Power distribution network power self-balancing control method based on virtual power plant aggregation
By constructing a virtual power plant aggregation layer and a coordination and optimization layer, the voltage over-limit problem caused by the large-scale distributed power source access was solved, realizing the safety and stability of the distribution network and the efficient absorption of distributed power sources, thus improving the economy and fairness of the system.
Patent Information
- Application Number
- CN202511023843.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-24
- Publication Date
- 2025-10-31
AI Technical Summary
The large-scale integration of distributed power sources into the distribution network leads to voltage over-limit and absorption problems. Existing centralized and distributed control methods are difficult to effectively solve the problems of grid security and stability and distributed power absorption rate, and traditional control methods are not suitable for environments with a high proportion of distributed power sources.
By constructing a virtual power plant aggregation layer, an independent optimization layer, a coordinated optimization layer, and an emergent benefit distribution layer, a community discovery algorithm is used to generate virtual power plants. A multi-virtual power plant coordinated interaction model is established to perform power self-balancing optimization control, thereby achieving voltage regulation and resource optimization allocation.
It improves the safety and stability of the distribution network and the absorption rate of distributed power sources, reduces the active power shearing of distributed power sources, and improves the overall economy and fairness of the system.
Smart Images
Figure CN120879818A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of virtual power plant regulation and control technology, specifically relating to a power self-balancing control method for distribution networks based on virtual power plant aggregation. Background Technology
[0002] Modern energy systems, represented by distributed generation, are developing rapidly and are quickly replacing traditional energy systems such as coal. However, distributed generation, such as wind and solar power, is characterized by randomness, intermittency, volatility, and anti-peak-shaving properties. Their large-scale integration presents a series of new challenges to power system balance and grid security. As the penetration rate of distributed generation in distribution networks continues to increase, the scale in some areas exceeds the capacity of the distribution network, leading to problems such as power flow reversal and frequent voltage exceedances. The situation regarding the safe operation of the distribution network and the absorption of distributed generation is severe.
[0003] Therefore, corresponding measures need to be taken to address the voltage exceeding limits in the distribution network, reduce wind and solar curtailment rates, and promote the absorption of distributed power sources. Virtual power plants, as a new operating mode, aggregate distributed generation (DG), energy storage systems (ESS), and other dispersed resources into a unified and controllable resource cluster through information and communication technologies and control strategies. This cluster acts as an independent entity participating in the operation and dispatch of the power system, fully utilizing the coordination and complementarity of distributed resources to achieve rational and optimized resource allocation and utilization. Externally, a virtual power plant operates as a special power plant, exhibiting the overall functions and effects of a traditional power plant, and can be controlled and managed like a traditional power plant. Internally, it functions as a comprehensive energy management system with multiple functions including self-coordination, self-management, and self-control.
[0004] Centralized control methods employ centralized modeling for distribution network optimization control models. However, with the large-scale integration of distributed power sources, the centralized control methods involve a large number of widely distributed devices. The large number of optimization control variables makes it difficult for the optimization control process to meet the time scale requirements, and may even lead to optimization difficulties due to the curse of dimensionality in the model.
[0005] Distributed control methods reduce the dimensionality of variables and simplify the optimization process through decomposition and coordination among stakeholders, achieving control effects close to global centralized optimization. However, most existing distributed optimization control methods are still based on the distribution network having absolute dispatching power over distributed sources and aiming to maximize the interests of power companies. In reality, distributed sources belong to the user side, and power companies do not have the authority to directly manage them. The traditional control method, which aims to maximize grid revenue, is no longer suitable for the new operating environment of grids with a high proportion of distributed sources. The wishes of other stakeholders (virtual power plants) should be fully considered. How to fully mobilize the enthusiasm of all stakeholders to participate in distribution network control and achieve fair and reasonable coordination and interaction still needs further resolution. Summary of the Invention
[0006] In view of the shortcomings of the prior art, the purpose of this invention is to provide a power self-balancing control method for distribution networks based on virtual power plant aggregation, which can effectively solve the voltage over-limit and absorption problems caused by the large-scale distributed power source access. Through the coordinated optimization of multiple virtual power plants and fair distribution of benefits, the safety and stability of the distribution network, the absorption rate of distributed power sources and the overall economy are improved.
[0007] To achieve the above objectives, this invention provides a power self-balancing control method for distribution networks based on virtual power plant aggregation, comprising the following steps: S1. Construct a virtual power plant aggregation layer. Based on the interest coupling degree index, resource sufficiency index, and coordination interaction degree index of the virtual power plant, construct the virtual power plant aggregation index of the distribution network, and generate several virtual power plants through the virtual power plant aggregation algorithm. S2. Construct an independent optimization layer for virtual power plants. For each generated virtual power plant, establish a power self-balancing optimization control model and perform independent optimization on each virtual power plant to achieve voltage regulation and control. S3. Construct a multi-virtual power plant coordination and optimization layer. Represent the coordination and interaction between virtual power plants through a link matrix. Establish a multi-virtual power plant coordination and interaction model. Perform coordination and optimization based on the results of each independent optimization to obtain emergent benefits. Emergent benefits are the reduction in the total operating cost of the multi-virtual power plant system coordination and interaction relative to the total operating cost of each virtual power plant's independent optimization. S4. Construct an emergent benefit distribution layer. Based on the results of each independent optimization and the final optimization result of coordinated optimization, distribute emergent benefits according to the contribution of each virtual power plant. S5. Solve the multi-virtual power plant power self-balancing optimization control architecture, which consists of a virtual power plant aggregation layer, a virtual power plant independent optimization layer, a multi-virtual power plant coordinated optimization layer, and an emergent benefit distribution layer. Calculate the final real operating control cost of each virtual power plant and complete the power self-balancing control of the distribution network based on virtual power plant aggregation.
[0008] As a preferred embodiment of the present invention, in S1, an interest coupling index is established. The process is as follows: S1.1.1 For nodes in the distribution network, the voltage sensitivity matrix is obtained by inverse matrix transformation of the power system load flow Jacobian matrix: (1); In the formula, This represents the change in the phase angle of the node voltage; Indicates the change in node voltage magnitude; sensitivity factor , These represent the changes in node voltage magnitude and phase angle per unit amount of active power injected into the node; sensitivity factor. , These represent the changes in node voltage amplitude and phase angle per unit amount of reactive power injected into the node; This represents the change in active power injected into the node; This represents the change in reactive power injected into the node; From equation (1), we can obtain that and , satisfy: (2); S1.1.2, Assume the cost required to adjust a unit of active power is... ,adjust The cost required for a certain amount of active power is The unit reactive power regulation cost is ,adjust The cost required for the quantity of reactive power is Then equation (2) is transformed into: (3); S1.1.3, Definition , The relationship between voltage changes at distribution network nodes and node regulation costs can be expressed as follows: (4); In the formula, , These are the active power coupling matrix and reactive power coupling matrix of the node's regulation cost, respectively, where each element represents the change in voltage amplitude per unit voltage regulation cost incurred by the node. but Represented as: (5); In the formula, N represents the total number of nodes in the distribution network; This represents the set of all virtual power plants in the power distribution network. This represents the virtual power plant to which node i belongs; Indicates node i and the virtual power plant The sum of the adjustment cost coupling between each node within the system. Indicates node i and excluding the virtual power plant The sum of the regulation cost coupling between nodes in the remaining virtual power plant.
[0009] As a preferred embodiment of the present invention, in S1, the resource sufficiency index Represented as: (6); In the formula, M represents the number of virtual power plants aggregated in the current distribution network; This indicates the resource sufficiency of the k-th virtual power plant: (7); In the formula, This represents the total amount of resources within the k-th virtual power plant that meet the control requirements; This represents the total amount of resource capacity that the k-th virtual power plant can regulate itself.
[0010] As a preferred embodiment of the present invention, in S1, a coordination interaction index is established. The process is as follows: S1.3.1 For a distribution network system with N nodes, the electrical distance based on the active power voltage sensitivity matrix is expressed as: (8); In the formula, This represents the electrical distance between node i and node j based on the active voltage sensitivity matrix; for The element in the i-th row and j-th column represents the relationship between the unit active power injected at node j and the voltage change at node i, where i, j = 1, 2, ..., N and i ≠ j, and N represents the total number of nodes in the distribution network. Similarly; Similarly, the electrical distance between node i and node j based on the reactive voltage sensitivity matrix is obtained. The electrical distance, based on the sensitivity matrix, is expressed as: (9); In the formula, This represents the electrical distance between node i and node j; S1.3.2 Weights of the edge connecting node i and node j The expression is: (10); In the formula, express Maximum value of the elements in the middle; Represented as: (11); In the formula, It is the sum of the weights of all edges in the network; , These are the sum of the weights of all edges connected to nodes i and j, respectively.
[0011] As a preferred embodiment of the present invention, in S1, the aggregation index of the virtual power plant in the distribution network Represented as: (12); In the formula, As an indicator of the degree of coupling of interests; As an indicator of resource sufficiency; To coordinate the degree of interaction; The virtual power plant aggregation algorithm uses a community discovery algorithm, and the generated communities are the virtual power plants in the distribution network.
[0012] As a preferred embodiment of the present invention, in S2, the method for establishing the power self-balancing optimization control model is to establish the objective function of the power self-balancing optimization control model with the goal of minimizing the operating cost of each virtual power plant: (13); In the formula, For the k-th virtual power plant in independent optimization control Total operating cost; for The cost of active power regulation losses; for Inverter loss costs for adjusting reactive power; for Equipment loss costs associated with charging and discharging energy storage devices; for Network loss costs; in, Represented as: (14); In the formula, This represents the active power reduction cost factor for distributed power sources. express The actual value of the photovoltaic output active power of the internal node j; express The optimized value of the photovoltaic output active power of the inner node j; express The actual value of the active power output of the wind turbine at internal node j; express The optimized value of the active power output of the wind turbine at internal node j; Represented as: (15); In the formula, This represents the reactive power regulation cost factor of the inverter; express The reactive power regulation of the photovoltaic inverter at internal node j; express The reactive power regulation of the wind turbine inverter at internal node j; Represented as: (16); In the formula, This represents the loss cost coefficient of energy storage equipment; express Charging power of internal energy storage devices; express Discharge power of internal energy storage device; Represented as: (17); In the formula, This represents the network loss cost coefficient; This represents the resistance on line ij; This represents the reactance on line ij; This represents the square of the current amplitude on line ij.
[0013] As a preferred embodiment of the present invention, the constraints of the power self-balancing optimization control model include: Power balance constraints: (18); (19); (20); (twenty one); In the formula, , These represent the active and reactive power transmitted on line ij, respectively. , These represent the active and reactive power transmitted on line jl, respectively. Let j be the set of upstream nodes; Let l be the set of downstream nodes of node j, where l represents one of the nodes. , These represent the active and reactive power demands of the load at node j, respectively. , These are the optimized values of the active power output of the photovoltaic and wind turbine inverters at node j, respectively. , These are the optimized values for the charging and discharging power of the energy storage device at node j, respectively. , These are the optimized values of reactive power regulation for the photovoltaic and wind turbine inverters at node j, respectively. Node voltage constraints: (twenty two); (twenty three); In the formula, , These are the squares of the voltage amplitudes at nodes i and j, respectively. , These are the minimum and maximum allowable voltages at distribution network nodes, respectively. Line capacity second-order cone relaxation constraint: (twenty four); Node current constraints: (25); In the formula, This represents the maximum permissible current on line ij. Distributed power generation output constraints: (26); (27); (28); (29); (30); (31); In the formula, , These represent the upper limits of the active power output of the photovoltaic and wind turbines at node j, respectively. , These represent the maximum adjustable reactive power of the photovoltaic and wind turbine inverters at node j, respectively. Energy storage operation constraints: (32); (33); In the formula, D is a 0-1 variable, representing the charging and discharging state of the energy storage device. A value of 1 indicates discharging, and a value of 0 indicates charging. This refers to the maximum allowable charging and discharging power of the energy storage device.
[0014] As a preferred embodiment of the present invention, the process of establishing a multi-virtual power plant coordination and interaction model in S3 is as follows: S3.1. Graph theory is used to describe the changes in physical and information links between multiple virtual power plants to represent the link behavior of each virtual power plant. A link matrix H containing the physical and information links of the multi-virtual power plant system is defined: (34); In the formula, a and b are indices of the virtual power plant, where a, b = 1, 2, ..., M and a ≠ b. , Let a and b represent the a-th and b-th virtual power plants, respectively. The number of virtual power plants aggregated in the current distribution network is M. express The information link status with the information exchange platform is indicated by a value of 1. There is information interaction with the information exchange platform; a value of 0 indicates that there is no information interaction. Similarly; express and The physical link between them is indicated by a value of 1, which means there is a physical link between them, and a value of 0 means there is no physical link. S3.2. Establish the objective function of the multi-virtual power plant coordination and interaction model, with the goal of minimizing the operating cost of the virtual power plant: (35); In the formula, When indicating coordinated interactive optimization control Total operating cost; express Interaction costs with other virtual power plants; Represented as: (36); In the formula, for The link vector represents Links to other virtual power plants; express The amount of power interacting with other virtual power plants. For positive time representation Output power, negative indicates Obtain power; This represents the set of all virtual power plants in the power distribution network; The constraints are: (37); (38); (39); In the formula, This represents the squared voltage value of the upstream virtual power plant boundary node s; This represents the square of the voltage magnitude at the boundary node s; Represents the virtual balance node of the downstream virtual power plant The square of the voltage; , These represent the active and reactive power of the virtual load at the boundary node s of the upstream virtual power plant, respectively. , These represent the active and reactive power transmitted via the virtual power plant inter-line sm, respectively. This represents the global value of active power transmitted through the virtual power plant inter-line sm. This represents a set of virtual power plant interconnections. This represents the global value of reactive power transmitted through the virtual power plant inter-line sm.
[0015] As a preferred embodiment of the present invention, the method for distributing emerging benefits in S4 is as follows: S4.1 Total Emergent Benefits of Multi-Virtual Power Plant Systems for: (40); S4.2 When multiple virtual power plants coordinate and interact, the adjustment amounts of active and reactive power of distributed sources within each virtual power plant are different. A contribution model for each virtual power plant is constructed based on the reduction amount of active power and the adjustment amount of reactive power. (41); In the formula, express Contribution index; For coordinated interaction The reduction in internal active power; For coordinated interaction The amount of internal reactive power adjustment; S4.3. Distribute the emerging benefits based on the contribution indicators of each virtual power plant: (42); In the formula, for Emergent benefits gained; S4.4. By distributing the benefits emerging from multiple virtual power plants, the actual operating costs of each virtual power plant are obtained: (43); In the formula, for The actual operating cost.
[0016] As a preferred embodiment of the present invention, in step S5, the solution method is based on the Matlab platform and uses the YALMIP toolbox to call CPLREX to solve the model, as follows: S5.1 Perform virtual power plant aggregation on the distribution network in the virtual power plant aggregation layer, and send the virtual power plant aggregation results to the virtual power plant independent optimization layer; S5.2 Initialize and assign values to the equipment in each virtual power plant: (44); (45); (46); S5.3. In the independent optimization layer of the virtual power plant, the optimization control cost within each virtual power plant is calculated based on the adjustment quantities of each device. The power self-balancing optimization control models of each virtual power plant are solved in parallel to obtain the optimization results for each virtual power plant. , , , , , The independent optimization results of each virtual power plant are sent to the virtual power plant coordination optimization layer, and the independent optimization control costs are sent to the emergent benefit distribution layer. S5.4 In the virtual power plant coordination optimization layer, based on the optimization results of each virtual power plant in the independent optimization layer, calculate the square of the voltage amplitude of the boundary node s of the virtual power plant. Active power transmission between virtual power plants (SM) and reactive power Solving the multi-virtual power plant coordination and interaction model, and calculating the optimal control cost within each virtual power plant, is the solution. Obtain the optimization results of each virtual power plant, and update the boundary node voltage and power data between virtual power plants. , , Where q represents the iteration number, that is, the value at the q-th iteration. , , ; , , Similarly, the convergence threshold for a given algorithm iteration process is... ,definition for: (47); like, If the solution is successful, the iteration stops, the optimal solution is returned, and the control cost of the virtual power plant coordination optimization is transferred to the emerging benefit distribution layer; otherwise, a new round of coordination and interaction optimization control model solution is performed until convergence. S5.5. In the emerging benefit distribution layer, calculate according to formula (40). Then, according to equations (41)-(43), the emerging benefits are allocated to calculate the final real operation control cost of each virtual power plant.
[0017] The beneficial effects of this invention are: To rationally achieve the aggregation of virtual power plants in a distribution network, this invention proposes a virtual power plant aggregation method that considers the coupling degree of interests, resource sufficiency, and coordination interaction of virtual power plants. This method fully considers the voltage regulation capability, participation in optimization control, and close connection of each virtual power plant, effectively improving the independent regulation capability and resource coordination of each virtual power plant in the power self-balancing optimization control process.
[0018] This invention coordinates and interacts with each virtual power plant based on independent optimization control, so as to jointly complete the control task, thereby realizing voltage control and power balance of the distribution network. It can make full use of the resources of each virtual power plant, reduce the active power shearing of distributed power sources, and increase the absorption rate of distributed power sources.
[0019] This invention proposes an emergent benefit allocation model based on contribution. It calculates the contribution of each virtual power plant to the multi-virtual power plant system of the distribution network based on the active power shearing and reactive power adjustment of each virtual power plant, and finally completes the allocation of emergent benefits, ensuring the fair distribution of emergent benefits and improving the overall economy and fairness of the system. Attached Figure Description
[0020] Figure 1 This is a flowchart illustrating the principle of the method of the present invention; Figure 2 This is a schematic diagram of the multi-virtual power plant power self-balancing optimization control architecture in this invention; Figure 3 This is a flowchart illustrating the virtual power plant aggregation algorithm (community discovery algorithm) in this invention; Figure 4 This is a schematic diagram of the 10kV line topology and virtual power plant aggregation results during the verification process of this invention; Figure 5 This is a schematic diagram illustrating the changes in the aggregation index of the virtual power plant in the distribution network during the verification process of this invention; Figure 6 These are voltage distribution diagrams under different methods during the verification process of this invention; Figure 7 This is a schematic diagram of the active and reactive power regulation of photovoltaic power under two methods during the verification process of this invention; Figure 8 This is a schematic diagram showing the active and reactive power adjustment of the fan under two methods during the verification process of this invention. Detailed Implementation
[0021] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: As Figure 1 and Figure 2As shown, a power self-balancing control method for distribution networks based on virtual power plant aggregation includes the following steps: S1. Construct a virtual power plant aggregation layer. Based on the interest coupling degree index, resource sufficiency index, and coordination interaction degree index of the virtual power plant, construct the virtual power plant aggregation index of the distribution network, and generate several virtual power plants through the virtual power plant aggregation algorithm. S2. Construct an independent optimization layer for virtual power plants. For each generated virtual power plant, establish a power self-balancing optimization control model and perform independent optimization on each virtual power plant to achieve voltage regulation and control. S3. Construct a multi-virtual power plant coordination and optimization layer. Represent the coordination and interaction between virtual power plants through a link matrix. Establish a multi-virtual power plant coordination and interaction model. Perform coordination and optimization based on the results of each independent optimization to obtain emergent benefits. Emergent benefits are the reduction in the total operating cost of the multi-virtual power plant system coordination and interaction relative to the total operating cost of each virtual power plant's independent optimization. S4. Construct an emergent benefit distribution layer. Based on the results of each independent optimization and the final optimization result of coordinated optimization, distribute emergent benefits according to the contribution of each virtual power plant. S5. Solve the multi-virtual power plant power self-balancing optimization control architecture, which consists of a virtual power plant aggregation layer, a virtual power plant independent optimization layer, a multi-virtual power plant coordinated optimization layer, and an emergent benefit distribution layer. Calculate the final real operating control cost of each virtual power plant and complete the power self-balancing control of the distribution network based on virtual power plant aggregation.
[0022] In S1, an interest coupling index is established. The process is as follows: S1.1.1 For nodes in the distribution network, the voltage sensitivity matrix is obtained by inverse matrix transformation of the power system load flow Jacobian matrix: (48); In the formula, This represents the change in the phase angle of the node voltage; Indicates the change in node voltage magnitude; sensitivity factor , These represent the changes in node voltage magnitude and phase angle per unit amount of active power injected into the node; sensitivity factor. , These represent the changes in node voltage amplitude and phase angle per unit amount of reactive power injected into the node; This represents the change in active power injected into the node; This represents the change in reactive power injected into the node; From equation (48), we can obtain that and , satisfy: (49); S1.1.2, Assume the cost required to adjust a unit of active power is... ,adjust The cost required for a certain amount of active power is The unit reactive power regulation cost is ,adjust The cost required for the quantity of reactive power is Then equation (49) is transformed into: (50); S1.1.3, Definition , The relationship between voltage changes at distribution network nodes and node regulation costs can be expressed as follows: (51); In the formula, , These are the active power coupling matrix and reactive power coupling matrix of the node's regulation cost, respectively, where each element represents the change in voltage amplitude per unit voltage regulation cost incurred by the node. but Represented as: (52); In the formula, N represents the total number of nodes in the distribution network; This represents the set of all virtual power plants in the power distribution network. This represents the virtual power plant to which node i belongs; Indicates node i and the virtual power plant The sum of the adjustment cost coupling between each node within the system. Indicates node i and excluding the virtual power plant The sum of the regulation cost coupling between all nodes in the remaining virtual power plant (i.e. In the corresponding equations 53 and 54 ); Represented as: (53); (54); (55); In the formula, E represents the sum of the regulation cost coupling degree among the nodes in the distribution network; This represents the degree of coupling between node i and node j in terms of adjustment cost. and These are the active coupling degree matrices for node adjustment costs. Elements in; and These are the node adjustment cost reactive power coupling degree matrices. The elements in.
[0023] Resource sufficiency index Represented as: (56); In the formula, M represents the number of virtual power plants aggregated in the current distribution network; This indicates the resource sufficiency of the k-th virtual power plant: (57); In the formula, This represents the total amount of resources within the k-th virtual power plant that meet the control requirements; This represents the total amount of resource capacity that the k-th virtual power plant can regulate itself.
[0024] Establish coordination and interaction index The process is as follows: S1.3.1 For a distribution network system with N nodes, the electrical distance based on the active power voltage sensitivity matrix is expressed as: (58); In the formula, This represents the electrical distance between node i and node j based on the active voltage sensitivity matrix; for The element in the i-th row and j-th column represents the relationship between the unit active power injected at node j and the voltage change at node i, where i, j = 1, 2, ..., N and i ≠ j, and N represents the total number of nodes in the distribution network. Similarly; Similarly, the electrical distance between node i and node j based on the reactive voltage sensitivity matrix is obtained. The electrical distance, based on the sensitivity matrix, is expressed as: (59); In the formula, This represents the electrical distance between node i and node j; S1.3.2 Weights of the edge connecting node i and node j The expression is: (60); In the formula, express Maximum value of the elements in the middle; Represented as: (61); In the formula, It is the sum of the weights of all edges in the network; , These are the sums of the weights of all edges connected to nodes i and j, respectively. The expression is: (62); , The expression is: (63); (64).
[0025] Distribution Network Virtual Power Plant Aggregation Index Represented as: (65); In the formula, As an indicator of the degree of coupling of interests; As an indicator of resource sufficiency; To coordinate the degree of interaction; like Figure 3 As shown, the virtual power plant aggregation algorithm uses a community discovery algorithm, and the generated communities are the virtual power plants within the distribution network. The specific process is as follows: Start: Process initiated.
[0026] Initialize the distribution network community: Each node in the distribution network is considered an independent community; Calculate the initial value according to formula (65). ; Club merger process: For a given club, randomly select another club from the remaining clubs to merge with; Calculate the merged And the incremental value of the virtual power plant aggregation index in the distribution network. ; Calculate all possible merge results: Calculate the results of merging all possible clubs in pairs. and value; Select The largest merge result is taken as the best merge result generated in this instance. Determine whether to continue merging: Determine if there are any communities in the network that can be merged further; If it exists, return to step 3 to continue the merging operation; If it does not exist, proceed to the next step; Determine the final aggregation result: Select The largest community merger result is taken as the final virtual power plant aggregation result of the distribution network; The nodes within the aggregated community are treated as a single virtual power plant; End: The process has ended.
[0027] In S2, the method for establishing the power self-balancing optimization control model is to set the objective function of the power self-balancing optimization control model with the goal of minimizing the operating cost of each virtual power plant: (66); In the formula, For the k-th virtual power plant in independent optimization control Total operating cost; for The cost of active power regulation losses; for Inverter loss costs for adjusting reactive power; for Equipment loss costs associated with charging and discharging energy storage devices; for Network loss costs; in, Represented as: (67); In the formula, This represents the active power reduction cost factor for distributed power sources. express The actual value of the photovoltaic output active power of the internal node j; express The optimized value of the photovoltaic output active power of the inner node j; express The actual value of the active power output of the wind turbine at internal node j; express The optimized value of the active power output of the wind turbine at internal node j; Represented as: (68); In the formula, This represents the reactive power regulation cost factor of the inverter; express The reactive power regulation of the photovoltaic inverter at internal node j; express The reactive power regulation of the wind turbine inverter at internal node j; Represented as: (69); In the formula, This represents the loss cost coefficient of energy storage equipment; express Charging power of internal energy storage devices; express Discharge power of internal energy storage device; Represented as: (70); In the formula, This represents the network loss cost coefficient (each coefficient corresponds to the actual unit cost, such as how much it costs to reduce 1kW of photovoltaic active power, how much it costs to adjust 1kvar of reactive power, how much it costs to charge and discharge 1kW of energy storage, and how much it costs to lose 1kW of electricity). This represents the resistance on line ij; This represents the reactance on line ij; This represents the square of the current amplitude on line ij; This represents all paths from node i to node j; The constraints of the power self-balancing optimization control model include: Power balance constraints: (71); (72); (73); (74); In the formula, , These represent the active and reactive power transmitted on line ij, respectively. , These represent the active and reactive power transmitted on line jl, respectively. Let j be the set of upstream nodes; Let l be the set of downstream nodes of node j, where l represents one of the nodes. , These represent the active and reactive power demands of the load at node j, respectively. , These are the optimized values of the active power output of the photovoltaic and wind turbine inverters at node j, respectively. , These are the optimized values for the charging and discharging power of the energy storage device at node j, respectively. , These are the optimized values of reactive power regulation for the photovoltaic and wind turbine inverters at node j, respectively. Node voltage constraints: (75); (76); In the formula, , These are the squares of the voltage amplitudes at nodes i and j, respectively. , These are the minimum and maximum allowable voltages at distribution network nodes, respectively. Line capacity second-order cone relaxation constraint: (77); Node current constraints: (78); In the formula, This represents the maximum permissible current on line ij. Distributed power generation output constraints: (79); (80); (81); (82); (83); (84); In the formula, , These represent the upper limits of the active power output of the photovoltaic and wind turbines at node j, respectively. , These represent the maximum adjustable reactive power of the photovoltaic and wind turbine inverters at node j, respectively. Energy storage operation constraints: (85); (86); In the formula, D is a 0-1 variable, representing the charging and discharging state of the energy storage device. A value of 1 indicates discharging, and a value of 0 indicates charging. This refers to the maximum allowable charging and discharging power of the energy storage device.
[0028] In S3, the process of establishing a multi-virtual power plant coordination and interaction model is as follows: S3.1. Graph theory is used to describe the changes in physical and information links between multiple virtual power plants to represent the link behavior of each virtual power plant. A link matrix H containing the physical and information links of the multi-virtual power plant system is defined: (87); In the formula, a and b are indices of the virtual power plant, where a, b = 1, 2, ..., M and a ≠ b. , Let a and b represent the a-th and b-th virtual power plants, respectively. The number of virtual power plants aggregated in the current distribution network is M. express The information link status with the information exchange platform is indicated by a value of 1. There is information interaction with the information exchange platform; a value of 0 indicates that there is no information interaction. Similarly; express and The physical link between them is indicated by a value of 1, which means there is a physical link between them, and a value of 0 means there is no physical link. S3.2. Establish the objective function of the multi-virtual power plant coordination and interaction model, with the goal of minimizing the operating cost of the virtual power plant: (88); In the formula, When indicating coordinated interactive optimization control Total operating cost; express Interaction costs with other virtual power plants; Represented as: (89); In the formula, for The link vector represents Links to other virtual power plants; express The amount of power interacting with other virtual power plants. For positive time representation Output power, negative indicates Obtain power; This represents the set of all virtual power plants in the power distribution network; The constraints are: (90); (91); (92); In the formula, This represents the squared voltage value of the upstream virtual power plant boundary node s; This represents the square of the voltage magnitude at the boundary node s; Represents the virtual balance node of the downstream virtual power plant The square of the voltage; , These represent the active and reactive power of the virtual load at the boundary node s of the upstream virtual power plant, respectively. , These represent the active and reactive power transmitted via the virtual power plant inter-line sm, respectively. This represents the global value of active power transmitted through the virtual power plant inter-line sm. This represents a set of virtual power plant interconnections. This represents the global value of reactive power transmitted through the virtual power plant inter-line sm.
[0029] In S4, the method for distributing emerging benefits is as follows: S4.1 Total Emergent Benefits of Multi-Virtual Power Plant Systems for: (93); S4.2 When multiple virtual power plants coordinate and interact, the adjustment amounts of active and reactive power of distributed sources within each virtual power plant are different. A contribution model for each virtual power plant is constructed based on the reduction amount of active power and the adjustment amount of reactive power. (94); In the formula, express Contribution index; For coordinated interaction The reduction in internal active power; For coordinated interaction The amount of internal reactive power adjustment; S4.3. Distribute the emerging benefits based on the contribution indicators of each virtual power plant: (95); In the formula, for Emergent benefits gained; S4.4. By distributing the benefits emerging from multiple virtual power plants, the actual operating costs of each virtual power plant are obtained: (96); In the formula, for The actual operating cost.
[0030] In S5, the solution method is based on the Matlab platform, and the YALMIP toolbox is used to call CPLREX to solve the model. The process is as follows: S5.1 Perform virtual power plant aggregation on the distribution network in the virtual power plant aggregation layer, and send the virtual power plant aggregation results to the virtual power plant independent optimization layer; S5.2 Initialize and assign values to the equipment in each virtual power plant: (97); (98); (99); S5.3. In the independent optimization layer of the virtual power plant, the optimization control cost within each virtual power plant is calculated based on the adjustment quantities of each device. The power self-balancing optimization control models of each virtual power plant are solved in parallel to obtain the optimization results for each virtual power plant. , , , , , The independent optimization results of each virtual power plant are sent to the virtual power plant coordination optimization layer, and the independent optimization control costs are sent to the emergent benefit distribution layer. S5.4 In the virtual power plant coordination optimization layer, based on the optimization results of each virtual power plant in the independent optimization layer, calculate the square of the voltage amplitude of the boundary node s of the virtual power plant. Active power transmission between virtual power plants (SM) and reactive power Solving the multi-virtual power plant coordination and interaction model, and calculating the optimal control cost within each virtual power plant, is the solution. Obtain the optimization results of each virtual power plant, and update the boundary node voltage and power data between virtual power plants. , , Where q represents the iteration number, that is, the value at the q-th iteration. , , ; , , Similarly, the convergence threshold for a given algorithm iteration process is... ,definition for: (100); like, If the solution is successful, the iteration stops, the optimal solution is returned, and the control cost of the virtual power plant coordination optimization is transferred to the emerging benefit distribution layer; otherwise, a new round of coordination and interaction optimization control model solution is performed until convergence. S5.5. In the emerging benefit distribution layer, calculate according to formula (93). Then, according to Equations (94)-(96), the emerging benefits are allocated to calculate the final real operation control cost of each virtual power plant.
[0031] The verification process is as follows: Using a real 10kV power line as the analysis object, the effectiveness of the method proposed in this embodiment is verified. The topology of this line and the installation nodes of distributed power sources and energy storage devices are as follows: Figure 4 As shown in Table 1, the total installed capacity of distributed power generation is 2.15MW, distributed across 17 nodes, and the total capacity of energy storage devices is 0.26MW, distributed across 5 nodes. Specific parameters are shown in Table 1. Furthermore, the active power shearing cost factor f for distributed power generation is set. RES The inverter reactive power regulation cost factor is 0.8 yuan / kW. Q The loss cost coefficient f of the energy storage equipment is 0.4 yuan / kVar. Es The network loss cost coefficient is 0.4 yuan / kW. Net The price is 0.2 yuan / kW.
[0032] Table 1. Installation Locations and Capacities of Distributed Power Sources and Energy Storage Equipment
[0033] Aggregated analysis of multiple virtual power plants in power distribution networks: The first step in the optimized control strategy proposed in this embodiment is virtual power plant aggregation, the aggregation effect of which directly affects the overall performance of subsequent power self-balancing optimized control. Using the distribution network virtual power plant aggregation method proposed in this embodiment, aggregation analysis was performed on this 10kV line. During the aggregation of multiple virtual power plants, as different virtual power plants aggregate, the distribution network virtual power plant aggregation index... Changes, such as Figure 5 As shown. The results indicate that when the distributed resources of this line are aggregated into three virtual power plants, Reaching the maximum value The value is 0.713, indicating that the aggregation of virtual power plants is most reasonable at this point. Virtual power plants are formed by... Figure 4 The dashed box contains nodes with adjustable resources.
[0034] Voltage Coordination Optimization Control Analysis: Without any control measures in place at noon, a severe overvoltage occurred on this line. At this time, the proportion of nodes in the network with voltage amplitudes exceeding 1.05 pu reached 66.7%. The voltage situation of the line at this time is as follows: Figure 6 As shown, corresponding control measures need to be taken to solve the voltage over-limit problem.
[0035] To analyze the performance of voltage optimization control in this embodiment, a comparative analysis is conducted using a virtual power plant independent control method and the method in this embodiment. The voltage distribution diagrams of the distribution network under both methods are shown below. Figure 6 As shown, the active and reactive power regulation of the virtual power plant photovoltaic inverter is as follows: Figure 7 As shown, the active and reactive power regulation of the wind turbine inverter is as follows: Figure 8 As shown.
[0036] A comprehensive analysis of the results obtained under the two control methods shows that, after appropriate control strategies, both methods can regulate the voltage of each node to within the normal operating range. However, when a virtual power plant operates independently, it can only control its own internal equipment and utilize internal resources. By adjusting the inverter, it reduces the active power of distributed generation and absorbs reactive power. Although independent optimization control of the virtual power plant can eliminate voltage exceedances in the distribution network, it cannot dispatch external reactive resources, which can easily lead to unnecessary distributed generation losses. In contrast, the method in this embodiment coordinates and interacts based on the independent optimization control of each virtual power plant to jointly complete the control task. This allows for the full utilization of the resources of each virtual power plant, reduces the reduction of active power of distributed generation, and increases the absorption rate of distributed generation.
[0037] Analysis of the benefits of the emergence of multiple virtual power plants in the power distribution network: First, based on the total cost required for independent control of the virtual power plant and the total cost required for coordinated interaction of the virtual power plants, the total emergent benefit of multiple virtual power plants is calculated using formula (93) to be 154.9 yuan. Details of the control costs for each virtual power plant are shown in Table 2.
[0038] Table 2 Virtual Power Plant Control Costs
[0039] Secondly, the contribution of each virtual power plant to the coordinated interaction is calculated by formula (94) based on the active and reactive power regulation of the distributed power inverter during the coordinated interaction of each virtual power plant. The calculation results are shown in Table 3.
[0040] Table 3 Contribution of each virtual power plant
[0041] Finally, the emerging benefits are allocated according to the contribution of each virtual power plant using equation (95), and the actual control cost of each virtual power plant is calculated using equation (96). The calculation results are shown in Table 4.
[0042] Table 4 Emerging Profit Distribution and Real Costs of Virtual Power Plant Control
[0043] As shown in Table 4, the actual control costs of virtual power plants 1, 2 and 3 are RMB 59.52, RMB 92.76 and RMB 88.15 respectively, all of which are less than the cost of independent control, thus satisfying the demand of virtual power plants to improve their own interests by joining a multi-virtual power plant system.
[0044] In summary, the optimized control strategy proposed in this embodiment is more in line with the future development trend of large-scale distributed power generation access to the distribution network.
[0045] Example 2: A power self-balancing control device for distribution networks based on virtual power plant aggregation, comprising: One or more processors; Memory, used to store one or more computer programs; When one or more programs are executed by one or more processors, the one or more processors execute the method in Example 1.
[0046] Example 3: A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method in Example 1.
Claims
1. A power self-balancing control method for distribution networks based on virtual power plant aggregation, characterized in that... Includes the following steps: S1. Construct a virtual power plant aggregation layer. Based on the interest coupling degree index, resource sufficiency index, and coordination interaction degree index of the virtual power plant, construct the virtual power plant aggregation index of the distribution network, and generate several virtual power plants through the virtual power plant aggregation algorithm. S2. Construct an independent optimization layer for virtual power plants. For each generated virtual power plant, establish a power self-balancing optimization control model and perform independent optimization on each virtual power plant to achieve voltage regulation and control. S3. Construct a multi-virtual power plant coordination and optimization layer. Represent the coordination and interaction between virtual power plants through a link matrix. Establish a multi-virtual power plant coordination and interaction model. Perform coordination and optimization based on the results of each independent optimization to obtain emergent benefits. Emergent benefits are the reduction in the total operating cost of the multi-virtual power plant system coordination and interaction relative to the total operating cost of each virtual power plant's independent optimization. S4. Construct an emergent benefit distribution layer. Based on the results of each independent optimization and the final optimization result of coordinated optimization, distribute emergent benefits according to the contribution of each virtual power plant. S5. Solve the multi-virtual power plant power self-balancing optimization control architecture, which consists of a virtual power plant aggregation layer, a virtual power plant independent optimization layer, a multi-virtual power plant coordinated optimization layer, and an emergent benefit distribution layer. Calculate the final real operating control cost of each virtual power plant and complete the power self-balancing control of the distribution network based on virtual power plant aggregation.
2. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 1, characterized in that, In S1, an interest coupling index is established. The process is as follows: S1.1.1 For nodes in the distribution network, the voltage sensitivity matrix is obtained by inverse matrix transformation of the power system load flow Jacobian matrix: （1）； In the formula, This represents the change in the phase angle of the node voltage; Indicates the change in node voltage magnitude; sensitivity factor , These represent the changes in node voltage magnitude and phase angle per unit amount of active power injected into the node; sensitivity factor. , These represent the changes in node voltage amplitude and phase angle per unit amount of reactive power injected into the node; This represents the change in active power injected into the node; This represents the change in reactive power injected into the node; From equation (1), we can obtain that and , satisfy: （2）； S1.1.2, Assume the cost required to adjust a unit of active power is... ,adjust The cost required for a certain amount of active power is The unit reactive power regulation cost is ,adjust The cost required for the quantity of reactive power is Then equation (2) is transformed into: （3）； S1.1.3, Definition , The relationship between voltage changes at distribution network nodes and node regulation costs can be expressed as follows: （4）； In the formula, , These are the active power coupling matrix and reactive power coupling matrix of the node's regulation cost, respectively, where each element represents the change in voltage amplitude per unit voltage regulation cost incurred by the node. but Represented as: （5）； In the formula, N represents the total number of nodes in the distribution network; This represents the set of all virtual power plants in the power distribution network. This represents the virtual power plant to which node i belongs; Indicates node i and the virtual power plant The sum of the adjustment cost coupling between each node within the system. Indicates node i and excluding the virtual power plant The sum of the regulation cost coupling between nodes in the remaining virtual power plant.
3. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 1, characterized in that, In S1, the resource sufficiency index Represented as: （6）； In the formula, M represents the number of virtual power plants aggregated in the current distribution network; This indicates the resource sufficiency of the k-th virtual power plant: （7）； In the formula, This represents the total amount of resources within the k-th virtual power plant that meet the control requirements; This represents the total amount of resource capacity that the k-th virtual power plant can regulate itself.
4. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 2, characterized in that, In S1, a coordination interaction index is established. The process is as follows: S1.3.1 For a distribution network system with N nodes, the electrical distance based on the active power voltage sensitivity matrix is expressed as: （8）； In the formula, This represents the electrical distance between node i and node j based on the active voltage sensitivity matrix; for The element in the i-th row and j-th column represents the relationship between the unit active power injected at node j and the voltage change at node i, where i, j = 1, 2, ..., N and i ≠ j, and N represents the total number of nodes in the distribution network. Similarly; Similarly, the electrical distance between node i and node j based on the reactive voltage sensitivity matrix is obtained. The electrical distance, based on the sensitivity matrix, is expressed as: （9）； In the formula, This represents the electrical distance between node i and node j; S1.3.2 Weights of the edge connecting node i and node j The expression is: （10）； In the formula, express Maximum value of the elements in the middle; Represented as: （11）； In the formula, It is the sum of the weights of all edges in the network; , These are the sum of the weights of all edges connected to nodes i and j, respectively.
5. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 1, characterized in that, In S1, the aggregation index of virtual power plants in the distribution network Represented as: （12）； In the formula, As an indicator of the degree of coupling of interests; As an indicator of resource sufficiency; For coordination and interaction metrics; The virtual power plant aggregation algorithm uses a community discovery algorithm, and the generated communities are the virtual power plants in the distribution network.
6. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 1, characterized in that, In S2, the method for establishing the power self-balancing optimization control model is as follows: taking the minimization of the operating cost of each virtual power plant as the objective, the objective function of the power self-balancing optimization control model is established: （13）； In the formula, For the k-th virtual power plant in independent optimization control Total operating cost; for The cost of active power regulation losses; for Inverter loss costs for adjusting reactive power; for Equipment loss costs associated with charging and discharging energy storage devices; for Network loss costs; in, Represented as: （14）； In the formula, This represents the active power reduction cost factor for distributed power sources. express The actual value of the photovoltaic output active power of the internal node j; express The optimized value of the photovoltaic output active power of the inner node j; express The actual value of the active power output of the wind turbine at internal node j; express The optimized value of the active power output of the wind turbine at internal node j; Represented as: （15）； In the formula, This represents the reactive power regulation cost factor of the inverter; express The reactive power regulation of the photovoltaic inverter at internal node j; express The reactive power regulation of the wind turbine inverter at internal node j; Represented as: （16）； In the formula, This represents the loss cost coefficient of energy storage equipment; express Charging power of internal energy storage devices; express Discharge power of internal energy storage device; Represented as: （17）； In the formula, This represents the network loss cost coefficient; This represents the resistance on line ij; This represents the reactance on line ij; This represents the square of the current amplitude on line ij.
7. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 6, characterized in that, The constraints of the power self-balancing optimization control model include: Power balance constraints: （18）； （19）； （20）； （21）； In the formula, , These represent the active and reactive power transmitted on line ij, respectively. , These represent the active and reactive power transmitted on line jl, respectively. Let j be the set of upstream nodes; Let l be the set of downstream nodes of node j, where l represents one of the nodes. , These represent the active and reactive power demands of the load at node j, respectively. , These are the optimized values of the active power output of the photovoltaic and wind turbine inverters at node j, respectively. , These are the optimized values for the charging and discharging power of the energy storage device at node j, respectively. , These are the optimized values of reactive power regulation for the photovoltaic and wind turbine inverters at node j, respectively. Node voltage constraints: （22）； （23）； In the formula, , These are the squares of the voltage amplitudes at nodes i and j, respectively. , These are the minimum and maximum allowable voltages at distribution network nodes, respectively. Line capacity second-order cone relaxation constraint: （24）； Node current constraints: （25）； In the formula, This represents the maximum permissible current on line ij. Distributed power generation output constraints: （26）； （27）； （28）； （29）； （30）； （31）； In the formula, , These represent the upper limits of the active power output of the photovoltaic and wind turbines at node j, respectively. , These represent the maximum adjustable reactive power of the photovoltaic and wind turbine inverters at node j, respectively. Energy storage operation constraints: （32）； （33）； In the formula, D is a 0-1 variable, representing the charging and discharging state of the energy storage device. A value of 1 indicates discharging, and a value of 0 indicates charging. This refers to the maximum allowable charging and discharging power of the energy storage device.
8. The power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 6, characterized in that, In S3, the process of establishing a multi-virtual power plant coordination and interaction model is as follows: S3.
1. Graph theory is used to describe the changes in physical and information links between multiple virtual power plants to represent the link behavior of each virtual power plant. A link matrix H containing the physical and information links of the multi-virtual power plant system is defined: （34）； In the formula, a and b are indices of the virtual power plant, where a, b = 1, 2, ..., M and a ≠ b. , Let a and b represent the a-th and b-th virtual power plants, respectively. The number of virtual power plants aggregated in the current distribution network is M. express The information link status with the information exchange platform is indicated by a value of 1. There is information interaction with the information exchange platform; a value of 0 indicates that there is no information interaction. Similarly; express and The physical link between them is indicated by a value of 1, which means there is a physical link between them, and a value of 0 means there is no physical link. S3.
2. Establish the objective function of the multi-virtual power plant coordination and interaction model, with the goal of minimizing the operating cost of the virtual power plant: （35）； In the formula, When indicating coordinated interactive optimization control Total operating cost; express Interaction costs with other virtual power plants; Represented as: （36）； In the formula, for The link vector represents Links to other virtual power plants; express The amount of power interacting with other virtual power plants. For positive time representation Output power, when negative, indicates Obtain power; This represents the set of all virtual power plants in the power distribution network; The constraints are: （37）； （38）； （39）； In the formula, This represents the squared voltage value of the upstream virtual power plant boundary node s; This represents the square of the voltage magnitude at the boundary node s; Represents the virtual balance node of the downstream virtual power plant The square of the voltage; , These represent the active and reactive power of the virtual load at the boundary node s of the upstream virtual power plant, respectively. , These represent the active and reactive power transmitted via the virtual power plant inter-line sm, respectively. This represents the global value of active power transmitted through the virtual power plant inter-line sm. This represents a set of virtual power plant interconnections. This represents the global value of reactive power transmitted through the virtual power plant inter-line sm.
9. A power self-balancing control method for distribution networks based on virtual power plant aggregation according to claim 8, characterized in that, In S4, the method for distributing emerging benefits is as follows: S4.1 Total Emergent Benefits of Multi-Virtual Power Plant Systems for: （40）； S4.2 When multiple virtual power plants coordinate and interact, the adjustment amounts of active and reactive power of distributed sources within each virtual power plant are different. A contribution model for each virtual power plant is constructed based on the reduction amount of active power and the adjustment amount of reactive power. （41）； In the formula, express Contribution index; For coordinated interaction The reduction in internal active power; For coordinated interaction The amount of internal reactive power adjustment; S4.
3. Distribute the emerging benefits based on the contribution indicators of each virtual power plant: （42）； In the formula, for Emergent benefits gained; S4.
4. By distributing the benefits emerging from multiple virtual power plants, the actual operating costs of each virtual power plant are obtained: （43）； In the formula, for The actual operating cost.
10. A power self-balancing control method for a distribution network based on virtual power plant aggregation according to claim 9, characterized in that, In S5, the solution method is based on the Matlab platform, and the YALMIP toolbox is used to call CPLREX to solve the model. The process is as follows: S5.1 Perform virtual power plant aggregation on the distribution network in the virtual power plant aggregation layer, and send the virtual power plant aggregation results to the virtual power plant independent optimization layer; S5.2 Initialize and assign values to the equipment in each virtual power plant: （44）； （45）； （46）； S5.
3. In the independent optimization layer of the virtual power plant, the optimization control cost within each virtual power plant is calculated based on the adjustment quantities of each device. The power self-balancing optimization control models of each virtual power plant are solved in parallel to obtain the optimization results for each virtual power plant. , , , , , The independent optimization results of each virtual power plant are sent to the virtual power plant coordination optimization layer, and the independent optimization control costs are sent to the emerging benefit distribution layer. S5.4 In the virtual power plant coordination optimization layer, based on the optimization results of each virtual power plant in the independent optimization layer, calculate the square of the voltage amplitude of the boundary node s of the virtual power plant. Active power transmission between virtual power plants (SM) and reactive power Solving the multi-virtual power plant coordination and interaction model, and calculating the optimal control cost within each virtual power plant, is the solution. Obtain the optimization results of each virtual power plant, and update the boundary node voltage and power data between virtual power plants. , , Where q represents the iteration number, that is, the value at the q-th iteration. , , ; , , Similarly, the convergence threshold for a given algorithm iteration process is... ,definition for: （47）； like, If the solution is successful, the iteration stops, the optimal solution is returned, and the control cost of the virtual power plant coordination optimization is transferred to the emerging benefit distribution layer; otherwise, a new round of coordination and interaction optimization control model solution is performed until convergence. S5.
5. In the emerging benefit distribution layer, calculate according to formula (40). Then, according to equations (41)-(43), the emerging benefits are allocated to calculate the final real operation control cost of each virtual power plant.