Method and device for quickly evaluating optimal adjustable capability of virtual power plant

Abstract

The invention discloses a method and a device for quickly evaluating the optimal adjustable capability of a virtual power plant, and relates to the field of virtual power plants. The method comprises the steps that in the training process, the target of a reconstruction layer is to determine a reconstruction strategy from a large number of feasible network topologies, meanwhile, a target value fed back by an evaluation layer is received, and the reconstruction strategy of the power distribution network is updated through interactive iteration with the evaluation layer; the evaluation layer continuously receives the network structure determined by the reconstruction layer, considers node characteristic constraints and network topology constraints at the same time, and evaluates a feasible region of the virtual power plant under a given topology by adopting a vertex search-based virtual power plant adjustable capability evaluation method until an optimal feasible region is found; during application, the optimal power distribution network reconstruction strategy of the target power distribution network is obtained by using the integrated network of the trained graph convolutional network and the bidirectional long-short-term memory network, and then the optimal adjustable capability of the virtual power plant is solved. According to the method, the optimal adjustable capability of the virtual power plant can be quickly calculated in a dynamic power distribution network structure.

Description

Technical Field

[0001] The present application relates to the field of virtual power plants, and in particular to a method and device for quickly evaluating the optimal adjustable capacity of a virtual power plant. Background Art

[0002] With the rapid development of renewable energy technology, the penetration rate of distributed resources with regulation potential, such as distributed photovoltaics, energy storage, temperature control loads, and electric vehicles, in the distribution network has increased significantly. However, distributed resources are small-scale, distributed, and heterogeneous, which poses a major challenge to the efficient use of the flexible regulation potential of individual distributed resources. Virtual Power Plant (VPP) has emerged as a feasible solution. It uses advanced communication and control technologies to aggregate distributed resources scattered in the distribution network into a whole, participate in power market transactions and grid ancillary services, and thus enhance the flexibility and stability of the grid. The determination of the adjustable capacity is a prerequisite for the participation of virtual power plants in ancillary services. However, virtual power plants usually contain a large number of distributed resources with different operating response characteristics, so evaluating the adjustable capacity of virtual power plants is a complex and challenging problem. In addition, virtual power plants must consider the impact of network topology on their adjustable capacity to prevent the issuance of unexecutable dispatching commands or endangering the security of the distribution network. In this case, the operational safety constraints related to the network topology will limit the regulation capacity of virtual power plants and hinder them from fully utilizing the regulation potential of distributed resources.

[0003] At present, the evaluation methods of the adjustable capacity of virtual power plants can be divided into bottom-up and top-down. Specifically, bottom-up methods, such as virtual energy storage modeling methods, involve a detailed description of the operating characteristics of each distributed resource, which is then gradually summarized through Minkowski summation to determine the overall adjustable capacity of all distributed resources. In addition, approximate evaluation methods including embedded hyperboxes, embedded isomorphic polyhedra, and Chino polyhedron transformations usually set specific shapes to approximate the adjustment capacity of virtual power plants, sacrificing certain flexibility to improve evaluation efficiency. However, these methods have difficulty in incorporating network topology constraints into the evaluation process. In contrast, top-down methods, such as Fourier-Motzkin elimination and vertex enumeration methods, are evaluated from a holistic perspective, essentially involving projections from high-dimensional polyhedra to low-dimensional feasible domain polyhedra. However, these methods usually generate a large number of new constraints or enumerate a large number of vertices, resulting in low computational efficiency.

[0004] Secondly, different network topologies lead to different power flows and voltage distributions, which are essential for maintaining power flow balance, ensuring voltage safety, and complying with line capacity constraints. These changes have a direct impact on the operational flexibility and regulation capabilities of virtual power plants. In some unreasonable network structures, the aggregation of distributed resources is more likely to cause problems such as line overload and voltage over-limit, making it difficult to fully utilize the regulation potential of distributed resources. Fortunately, most distribution networks have the ability to adjust their topology, thereby enabling network reconstruction strategies. The network reconstruction strategy adjusts the topology of the distribution network by changing the connection status of the power lines, and has been widely used in fault recovery, optimizing power flows, reducing network losses, and improving voltage. However, network reconstruction has not yet been applied to improve the adjustable capacity of virtual power plants. Taking advantage of the advantages of network reconstruction in improving the operational performance of the power grid may help to tap the maximum regulation capacity of virtual power plants under dynamic network topologies.

[0005] Finally, the network reconfiguration problem is usually formulated as a nonlinear integer programming problem, and its solution itself is very complex. The current solution methods are roughly divided into mathematical programming methods and heuristic algorithms. Branch and bound, dynamic programming, and second-order cone programming are the earliest mathematical programming methods that accurately solve the simple network topology reconfiguration problem. They do not depend on the initial network topology, but face the challenge of dimensionality explosion as the scale of the power grid increases. Heuristic algorithms, such as branch exchange algorithm and particle swarm algorithm, have slightly improved the solution efficiency, but it is difficult to guarantee the quality of the solution. In recent years, machine learning methods such as long short-term memory networks and graph convolutional networks have received increasing attention in solving optimization problems, especially voltage regulation and power flow calculation. Machine learning methods can not only achieve fast mapping from input data to output results, but also avoid errors caused by simplification or relaxation of traditional physical models. However, the application of machine learning methods to solve network reconfiguration to enhance the adjustability of virtual power plants remains to be explored.

[0006] The defects and difficulties in the above-mentioned virtual power plant optimal adjustable capacity evaluation process are mainly reflected in the following 1)-3).

[0007] 1) It is difficult to evaluate the adjustable capacity of virtual power plants: For the aggregation of the adjustable capacity of virtual power plants, bottom-up methods find it difficult to consider the relevant distribution network safety operation constraints caused by the network structure in the process of aggregating distributed resources, while the existing top-down methods cannot strike a balance between solution accuracy and computational efficiency.

[0008] 2) The adjustable potential of virtual power plants needs to be explored: Due to the need to ensure the safe operation of the distribution network, the related safety operation constraints brought by the network structure will limit the adjustable capacity of the virtual power plant. Especially in some network structures with unreasonable initial designs, there is a technical gap in the research on introducing network reconstruction to seek the maximum adjustable capacity of the virtual power plant.

[0009] 3) Consider the complexity of solving the network reconstruction model: The network reconstruction model is usually expressed as a nonlinear integer programming problem, which is relatively complex to solve. Traditional mathematical programming methods are difficult to guarantee the efficiency of solving the reconstruction strategy, and heuristic algorithms cannot ensure the quality of the solution. Summary of the invention

[0010] The purpose of this application is to provide a method and device for quickly evaluating the optimal adjustable capacity of a virtual power plant, so as to quickly calculate the optimal adjustable capacity of a virtual power plant in a dynamic distribution network structure.

[0011] To achieve the above objectives, this application provides the following solutions.

[0012] In the first aspect, the present application provides a method for rapidly evaluating the optimal adjustable capacity of a virtual power plant, including: establishing a two-layer evaluation architecture including a reconstruction layer and an evaluation layer; generating a distribution network reconstruction strategy for the distribution network under each historical operating state in the reconstruction layer, and transmitting it to the evaluation layer; in the evaluation layer, the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time are jointly formed into a historical state space; a breadth-first search method is used to perform dimensionality reduction projection on the historical state space, and a feasible domain of the output power of the virtual power plant under the distribution network reconstruction strategy received this time is obtained, and a calculation is performed. Calculate the feasible domain area this time; according to the feasible domain area this time, take the output power of the common coupling point in the received distribution network reconstruction strategy as the decision variable, calculate the target value when the distribution network reconstruction cost is minimized and the adjustable capacity is maximized, and transmit both the target value this time and the feasible domain area this time to the reconstruction layer; in the reconstruction layer, select the larger target value between the target value received this time and the target value received last time, and according to the feasible domain area corresponding to the larger target value, take the line connection state of the distribution network as the decision variable, obtain the distribution network reconstruction strategy when the distribution network reconstruction cost is minimized and the adjustable capacity is maximized, and transmit it to The evaluation layer returns to the step of executing the step of forming a historical state space of the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time in the evaluation layer, until the target values ​​received twice are equal, and the latest distribution network reconstruction strategy in the reconstruction layer is determined as the optimal distribution network reconstruction strategy under each historical operating state; with each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, the integrated network of the graph convolutional network and the bidirectional long short-term memory network is trained to obtain the strategy solution model; in the reconstruction layer, according to the current The optimal distribution network reconstruction strategy of the target distribution network is obtained by using the strategy solving model, and is transmitted to the evaluation layer; the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network together constitute the current state space; in the evaluation layer, a breadth-first search method is used to perform dimensionality reduction projection on the current state space to obtain the feasible domain of the output power of the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network, and the feasible domain area of ​​the target distribution network is calculated and transmitted to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

[0013] In the second aspect, the present application provides a device for quickly evaluating the optimal adjustable capacity of a virtual power plant, including: an establishment module, a strategy generation module, a state space composition module, a dimensionality reduction projection module, an area calculation module, a strategy update module, a training module, a model application module, a constraint integration module and an optimal adjustable capacity determination module.

[0014] Establish a module for establishing a two-layer evaluation architecture including a reconstruction layer and an evaluation layer. A strategy generation module for generating a distribution network reconstruction strategy for the distribution network under each historical operating state in the reconstruction layer and transmitting it to the evaluation layer. A state space construction module for forming a historical state space from the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time in the evaluation layer. A dimension reduction projection module for performing dimension reduction projection on the historical state space using a breadth-first search method to obtain the feasible domain of the output power of the virtual power plant under the distribution network reconstruction strategy received this time, and calculate the area of ​​the feasible domain this time.

[0015] The area calculation module is used to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity based on the feasible domain area of ​​this time and the output power of the common coupling point in the received distribution network reconstruction strategy as the decision variable, and transmit both the target value and the feasible domain area of ​​this time to the reconstruction layer.

[0016] The strategy updating module is used to select the larger target value between the target value received this time and the target value received last time in the reconstruction layer, and according to the feasible domain area corresponding to the larger target value, take the line connection state of the distribution network as the decision variable, obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit it to the evaluation layer, call the state space composition module, until the target values ​​received twice adjacently are equal, and determine the latest distribution network reconstruction strategy in the reconstruction layer as the optimal distribution network reconstruction strategy under each historical operating state.

[0017] The training module is used to take each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, train the integrated network of the graph convolutional network and the bidirectional long short-term memory network, and obtain the strategy solution model.

[0018] The model application module is used to obtain the optimal distribution network reconstruction strategy of the target distribution network in the reconstruction layer according to the current operating state of the target distribution network by using the strategy solving model, and transmit it to the evaluation layer.

[0019] The constraint integration module is used to combine the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network to form the current state space. The optimal adjustable capacity determination module is used to use the breadth-first search method in the evaluation layer to perform dimensionality reduction projection on the current state space, obtain the feasible domain of the virtual power plant output power under the optimal distribution network reconstruction strategy of the target distribution network, and calculate the feasible domain area of ​​the target distribution network, and transmit it to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

[0020] According to the specific embodiments provided in this application, this application has the following technical effects.

[0021] The present application provides a method and device for quickly evaluating the optimal adjustable capacity of a virtual power plant. In a two-layer evaluation architecture, the goal of the reconstruction layer is to determine the reconstruction strategy from a large number of feasible network topologies, while receiving the evaluation results (target values) fed back by the evaluation layer, and updating the distribution network reconstruction strategy through interactive iteration with the evaluation layer; the evaluation layer continuously receives the network structure determined by the reconstruction layer, while considering the node characteristic constraints and the network topology constraints, and adopts a vertex search-based virtual power plant adjustable capacity evaluation method to evaluate the feasible domain of the virtual power plant under a given topology until the optimal feasible domain is found; the graph convolutional network and the bidirectional long short-term memory network respectively extract the spatial dependency and long-term dependency of the input data, realize the mapping from the distribution network operation state to its optimal reconstruction strategy, and efficiently obtain the optimal network reconstruction strategy corresponding to the optimal adjustable capacity of the virtual power plant, thereby realizing the rapid calculation of the optimal adjustable capacity of the virtual power plant in the dynamic distribution network network structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flowchart of a method for rapidly evaluating the optimal adjustable capacity of a virtual power plant provided in one embodiment of the present application.

[0024] Figure 2 A schematic diagram of a framework of a method for rapidly evaluating the optimal adjustable capacity of a virtual power plant provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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 are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] In view of the defects and difficulties in the process of evaluating the optimal adjustable capacity of a virtual power plant, in an exemplary embodiment, Figure 1 As shown, the present application provides a method for quickly evaluating the optimal adjustable capacity of a virtual power plant, including the following steps 101 to 110.

[0028] Step 101: Establish a two-layer evaluation framework including a reconstruction layer and an evaluation layer.

[0029] Step 102: Generate a distribution network reconstruction strategy for the distribution network in each historical operating state in the reconstruction layer, and transmit it to the evaluation layer.

[0030] Step 103: At the evaluation layer, the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time are combined to form a historical state space.

[0031] Step 104: Use the breadth-first search method to perform dimensionality reduction projection on the historical state space, obtain the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time, and calculate the area of ​​the feasible domain this time.

[0032] Step 105: According to the feasible domain area of ​​this time, the output power of the common coupling point in the received distribution network reconstruction strategy is used as the decision variable to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit both the target value and the feasible domain area of ​​this time to the reconstruction layer.

[0033] The common coupling point refers to the common coupling point with the upper-level power grid.

[0034] Step 106: In the reconstruction layer, select the larger target value between the target value received this time and the target value received last time, and based on the feasible domain area corresponding to the larger target value, take the line connection state of the distribution network as the decision variable to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit it to the evaluation layer, return to execute step 103, until the target values ​​received twice adjacently are equal, and determine the latest distribution network reconstruction strategy in the reconstruction layer as the optimal distribution network reconstruction strategy under each historical operating state.

[0035] Step 107: Taking each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, an integrated network of a graph convolutional network and a bidirectional long short-term memory network is trained to obtain a strategy solution model.

[0036] Step 108: In the reconstruction layer, according to the current operating state of the target distribution network, the optimal distribution network reconstruction strategy of the target distribution network is obtained by using the strategy solution model, and transmitted to the evaluation layer.

[0037] Step 109: The node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network together constitute the current state space.

[0038] Step 110: In the evaluation layer, a breadth-first search method is used to perform dimensionality reduction projection on the current state space to obtain the feasible domain of the virtual power plant output power under the optimal distribution network reconstruction strategy of the target distribution network, and the feasible domain area of ​​the target distribution network is calculated and transmitted to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

[0039] This application defines the concept of a feasible domain representing the adjustable capacity of a virtual power plant, and considers node characteristics and network topology constraints at the same time. The breadth-first search method can be used to evaluate the feasible domain of the output power of a virtual power plant under a fixed topology; a two-layer evaluation architecture is used to explore the optimal adjustment capacity of a virtual power plant under a dynamic network topology. In the two-layer evaluation architecture, the goal of the reconstruction layer (also known as the upper layer) is to determine the reconstruction strategy from a large number of feasible network topologies, while receiving the evaluation results fed back by the evaluation layer (also known as the lower layer), and updating the reconstruction strategy through interactive iteration with the evaluation layer. The evaluation layer continuously receives the network structure determined by the upper layer, and uses a vertex search-based virtual power plant adjustable capacity evaluation method to evaluate the feasible domain of the virtual power plant under a given topology until the optimal feasible domain is found; finally, a data-driven fast solution model is established: the spatial dependency and long-term dependency of the input data are extracted respectively through graph convolution and bidirectional long short-term memory networks, and the mapping from the operating state of the distribution network to its optimal reconstruction strategy is realized, so as to efficiently obtain the optimal network reconstruction strategy corresponding to the optimal adjustment capacity of the virtual power plant. Among them, the long-term dependency refers to the relationship between the inputs that the model needs to consider when processing sequence data.

[0040] Although it is challenging to incorporate network reconstruction into the evaluation process of the feasible domain of virtual power plants, it is necessary to fully exploit the flexibility of distributed resources in virtual power plants. On the one hand, when the network topology is not ideal, the aggregation of distributed resources is more likely to violate the safe operation restrictions of the distribution network, such as line capacity or voltage safety constraints, resulting in reduced adjustability. On the other hand, determining the optimal feasible domain of the virtual power plant requires repeatedly solving the optimization problem until the termination condition is met, and each iteration must consider the network topology constraints. If the network topology is used as a variable in the evaluation process of the feasible domain of the virtual power plant, it is difficult to ensure the consistency of the network topology throughout the iteration process, which is likely to lead to erroneous evaluation results. To this end, a two-layer evaluation architecture is proposed. The two-layer evaluation architecture adjusts the distribution of the distribution network flow in combination with the distribution network reconstruction strategy, thereby improving the overall adjustability of the virtual power plant.

[0041] When considering the reconstruction of the distribution network, the reconstruction cost is an important economic indicator, which directly reflects the economic investment required for the adjustment of the network topology. Therefore, it is necessary to incorporate this economic indicator into the evaluation framework. In addition, improving the adjustable capacity of the virtual power plant is the ultimate goal of implementing the reconstruction strategy. To this end, the feasible domain area of ​​the virtual power plant is introduced as an evaluation indicator for selecting the optimal feasible domain. The larger the feasible domain area, the better the adjustable capacity of the virtual power plant. The optimal network topology should strike a balance between economy and flexibility. Therefore, the two-layer evaluation architecture comprehensively considers the distribution network reconstruction cost and the adjustable capacity of the virtual power plant in its objective function.

[0042] The reconstruction layer uses the line connection status as a decision variable to generate a feasible and economical network reconstruction strategy. Its goal is to continuously optimize the objective function based on the evaluation results fed back by the lower layer, update the reconstruction strategy, and pass the new network topology to the evaluation layer of the lower layer, and finally identify the optimal network topology from many feasible network topologies. In addition, the reconstruction layer records the optimal network topology obtained by the distribution network under different historical operating conditions. These data are then organized into a dataset for training an integrated network of graph convolutional networks and bidirectional long short-term memory networks, aiming to establish a fast mapping mechanism from the operating status of the distribution network to the optimal network topology.

[0043] Under the network topology given by the reconstruction layer, the evaluation layer takes the operating status of the distribution network as input, executes the virtual power plant adjustable capacity evaluation method based on breadth-first search, and obtains the target value and feasible domain area while satisfying the node characteristic constraints and the distribution network safe operation constraints. Then the target value and feasible domain area are fed back to the reconstruction layer to compare the advantages and disadvantages of different network reconstruction strategies, and the reconstruction strategy with a larger target value is given priority. In summary, the two-layer evaluation architecture determines the optimal network reconstruction strategy at the reconstruction layer through iterative interaction between the reconstruction layer and the evaluation layer, and evaluates the optimal feasible domain of the virtual power plant under the optimal network topology at the evaluation layer.

[0044] In another exemplary embodiment of the present application, the definition of the feasible domain is: the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant together constitute a state space , the state space is a high-dimensional state space. The state space is expressed as formula (1).

[0045] (1) In the formula, represents the state space; represents the projection variable, , and They represent the active power and reactive power of the virtual power plant at the common coupling point respectively; Represents internal decision variables and state variables; , All represent coefficient matrices; represents a constant vector; Represents 2-dimensional real number space, referring to is a two-dimensional vector; express dimensional real number space, referring to is a dimensional vector.

[0046] Then, Project to a projected variable The low-dimensional state space determined , which can be expressed as formula (2) to concisely and clearly represent the feasible domain of the virtual power plant on the PQ coupling plane.

[0047] (2) In the formula, The feasible region of the virtual power plant output power is described, and each point in the region can be achieved by at least one feasible combination of internal distributed resources without violating node characteristic constraints or network topology constraints. arrive The projection of describes the feasible domain of the virtual power plant, which represents the flexible and adjustable capacity of the virtual power plant at the common coupling point.

[0048] In another exemplary embodiment of the present application, the distributed resources in the virtual power plant are distributed on different distribution network nodes and have different operating characteristics, including distributed photovoltaic, energy storage, temperature control load and electric vehicles. Node characteristics refer to the specific attributes and behavioral characteristics of the distribution network nodes caused by the access of distributed resources. These characteristics determine the performance of the nodes when evaluating the adjustable capacity of the virtual power plant.

[0049] Node characteristic constraints include: photovoltaic node operation characteristics, energy storage operation characteristics, electric vehicle operation characteristics and temperature control load operation characteristics.

[0050] As a typical power resource, photovoltaics can adjust active or reactive power output through grid-connected inverters and have certain flexibility. The operating characteristics of photovoltaic nodes are shown in equations (3) to (5).

[0051] (3) (4) (5) In the formula, , and Respectively represent nodes Photovoltaic Active power, reactive power and grid-connected inverter capacity generated at all times; and Respectively represent nodes The upper and lower limits of the active power of photovoltaic power; and Respectively represent nodes The maximum power factor angle and the minimum power factor angle.

[0052] Energy storage can maintain the supply and demand balance of the distribution network through rapid charging and discharging, thereby improving the reliability and stability of the grid operation. The energy storage operation characteristics are shown in equations (6) to (10).

[0053] (6) (7) (8) (9) (10) In the formula, , and Respectively represent nodes Energy storage in Active power, reactive power and grid-connected inverter capacity at the moment; and Representation Node Energy storage in The charging and discharging power at each moment; , Respectively represent nodes The maximum charging power and minimum charging power of the energy storage; , Respectively represent nodes The maximum discharge power and minimum discharge power of the energy storage; Representation Node Energy storage in The state of charge at the moment, and Respectively represent nodes The upper and lower limits of the state of charge of the energy storage; and Respectively represent the charge and discharge efficiency; Indicates the time interval of the charging / discharging process; Representation Node Energy storage in State of charge at the moment.

[0054] Electric vehicles can dynamically adjust the charging time and charging power through reasonable control strategies and participate in the power regulation of the power grid. The operating characteristics of electric vehicles are as follows: (11)-(19).

[0055] (11) (12) (13) (14) (15) (16) (17) (18) (19) In the formula, Representation Node of electric vehicles in The energy state at any moment, and Representation Node of electric vehicles in The upper and lower limits of energy state at each moment; Representation Node The maximum energy state technically allowed for electric vehicles, and Representation Node The energy status of electric vehicles during the time of grid connection and off-grid connection; and They represent the grid-connected time and off-grid time of electric vehicles respectively; Representation Node The maximum charging power technically allowed for electric vehicles; Representation Node The technically permissible charging efficiency of electric vehicles; Representation Node of electric vehicles in The actual charging power at the moment, and Respectively represent nodes of electric vehicles in Maximum charging power and minimum charging power at the moment; and Respectively represent nodes of electric vehicles in The upper and lower limits of energy state at each moment; Representation Node of electric vehicles in The energy state at any moment; and Respectively represent nodes The surplus energy of electric vehicles when connected to the grid and the minimum energy required when off-grid.

[0056] Specifically, if an electric vehicle starts charging at the maximum charging power from the moment it is connected to the grid, The maximum energy state at the moment can be calculated by formula (11). Similarly, by ensuring the expected charging demand of the electric vehicle when it is disconnected from the grid, it can be inferred from formula (12) The minimum energy demand at the moment. The trajectories between these two charging trajectories and satisfying the charging power constraint are all feasible charging schemes for electric vehicles, which fully demonstrates its flexibility.

[0057] Temperature control loads can be controlled by adjusting the temperature at the node without affecting the user's comfort. Maximum temperature and nodes Minimum temperature The temperature set point is adjusted between the two periods to reduce the peak and fill the valley. An equivalent thermal parameter model of the temperature control load is established to describe the dynamic change of indoor temperature caused by the cooling power. The equivalent thermal parameter model is formula (20).

[0058] (20) in, Representation Node The temperature control load is Refrigeration power at the time; Representation Node exist The indoor temperature at the moment; Representation Node exist The outdoor ambient temperature at the moment; and They represent equivalent thermal resistance and equivalent heat capacity respectively; Indicates the cooling coefficient.

[0059] It can be seen from formula (20) that the change of indoor temperature is continuous, reflecting the change of room heat storage, so the temperature control load can be equivalently modeled as an energy storage device. Specifically, the increase or decrease of indoor temperature corresponds to the discharge or charge behavior of the energy storage device. exist The indoor temperature at the time is When , the storage energy and maximum storage capacity of the temperature control load are defined by equations (21) and (22) respectively. Therefore, the state of charge of the temperature control load is expressed by equation (23).

[0060] (twenty one) (twenty two) (twenty three) In the formula, Representation Node The temperature control load is The energy stored at all times, Representation Node The temperature control load is The maximum storage energy at the moment, Representation Node The temperature control load is State of charge at the moment.

[0061] Then, the equivalent thermal parameter model [see formula (20)] is approximated by the differential equation as formula (24). Substituting formula (23) into formula (24), the temperature control load regulation characteristics shown in formulas (25) to (28) can be obtained.

[0062] (twenty four) (25) (26) (27) (28) In the formula, Representation Node The temperature control load is The state of charge at the moment; Representation Node The temperature control load is Refrigeration power at the time; Representation Node exist The outdoor ambient temperature at the moment; , , , All represent characteristic parameters of the equivalent energy storage model of the temperature control load; and Respectively represent nodes The minimum cooling power and maximum cooling power of the temperature control load.

[0063] In another exemplary embodiment of the present application, in addition to node characteristic constraints, it is also necessary to consider network topology constraints, which include power flow balance constraints, voltage safety constraints, and line capacity constraints, to ensure the accuracy of the evaluation results.

[0064] The power flow balance constraints are as follows: (29) to (32).

[0065] (29) (30) (31) (32) In the formula, and Respectively represent nodes exist Active power and reactive power generated at all times; and Representation Node exist Active load and reactive load at the moment; and Indicated in Timeline Active power flow and reactive power flow on the and Node and nodes The resistance and reactance between Indicated in Timeline The square of the upper current; and Indicated in Timeline Active power flow and reactive power flow on the and Respectively represent nodes The collection of parent nodes and child nodes; Indicated in Time Node The voltage at Indicated in Time Node The voltage at represents the L2 norm.

[0066] The voltage safety constraint is expressed as formula (33).

[0067] (33) In the formula, and Representation Node exist The upper and lower limits of voltage at any given moment.

[0068] The line capacity constraints are as follows: (34)-(35).

[0069] (34) (35) In the formula, and Respectively The active power and reactive power of the virtual power plant at the common coupling point at each moment; Indicates line The line capacity limit on Represents the line capacity limit at the virtual power plant common coupling point.

[0070] In another exemplary embodiment of the present application, The calculation of is a high-dimensional polyhedron projection problem, and the projection result will also be a polyhedron. and are all bounded, so that and must be a bounded polyhedron. As a bounded polyhedron, and A finite set of vertices The constructed convex hull is fully characterized. The basic idea of ​​breadth-first search is to iteratively translate the cut faces of the approximate polyhedron outward to find new vertices. Then, the final The convex hull of , which represents the feasible domain of the virtual power plant on the PQ coupling plane. The key iterative process is as follows.

[0071] 1) Optimization problem M0: It is easy to observe that when the normalized outward normal vector of the cut surface is multiplied by any point on the cut surface, the result is always 1. If there are still undiscovered vertices outside this cut surface, there must be an optimal solution greater than 1 Therefore, considering the high-dimensional state space constraints composed of node feature constraints of distributed resources and network topology constraints, an optimization problem M0 is formulated to translate each face of the obtained polyhedron outward as much as possible to search for new vertices. Each optimal solution obtained by solving the optimization problem M0 represents the optimal vertex identified in the current state, as shown in Equation (36).

[0072] (36) in, Indicates along The width of the movement when searching for vertices; represents the unit outward normal vector of the tangent plane searching for new vertices, , and Respectively represent the direction vectors on the P axis and Q axis; express Nodes in the feasible region at time , which corresponds to the vertex search process Income The vertex of represents a vertex set, ,in and They represent the feasible domain of the virtual power plant. Active and reactive power at the point of common coupling.

[0073] 2) Termination condition: Introducing relative motion width To quantify the contribution of the newly identified vertices to the improvement of the existing convex hull shape as shown in formula (37).

[0074] (37) In the formula, Indicates along The maximum movement width when searching for vertices, It is defined as, Represents the norm.

[0075] Usually, assuming a sufficiently small threshold ,when , it indicates that the new vertex effectively improves the shape of the existing convex hull. In this case, save the vertex in If Set to zero, then you can identify All vertices of , thus obtaining the exact feasible region of the virtual power plant. Otherwise, a positive will result in The omission of some vertices in the convex hull makes little contribution to the improvement of the convex hull shape, thus obtaining a conservative approximation of the feasible region of the virtual power plant.

[0076] 3) Algorithm flow: First initialize the direction vector set and vertex set First, search for the initial vertices along the two ends of the PQ coupling plane coordinate axis, that is, initialize for Then, The direction vectors in Substitute into the optimization problem M0 and solve the corresponding initial optimal solution These initial solutions are stored directly in to complete its initialization.

[0077] Then, update the direction vector set Calculated by formula (38) The unit outward normal vector between all adjacent vertices in is stored as the direction vector for the next iteration search, thereby updating .

[0078] (38) Next, update the vertex set By solving the optimization problem M0, along All updated direction vectors in the search for new vertices. Then, compare the corresponding relative motion widths and .if Greater than , then save the vertex and update Repeat the above two steps until all All less than . At this point, the termination condition is met, the search process is stopped, and the optimal .

[0079] Finally, find the optimal The convex hull of determines the feasible domain of the virtual power plant on the PQ coupling plane, that is, the adjustable capacity of the virtual power plant at the common coupling point.

[0080] In the above step 104, the breadth-first search method is used to perform dimensionality reduction projection on the historical state space to obtain the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time, which can be specifically summarized as the following steps 201 to 204.

[0081] Step 201: Determine the optimization problem as: .

[0082] Step 202: Establish the termination condition: Less than threshold .

[0083] Step 203: According to the optimization problem and the termination condition, a breadth-first search method is used to perform dimension reduction projection on the historical state space to obtain an optimal vertex set.

[0084] Step 204: Calculate the convex hull of the optimal vertex set and determine it as the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time.

[0085] In another exemplary embodiment of the present application, the reconstruction layer and the evaluation layer interact and coordinately optimize through objective functions [see equations (39) and (40)] to ultimately determine the optimal network topology.

[0086] (39) (40) (41) (42) in, It represents the target value when the distribution network reconstruction cost is the minimum and the adjustable capacity is the maximum in the evaluation layer; It represents the target value when the distribution network reconstruction cost is minimized and the adjustable capacity is maximized in the reconstruction layer. and are weight coefficients, and ; represents the reconstruction cost, represents the cost of a single line switch operation, represents the set of common nodes in the distribution network, and Respectively indicate lines exist Moment and A binary variable that indicates the switch state at the moment, where Indicates line The switch on is closed, Indicates the switch is on; represents the feasible region area of ​​this time; and They represent the minimum cost and maximum cost of network reconstruction under the initial network topology respectively; and They respectively represent the minimum feasible domain area and the maximum feasible domain area of ​​the virtual power plant under the initial network topology. Represents the feasible region area corresponding to the larger target value.

[0087] Specifically, the upper layer uses the line connection status as a decision variable to generate a feasible and economical network reconstruction strategy. Its goal is to continuously optimize the objective function based on the evaluation results fed back by the lower layer, update the reconstruction strategy, and pass the new network topology to the evaluation layer of the lower layer, and finally identify the optimal network topology from many feasible network topologies. In addition, the upper layer records the optimal network topology obtained by the distribution network under different historical operating conditions. These data are then organized into a data set for training data-driven solution methods, aiming to establish a fast mapping mechanism from the operating status of the distribution network to the optimal network topology. In order to ensure that the distribution network still maintains a radial network structure after reconstruction, the radial network structure constraints are formulated as Equations (43)-(46).

[0088] (43) (44) (45) (46) In the formula, represents a binary variable, Representation Node Is a node The parent node of Representation Node Not a node The parent node of Represents the set of generator nodes. Equation (43) shows that the reconstructed network topology still needs to be a tree structure. Equation (44) ensures that in each branch, only one node can be the parent node of another node. Equation (45) restricts each node to have at most one parent node. Equation (46) specifies that generator nodes cannot be parent nodes.

[0089] Under the network topology given by the reconstruction layer, the evaluation layer takes the operating status of the distribution network as input and executes the virtual power plant adjustable capacity evaluation method based on breadth-first search. Under the condition of satisfying the node characteristic constraints [see equations (3)-(28)] and the distribution network safe operation constraints [see equations (29)-(35)], the evaluation index is obtained. The evaluation index is then fed back to the reconstruction layer to compare the advantages and disadvantages of different network reconstruction strategies, giving priority to those with greater In summary, the two-layer evaluation architecture determines the optimal network reconstruction strategy in the upper reconstruction layer through iterative interaction between the reconstruction layer and the evaluation layer, and evaluates the optimal feasible domain of the virtual power plant under the optimal network topology in the lower evaluation layer.

[0090] Then the above step 105 can be replaced by the following steps 301 to 302.

[0091] Step 301: Determine the objective function of the evaluation layer as: .

[0092] Step 302: According to the feasible region area of ​​this time, the active power and reactive power of the common coupling point in the received distribution network reconstruction strategy are used as decision variables, and the objective function of the evaluation layer is used to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity.

[0093] In the above step 106, according to the feasible region area corresponding to the larger target value, the line connection state of the distribution network is used as the decision variable to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, which can be replaced by the following steps 401 to 403.

[0094] Step 401: Determine the objective function of the reconstruction layer as: .

[0095] Step 402: Establish radial network structure constraints as follows: ; ; ; .

[0096] Step 403: According to the feasible region area corresponding to the larger target value, the line connection status of the distribution network is used as the decision variable, and the objective function of the reconstruction layer and the radial network structure constraints are used to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity.

[0097] In another exemplary embodiment of the present application, efficiently identifying the optimal strategy from a large number of feasible network reconstruction strategies is the key to evaluating the optimal feasible domain of a virtual power plant. Utilizing the graph structure characteristics of the distribution network, the graph convolutional network can effectively process such graph structure data and extract key spatial dependencies. In addition, by introducing a bidirectional long short-term memory network, the long-term dependencies of the input data can also be captured, thereby more comprehensively describing the reconstruction behavior of the distribution network. Therefore, the graph convolutional network and the bidirectional long short-term memory network are integrated together to propose an integrated network, which includes a spatial convolution module, a sequence learning module, and a fully connected layer connected in sequence. Taking the operating state of the distribution network as input data, the mapping relationship between the operating state of the distribution network and the optimal network reconstruction strategy is constructed by extracting spatial dependencies and long-term dependencies from the input data. The process of solving the optimal distribution network reconstruction strategy using the integrated network becomes a data-driven optimal reconstruction strategy fast solution method.

[0098] (1) Spatial convolution module.

[0099] The spatial convolution module consists of multiple graph convolutional network layers. Through multiple layers of convolution operations, the spatial convolution module gradually obtains the spatial dependencies between distribution network nodes, which helps to explore how network topology changes affect the operating status of the distribution network and the adjustable capacity of the virtual power plant, thereby providing support for determining network reconstruction strategies. For each graph convolutional network layer, the distribution network is initialized as an undirected weighted graph ,in represents an undirected weighted graph, Indicates the power distribution network A collection of nodes, represents the set of power lines, is the adjacency matrix, Represents the adjacency matrix is a N × N The admittance matrix of the distribution network is used as the adjacency matrix , to effectively characterize the connection strength and mutual influence between nodes. Input into the graph convolutional network, where represents the input feature matrix, It is The input feature matrix of the layer graph convolutional network is composed of data related to the operating status of the distribution network, including the power distribution, voltage distribution, location and operating status of the distributed resources connected to the distribution network. represents the dimension of the feature matrix, express is a N × D dimensional matrix. The convolution operation of the layer graph convolutional network can be expressed as formula (47).

[0100] (47) In the formula, , is the identity matrix; is a diagonal matrix, indicating The degree matrix of represents the adjacency matrix after adding the self-loop; For the Trainable weight matrices for layer graph convolutional networks; is a non-linear activation function.

[0101] Assumptions is the output of the spatial convolution module, and the convolution operation of multiple graph convolutional network layers can be expressed as formula (48).

[0102] (48) (2) Sequence learning module.

[0103] The sequence learning module consists of multiple bidirectional long short-term memory network layers, which is designed to further capture the output of the spatial convolution module. long-term dependencies, where The spatial dependency is already included. The bidirectional LSTM network integrates the forward LSTM network and the backward LSTM network, where each LSTM network consists of a memory unit and three gates. For each LSTM network, the input sequence It is the sequence data of the distribution network operation status, and the hidden state Represents the corresponding historical optimal reconstruction strategy. , Input Gate and output gate can be obtained from the previous hidden state and the current input Improved. All gates will selectively move to unit status In addition, the unit state It will also combine the previous state and Then, the current optimal reconstruction strategy and Will be periodically fed into the next layer. Using the bidirectional structure, the sequence learning module can extract forward and backward information from the input data at the same time. Specifically, the sequence learning module can not only use the forward data to analyze the mapping relationship between the distribution network operation status and the optimal reconstruction strategy, but also improve the mapping mechanism through the backward data. This bidirectional capability can improve the ability to capture long-term dependencies, as shown in the following equations (49)-(57).

[0104] (49) (50) (51) (52) (53) (54) (55) (56) (57) In the formula, represents a candidate memory unit; , , and is the weight matrix corresponding to each gate; , , and is the bias matrix; and Respectively represent the output of the forward long short-term memory network and the backward long short-term memory network; LSTM The steps defined by equations (49) to (54) are expressed as follows; , and It is the trainable weight matrix and bias matrix when combining the forward and backward long short-term memory networks.

[0105] (3) Fully connected layer.

[0106] The hidden state of the last bidirectional LSTM network layer is expressed as , as the output of the sequence learning module, and is fed to the fully connected layer. At this stage, the spatial dependency and long-term dependency of the input data are extracted and integrated by the spatial convolution module and the sequence learning module respectively. A binary sequence is generated using the fully connected layer, which represents the control decision of the branch switch, thereby converting the spatial dependency and long-term dependency of the input data into a binary sequence. Mapping to the optimal network reconstruction strategy , as shown in formula (58). Subsequently, the obtained optimal network topology is passed to the lower layer of the two-layer evaluation architecture, and the virtual power plant adjustable capacity evaluation method based on breadth-first search is executed in the lower layer to determine the feasible domain with the largest area of ​​the virtual power plant, thereby completing the evaluation of the optimal adjustable capacity of the virtual power plant.

[0107] (58) In the formula, and They represent the trainable weight matrix and bias matrix of the fully connected layer respectively. represents a non-linear activation function.

[0108] The key points of this application are as follows.

[0109] 1. A method for evaluating the adjustable capacity of virtual power plants based on breadth-first search is proposed: first, the concept of the feasible domain representing the adjustable capacity of virtual power plants is defined, and then the feasible domain of virtual power plants on the PQ coupling plane, i.e., the adjustable capacity of virtual power plants, is evaluated under a fixed topology by considering both node characteristic constraints and network topology constraints.

[0110] 2. A two-layer virtual power plant optimal feasible domain evaluation framework considering network reconstruction is proposed to explore the optimal adjustable capacity of virtual power plants under dynamic network topology. Through continuous interactive iteration between the upper and lower layers, the reconstruction strategy is continuously updated, and the virtual power plant adjustable capacity evaluation method based on breadth-first search is used to calculate the feasible domain of the virtual power plant under the current topology until the optimal feasible domain is found.

[0111] 3. A data-driven optimal reconstruction strategy fast solution method is proposed. The spatial dependency and long-term dependency of the input feature data are extracted through a graph convolutional network and a bidirectional long short-term memory network, respectively, to achieve a fast mapping from the distribution network operation state to its optimal reconstruction strategy, so as to efficiently obtain the optimal network reconstruction strategy corresponding to the optimal regulation capacity of the virtual power plant.

[0112] It is a very complex problem to accurately evaluate the adjustable capacity of virtual power plants while considering both node characteristics and network structure. Traditional evaluation methods either fail to consider network topology constraints during the evaluation process, or fail to balance evaluation accuracy and computational efficiency; unreasonable network structures are more likely to cause line overloads, voltage over-limits and other problems during the aggregation of distributed resources, so that distributed resources are difficult to fully exert their adjustable potential. Therefore, it is considered to introduce network reconstruction to improve the maximum adjustable capacity of virtual power plants; the network reconstruction problem is essentially a nonlinear integer programming problem and is difficult to solve directly. Traditional solution methods either fail to solve complex networks or have difficulty in ensuring the quality of the solution. The above problems put forward higher requirements for evaluating the adjustable capacity of virtual power plants while considering node characteristics and network structure.

[0113] In view of the defects and difficulties in the above-mentioned virtual power plant optimal adjustable capacity evaluation process, the present application can quickly calculate the optimal adjustable capacity of the virtual power plant in a dynamic network structure. The proposed virtual power plant adjustable capacity evaluation method based on breadth-first search can not only consider the node characteristics and network topology constraints in the evaluation process, but also ensure that the adjustable capacity of the virtual power plant is accurately solved in a shorter time. Then, considering the potential restrictions of network topology constraints on the adjustable capacity of virtual power plants, a two-layer virtual power plant optimal feasible domain evaluation architecture considering network reconstruction is proposed to deeply explore the regulation potential of distributed resources and seek the optimal adjustable capacity of virtual power plants under dynamic network topology. Finally, a data-driven optimal reconstruction strategy fast solution method is proposed, which uses graph convolutional networks and bidirectional long short-term memory networks to extract the spatial dependency and long-term dependency of input data respectively, realizes the mapping from the operating state of the distribution network to its optimal reconstruction strategy, and efficiently obtains the optimal network reconstruction strategy corresponding to the optimal regulation capacity of the virtual power plant, thereby realizing the rapid evaluation of the optimal regulation capacity of the virtual power plant.

[0114] Based on the same inventive concept, the embodiment of the present application also provides a virtual power plant optimal adjustable capacity rapid assessment device for implementing the above-mentioned virtual power plant optimal adjustable capacity rapid assessment method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more virtual power plant optimal adjustable capacity rapid assessment device embodiments provided below can refer to the above-mentioned limitations on the virtual power plant optimal adjustable capacity rapid assessment method, which will not be repeated here.

[0115] In an exemplary embodiment, a virtual power plant optimal adjustable capacity rapid assessment device is provided, including: an establishment module, a strategy generation module, a state space construction module, a dimensionality reduction projection module, an area calculation module, a strategy update module, a training module, a model application module, a constraint integration module and an optimal adjustable capacity determination module.

[0116] Establish a module for establishing a two-layer evaluation architecture including a reconstruction layer and an evaluation layer. A strategy generation module for generating a distribution network reconstruction strategy for the distribution network under each historical operating state in the reconstruction layer and transmitting it to the evaluation layer. A state space construction module for forming a historical state space from the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time in the evaluation layer. A dimension reduction projection module for performing dimension reduction projection on the historical state space using a breadth-first search method to obtain the feasible domain of the output power of the virtual power plant under the distribution network reconstruction strategy received this time, and calculate the area of ​​the feasible domain this time.

[0117] The area calculation module is used to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity based on the feasible domain area of ​​this time and the output power of the common coupling point in the received distribution network reconstruction strategy as the decision variable, and transmit both the target value and the feasible domain area of ​​this time to the reconstruction layer.

[0118] The strategy updating module is used to compare the target value received this time with the target value received last time in the reconstruction layer, and according to the feasible domain area with a larger target value, take the line connection state of the distribution network as the decision variable to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit it to the evaluation layer, call the state space composition module, until the target values ​​received twice adjacently are equal, and determine the latest distribution network reconstruction strategy in the reconstruction layer as the optimal distribution network reconstruction strategy under each historical operating state.

[0119] The training module is used to take each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, train the integrated network of the graph convolutional network and the bidirectional long short-term memory network, and obtain the strategy solution model.

[0120] The model application module is used to obtain the optimal distribution network reconstruction strategy of the target distribution network in the reconstruction layer according to the current operating state of the target distribution network by using the strategy solving model, and transmit it to the evaluation layer.

[0121] The constraint integration module is used to combine the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network to form the current state space. The optimal adjustable capacity determination module is used to use the breadth-first search method in the evaluation layer to perform dimensionality reduction projection on the current state space, obtain the feasible domain of the virtual power plant output power under the optimal distribution network reconstruction strategy of the target distribution network, and calculate the feasible domain area of ​​the target distribution network, and transmit it to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

[0122] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for rapidly evaluating the optimal adjustable capacity of a virtual power plant, characterized in that: include: Establish a two-layer evaluation framework including reconstruction layer and evaluation layer; A distribution network reconstruction strategy is generated in the reconstruction layer under each historical operation state of the distribution network, and transmitted to the evaluation layer; At the evaluation layer, the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time are combined to form a historical state space; The breadth-first search method is used to reduce the dimension of the historical state space, obtain the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time, and calculate the area of ​​the feasible domain this time; According to the feasible area of ​​this time, the output power of the common coupling point in the received distribution network reconstruction strategy is used as the decision variable to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit the target value and the feasible area of ​​this time to the reconstruction layer; In the reconstruction layer, the larger target value between the target value received this time and the target value received last time is selected, and according to the feasible domain area corresponding to the larger target value, the line connection state of the distribution network is used as the decision variable to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit it to the evaluation layer, and return to execute the step of forming a historical state space of the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time in the evaluation layer, until the target values ​​received twice adjacently are equal, and the latest distribution network reconstruction strategy in the reconstruction layer is determined as the optimal distribution network reconstruction strategy under each historical operating state; Taking each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, an integrated network of graph convolutional network and bidirectional long short-term memory network is trained to obtain a strategy solution model; In the reconstruction layer, according to the current operating state of the target distribution network, the optimal distribution network reconstruction strategy of the target distribution network is obtained by using the strategy solution model, and transmitted to the evaluation layer; The node characteristic constraints and network topology constraints of distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network together constitute the current state space; In the evaluation layer, the breadth-first search method is used to perform dimensionality reduction projection on the current state space to obtain the feasible domain of the virtual power plant output power under the optimal distribution network reconstruction strategy of the target distribution network, and the feasible domain area of ​​the target distribution network is calculated and transmitted to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

2. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The node characteristic constraints include: photovoltaic node operation characteristics, energy storage operation characteristics, electric vehicle operation characteristics and temperature control load operation characteristics; The operating characteristics of the photovoltaic node are: ； ； ； In the formula, , and Respectively represent nodes Photovoltaic Active power, reactive power and grid-connected inverter capacity generated at all times; and Respectively represent nodes The upper and lower limits of the active power of photovoltaic power; and Respectively represent nodes The maximum power factor angle and the minimum power factor angle; The energy storage operation characteristics are: ； ； ； ； ； In the formula, , and Respectively represent nodes Energy storage in Active power, reactive power and grid-connected inverter capacity at the moment; and Representation Node Energy storage in The charging and discharging power at the moment; , Respectively represent nodes The maximum charging power and minimum charging power of the energy storage; , Respectively represent nodes The maximum discharge power and minimum discharge power of the energy storage; Representation Node Energy storage in The state of charge at the moment, and Respectively represent nodes The upper and lower limits of the state of charge of the energy storage; and Respectively represent the charge and discharge efficiency; Indicates the time interval of the charging / discharging process; Representation Node Energy storage in The state of charge at the moment; The operating characteristics of electric vehicles are: ； ； ； ； ； ； ； ； ； In the formula, Representation Node of electric vehicles in The energy state at any moment, and Representation Node of electric vehicles in The upper and lower limits of energy state at each moment; Representation Node The maximum energy state technically allowed for electric vehicles, and Representation Node The energy status of electric vehicles during the time of grid connection and off-grid connection; and They represent the grid-connected time and off-grid time of electric vehicles respectively; Representation Node The maximum charging power technically allowed for electric vehicles; Representation Node The technically permissible charging efficiency of electric vehicles; Representation Node of electric vehicles in The actual charging power at the moment, and Respectively represent nodes of electric vehicles in Maximum charging power and minimum charging power at the moment; and Respectively represent nodes of electric vehicles in The upper and lower limits of energy state at each moment; Representation Node of electric vehicles in The energy state at any moment; and Respectively represent nodes The remaining energy of electric vehicles when connected to the grid and the minimum energy required when off-grid; The operating characteristics of temperature control load are: ； ； ； ； In the formula, Representation Node The temperature control load is The state of charge at the moment, Representation Node The temperature control load is The state of charge at the moment; Representation Node The temperature control load is The cooling power at the time, Representation Node The temperature control load is Refrigeration power at the time; Representation Node exist The outdoor ambient temperature at the moment; , , , All represent characteristic parameters of the equivalent energy storage model of the temperature control load; and They represent equivalent thermal resistance and equivalent heat capacity respectively; It indicates the cooling coefficient; and Respectively represent nodes the maximum and minimum temperatures; and Respectively represent nodes The minimum cooling power and maximum cooling power of the temperature control load; Representation Node exist The indoor temperature at the moment.

3. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The network topology constraints include: power flow balance constraints, voltage safety constraints and line capacity constraints; The power flow balance constraint is: ； ； ； ； In the formula, and Respectively represent nodes exist Active power and reactive power generated at all times; and Representation Node exist Active load and reactive load at the moment; and Indicated in Timeline Active power flow and reactive power flow on the and Node and nodes The resistance and reactance between Indicated in Timeline The square of the upper current; and Indicated in Timeline Active power flow and reactive power flow on the and Respectively represent nodes The collection of parent nodes and child nodes; Indicated in Time Node The voltage at Indicated in Time Node The voltage at represents the L2 norm; The voltage safety constraint is: ； In the formula, and Representation Node exist The upper and lower limits of voltage at all times; The line capacity constraint is: ； ； In the formula, and Respectively The active power and reactive power of the virtual power plant at the common coupling point at each moment; Indicates line The line capacity limit on Represents the line capacity limit at the virtual power plant common coupling point.

4. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The state space is represented as: ； In the formula, represents the state space; represents the projection variable, , and They represent the active power and reactive power of the virtual power plant at the common coupling point respectively; Represents internal decision variables and state variables; , All represent coefficient matrices; represents a constant vector; Represents 2-dimensional real number space, referring to is a two-dimensional vector; express dimensional real number space, referring to is a dimensional vector.

5. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The breadth-first search method is used to reduce the dimension of the historical state space and obtain the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time, including: The optimization problem is determined as: ; In the formula, Indicates along The width of the movement when searching for vertices; represents the unit outward normal vector of the tangent plane searching for new vertices, , and Respectively represent the direction vectors on the P axis and Q axis; express Nodes in the feasible region at time , at this time node Income during vertex search The vertex of represents a vertex set, , and Respectively In the feasible domain of virtual power plant Active and reactive power at the point of common coupling; represents the feasible domain of the output power of the virtual power plant; represents the projection variable; The termination condition is established as: Less than threshold ;in, Indicates the relative motion width; According to the optimization problem and the termination condition, a breadth-first search method is used to perform dimension reduction projection on the historical state space to obtain an optimal vertex set; The convex hull of the optimal vertex set is calculated and determined as the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time.

6. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The calculation formula of the feasible region area is: ； In the formula, represents the feasible region area; and Respectively Moment and In the feasible domain of virtual power plant Active power at the point of common coupling, express Nodes in the feasible region at time , at this time node Income during vertex search The vertex of represents a vertex set, ; and Respectively Moment and In the feasible domain of virtual power plant Reactive power at the point of common coupling; det represents the determinant of the matrix.

7. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: According to the feasible area of ​​this time, the output power of the common coupling point in the received distribution network reconstruction strategy is used as the decision variable to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, including: The objective function of the evaluation layer is determined as: ; In the formula, It represents the target value when the distribution network reconstruction cost is the minimum and the adjustable capacity is the maximum in the evaluation layer; and are weight coefficients, and ; represents the reconstruction cost, , represents the cost of a single line switch action, represents the set of common nodes in the distribution network, and Respectively indicate lines exist Moment and A binary variable that indicates the switch state at the moment, where Indicates line The switch on is closed, Indicates the switch is on; represents the feasible region area of ​​this time; and They represent the minimum cost and maximum cost of network reconstruction under the initial network topology respectively; and They represent the minimum feasible area and the maximum feasible area of ​​the virtual power plant under the initial network topology respectively; and They represent the feasible domain of the virtual power plant. Active and reactive power at the point of common coupling, express Nodes in the feasible region at time , at this time node Income during vertex search The vertex of represents a vertex set, ; According to the feasible region area, the active power and reactive power of the common coupling point in the received distribution network reconstruction strategy are taken as decision variables, and the objective function of the evaluation layer is used to calculate the target value that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity.

8. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: According to the feasible region area corresponding to the larger target value, the line connection state of the distribution network is used as the decision variable to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, which specifically includes: The objective function of the reconstruction layer is determined as: ; In the formula, It represents the target value when the distribution network reconstruction cost is minimum and the adjustable capacity is maximum in the reconstruction layer; and are weight coefficients, and ; represents the reconstruction cost, , represents the cost of a single line switch action, represents the set of common nodes in the distribution network, and Respectively indicate lines exist Moment and A binary variable that indicates the switch state at the moment, where Indicates line The switch on is closed, Indicates the switch is on; represents the feasible region area of ​​this time; and They represent the minimum cost and maximum cost of network reconstruction under the initial network topology respectively; and They represent the minimum feasible area and the maximum feasible area of ​​the virtual power plant under the initial network topology respectively; Indicates the feasible region area corresponding to the larger target value; The constraints for establishing a radial network structure are: ； ； ； ； In the formula, represents a binary variable, Representation Node Is a node The parent node of Representation Node Not a node The parent node of represents the set of generator nodes; According to the feasible region area corresponding to the larger target value, the line connection status of the distribution network is taken as the decision variable, and the objective function of the reconstruction layer and the radial network structure constraints are utilized to obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity.

9. The method for rapidly evaluating the optimal adjustable capacity of a virtual power plant according to claim 1, characterized in that: The integrated network includes a spatial convolution module, a sequence learning module and a fully connected layer connected in sequence; The spatial convolution module includes a plurality of graph convolution network layers connected in sequence; the spatial convolution module is used to obtain the spatial dependency relationship between the nodes of the distribution network and output the spatial dependency features; The sequence learning module includes a plurality of bidirectional long short-term memory network layers connected in sequence; the sequence learning module is used to capture the long-term dependency relationship of the spatial dependency features output by the spatial convolution module, and output the spatial and long-term dependency features; The fully connected layer is used to calculate the spatial and long-term dependency features using the formula , output the optimal distribution network reconstruction strategy; where, represents the optimal distribution network reconstruction strategy, represents a nonlinear activation function, Represents spatial and long-term dependency features, and They represent the trainable weight matrix and bias matrix of the fully connected layer respectively.

10. A device for rapidly evaluating the optimal adjustable capacity of a virtual power plant, characterized in that: The virtual power plant optimal adjustable capacity rapid assessment device comprises: Establishing a module for establishing a two-layer evaluation framework including a reconstruction layer and an evaluation layer; A strategy generation module is used to generate a distribution network reconstruction strategy for the distribution network under each historical operating state in the reconstruction layer and transmit it to the evaluation layer; The state space construction module is used to construct a historical state space by combining the node characteristic constraints and network topology constraints of the distributed resources in the virtual power plant under the distribution network reconstruction strategy received this time at the evaluation layer; The dimension reduction projection module is used to perform dimension reduction projection on the historical state space using a breadth-first search method, obtain the feasible domain of the virtual power plant output power under the distribution network reconstruction strategy received this time, and calculate the area of ​​the feasible domain this time; The area calculation module is used to calculate the target value when the distribution network reconstruction cost is minimized and the adjustable capacity is maximized according to the feasible domain area of ​​this time and the output power of the common coupling point in the received distribution network reconstruction strategy as the decision variable, and transmit the target value and the feasible domain area of ​​this time to the reconstruction layer; The strategy updating module is used to select the larger target value between the target value received this time and the target value received last time in the reconstruction layer, and according to the feasible domain area corresponding to the larger target value, take the line connection state of the distribution network as the decision variable, obtain the distribution network reconstruction strategy that minimizes the distribution network reconstruction cost and maximizes the adjustable capacity, and transmit it to the evaluation layer, call the state space composition module, until the target values ​​received twice adjacently are equal, and determine the latest distribution network reconstruction strategy in the reconstruction layer as the optimal distribution network reconstruction strategy under each historical operating state; A training module is used to take each historical operating state as input and the optimal distribution network reconstruction strategy under each historical operating state as a label, train an integrated network of a graph convolutional network and a bidirectional long short-term memory network, and obtain a strategy solution model; A model application module is used to obtain the optimal distribution network reconstruction strategy of the target distribution network by using the strategy solving model according to the current operating state of the target distribution network in the reconstruction layer, and transmit it to the evaluation layer; A constraint integration module is used to combine the node characteristic constraints and network topology constraints of distributed resources in the virtual power plant under the optimal distribution network reconstruction strategy of the target distribution network to form the current state space; The optimal adjustable capacity determination module is used to use the breadth-first search method in the evaluation layer to perform dimensionality reduction projection on the current state space, obtain the feasible domain of the virtual power plant output power under the optimal distribution network reconstruction strategy of the target distribution network, and calculate the feasible domain area of ​​the target distribution network, and transmit it to the reconstruction layer; the feasible domain area of ​​the target distribution network represents the optimal adjustable capacity of the virtual power plant.

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