Cooperative resource allocation method for virtual power plant cluster and power distribution network

Abstract

A collaborative resource allocation method for a virtual power plant cluster and a power distribution network comprises the steps that a virtual power plant cluster-power distribution network joint operation framework is constructed, a double-layer optimization model is constructed, and an upper-layer optimization model formulates an electric energy and carbon quota P2P transaction plan through a cooperative game between virtual power plants; the lower-layer optimization model solves the optimal power flow of the power distribution network based on the transaction plan and feeds back the electricity-carbon comprehensive price; and a cooperative game model is constructed, and the double-layer optimization model is solved by adopting an alternating direction multiplier algorithm, so that cooperative resource allocation is realized. According to the method, a joint operation framework of the virtual power plant cluster and the power distribution network is constructed, and a double-layer optimization model and an asymmetric Nash bargaining theory are combined, so that P2P transaction of electric energy and carbon quota is realized, and the optimal power flow of the power distribution network is optimized.

Description

Technical Field

[0001] The present invention relates to a technology in the field of power grid resource configuration, specifically a collaborative resource configuration method for a virtual power plant cluster and a distribution network. Background Art

[0002] Existing market-driven decision-making techniques within power-carbon synergy mechanisms suffer from the following limitations: First, they primarily focus on the power generation side, lacking in-depth exploration of user-side participation mechanisms such as virtual power plants. Second, they rarely incorporate interactive constraints within the distribution network into decision-making models, failing to fully capture the overall synergy of the power system. Existing techniques, when applying cooperative game theory to optimize the shared interests of groups, primarily focus on maximizing overall benefits, but fail to fully consider the differentiated contributions of each participant in the collaborative process. This results in a lack of precision and fairness in the benefit distribution mechanism, making it difficult to effectively motivate and motivate the collaborating parties. Summary of the Invention

[0003] In response to the above-mentioned shortcomings of the existing technology, the present invention proposes a collaborative resource allocation method for virtual power plant clusters and distribution networks. By constructing a joint operation framework for virtual power plant clusters and distribution networks, combined with a two-layer optimization model and asymmetric Nash bargaining theory, P2P trading of electricity and carbon quotas is realized, and the optimal power flow of the distribution network is optimized.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a collaborative resource allocation method for a virtual power plant cluster and a distribution network. By constructing a virtual power plant cluster-distribution network joint operation framework and a two-layer optimization model, the upper-layer optimization model formulates a P2P trading plan for electricity and carbon quotas through cooperative games between virtual power plants, and the lower-layer optimization model solves the optimal power flow of the distribution network based on the trading plan and feeds back the comprehensive price of electricity and carbon. Then, by constructing a cooperative game model, the alternating direction multiplier algorithm is used to solve the two-layer optimization model to achieve collaborative resource allocation.

[0006] Technical Effects

[0007] The present invention provides a systematic solution for multi-agent interaction in the electricity-carbon collaborative market by considering the two-layer optimization modeling and solution of cooperative game and coordinated operation of distribution network, thereby improving the overall operational efficiency. Through the two-layer optimization model and asymmetric Nash bargaining theory, the differentiated contribution of each member in the virtual power plant cluster in the transaction of electricity and carbon quota is fully considered to optimize resource allocation. The introduction of asymmetric Nash bargaining theory ensures the fairness of the internal distribution of benefits of the virtual power plant cluster and enhances the stability and sustainability of cooperation. Safe and economical operation of the distribution network: The lower-level optimization model solves the optimal power flow of the distribution network based on the transaction plan, and feeds back the comprehensive price of electricity and carbon to ensure the safety and economic operation efficiency of the distribution network. The improved alternating multiplier algorithm is used for efficient solution to improve the computational efficiency and practicality of the model, which is suitable for the collaborative optimization of large-scale virtual power plant clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 To implement the virtual power plant cluster joint electricity carbon market operation framework;

[0009] Figure 2 This is a schematic diagram of a virtual power plant cluster connected to a distribution network in an embodiment. DETAILED DESCRIPTION

[0010] like Figure 1 and Figure 2 As shown, this embodiment relates to a collaborative resource configuration method for a virtual power plant cluster and a distribution network based on asymmetric Nash bargaining, including:

[0011] Step 1: Construct a virtual power plant cluster joint operation framework model, including:

[0012] 1.1 Virtual Power Plants and Clusters: A single virtual power plant manages local distributed resources, including distributed renewable energy sources (such as wind and photovoltaic power), controllable generators (such as gas turbines), local energy storage, flexible loads, and electric vehicles. A local energy management system manages and dispatches the energy of these distributed resources. Multiple virtual power plants within a region are connected at different voltage levels and nodes in the distribution network, forming a regional virtual power plant cluster.

[0013] 1.2 Carbon quotas, carbon emission flows and comprehensive electricity-carbon prices: The carbon emissions caused by virtual power plants are considered in two parts. The first part is the carbon emissions of the distributed resources within the virtual power plant itself, which is caused by the operation of gas turbine generators in this method; the second part is the carbon emissions of coal-fired units on the power generation side indirectly caused by the virtual power plant purchasing electricity from the distribution network.

[0014] Carbon quotas are set by governments or regulatory agencies and represent the maximum permissible carbon emissions for each emitter within a specific time period. To manage and reduce carbon emissions from virtual power plants, this method incorporates virtual power plants into a carbon emissions trading market, allocating a certain number of free carbon quotas to each gas turbine generator and renewable energy generator within the virtual power plant. Through carbon quota trading, virtual power plants with surplus carbon quotas can sell their excess quotas to those with insufficient carbon quotas. Any excess carbon emissions after the transaction will incur a penalty fee.

[0015] The carbon emission flow (CEF), a virtual network flow corresponding to the power flow, can be used to quantify the carbon emission responsibility generated by the electricity consumption side. Carbon emission flow indicators include node carbon potential, branch carbon flow density, and network loss carbon flow rate. Node carbon potential is the equivalent generation-side carbon emissions contained in the unit of electrical energy injected into the node. The carbon potential of each node in the system is: ,in: For nodes exist Nodal carbon potential of the time period; To connect to the node All coal-fired units A collection of For the crew exist Output power during the time period; For the crew exist The carbon emission intensity of the time period, that is, the carbon emissions contained in the unit energy output of the generator; Connect nodes in the distribution network All the first nodes A collection of To connect nodes and nodes The branch power, is the network loss power on this branch, For this branch The branch carbon flow density of the time period is the carbon emission when the branch transmits unit power; For nodes exist The sum of other powers injected during the period, such as those connected at the node The power of wind power and photovoltaic units injected into the distribution network, the power of virtual power plants injected into the distribution network, etc.

[0016] According to the proportional sharing principle, from the node The carbon flow density of all outflow branches is equal to the carbon potential of the node, so the carbon potential of each node is converted to: , and then, when the distribution network power flow distribution is known, only the carbon emission intensity of the coal-fired units on the power generation side is needed to calculate the carbon potential of all nodes in the distribution network and the branch carbon flow density, and then calculate the real-time carbon emissions in the distribution network.

[0017] The electricity-carbon price is based on the node carbon potential and adds the electricity price to the carbon emission cost to comprehensively reflect the environmental impact of electricity transmission and consumption. The use of the electricity-carbon price can guide electricity producers and consumers to choose low-carbon or zero-carbon energy and reduce carbon emissions. The electricity-carbon price is: ,in: For nodes exist The comprehensive price of electricity and carbon in the time period; For nodes exist The locational marginal price (LMP) of the time period; For carbon tax.

[0018] 1.3 Joint operation of virtual power plant clusters: Figure 1 The joint operation framework of the virtual power plant cluster shown in the figure cooperates within the cluster to increase individual benefits and reduce carbon emissions by sharing electricity and carbon quotas. Specifically, it includes: in the first stage, organizing shared transactions within the cluster to form a peer-to-peer (P2P) trading plan. In this stage, the virtual power plant cluster operator is only responsible for formulating the rules of internal shared transactions and organizing transactions, and does not interfere with the pricing decisions between virtual power plants; in the second stage, trading the cluster's unbalanced electricity energy with the distribution network operator.

[0019] The responsibilities of the distribution network operator include: 1) calculating the optimal power flow of the regional distribution network, and performing routine work such as dispatching control, trading operations, etc.; 2) calculating the carbon emission flow and node carbon potential of the network based on the distribution network power flow, and forming a comprehensive electricity-carbon price for selling electricity to the virtual power plant cluster; 3) allocating carbon quotas to each node and charging penalties for excess carbon emissions, while purchasing electricity from the superior power grid to maintain the power balance of the distribution network.

[0020] Step 2: Construct a virtual power plant and its internal resource model, including:

[0021] 2.1 Construct a distributed resource model, including: distributed renewable energy sources (such as wind power and photovoltaics), controllable generators (such as gas turbine generators), local energy storage, flexible loads and electric vehicles.

[0022] The renewable energy power supply model is: ,in: 、 、 Node The virtual power plant connected The forecast output of renewable energy power generation, photovoltaic power generation and wind power generation for each period.

[0023] The gas turbine generator model is: , ,in: For nodes The virtual power plant connected The gas turbine generator output power during the period; 、 are the upper and lower limits of gas turbine generator output respectively; It is the upper limit of the ramp rate of the gas turbine generator.

[0024] The energy storage model is: , , , , , ,in: For nodes The virtual power plant connected Energy storage capacity during the time period; 、 They are Energy storage charging and discharging power during the time period; 、 are the energy storage charging and discharging efficiency; 、 are the upper and lower limits of energy storage capacitance respectively; It is the upper limit of energy storage charging and discharging power.

[0025] The load model is: , , , ,in: For nodes The virtual power plant connected Total load during the time period; 、 、 They are original predicted load, curtailable load and shiftable load respectively; If it is positive, the load is transferred out during this period, otherwise it is transferred into this period; is the translation coefficient, which represents the upper limit of the translation load ratio; The upper limit of the load that can be reduced by the virtual power plant.

[0026] The electric vehicle charging and discharging model mentioned above means that the electric vehicle cluster aggregated by the virtual power plant can participate in scheduling as virtual energy storage. The model includes: , , , , , ,in: For nodes The virtual power plant connected The total electricity consumption of the electric vehicle cluster during the period; 、 They are The total charging and discharging power of the electric vehicle cluster during the time period; 、 They are the upper and lower limits of the total power respectively; It is the upper limit of the total charge and discharge power.

[0027] 2.2 Construct the virtual power plant operation cost model, specifically: ,in: For nodes The total cost of operating the connected virtual power plant; The cost of interacting with the distribution grid for the virtual power plant; The cost of virtual power plant load demand response; Degradation costs for energy storage operations in virtual power plants; operating degradation costs for electric vehicle clusters in virtual power plants; operating costs of gas turbine generators for virtual power plants; The cost of electricity transmission during P2P transactions of virtual power plants; is the P2P transaction cost of virtual power plants.

[0028] The interaction cost between the virtual power plant and the distribution network is: , , ,in: 、 Node The virtual power plant connected The power purchased and sold from the distribution network during the time period; The price of electricity sold by the virtual power plant to the distribution network; Penalty costs for excess carbon emissions, Penalty price for excess carbon emissions, is the net carbon quota after the shared transaction. The virtual power plant load demand response cost is: ,in: 、 are the compensation costs for load reduction and load shifting, respectively. To avoid repeated calculations, the degradation cost of the energy storage operation of the virtual power plant is: ,in: is the degradation cost coefficient of energy storage charging and discharging. The degradation cost of the virtual power plant electric vehicle cluster operation is: ,in: is the degradation cost coefficient of the electric vehicle cluster. The operating cost of the virtual power plant gas turbine generator is: ,in: is the cost coefficient of the gas turbine generator. The P2P transaction cost of the virtual power plant is: ,in: 、 、 、 Node Connected virtual power plants and nodes The P2P transaction volume and transaction price of electricity and carbon quotas between connected virtual power plants. Under this subscript, For positive and for the node The connected virtual power plant sends The connected virtual power plant purchases electricity, and negative electricity is sold. Similarly; For positive and for the node The connected virtual power plant sends The electricity fee paid by the connected virtual power plant is negative for the electricity fee collected. Similarly, the power transmission cost is: ,in: is the loss cost parameter of electric energy transmission; is the collection of all virtual power plants participating in the joint operation; only The scenario is that only the electricity transmission fee is charged to the electricity buyer's virtual power plant.

[0029] Step 3: Construct a two-layer model of cluster trading strategy in the electricity-carbon collaborative market, specifically including:

[0030] 3.1 Constructing a Two-Level Optimization Model Framework: By developing an optimized trading strategy, we minimize the electricity and environmental costs of virtual power plant cluster operations, expand local trading, and reduce carbon emissions. For the day-ahead period (which can be extended to other time scales), we establish a two-level optimization model to formulate virtual power plant cluster trading strategies in the electricity-carbon collaborative market. The upper-level optimization model aims to minimize the cluster's total operating costs. Within the bounds of the comprehensive electricity-carbon price set by the distribution network operator, the cluster's virtual power plants, through cooperative game play, develop a P2P trading plan for electricity and carbon quotas, as well as an optimized dispatch plan for each virtual power plant. The lower-level model aims to minimize the distribution network's operating costs. Based on the P2P trading plans submitted by the virtual power plant cluster operators, it calculates the optimal power flow of the distribution network and determines the comprehensive electricity-carbon price. The two-level optimization model uses the P2P trading plan and the comprehensive electricity-carbon price as interacting variables. Through iterative coordination of decisions between the upper and lower-level models, it achieves optimal operation of the virtual power plant cluster in the electricity-carbon collaborative market until convergence conditions are met.

[0031] 3.2 Construct the upper-level optimization model, including the objective function of minimizing the total operating cost of the virtual power plant cluster: , where the active power balance constraint of each virtual power plant is: , the carbon emission balance constraint of each virtual power plant is: .

[0032] In the virtual power plant model of this method, resources that use fossil energy to generate electricity will not receive free carbon quotas, while resources that use clean energy to generate electricity can receive a certain proportion of free carbon quotas based on their historical carbon emission intensity to encourage the production of more clean energy. Carbon emissions from gas turbine generators, The initial allocation of carbon quotas.

[0033] The core of the upper-level optimization model is to achieve optimal overall and individual benefits within a given price signal boundary through an internal cooperative game. This requires ensuring two things: 1) the existence of a cooperative game; and 2) the fairness of the distribution of benefits. The existence of a cooperative game requires proving that member cooperation truly brings increased benefits to the cluster; otherwise, cooperation would be meaningless. According to cooperative game theory, the existence of a cooperative game can be proven by the superadditivity of the characteristic function of the cooperative alliance. In this model, the characteristic function is modeled as the total operating cost of the virtual power plant cluster. Existing techniques have proven that such characteristic functions are superadditivity, indicating the existence of a cooperative game.

[0034] The cooperative game model is: 、 、 、 ,in: is a cooperative game model, As the main participants in the game, is the characteristic function of the cooperative game. In the cooperative game, the Nash bargaining model can take into account both individual and collective interests and realize the distribution of benefits among multiple subjects. The present invention adopts asymmetric Nash bargaining based on contribution index to realize the distribution of benefits of joint operation, which specifically includes: 、 、 、 、 ,in: It is the contribution index in electric energy trading. By analogy, we can get the contribution index in carbon quota trading. The formula is no longer listed. The contribution index of integrated electricity energy trading and carbon quota trading is obtained .

[0035] 3.3 Construct an asymmetric Nash bargaining model to solve the upper-level optimization model, specifically: , , ,in: Node before participating in cooperative game The optimal operating cost of the connected virtual power plant.

[0036] To facilitate the solution, the upper optimization model is logarithmized and the maximum value problem is converted into a minimum value problem. Finally, the asymmetric Nash bargaining model is transformed into two sub-problems to be solved sequentially, namely the sub-problem of minimizing the total cost of the cooperative alliance (problem 1, P1) and the sub-problem of maximizing the profit distribution of alliance entities (problem 2, P2). Then, the P2P transaction volume and P2P transaction price in the model are solved by the alternating direction multiplier method (ADMM).

[0037] The cooperative alliance total cost minimum sub-problem is solved in the following way: The cooperative alliance total cost minimum sub-problem (P1) is: , ,in: 、 is the coupling variable, which is the node and nodes A generalized expression of the shared electricity trading volume and shared carbon quota trading volume between connected virtual power plants, and must meet . is the operating cost expression of a single virtual power plant in subproblem P1. Therefore, there is no need to consider the cost of shared transactions in a single virtual power plant. The ADMM algorithm is used to solve the total cost minimization sub-problem P1 of the virtual power plant cluster cooperation alliance.

[0038] The augmented Lagrangian function of the total cost minimization subproblem of the cooperative alliance is ,in: 、 are the Lagrange multipliers and penalty factors of subproblem P1, respectively. The updates of coupling variables and Lagrange multipliers include: ,in: is the number of distributed solution iterations; the convergence conditions for the primal and dual residuals include: ,in: 、 Convergence thresholds for the primal and dual residuals set for subproblem P1 respectively.

[0039] The sub-problem of maximizing the distribution of benefits among the alliance entities is solved in the following way: by solving the sub-problem of minimizing the total cost of the cooperative alliance, the optimal shared transaction volume between the virtual power plants is obtained, and the optimal shared transaction volume is substituted into the asymmetric Nash bargaining model to obtain the sub-problem P2: ,in: 、 is the coupling variable, which is the node and nodes The generalized expression of the shared electricity transaction price and shared carbon quota transaction price between the connected virtual power plants satisfies . The optimal solution for P1 is obtained. The ADMM algorithm is used to solve the sub-problem P2 of maximizing the profit distribution of the virtual power plant cluster cooperative alliance.

[0040] The augmented Lagrangian function of the virtual power plant cluster cooperative alliance main body profit distribution maximization sub-problem ,in: 、 are the Lagrange multiplier and penalty factor of subproblem P2 respectively. The subsequent solution process is roughly the same as that of P1 and will not be repeated here.

[0041] 3.4 Constructing the lower-level model: Taking the minimization of the distribution network operation cost as the goal, the optimal power flow of the distribution network is calculated. The objective function of the lower-level model is: , , ,in: is the operating cost of the thermal power unit, The cost of purchasing electricity from the upper-level distribution network. is the operating power of the thermal power unit, is the operating cost coefficient of thermal power units. The electricity purchase price from the upper distribution network, The power purchased from the upper distribution network; the constraints of the lower model include: 1) active power balance constraint ; ,in: 、 They are photovoltaic and wind farm stations connected to the distribution network. To deal with the Lagrange multiplier for active power balance constraints, ;2) Reactive power balance constraint; ; ;3) Other constraints on distribution network operation; ; ; ; ; ;4) Thermal power unit output constraints; ; .

[0042] 3.5 Solving the Two-Level Model: An optimization strategy based on back-and-forth iteration is employed, alternating between solving the upper-level and lower-level problems to gradually approach the optimal solution. Specifically, the upper-level decision variables are first fixed, and the lower-level problem is solved to obtain the corresponding lower-level decision variables. Subsequently, the upper-level decision variables are updated based on the solution to the lower-level problem, and the upper-level objective function is optimized. This process is repeated in each iteration until the preset convergence conditions are met. This method ensures the convergence of the solution process through the coordinated optimization of the upper and lower-level problems, while effectively taking into account the complex structural characteristics of the two-level model.

[0043] Through specific practical experiments, a systematic solution is provided for the interaction of multiple subjects in the electricity-carbon collaborative market by constructing a virtual power plant cluster-distribution network joint operation framework; based on the operation framework, a two-layer optimization model is established to formulate the virtual power plant cluster trading strategy, in which the upper-layer optimization model aims to minimize the total operating cost of the cluster. Under the boundary of the comprehensive electricity-carbon price formulated by the distribution network operator, the P2P trading plan and optimized scheduling scheme of electricity and carbon quota are formed through the cooperative game between the virtual power plants within the cluster; the lower-layer model aims to minimize the operating cost of the distribution network, solves the optimal power flow according to the trading plan submitted by the virtual power plant cluster, and feeds back the comprehensive electricity-carbon price; the asymmetric Nash bargaining theory is introduced to construct a cooperative game model, and the improved alternating multiplier algorithm is used for efficient solution.

[0044] Compared with existing technologies, this method solves problems such as insufficient consideration of differentiated contributions and unfair benefit distribution mechanisms in traditional models, significantly improving the overall economic benefits and cooperative stability of virtual power plant clusters, while ensuring the safe and economical operation of distribution networks, and providing new theoretical support and practical guidance for multi-agent collaborative optimization in the electricity-carbon collaborative market.

[0045] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principles and purpose of the present invention. The scope of protection of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. All implementation schemes within its scope shall be subject to the constraints of the present invention.

Claims

1. A collaborative resource configuration method for a virtual power plant cluster and a distribution network, characterized in that: By constructing a virtual power plant cluster-distribution network joint operation framework and building a two-layer optimization model, the upper-layer optimization model formulates a P2P trading plan for electricity and carbon quotas through cooperative games between virtual power plants, and the lower-layer optimization model solves the optimal power flow of the distribution network based on the trading plan and feeds back the comprehensive price of electricity and carbon; then, by constructing a cooperative game model, the alternating direction multiplier algorithm is used to solve the two-layer optimization model to achieve collaborative resource allocation.

2. The collaborative resource configuration method of the virtual power plant cluster and the distribution network according to claim 1 is characterized in that: include: Step 1: Construct a virtual power plant cluster joint operation framework model, including: virtual power plants and their clusters, carbon quotas, carbon emission flows, and comprehensive electricity-carbon prices; Step 2: Construct a virtual power plant and its internal resource model; Step 3: Construct a two-layer model of cluster trading strategy in the electricity-carbon collaborative market, specifically including: 3.1 Construct a two-layer optimization model framework; 3.2 Construct upper optimization model; 3.3 Construct an asymmetric Nash bargaining model to solve the upper-level optimization model; 3.4 Construct the lower layer model; 3.5 Solve the two-layer model.

3. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2 is characterized in that: The virtual power plant cluster joint operation framework cooperates within the cluster to increase individual benefits and reduce carbon emissions by sharing electricity and carbon quotas. Specifically, it includes: in the first stage, organizing internal shared transactions within the cluster to form a point-to-point trading plan. In this stage, the virtual power plant cluster operator is only responsible for formulating the rules of internal shared transactions and organizing transactions, and does not interfere with the pricing decisions between virtual power plants; in the second stage, trading the cluster's unbalanced electricity with the distribution network operator.

4. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2, wherein: The distributed resource model includes: distributed renewable energy power sources (such as wind power and photovoltaic power), controllable generators (such as gas turbine generators), local energy storage, flexible loads and electric vehicles; The renewable energy power supply model is: ,in: 、 、 Node The virtual power plant connected The forecast output of renewable energy power generation, photovoltaic power generation and wind power generation for each period; The gas turbine generator model is: , ,in: For nodes The virtual power plant connected The gas turbine generator output power during the period; 、 are the upper and lower limits of gas turbine generator output respectively; is the upper limit of the ramp rate of the gas turbine generator; The energy storage model is: , , , , , ,in: For nodes The virtual power plant connected Energy storage capacity during the time period; 、 They are Energy storage charging and discharging power during the time period; 、 are the energy storage charging and discharging efficiency; 、 are the upper and lower limits of energy storage capacitance respectively; The upper limit of energy storage charging and discharging power; The load model is: , , , ,in: For nodes The virtual power plant connected Total load during the time period; 、 、 They are original predicted load, curtailable load and shiftable load respectively; If it is positive, the load is transferred out during this period, otherwise it is transferred into this period; is the translation coefficient, which represents the upper limit of the translation load ratio; The upper limit of the load that can be reduced by the virtual power plant; The electric vehicle charging and discharging model mentioned above means that the electric vehicle cluster aggregated by the virtual power plant can participate in scheduling as virtual energy storage. The model includes: , , , , , ,in: For nodes The virtual power plant connected The total electricity consumption of the electric vehicle cluster during the period; 、 They are The total charging and discharging power of the electric vehicle cluster during the time period; 、 They are the upper and lower limits of the total power respectively; It is the upper limit of the total charge and discharge power.

5. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2, wherein: The virtual power plant operating cost model is specifically as follows: ,in: For nodes The total cost of operating the connected virtual power plant; The cost of interacting with the distribution grid for the virtual power plant; The cost of virtual power plant load demand response; Degradation costs for energy storage operations in virtual power plants; operating degradation costs for electric vehicle clusters in virtual power plants; operating costs of gas turbine generators for virtual power plants; The cost of electricity transmission during P2P transactions of virtual power plants; P2P transaction costs for virtual power plants; The interaction cost between the virtual power plant and the distribution network is: , , ,in: 、 Node The virtual power plant connected The power purchased and sold from the distribution network during the time period; The price of electricity sold by the virtual power plant to the distribution network; Penalty costs for excess carbon emissions, Penalty price for excess carbon emissions, To share the net carbon quota after the transaction, the virtual power plant load demand response cost is: ,in: 、 are the compensation costs for load reduction and load shifting, respectively. To avoid repeated calculations, the degradation cost of the energy storage operation of the virtual power plant is: ,in: is the degradation cost coefficient of energy storage charging and discharging, and the operational degradation cost of the virtual power plant electric vehicle cluster is: ,in: is the degradation cost coefficient of the electric vehicle cluster, and the operating cost of the virtual power plant gas turbine generator is: ,in: is the cost coefficient of the gas turbine generator; The P2P transaction cost of the virtual power plant is: ,in: 、 、 、 Node Connected virtual power plants and nodes The P2P transaction volume and transaction price of electricity and carbon quotas between connected virtual power plants, under this subscript, For positive and for the node The connected virtual power plant sends The connected virtual power plant purchases electricity, and negative electricity is sold. Similarly; For positive and for the node The connected virtual power plant sends The electricity fee paid by the connected virtual power plant is negative for the electricity fee collected. Similarly, the power transmission cost is: ,in: is the loss cost parameter of electric energy transmission; is the collection of all virtual power plants participating in the joint operation; only The scenario is that only the electricity transmission fee is charged to the electricity buyer's virtual power plant.

6. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2, characterized in that: The two-tier optimization model framework develops optimized trading strategies to minimize the electricity and environmental costs of virtual power plant cluster operations, expand local trading scale, and reduce carbon emissions. A two-layer optimization model is established for the day-ahead phase to formulate virtual power plant cluster trading strategies in the electricity-carbon collaborative market. The upper-layer optimization model aims to minimize the total operating cost of the cluster. Within the comprehensive electricity-carbon price boundary set by the distribution network operator, the upper-layer optimization model forms a P2P trading plan for electricity and carbon quotas, as well as an optimized scheduling plan for each virtual power plant, through cooperative game between virtual power plants within the cluster. The lower-layer model aims to minimize the operating cost of the distribution network. Based on the P2P trading plan submitted by the virtual power plant cluster operator, the optimal power flow of the distribution network is calculated to form a comprehensive electricity-carbon price. The two-layer optimization model uses the P2P trading plan and the comprehensive price of electricity and carbon as interactive variables. It coordinates the decisions between the upper and lower models through cyclic iteration until the convergence conditions are met, thereby achieving the optimal operation of the virtual power plant cluster under the electricity and carbon collaborative market.

7. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 6 is characterized in that: The upper-level optimization model includes the objective function of minimizing the total operating cost of the virtual power plant cluster: , where the active power balance constraint of each virtual power plant is: , the carbon emission balance constraint of each virtual power plant is: ,in Carbon emissions from gas turbine generators, The initial allocation of carbon allowances; The cooperative game model is: 、 、 、 ,in: is a cooperative game model, As the main participants in the game, is the characteristic function of cooperative games. In cooperative games, the Nash bargaining model can take into account both individual and collective interests and realize the distribution of benefits among multiple subjects. The present invention adopts asymmetric Nash bargaining based on contribution index to realize the distribution of benefits of joint operations, which specifically includes: 、 、 、 、 ,in: It is the contribution index in electric energy trading. By analogy, it can be obtained as the contribution index in carbon quota trading. , the contribution index of integrated electricity energy trading and carbon quota trading is obtained .

8. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 1 is characterized in that: The construction of the asymmetric Nash bargaining model to solve the upper-level optimization model is specifically as follows: , , ,in: Node before participating in cooperative game The optimal operating cost of the connected virtual power plant is calculated by taking the logarithm of the upper optimization model and converting the maximum value problem into a minimum value problem. Finally, the asymmetric Nash bargaining model is transformed into two sub-problems to be solved sequentially, namely the sub-problem of minimizing the total cost of the cooperative alliance (P1) and the sub-problem of maximizing the profit distribution of alliance entities (P2). Then, the P2P transaction volume and P2P transaction price in the model are solved by the alternating direction multiplier method (ADMM).

9. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2, characterized in that: The lower model is designed to minimize the operating cost of the distribution network and calculate the optimal power flow of the distribution network. The objective function of the lower model is: , , ,in: is the operating cost of the thermal power unit, is the cost of purchasing electricity from the upper-level distribution network, is the operating power of the thermal power unit, is the operating cost coefficient of thermal power units, The electricity purchase price from the upper-level distribution network, The power purchased from the upper distribution network; the constraints of the lower model include: 1) active power balance constraint ; ,in: 、 They are photovoltaic and wind farm stations connected to the distribution network. To deal with the Lagrange multiplier for active power balance constraints, ;2) Reactive power balance constraint; ; ;3) Other constraints on distribution network operation; ; ; ; ; ;4) Thermal power unit output constraints; ; .

10. The collaborative resource configuration method for a virtual power plant cluster and a distribution network according to claim 2, characterized in that: The solution to the two-layer model is as follows: an optimization strategy based on back-and-forth iteration is adopted, and the optimal solution is gradually approached by alternating between solving the upper and lower layer problems. Specifically, the upper layer decision variables are first fixed, and the lower layer problem is solved to obtain the corresponding lower layer decision variables; then, the upper layer decision variables are updated based on the solution of the lower layer problem, and the upper layer objective function is optimized. This process is repeated in each iteration until the preset convergence conditions are met.

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