Markov decision process, and a quantum genetic algorithm is used to solve the optimization problem to improve computational efficiency, thereby solving the technical problem of low economic efficiency of virtual power plant regulation in the existing technology.

[0104] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0105] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0106] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A distributed virtual power plant collaborative management method, characterized in that: The following steps are involved: Constructing a multi-virtual power plant collaborative management model, wherein the collaborative management model includes an objective function and constraint conditions; The collaborative management model is relaxed by using the Lagrangian dual relaxation algorithm, and the original problem of the collaborative management model is transformed into a distributed optimization problem of multiple virtual power plants; A distributed partially observable Markov decision process is used to reconstruct the multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem. The collaborative management model is solved by a quantum genetic algorithm.

2. The distributed virtual power plant collaborative management method according to claim 1, characterized in that: The construction of the objective function comprises the following steps: The cost function of each virtual power plant in the T time domain is calculated as follows: in, Based on the cost function of each virtual power plant, the objective function is constructed as: In the above formula, N represents the number of virtual power plants; W VPP，n is the cost function of the nth virtual power plant; C grid,t Represents the income from purchasing and selling electricity to the power grid; C vpp,t Represents the income from purchasing and selling electricity from other VPPs; C cl,t represents the load shedding compensation cost; C om,t represents the equipment operating cost; k grid,s,t represents the unit price of electricity sold by the virtual power plant to the grid at time t; k grid,b,t represents the unit price of electricity purchased by the virtual power plant from the power grid at time t; k vpp,t represents the transaction electricity price between virtual power plants at time t; k cl represents the controllable load compensation cost; k om Indicates the equipment operating cost; P grid,s,t P represents the power sold by the virtual power plant to the grid at time t; grid,b,t P represents the power purchased by the virtual power plant from the grid at time t; vpp,s,t P represents the power sold by the virtual power plant to other virtual power plants at time t; vpp,b,t P represents the power purchased by the virtual power plant from other virtual power plants at time t; cl,i,t Indicates load interruption power; P om,i,t Indicates the power generated by the equipment; M cl Represents the number of controllable loads in the virtual power plant; M om Indicates the number of devices in the virtual power plant.

3. The distributed virtual power plant collaborative management method according to claim 2, characterized in that: The transaction price k between virtual power plants vpp,t The calculation formula includes: in, In the above formula, Q vpp-s,n.t Indicates the remaining power of the virtual power plant; Q vpp-q,n.t Indicates the power shortage of the virtual power plant.

4. The distributed virtual power plant collaborative management method according to claim 1, characterized in that: The constraints include internal power flow constraints of virtual power plants, internal safe operation constraints of virtual power plants, transaction constraints of virtual power plants, and transaction balance constraints between virtual power plants.

5. The distributed virtual power plant collaborative management method according to claim 4, characterized in that: The internal power flow constraint of the virtual power plant is: In the above formula, M represents the number of nodes in the virtual power plant; P i,t represents the active power injected by node i at time t; Q i,t represents the reactive power injected by node i at time t; V i,t Represents the voltage of node i; V j,t represents the voltage of node j; G ij represents the conductance between node i and node j; B ij represents the susceptance between node i and node j; δ ij,t Represents the phase difference between node i and node j at time t.

6. The distributed virtual power plant collaborative management method according to claim 5, characterized in that: The internal safety operation constraints of the virtual power plant are as follows: In the above formula, Indicates the upper limit of the voltage amplitude at node i; Indicates the lower limit of the voltage amplitude at node i; Indicates the upper limit of branch ij current; I ij,t Represents the current in branch ij at time t.

7. The distributed virtual power plant collaborative management method according to claim 1, characterized in that: The virtual power plant transaction constraints are as follows: In the above formula, Indicates the maximum interactive power between the virtual power plant and the power grid; represents the maximum interactive power between the virtual power plant and other virtual power plants; D s,t , D b,t Respectively represent the power selling status and power purchasing status of the virtual power plant, 1 means participating in the transaction, and 0 means not participating in the transaction; The transaction balance constraints between virtual power plants are as follows: In the above formula, P vpp,s,n,t Represents the remaining power of the virtual power plant; P vpp,q,n,t Indicates the power shortage of the virtual power plant.

8. A distributed virtual power plant collaborative management system, characterized in that: A method for implementing the distributed virtual power plant collaborative management method according to any one of claims 1 to 7, comprising: A construction module, which is used to construct a multi-virtual power plant collaborative management model, wherein the collaborative management model includes an objective function and constraint conditions; A conversion module, which is used to relax the collaborative management model using a Lagrangian dual relaxation algorithm, and convert the original problem of the collaborative management model into a multi-virtual power plant distributed optimization problem; The reconstruction module is used to reconstruct the multi-period coordinated optimization scheduling problem in the collaborative management model into a real-time optimization scheduling problem by using a distributed partially observable Markov decision process; A solution module is used to solve the collaborative management model through a quantum genetic algorithm.

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 distributed virtual power plant collaborative management method according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the distributed virtual power plant collaborative management method described in any one of claims 1 to 7.

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