Multi-virtual power plant power distribution network cooperative operation optimization method, medium, device, equipment and medium

Abstract

The invention belongs to the technical field of virtual power grids, and particularly relates to a multi-virtual power plant power distribution network cooperative operation optimization method and device, equipment and a medium. The method comprises the following steps: determining a multi-virtual power plant aggregation model; determining a feasible region model based on the virtual power plant aggregation model, and solving the feasible region model by adopting a vertex enumeration method to obtain a feasible region projection; establishing a first objective function by taking the lowest overall power supply cost of the system as an objective; the first objective function comprises power distribution network power supply cost and virtual power plant power supply cost; constraint conditions of the first objective function comprise upper and lower limits of a feasible region; the upper and lower limits of the feasible region are obtained by solving a preset second objective function; the constraint condition of the second objective function comprises feasible region projection; and solving the first objective function to obtain a multi-virtual power plant power distribution network cooperative operation optimization scheme. The two-stage power grid of the power distribution network and the virtual power plant can be integrally optimized from the global perspective, alternate solution of the two-stage power grid is not needed, and the calculation speed and efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of virtual power grid technology, specifically relating to a method, device, equipment, and medium for optimizing the collaborative operation of a multi-virtual power plant distribution network. Background Technology

[0002] With the large-scale integration of flexible devices such as electric vehicles, distributed power sources, energy storage, and flexible loads into the distribution system, the integration of power generation, load, and storage, as well as vehicle-grid interaction and coordinated participation in grid dispatch, has become an unstoppable trend. The aggregation of flexible resources such as electric vehicles into virtual power plants is an important means for the load side to participate in grid dispatch. Virtual power plants have attracted widespread attention due to their advantages, such as containing numerous flexible resources and rapid adjustment speed. As a large number of virtual power plants are connected to the distribution network, how to efficiently coordinate and optimize the distribution network and virtual power plants to improve the safe operation of the power grid has become a widely concerned issue.

[0003] Currently, in the field of two-level coordinated optimization research in power grids, most methods utilize distributed algorithms to decompose the ensemble problem into several sub-problems, solving these sub-problems by exchanging boundary coupling variables between the transmission and distribution networks. These methods mainly include Lagrange relaxation-based methods, heterogeneous decomposition methods, Benders decomposition methods, optimal condition decomposition methods, and generalized master-slave splitting methods. Lagrange relaxation-based methods include the Alternating Multiplier Method (ADMM), Objective Cascade Analysis (ATC), Proximity Message Transfer (PMP), and Auxiliary Problem Principle (APP). In the field of coordinated optimization research between virtual power plants and distribution networks, a two-level coordinated optimization model based on the feasible region of virtual power plant flexibility resource aggregation provides a good demonstration reference for the model design of flexibility resource aggregation participating in power grid regulation. By treating the virtual power plant as a whole as a node within the distribution network for transmission network optimization scheduling, it avoids iterative solutions between the distribution network and the virtual power plant, and also ensures the privacy of information between the two levels of the power grid. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, equipment, and medium for optimizing the collaborative operation of multiple virtual power plant distribution networks, thereby solving the problems of low matching degree and low resource utilization in the collaborative operation of virtual aggregates in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the coordinated operation of a multi-virtual power plant distribution network, comprising the following specific steps: Determine the aggregation model for multiple virtual power plants; Based on the virtual power plant aggregation model, the feasible region model is determined, and the vertex enumeration method is used to solve the feasible region model to obtain the feasible region projection. With the goal of minimizing the overall power supply cost of the system, a first objective function is established; the first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant; the constraints of the first objective function include the upper and lower limits of the feasible region; the upper and lower limits of the feasible region are obtained by solving a preset second objective function; the constraints of the second objective function include the projection of the feasible region. Solving the first objective function yields an optimization scheme for the coordinated operation of a multi-virtual power plant distribution network.

[0006] Preferably, in the step of determining the multi-virtual power plant aggregation model, the multi-virtual power plant aggregation model includes a fully controlled power source model, a semi-controlled power source model, and an energy storage resource model.

[0007] Preferably, the step of determining the feasible region model based on the virtual power plant aggregation model and solving the feasible region model using the vertex enumeration method to obtain the feasible region projection includes: The solution dimension of the feasible region model is defined as the active power P and reactive power Q at the common coupling point, forming a 2-dimensional optimization space, and the initial set of projected vertices is determined. Based on the initial set of projected vertices, an initial convex polyhedron is constructed. Each face of the initial convex polyhedron is then transformed to obtain the effective faces. Collect the vertices of the valid surface to form a new set of projected vertices; The process iteratively generates a new set of projected vertices. When the termination condition is met, the iteration terminates, and the final set of vertices is obtained as the feasible region projection.

[0008] Preferably, in the step of terminating the iteration when the termination condition is met: Calculate the relative displacement between each new vertex in the new set of projected vertices and the original boundary. Terminate the iteration when the relative displacement is less than or equal to a preset value.

[0009] Preferably, the first objective function is: ; In the formula, in the formula, Cost of power supply to the distribution network; Cost of supplying electricity to virtual power plants; A collection of virtual power plants connected to the power distribution network; A collection of generators in a power distribution network; For the distribution network during the time period t The power supply cost function; For the first in the distribution network i The generators during the time period t Those who have made meritorious contributions; and These are the costs of wind curtailment and solar curtailment, respectively. For energy storage maintenance costs; Cost of incentivized DR calls; For virtual power plants k During the period t Equivalent active power of coupled nodes; , and For the first i Cost function coefficients of a generator; T This is the set of time steps.

[0010] Preferably, the second objective function is: ; In the formula, , and Distribution network units i Consumption characteristic coefficient, , , , These represent the costs of wind and solar power curtailment, energy storage maintenance, and incentive-based D-type dispatch in the lower-level power grid, respectively. For the time period t The cost of transmitting electricity from the upper-level power grid to the lower-level power grid; This refers to the set of nodes in the power distribution system.

[0011] Preferably, the constraints of the second objective function are: Line power constraints: ; ; ; In the formula, and These are nodes b and q The conductance and susceptance values ​​of the lines between them; , and , These are nodes b and q In time t The square of the voltage amplitude and the phase angle; and Representing nodes respectively b and q The active and reactive power of the lines between them; It is a node b and q Line capacity between; Voltage amplitude and phase angle constraints: ; In the formula, and These are the upper and lower bounds of the square of the voltage amplitude, respectively. and These are the upper and lower bounds of the voltage phase angle, respectively, which are taken as 60° and -60° here; Controllable range constraints of distributed power supply output: ; ; ; In the formula, , They are the first j A virtual power plant In time t The actual output power and maximum output power at that time; , They are The actual output power and maximum output power at time t; This is the upper limit of the wind turbine's ramping power; Active and reactive power balance constraints: ; ; In the formula, Let the load of node b at time t be denoted as 'b'. Energy storage constraints for electric vehicles, etc. ; ; ; ; ; In the formula, and The states of charge of the electrochemical energy storage at time 0 and time 1 are respectively. and They are respectively t and t The state of charge of electrochemical energy storage at time -1; and These are charging efficiency and discharging efficiency, respectively. and These represent the minimum and maximum states of charge for electrochemical energy storage, respectively. and These are charging power and discharging power, respectively. and These are the rated charge and discharge powers, respectively. Coupled node constraints: ; In the formula, Let be the two-dimensional feasible region of the common coupled nodes of the virtual power plant k.

[0012] In a second aspect, the present invention provides a multi-virtual power plant distribution network collaborative operation optimization device, comprising: The modeling module is used to determine the aggregation model of multiple virtual power plants; The feasible region module is used to determine the feasible region model based on the virtual power plant aggregation model, and solve the feasible region model using the vertex enumeration method to obtain the feasible region projection. The objective function module is used to establish a first objective function with the goal of minimizing the overall power supply cost of the system. The first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant. The constraints of the first objective function include upper and lower limits of the feasible region. The upper and lower limits of the feasible region are obtained by solving a preset second objective function. The constraints of the second objective function include the projection of the feasible region. The solution module is used to solve the first objective function to obtain the optimization scheme for the coordinated operation of the multi-virtual power plant distribution network.

[0013] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the aforementioned multi-virtual power plant distribution network collaborative operation optimization method.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the aforementioned method for optimizing the coordinated operation of a multi-virtual power plant distribution network.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Compared with existing collaborative optimization methods for distribution networks and virtual power plants, the collaborative operation optimization method based on the feasible region of virtual power plants proposed in this technical solution can optimize the two-level power grids of distribution networks and virtual power plants from a global perspective, without the need for alternating solutions for the two levels of power grids, thus improving computational speed and efficiency. By combining various resources within the virtual power plant, considering distributed wind and solar power, energy storage, and flexible loads, a feasible region for virtual power plant scheduling is formed, coordinating various resources within the virtual power plant to participate in system scheduling; the equivalent coupled nodes of the virtual power plant participate in the optimal scheduling of the distribution network as constraints, realizing the optimization of the entire distribution network from a global perspective, avoiding alternating iterations and information exchange between the two levels of power grids, and ensuring the privacy of information at each level of the power grid.

[0016] Solving the feasible region projection of the virtual power plant by vertex enumeration method can clarify the active and reactive power interaction boundaries of the common coupling point between the virtual power plant and the distribution network, define the upper limit of resource regulation capacity, realize the quantitative characterization of the regulation potential of the virtual power plant, and avoid invalid solutions that exceed the resource regulation capacity in collaborative optimization.

[0017] By first optimizing the power supply cost of the virtual power plant and then solving the overall objective function in a hierarchical manner, the difficulty of solving complex collaborative models can be simplified, the tedious calculations of alternating iterations can be avoided, the optimization scheme can be solved efficiently, and the engineering practicality and scheduling decision efficiency of the model can be improved. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a multi-virtual power plant distribution network collaborative operation optimization method according to Embodiment 1 of the present invention. Figure 2 This is a topology diagram of the virtual power plant in Embodiment 1 of the present invention; Figure 3 This is a flowchart of the vertex enumeration algorithm in Embodiment 1 of the present invention; Figure 4 This is a structural block diagram of the multi-virtual power plant distribution network collaborative operation optimization device according to Embodiment 2 of the present invention; Figure 5 This is a structural block diagram of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0020] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0021] Example 1 A distribution network optimization method based on the feasible region of a virtual power plant includes the following steps: S1. Determine the multi-resource model of the virtual power plant.

[0022] like Figures 1-3As shown, the flexible resources within a virtual power plant can be categorized into power sources, loads, and energy storage. Power sources include fully controlled power sources such as micro gas turbines, and semi-controlled power sources such as distributed wind power and distributed photovoltaic power. Loads include flexible loads such as air conditioning and non-adjustable rigid loads. Energy storage includes resources with energy accumulation, such as distributed energy storage and electric vehicles. The power flow calculation for the power network topology uses the DistFlow power flow model.

[0023] The fully controlled power supply model, represented by a micro gas turbine, is as follows: ; In the formula: and , which are the active and reactive power outputs of the fully controlled power supply at time t, respectively; and These are the minimum and maximum active power outputs of a fully controlled power supply, respectively. The rated capacity of a fully controlled power supply; and These are the upper and lower limits of ramp rate for a fully controlled power supply.

[0024] The semi-controlled power source model, represented by distributed wind power and distributed photovoltaic power, is as follows: ; In the formula, It is a collection of photovoltaic units; It is a collection of wind turbine units; , They are the first j A virtual power plant The actual output power and maximum output power at time t; , They are the first k The wind turbine MT in time t The actual output power and maximum output power at that time; This is the upper limit of the wind turbine's ramping power.

[0025] The energy storage resource model, represented by electric vehicles, is as follows: ; In the formula, and The states of charge of the electrochemical energy storage at time 0 and time 1 are respectively. and They are respectively t and t-1 The state of charge of electrochemical energy storage at any given time; and These are charging efficiency and discharging efficiency, respectively. and These represent the minimum and maximum states of charge for electrochemical energy storage, respectively. and These are charging power and discharging power, respectively. and These are the rated charge and discharge powers, respectively.

[0026] To account for the randomness and uncertainty of large-scale electric vehicles, a spectrum-based neural network technique is used to calculate the power of the entire electric vehicle cluster based on data-driven methods, and this power is then used in an energy storage resource model.

[0027] The neural network is designed as follows: Definition includes n Each node and m Graph with stripes .in , They are respectively The set of nodes and the set of edges, If we have an adjacency matrix, then we have: ; In the formula, L is the normalized Laplace matrix. It is the identity matrix. D It is a diagonal matrix, and its diagonal elements are: ; The eigenvector matrix of the normalized Laplacian matrix L is defined as follows: ; The eigenvalue matrix is: , ; The graph Fourier transform is defined as: ; In the formula, Original signal After orthogonal basis The representation projected onto the spectral domain.

[0028] Define the graph convolution operator as " The matrix dot product operator is "☉", then two signals... The convolution of the spectrograms of y and y is: ; make ,definition If the kernel is a convolution kernel, then the convolution form in the spectral domain is: ; To improve computational efficiency, a Chebyshev multinomial expansion graph convolution kernel is used. An approximation is performed. The number of terms K retained in the expansion is used to change the size of the aggregation range of related information across multiple time scales. The Chebyshev expansion of the convolution kernel is: The convolution expression obtained after approximating the Chebyshev expansion is: ; This is a partial design scheme for a graph neural network.

[0029] In this scenario, the input data includes the charging and discharging load data of electric vehicles, the voltage and current of the charging station's aggregation link point, and external settings such as date and weather. The training target data is the rated charging and discharging power after aggregation in the energy storage resource model. This rated charging and discharging power is not the actual charging and discharging data, but rather reflects the control capability of electric vehicles and energy storage resources after aggregation at the local node. By adopting a data-driven approach, user charging data is integrated, and a spectral neural network is used to aggregate and represent it.

[0030] The constraints of flexible loads, exemplified by air conditioners, are as follows: ; In the formula, This refers to the real-time cooling (or heating) capacity of a variable frequency air conditioner. This refers to the real-time electrical power of the variable frequency air conditioner. This refers to the operating frequency of the compressor; a , b All are constant coefficients for cooling capacity (heating capacity); m , n All are constant power coefficients. Assume the operating frequency range of the inverter air conditioner is [...]. , Room temperature is maintained at The vicinity, let's assume that the vicinity is [ , ]; e For controller accuracy, it is usually set to 1.

[0031] The virtual power plant network model includes node and line constraints, voltage and current safety constraints, etc. ; In the formula: for t Power at time node; Time nodes i The square of the voltage; and These are the upper and lower limits of the square of the voltage, respectively. and They are respectively t The active and reactive power output of the virtual power plant at the common coupling point at any given time.

[0032] S2. Based on the virtual power plant aggregation model, determine the feasible region model, and use the vertex enumeration method to solve the feasible region model to obtain the feasible region projection.

[0033] To concretize the active-reactive power regulation capability of the virtual power plant, the output power at the common coupling point between the virtual power plant and the distribution network is calculated. P , Q The feasible region in the 2D plane where it is located.

[0034] Specifically, by establishing resource models and network models within the virtual power plant as constraints, the feasible region projection of the virtual power plant is obtained using a vertex enumeration algorithm. The specific process is as follows: The basic idea of ​​the Vertex Enumeration (PVE) algorithm is to search for new vertices by translating the outward facet of the obtained approximate polyhedron.

[0035] (1) Initialization First, initialize the algorithm; at least one should be found ( b +1) vertices of the projection, where the convex hull should be The nondegenerate polyhedron in [the region]. One method to obtain the initial vertices is to calculate the extreme points along each axis and opposite to each axis. After removing duplicate vertices, the set of initial vertices is represented as [formula missing]. The number of these vertices is represented as .

[0036] (2) Inner ring Based on the initial vertex set Construct the initial convex polyhedron For the initial convex polyhedron Each face All by By translating from the origin, a new vertex is obtained, resulting in an affine.

[0037] Based on this, the surface The affine can be standardized as: ; in, It is noodles The external normal vector.

[0038] Then, the surface transformation operation can be achieved by solving the following linear programming problem: ; ; make and These respectively indicate that the above problems correspond to The optimal solution and the maximum value.

[0039] (3) Outer ring and termination principle The polygon represented by the feasible region boundary is a set of vertices and edges. Among the different characteristics of the new vertex and the original vertex, relative displacement is the most intuitive and effective. Therefore, the termination condition is set as the relative displacement of the new vertex to its corresponding original line being less than a certain specific value. Let the relative displacement be... ,Right now: ; In the formula: The new vertex generated at time t; The vertex on the clockwise side of the newly generated vertex; Let L2 be the norm of the search direction vector; This is a preset termination condition.

[0040] The flowchart for solving the feasible region projection using the vertex enumeration algorithm is as follows: Figure 3 As shown. S3. Establish a first objective function with the goal of minimizing the overall system power supply cost. This first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant. The constraints of the first objective function include the upper and lower limits of the feasible region.

[0041] In the objective function of the two-level grid collaborative optimization scheduling of the distribution network and virtual power plant, the main consideration is to optimize scheduling to minimize the overall power supply cost of the system, which includes the power supply cost of the distribution network. and the cost of power supply from virtual power plants The two parts can be represented as: ; In the formula, Cost of power supply to the distribution network; Cost of supplying electricity to virtual power plants; A collection of virtual power plants connected to the power distribution network; A collection of generators in a power distribution network; For the distribution network during the time period t The power supply cost function; For the first in the distribution network i The generators during the time period t Those who have made meritorious contributions; and These are the costs of wind curtailment and solar curtailment, respectively. For energy storage maintenance costs; Cost of incentivized DR calls; For virtual power plants k During the period t Equivalent active power of coupled nodes; , and For the first i Cost function coefficients of a generator; T This is the set of time steps.

[0042] in It is expressed as follows: ; in, Total flexible load, This is a flag for flexible load dispatch; Price for flexible load dispatch; This refers to the amount of flexible load dispatch.

[0043] In actual power grid operation, comprehensive optimization is typically achieved through safety-constrained economic dispatch. This approach simultaneously considers generation costs, transmission losses, and line transmission constraints, enabling optimized utilization of resources at both levels of the power grid.

[0044] The upper and lower limits of the feasible region are obtained by solving a preset second objective function; the constraints of the second objective function include the projection of the feasible region.

[0045] Taking the lower-level distribution network under the virtual power plant coupled node (PCC) as an example model, the power supply cost of the lower-level distribution network mainly considers the generator generation cost. Based on the generator set consumption characteristics, the second objective function can be obtained as follows: ; In the formula, , and Distribution network units i Consumption characteristic coefficient, , , , These represent the costs of wind and solar power curtailment, energy storage maintenance, and incentive-based D-type dispatch in the lower-level power grid, respectively. For the time period t The cost of transmitting electricity from the upper-level power grid to the lower-level power grid; Let be the set of nodes in the power distribution system. The optimization of the second objective function, along with the following constraints, is used to solve for the upper and lower limits of the feasible region for upward aggregation of flexible resources. The constraints of the second objective function mainly include: 1) Line power constraints ; ; ; In the formula, and These are the conductance and susceptance values ​​of the line between nodes b and q, respectively. , and , These are the squares of the voltage magnitudes and the phase angles at nodes b and q at time t, respectively. and These represent the active and reactive power of the line between nodes b and q, respectively. It represents the line capacity between nodes b and q.

[0046] 2) Voltage amplitude and phase angle constraints ; In the formula, and These are the upper and lower bounds of the square of the voltage amplitude, respectively. and These are the upper and lower bounds of the voltage phase angle, respectively, which are taken as 60° and -60° here.

[0047] 3) Constraints on the controllable range of distributed power source output ; ; ; In the formula, , They are The actual output power and maximum output power at time t; , They are The actual output power and maximum output power at time t; This is the upper limit of the wind turbine's ramping power.

[0048] 4) Active and reactive power balance constraints ; ; In the formula, Let be the load of node b at time t.

[0049] 5) Energy storage constraints such as electric vehicles ; ; ; ; ; In the formula, and The states of charge of the electrochemical energy storage at time 0 and time 1 are respectively. and They are respectively tand t The state of charge of electrochemical energy storage at time -1; and These are charging efficiency and discharging efficiency, respectively. and These represent the minimum and maximum states of charge for electrochemical energy storage, respectively. and These are charging power and discharging power, respectively. and These are the rated charge and discharge powers, respectively.

[0050] 6) Coupled node constraints ; In the formula, Let be the two-dimensional feasible region of the common coupled nodes of the virtual power plant k.

[0051] First, based on the feasible region projection, solve the second objective function. After solving, obtain the extended feasible region projection. Based on the extended feasible region projection, obtain the upper and lower limits of the feasible region projection.

[0052] S4. Solve the first objective function to obtain the optimization scheme for the coordinated operation of the multi-virtual power plant distribution network.

[0053] The second objective function model in this invention considers flexible resources such as large-scale electric vehicles, and includes power flow and optimization calculation methods for two-level power grids: distribution network and virtual power plant. It also needs to consider the coupling relationship between the two levels of the power grid, making direct solution quite complex. This model first treats the feasible region projection of the virtual power plant as a constraint in the distribution network, and then optimizes the economic dispatch model of the distribution network, eliminating the need for tedious alternating solutions.

[0054] Example 2 like Figure 4 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a multi-virtual power plant distribution network collaborative operation optimization device, comprising: The modeling module is used to determine the aggregation model of multiple virtual power plants; The feasible region module is used to determine the feasible region model based on the virtual power plant aggregation model, and solve the feasible region model using the vertex enumeration method to obtain the feasible region projection. The objective function module is used to establish a first objective function with the goal of minimizing the overall power supply cost of the system. The first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant. The constraints of the first objective function include upper and lower limits of the feasible region. The upper and lower limits of the feasible region are obtained by solving a preset second objective function. The constraints of the second objective function include the projection of the feasible region. The solution module is used to solve the first objective function to obtain the optimization scheme for the coordinated operation of the multi-virtual power plant distribution network.

[0055] Example 3 like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a method for optimizing the coordinated operation of a multi-virtual power plant distribution network; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0056] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the multi-virtual power plant distribution network collaborative operation optimization method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0057] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0058] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0059] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for optimizing the coordinated operation of a multi-virtual power plant distribution network, and the processor 102 can execute multiple instructions to achieve the following: Determine the aggregation model for multiple virtual power plants; Based on the virtual power plant aggregation model, the feasible region model is determined, and the vertex enumeration method is used to solve the feasible region model to obtain the feasible region projection. With the goal of minimizing the overall power supply cost of the system, a first objective function is established; the first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant; the constraints of the first objective function include the upper and lower limits of the feasible region; the upper and lower limits of the feasible region are obtained by solving a preset second objective function; the constraints of the second objective function include the projection of the feasible region. Solving the first objective function yields an optimization scheme for the coordinated operation of a multi-virtual power plant distribution network.

[0060] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the coordinated operation of a multi-virtual power plant distribution network, characterized in that, The specific steps include the following: Determine the aggregation model for multiple virtual power plants; Based on the virtual power plant aggregation model, the feasible region model is determined, and the vertex enumeration method is used to solve the feasible region model to obtain the feasible region projection. With the goal of minimizing the overall power supply cost of the system, a first objective function is established; the first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant; the constraints of the first objective function include the upper and lower limits of the feasible region; the upper and lower limits of the feasible region are obtained by solving a preset second objective function; the constraints of the second objective function include the projection of the feasible region. Solving the first objective function yields an optimization scheme for the coordinated operation of a multi-virtual power plant distribution network.

2. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 1, characterized in that, In the step of determining the multi-virtual power plant aggregation model, the multi-virtual power plant aggregation model includes a fully controlled power source model, a semi-controlled power source model, and an energy storage resource model.

3. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 1, characterized in that, The steps of determining the feasible region model based on the virtual power plant aggregation model and solving the feasible region model using the vertex enumeration method to obtain the feasible region projection include: The solution dimension of the feasible region model is defined as the active power P and reactive power Q at the common coupling point, forming a 2-dimensional optimization space, and the initial set of projected vertices is determined. Based on the initial set of projected vertices, an initial convex polyhedron is constructed. Each face of the initial convex polyhedron is then transformed to obtain the effective faces. Collect the vertices of the valid surface to form a new set of projected vertices; The process iteratively generates a new set of projected vertices. When the termination condition is met, the iteration terminates, and the final set of vertices is obtained as the feasible region projection.

4. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 3, characterized in that, When the termination condition is met, the iteration termination step is as follows: Calculate the relative displacement between each new vertex in the new set of projected vertices and the original boundary. Terminate the iteration when the relative displacement is less than or equal to a preset value.

5. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 1, characterized in that, The first objective function is: ； In the formula, Cost of power supply to the distribution network; Cost of supplying electricity to virtual power plants; A collection of virtual power plants connected to the power distribution network; A collection of generators in a power distribution network; For the distribution network during the time period t The power supply cost function; For the first in the distribution network i The generators during the time period t Those who have made meritorious contributions; and These are the costs of wind curtailment and solar curtailment, respectively. For energy storage maintenance costs; Cost of incentivized DR calls; For virtual power plants k During the period t Equivalent active power of coupled nodes; , and For the first i Cost function coefficients of a generator; T This is the set of time steps.

6. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 5, characterized in that, The second objective function is: ； In the formula, , and Distribution network units i Consumption characteristic coefficient, , , , These represent the costs of wind and solar power curtailment, energy storage maintenance, and incentive-based D-type dispatch in the lower-level power grid, respectively. For the time period t The cost of transmitting electricity from the upper-level power grid to the lower-level power grid; This refers to the set of nodes in the power distribution system.

7. The method for optimizing the coordinated operation of a multi-virtual power plant distribution network as described in claim 6, characterized in that, The constraints of the second objective function are: Line power constraints: ； ； ； In the formula, and These are nodes b and q The conductance and susceptance values ​​of the lines between them; , and , These are nodes b and q In time t The square of the voltage amplitude and the phase angle; and Representing nodes respectively b and q The active and reactive power of the lines between them; It is a node b and q Line capacity between; Voltage amplitude and phase angle constraints: ； In the formula, and These are the upper and lower bounds of the square of the voltage amplitude, respectively. and These are the upper and lower bounds of the voltage phase angle, respectively. Controllable range constraints of distributed power supply output: ； ； ； In the formula, , They are the first j A virtual power plant In time t The actual output power and maximum output power at that time; , They are The actual output power and maximum output power at time t; This is the upper limit of the wind turbine's ramping power; Active and reactive power balance constraints: ； ； In the formula, Let the load of node b at time t be denoted as ; Energy storage constraints for electric vehicles, etc. ； ； ； ； ； In the formula, and The states of charge of the electrochemical energy storage at time 0 and time 1 are respectively. and They are respectively t and t The state of charge of electrochemical energy storage at time -1; and These are charging efficiency and discharging efficiency, respectively. and These represent the minimum and maximum states of charge for electrochemical energy storage, respectively. and These are charging power and discharging power, respectively. and These are the rated charge and discharge powers, respectively. Coupled node constraints: ； In the formula, Let be the two-dimensional feasible region of the common coupled nodes of the virtual power plant k.

8. A multi-virtual power plant distribution network collaborative operation optimization device, characterized in that, include: The modeling module is used to determine the aggregation model of multiple virtual power plants; The feasible region module is used to determine the feasible region model based on the virtual power plant aggregation model, and solve the feasible region model using the vertex enumeration method to obtain the feasible region projection. The objective function module is used to establish a first objective function with the goal of minimizing the overall power supply cost of the system. The first objective function includes the power supply cost of the distribution network and the power supply cost of the virtual power plant. The constraints of the first objective function include upper and lower limits of the feasible region. The upper and lower limits of the feasible region are obtained by solving a preset second objective function. The constraints of the second objective function include the projection of the feasible region. The solution module is used to solve the first objective function to obtain the optimization scheme for the coordinated operation of the multi-virtual power plant distribution network.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the multi-virtual power plant distribution network collaborative operation optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the multi-virtual power plant distribution network collaborative operation optimization method as described in any one of claims 1 to 7.

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