Three-phase power distribution network vehicle-network coordinated operation method based on generalized Nash bargaining

Abstract

The invention discloses a three-phase power distribution network vehicle-network coordinated operation method based on generalized Nash bargaining. The method comprises the following steps: obtaining power distribution network vehicle-network basic parameters; constructing an electric vehicle aggregator independent optimization operation model; substituting the vehicle network basic parameters of the power distribution network into the electric vehicle aggregator independent optimization operation model, and solving the electric vehicle aggregator independent optimization operation model to obtain a first solving result; constructing a power distribution operator independent optimization operation model; substituting the vehicle network basic parameters of the power distribution network and the first solving result into a power distribution operator independent optimization operation model to obtain a second solving result; constructing a cooperative operation model of an electric vehicle aggregator and a power distribution operator; and substituting the vehicle network basic parameters of the power distribution network, the first solving result and the second solving result into an electric vehicle aggregator and power distribution operator cooperative operation model to obtain a third solving result. According to the invention, cooperation between a power distribution operator and an electric vehicle aggregator can be enhanced, so that vehicle-network coordinated optimization operation is realized.

Description

Technical Field

[0001] The invention relates to the technical field of distribution network analysis, and in particular to a vehicle-grid coordinated operation method of a three-phase distribution network based on generalized Nash bargaining. Background Art

[0002] At present, with the integration of various new loads, the diversity and volatility of loads in distribution networks are increasing, which brings many unprecedented challenges. Among them, the three-phase imbalance problem has become a research focus due to its significant impact on the safe and economic operation of distribution networks. Three-phase imbalance not only increases the energy loss of the power system and shortens the service life of power equipment, but may also trigger chain reactions such as voltage fluctuations and harmonic pollution, threatening the safe operation of the distribution network. Therefore, developing an effective three-phase imbalance control method is of great significance for distribution operators to ensure power supply quality and improve the operating efficiency of distribution networks.

[0003] As a virtual energy storage unit, electric vehicles can adjust the charging time and charging power during the charging process. After being aggregated by aggregators, they can participate in intelligent charging management to redistribute the three-phase charging load to balance the current of each phase. However, existing technologies usually assume that electric vehicle aggregators are fully controllable and the power grid directly calls on the charging flexibility of electric vehicles to improve the three-phase imbalance, while ignoring the profit-seeking nature of electric vehicle aggregators. In this case, electric vehicle aggregators may choose not to participate in charging management, thereby limiting the practical feasibility of the three-phase imbalance management solution. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a three-phase distribution network vehicle-grid coordinated operation method based on generalized Nash bargaining, which solves the problem of unreasonable vehicle-grid coordination in the prior art.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a three-phase distribution network vehicle-grid coordinated operation method based on generalized Nash bargaining, comprising:

[0006] S1. Obtain basic parameters of the distribution network and vehicle network;

[0007] The basic parameters of the distribution network include basic optimization parameters, technical parameters of electric vehicle aggregators, charging demand forecast data of electric vehicle aggregators, basic technical data of the distribution system and distribution system operation forecast data;

[0008] S2. Build an independent optimization operation model for electric vehicle aggregators;

[0009] S3, substituting the basic parameters of the distribution network into the independent optimization operation model of the electric vehicle aggregator, solving the independent optimization operation model of the electric vehicle aggregator, and obtaining a first solution result;

[0010] S4. Construct an independent optimization operation model for distribution operators considering three-phase imbalance management;

[0011] S5. Substituting the basic parameters of the distribution network and the first solution result into the independent optimization operation model of the distribution operator to obtain a second solution result;

[0012] S6. Construct a cooperative operation model between electric vehicle aggregators and distribution operators based on generalized Nash bargaining considering three-phase imbalance management;

[0013] S7. Substitute the basic parameters of the distribution network and the vehicle-grid, the first solution result and the second solution result into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain a third solution result, and determine the third solution result as the coordinated operation plan of the three-phase distribution network and the vehicle-grid.

[0014] The beneficial effects of the above scheme are as follows: the present invention proposes a cooperation method based on generalized Nash bargaining, and designs flexibility contribution and cooperation dependence indicators to evaluate the bargaining power of each cooperation participant, thereby determining the remuneration that the distribution operator should pay to the aggregator after the electric vehicle aggregator participates in the three-phase imbalance management. The cooperation between the distribution operator and the electric vehicle aggregator is strengthened through reasonable and fair profit distribution, thereby achieving coordinated and optimized operation of the vehicle network. This method can effectively alleviate the three-phase imbalance, reduce the three-phase imbalance management costs of the distribution system operator, and reduce the related charging costs of the electric vehicle aggregator. This has a stronger guiding significance for guiding electric vehicle aggregators to participate in the practical application of three-phase imbalance management.

[0015] Furthermore, S2 specifically includes:

[0016] S21. Construct the objective function of the independent optimization operation model of electric vehicle aggregators;

[0017] The objective function of the independent optimization operation model of electric vehicle aggregators is:

[0018]

[0019]

[0020]

[0021] in, represents the total cost of an EV aggregator operating independently and optimally, is the charging cost of EV aggregator n, is the equivalent dissatisfaction cost of EV aggregator n when it fails to charge to the expected energy level, a t and b t is the dynamic pricing coefficient of time period t, x n,tis the charging power of aggregator n in time period t, Δt is the duration of each time period, and λ anx is the electric vehicle dissatisfaction coefficient, δ n is the difference between the expected charging amount and the actual charging amount of aggregator n;

[0022] S22. Construct constraints for the independent optimization operation model of electric vehicle aggregators;

[0023] The constraints of the independent optimization operation model of EV aggregators include individual EV charging constraints and EV aggregator charging constraints;

[0024] The individual charging constraints of electric vehicles include the maximum charging power constraint of the electric vehicle in the t-th simulation period, the minimum cumulative charging amount constraint of the electric vehicle in the t-th simulation period, and the maximum cumulative charging amount constraint of the electric vehicle in the t-th simulation period;

[0025] The maximum charging power constraint of the electric vehicle in the tth simulation period is:

[0026]

[0027] in, is the maximum charging power of electric vehicle i determined by the battery technology; and The time when electric vehicle i connects to and leaves the grid;

[0028] The minimum cumulative charge capacity constraint of the electric vehicle in the tth simulation period is:

[0029]

[0030] in, and are the initial charging power of electric vehicle i at the time of arrival and the expected charging power at the time of departure, η is the charging efficiency of the electric vehicle, is the maximum allowable deviation between the expected charging power and the actual charging power of electric vehicle i;

[0031] The maximum cumulative charge capacity constraint of the electric vehicle in the tth simulation period is:

[0032]

[0033] Where t is the tth simulation period, and are the initial charging capacity of electric vehicle i at the time of arrival and the expected charging capacity at the time of departure, The moment when electric vehicle i is connected to the grid;

[0034] The charging constraints of the electric vehicle aggregator include the charging power constraint of the electric vehicle aggregator in the t-th simulation period, the cumulative charging amount constraint of the electric vehicle aggregator in the t-th simulation period, and the charging amount deviation constraint of the electric vehicle aggregator;

[0035] The charging power constraint of the electric vehicle aggregator in the tth simulation period is:

[0036]

[0037] in, is the maximum charging power of EV aggregator n determined by the capacity limit of charging stations, is a collection of electric vehicles managed by aggregator n;

[0038] The cumulative charging capacity constraint of the electric vehicle aggregator in the tth simulation period is:

[0039]

[0040] Among them, ∑ τ≤t ηx n,t Δt is the cumulative charging amount of the EV aggregator in the tth simulation period;

[0041] The charging capacity deviation constraint of electric vehicle aggregators is:

[0042]

[0043] Among them, δ n is the deviation between the expected charging capacity and the actual charging capacity of the electric vehicle aggregator n;

[0044] S23, constraining the objective function of the independent optimization operation model of the electric vehicle aggregator using the constraint conditions of the independent optimization operation model of the electric vehicle aggregator to obtain the independent optimization operation model of the electric vehicle aggregator;

[0045] The independent optimization operation model of electric vehicle aggregators is:

[0046]

[0047] Among them, x n is the feasible set of electric vehicle aggregators n, x n =[x n,1 ,…,x n,T ]Strategies for electric vehicle aggregators n.

[0048] The beneficial effect of the above further scheme is that in the independent optimization operation mode, each EV aggregator focuses on minimizing its own charging cost without considering the interests of the distribution network. Therefore, the distribution network is considered to adopt a linear dynamic pricing scheme widely used in many smart grids to avoid the simultaneous charging of EV loads. In addition, the dissatisfaction cost of EV aggregators when they do not charge to the expected energy level is also considered.

[0049] Further, in S3, the first solution result includes the optimal charging strategy when each electric vehicle aggregator independently optimizes the operation and the optimal operating cost when the electric vehicle aggregator independently optimizes the operation.

[0050] Furthermore, S4 specifically includes:

[0051] S41. Constructing the objective function of the independent optimization operation model of the distribution operator;

[0052] The objective function of the independent optimization operation model of the distribution operator is:

[0053] min C D =C loss +C inv +C ope (2-1)

[0054]

[0055]

[0056] C ope =C SWH N SWH Δt (2-4)

[0057] Among them, C D is the total cost of the distribution operator in the independent optimization operation model, C loss is the transmission loss cost, C inv is the daily equivalent investment cost of the phase-changing switch, C ope is the operating cost of the phase-changing switch, λ loss and They are the unit cost of network loss and the network loss in time period t, C PSD is the purchase cost of a single phase-changing switch, is the number of phase-changing switches installed at node m, r is the discount rate of cash flow, y is the service life of the phase-changing switch, C SWH is the switching cost of a single switching of the commutation switch, N SWH is the total number of switching times of the phase-changing switch in one day;

[0058] S42, construct network constraints for the independent optimization operation model of distribution operators;

[0059] The network constraints of the distribution operator's independent optimization operation model include node voltage and injected active power and reactive power constraints, network loss constraints during the distribution period, and other constraints for independent optimization by the distribution operator;

[0060] The node voltage and injected active power and reactive power constraints are:

[0061]

[0062] in, is the voltage amplitude of all PQ nodes in time period t, Indicates the voltage amplitude of the three phases A, B, and C of bus m in time period t, is the voltage phasor of all PQ nodes when there is no load connected in the distribution system, and are the injected active power and reactive power of all PQ nodes in Y-connection and triangle connection in period t, and denote the complex power injection of Y-connection and triangle connection of all PQ nodes in period t, E Y and E Δ is the corresponding coefficient matrix;

[0063] The network loss constraint condition during the distribution period is:

[0064]

[0065] Among them, a is the coefficient vector, F Y and F Δ is the corresponding coefficient matrix, Respectively represent the horizontal quantities whose elements in 3D and 3M dimensions are all 1;

[0066] The distribution operator independently optimizes other constraints including the number of commutation switches to maintain the constraint, the maximum number of commutation switches to install the constraint, the total number of switching times of the commutation switches, the transfer load constraint when the commutation switches are switched, the active load constraint, the node voltage constraint and the three-phase imbalance constraint;

[0067] The constraints for the number of commutation switches are:

[0068]

[0069]

[0070] Among them, X∈{A,B,C} is the three-phase phase, is an integer decision variable, which indicates the number of commutation switches of phase X connected to node m in period t, t=0 indicates the initial time before the simulation starts, is the set of simulation time periods;

[0071] The maximum number of phase-changing switches installed is constrained by:

[0072]

[0073] Among them, N PSD+ The maximum number of phase-changing switches that can be installed in the distribution network is limited;

[0074] The total switching times constraint of the commutation switch is:

[0075]

[0076] in, is an integer decision variable, representing the number of commutation switches of phase X connected to node m in time period t;

[0077] The transfer load constraint condition when the phase-changing switch is switched is:

[0078]

[0079]

[0080] in, are the total active load and total reactive load of phase X at node m in period t, and are the basic active load and reactive load of phase X at node m in period t, respectively; Δp is the load that can be switched by a single phase-changing switch on average each time; is a binary variable, which is 1 when the EV aggregator n is connected to the X phase of the node m, otherwise it is 0, ξ is a constant related to the EV power factor, x n,t represents the optimal charging strategy of the nth EV aggregator when operating independently in time period t;

[0081] The active load constraint condition is:

[0082]

[0083] in, is the active load;

[0084] The node voltage constraints are:

[0085]

[0086] Among them, v - and v +are the voltage minimum and maximum vectors respectively;

[0087] The three-phase unbalance constraint condition is:

[0088]

[0089]

[0090] Among them, U m,t is the maximum three-phase voltage deviation of node m during period t, which is used to measure the unbalanced degree of the three-phase voltage at the node. + is the maximum limit of imbalance, and are the voltage amplitude of node m in phase X at time period t, and the average voltage amplitude of node m at time period t, respectively;

[0091] S43, constraining the objective function of the independent optimization operation model of the distribution operator using the network constraint conditions of the independent optimization operation model of the distribution operator, and obtaining the independent optimization operation model of the distribution operator considering the three-phase imbalance control;

[0092] The independent optimization operation model of the distribution operator considering three-phase unbalance management is:

[0093]

[0094] in, is the feasible set of distribution operators, represents the decision variable.

[0095] The beneficial effect of the above further scheme is that in the independent operation mode, the main goal of the distribution operator is to optimize the deployment and switching strategy of the phase-changing switches given the charging plan of the electric vehicle aggregator, and to switch the connection phase of the load by switching the switches, thereby evenly redistributing the power flow and alleviating the three-phase imbalance.

[0096] Furthermore, S5 specifically includes:

[0097] Substitute the basic parameters of the distribution network and the optimal charging strategy of each electric vehicle aggregator during independent optimization operation into the independent optimization operation model of the distribution operator to obtain the second solution;

[0098] The second solution result includes the optimal operation strategy when the distribution operator independently optimizes the operation and the optimal operation cost when the distribution operator independently optimizes the operation.

[0099] Furthermore, S6 specifically includes:

[0100] S61. Construct a collaborative framework based on broad inclusion bargaining;

[0101] The collaborative cooperation framework based on broad inclusion of bargaining is:

[0102]

[0103]

[0104]

[0105] Among them, φ n The financial compensation provided by the distribution operator to the electric vehicle aggregator n for participating in the three-phase imbalance management, represents the optimal operating cost of EV aggregator n when operating independently, C D* represents the optimal operating cost when the distribution operator operates independently;

[0106] S62. Calculate the flexibility contribution index, the cooperation dependence index and the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index;

[0107] The calculation formula of flexibility contribution index is:

[0108]

[0109]

[0110]

[0111] in, is the flexibility contribution index, k1 and k2 are weighting coefficients, is the unit charging energy deviation of EV aggregator n between independent operation and cooperative operation modes, is the unit charging target deviation of EV aggregator n between independent operation and cooperative operation modes, T is the number of simulation periods, are the optimal charging power of EV aggregator n in time period t under independent operation and cooperative operation modes respectively;

[0112] The calculation formula of cooperation dependence index is:

[0113]

[0114]

[0115]

[0116] in, denote the cooperation dependence index of distribution operators, the cooperation dependence index of all electric vehicle aggregators, and the cooperation dependence index of electric vehicle aggregators, respectively. and represents the optimal cost of the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator;

[0117] The calculation formula of the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index is:

[0118]

[0119]

[0120] Among them, α represents the bargaining power parameter of the distribution operator, β n represents the bargaining power parameter of each EV aggregator, k3 and k4 are weighting coefficients;

[0121] S63. Equivalent decomposition is performed on the collaborative cooperation framework based on broad inclusion bargaining, and the flexibility contribution index, cooperation dependence index and bargaining power parameters based on the flexibility contribution index and cooperation dependence index are used as constraints to obtain the cooperative operation model of electric vehicle aggregators and distribution operators;

[0122] The cooperation model between electric vehicle aggregators and distribution operators is:

[0123]

[0124]

[0125]

[0126]

[0127] (3-4)-(3-11)(3-16)

[0128] in, and represents the optimal cost between the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator.

[0129] The beneficial effect of the above further scheme is that under the cooperative operation mode, the distribution operator negotiates with the electric vehicle aggregator to jointly decide on the deployment and switching strategy of the phase-changing switches and the charging strategy of the electric vehicle aggregator. At this time, the electric vehicle aggregator will deviate from its profit-maximizing charging strategy to assist the distribution operator in alleviating the three-phase imbalance governance. This enables the distribution operator to minimize its reliance on phase-changing switches, thereby directly reducing the costs associated with phase-changing switches. As a beneficiary, the distribution operator should provide corresponding financial compensation to the electric vehicle aggregator. Therefore, a mutually beneficial negotiation agreement between the distribution operator and the electric vehicle aggregator is guaranteed and a fair profit distribution is ensured.

[0130] Further, in S7, the basic parameters of the distribution network, the first solution result and the second solution result are substituted into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain a third solution result, which specifically includes:

[0131] S71. Substitute the basic parameters of the distribution network into (3-12) and (3-13) in the cooperative operation model of the electric vehicle aggregator and the distribution operator to solve them, and obtain the optimal charging strategy of the electric vehicle aggregator, the optimal phase-changing switch deployment and switching strategy of the distribution operator, and the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode;

[0132] S72. Substitute the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode, the optimal operating cost of the electric vehicle aggregator when operating independently in the first solution result, and the optimal operating cost of the distribution operator when operating independently in the second solution result into (3-14), (3-15) and (3-16) in the cooperative operation model of the electric vehicle aggregator and the distribution operator for solution to obtain the third solution.

[0133] The beneficial effect of the above further proposal is to strengthen the cooperation between distribution operators and EV aggregators through reasonable and fair profit distribution, thereby achieving coordinated and optimized operation of the vehicle network. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 The figure is a flow chart of a vehicle-grid coordinated operation method for a three-phase distribution network based on generalized Nash bargaining. DETAILED DESCRIPTION

[0135] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0136] like Figure 1 As shown, a vehicle-grid coordinated operation method of a three-phase distribution network based on generalized Nash bargaining includes:

[0137] S1. Obtain the basic parameters of the distribution network and the vehicle network; the basic parameters of the distribution network and the vehicle network include basic optimization parameters, technical parameters of electric vehicle aggregators, charging demand forecast data of electric vehicle aggregators, basic technical data of the distribution system and operation forecast data of the distribution system.

[0138] Exemplarily, the basic parameters of the distribution network vehicle network may include basic optimization parameters, electric vehicle aggregator technical parameters, electric vehicle aggregator charging demand forecast data, distribution system basic technical data and distribution system operation forecast data.

[0139] Among them, the basic optimization parameters may include the duration of each period Δt, the set of simulation periods

[0140] EV aggregator technical parameters may include the maximum charging power of EVs determined by battery technology. EV charging efficiency η, the maximum charging power of EV aggregator n determined by the capacity limit of charging stations A collection of electric vehicles managed by an aggregator n A constant ξ related to the power factor of electric vehicles.

[0141] The charging demand forecast data for EV aggregators can include the times when EVs i connect to and leave the grid. and The initial charge capacity of electric vehicle i at the time of arrival and the expected charge level at the time of departure The maximum allowable deviation between the expected charging energy and the actual charging energy of electric vehicle i

[0142] The basic technical data of the power distribution system may include the dynamic pricing coefficient a for time period t t and b t , network loss unit cost λ loss , the purchase cost of a single phase-changing switch C PSD , the service life of the phase-changing switch y, the switching cost C generated by a single switching of the phase-changing switch SWH The maximum number of phase-changing switches that can be installed in the distribution network is N. PSD+ , the average load that can be switched per phase-changing switch is Δp, and the coefficient vector required to calculate the node voltage is And the coefficient matrix E Y and F Δ , the coefficient vector a and coefficient matrix F required to calculate the network loss Y and F Δ , the maximum limit of system node imbalance U + , the minimum and maximum limits of the system node voltage v - and v+ .

[0143] The distribution system operation prediction data may include the basic active load and reactive load of phase X at node m during period t. and

[0144] S2. Build an independent optimization operation model for electric vehicle aggregators.

[0145] In this embodiment, S2 specifically includes:

[0146] S21. Construct the objective function of the independent optimization operation model of electric vehicle aggregators;

[0147] The objective function of the independent optimization operation model of electric vehicle aggregators is:

[0148]

[0149]

[0150]

[0151] in, represents the total cost of an EV aggregator operating independently and optimally, is the charging cost of EV aggregator n, is the equivalent dissatisfaction cost of EV aggregator n when it fails to charge to the expected energy level, a t and b t is the dynamic pricing coefficient of time period t, x n,t is the charging power of aggregator n in time period t, Δt is the duration of each time period, and λ anx is the electric vehicle dissatisfaction coefficient, δ n is the difference between the expected charging amount and the actual charging amount of aggregator n;

[0152] S22. Construct constraints for the independent optimization operation model of electric vehicle aggregators;

[0153] The constraints of the independent optimization operation model of EV aggregators include individual EV charging constraints and EV aggregator charging constraints;

[0154] The individual charging constraints of electric vehicles include the maximum charging power constraint of the electric vehicle in the t-th simulation period, the minimum cumulative charging amount constraint of the electric vehicle in the t-th simulation period, and the maximum cumulative charging amount constraint of the electric vehicle in the t-th simulation period;

[0155] The maximum charging power constraint of the electric vehicle in the tth simulation period is:

[0156]

[0157] in, is the maximum charging power of electric vehicle i determined by the battery technology; and The time when electric vehicle i connects to and leaves the grid;

[0158] The minimum cumulative charge capacity constraint of the electric vehicle in the tth simulation period is:

[0159]

[0160] in, and are the initial charging power of electric vehicle i at the time of arrival and the expected charging power at the time of departure, η is the charging efficiency of the electric vehicle, is the maximum allowable deviation between the expected charging power and the actual charging power of electric vehicle i;

[0161] The maximum cumulative charge capacity constraint of the electric vehicle in the tth simulation period is:

[0162]

[0163] in, and are the initial charging capacity of electric vehicle i at the time of arrival and the expected charging capacity at the time of departure, The moment when electric vehicle i is connected to the grid;

[0164] The charging constraints of the electric vehicle aggregator include the charging power constraint of the electric vehicle aggregator in the t-th simulation period, the cumulative charging amount constraint of the electric vehicle aggregator in the t-th simulation period, and the charging amount deviation constraint of the electric vehicle aggregator;

[0165] The charging power constraint of the electric vehicle aggregator in the tth simulation period is:

[0166]

[0167] in, is the maximum charging power of EV aggregator n determined by the capacity limit of charging stations, is a collection of electric vehicles managed by aggregator n;

[0168] The cumulative charging capacity constraint of the electric vehicle aggregator in the tth simulation period is:

[0169]

[0170] Among them, ∑ τ≤t ηxn,t Δt is the cumulative charging amount of the EV aggregator in the tth simulation period;

[0171] The charging capacity deviation constraint of electric vehicle aggregators is:

[0172]

[0173] Among them, δ n is the deviation between the expected charging capacity and the actual charging capacity of the electric vehicle aggregator n;

[0174] S23, constraining the objective function of the independent optimization operation model of the electric vehicle aggregator using the constraint conditions of the independent optimization operation model of the electric vehicle aggregator to obtain the independent optimization operation model of the electric vehicle aggregator;

[0175] The independent optimization operation model of electric vehicle aggregators is:

[0176]

[0177] in, is the feasible set of electric vehicle aggregators n, x n =[x n,1 ,…,x n,T ]Strategies for electric vehicle aggregators n.

[0178] Optionally, the duration of each simulation period (ie, Δt) may be determined according to actual conditions and is not specifically limited herein.

[0179] Exemplarily, the charging power and cumulative energy constraints that the electric vehicle aggregator needs to meet are obtained by superimposing the charging power and cumulative energy constraints of a single electric vehicle it manages, so the charging constraints of the electric vehicle aggregator can be calculated.

[0180] For example, the goal of EV aggregator n Not only with its own strategy x n =[x n,1 ,…,x n,T ], and also the strategies of all other EV aggregators -n =[x1,…,x n-1 ,x n+1 ,…,x N ] Therefore, the problem of independent optimization operation of electric vehicle aggregators constitutes the Nash equilibrium problem shown in (1-10).

[0181] S3. Substitute the basic parameters of the distribution network into the independent optimization operation model of the electric vehicle aggregator, solve the independent optimization operation model of the electric vehicle aggregator, and obtain a first solution result.

[0182] In this embodiment, in S3, the first solution result includes the optimal charging strategy when each electric vehicle aggregator independently optimizes the operation and the optimal operating cost when the electric vehicle aggregator independently optimizes the operation.

[0183] For example, the optimal charging strategy when each EV aggregator optimizes independently can be expressed as The optimal operating cost of electric vehicle aggregators when operating independently can be expressed as

[0184] S4. Construct an independent optimization operation model for distribution operators considering three-phase imbalance management.

[0185] In this embodiment, S4 specifically includes:

[0186] S41. Constructing the objective function of the independent optimization operation model of the distribution operator;

[0187] The objective function of the independent optimization operation model of the distribution operator is:

[0188] min C D =C loss +C inv +C ope (2-1)

[0189]

[0190]

[0191] C ope =C SWH N SWH Δt (2-4)

[0192] Among them, C D is the total cost of the distribution operator in the independent optimization operation model, C loss is the transmission loss cost, C inv is the daily equivalent investment cost of the phase-changing switch, C ope is the operating cost of the phase-changing switch, λ loss and They are the unit cost of network loss and the network loss in time period t, C PSD is the purchase cost of a single phase-changing switch, is the number of phase-changing switches installed at node m, r is the discount rate of cash flow, y is the service life of the phase-changing switch, C SWH is the switching cost of a single switching of the commutation switch, N SWH is the total number of switching times of the phase-changing switch in one day;

[0193] S42, construct network constraints for the independent optimization operation model of distribution operators;

[0194] The network constraints of the distribution operator's independent optimization operation model include node voltage and injected active power and reactive power constraints, network loss constraints during the distribution period, and other constraints for independent optimization by the distribution operator;

[0195] The node voltage and injected active power and reactive power constraints are:

[0196]

[0197] in, is the voltage amplitude of all PQ nodes in time period t, Indicates the voltage amplitude of the three phases A, B, and C of bus m in time period t, is the voltage phasor of all PQ nodes when there is no load connected in the distribution system, and are the injected active power and reactive power of all PQ nodes in Y-connection and triangle connection in period t, and denote the complex power injection of Y-connection and triangle connection of all PQ nodes in period t, E Y and E Δ is the corresponding coefficient matrix;

[0198] The network loss constraint condition during the distribution period is:

[0199]

[0200] Among them, a is the coefficient vector, F Y and F Δ is the corresponding coefficient matrix, Respectively represent the horizontal quantities whose elements in 3D and 3M dimensions are all 1;

[0201] The distribution operator independently optimizes other constraints including the number of commutation switches to maintain the constraint, the maximum number of commutation switches to install the constraint, the total number of switching times of the commutation switches, the transfer load constraint when the commutation switches are switched, the active load constraint, the node voltage constraint and the three-phase imbalance constraint;

[0202] The constraints for the number of commutation switches are:

[0203]

[0204]

[0205] Among them, X∈{A,B,C} is the three-phase phase, is an integer decision variable, which indicates the number of commutation switches of phase X connected to node m in period t, t=0 indicates the initial time before the simulation starts, is the set of simulation time periods;

[0206] The maximum number of phase-changing switches installed is constrained by:

[0207]

[0208] Among them, N PSD+ The maximum number of phase-changing switches that can be installed in the distribution network is limited;

[0209] The total switching times constraint of the commutation switch is:

[0210]

[0211] in, is an integer decision variable, representing the number of commutation switches of phase X connected to node m in time period t;

[0212] The transfer load constraint condition when the phase-changing switch is switched is:

[0213]

[0214]

[0215] in, are the total active load and total reactive load of phase X at node m in period t, and are the basic active load and reactive load of phase X at node m in period t, respectively; Δp is the load that can be switched by a single phase-changing switch on average each time; is a binary variable, which is 1 when the EV aggregator n is connected to the X phase of the node m, otherwise it is 0, ξ is a constant related to the EV power factor, x n,t represents the optimal charging strategy of the nth EV aggregator when operating independently in time period t;

[0216] The active load constraint condition is:

[0217]

[0218] in, is the active load;

[0219] The node voltage constraints are:

[0220]

[0221] Among them, v- and v + are the voltage minimum and maximum vectors respectively;

[0222] The three-phase unbalance constraint condition is:

[0223]

[0224]

[0225] Among them, U m,t is the maximum three-phase voltage deviation of node m during period t, which is used to measure the unbalanced degree of the three-phase voltage at the node. + is the maximum limit of imbalance, and are the voltage amplitude of node m in phase X at time period t, and the average voltage amplitude of node m at time period t, respectively;

[0226] S43, constraining the objective function of the independent optimization operation model of the distribution operator using the network constraint conditions of the independent optimization operation model of the distribution operator, and obtaining the independent optimization operation model of the distribution operator considering the three-phase imbalance control;

[0227] The independent optimization operation model of the distribution operator considering three-phase unbalance management is:

[0228]

[0229] in, is the feasible set of distribution operators, represents the decision variable.

[0230] Exemplarily, in the independent operation mode, the main goal of the distribution operator is to optimize the deployment and switching strategy of phase-changing switches given the charging scheme of the electric vehicle aggregator. The connection phase of the load is switched by switching switches, thereby evenly redistributing the power flow and alleviating the three-phase imbalance. However, the use of phase-changing switches by distribution operators requires a lot of expenses, including investment and operating costs. Therefore, it is necessary to build an independent optimization operation model for distribution operators.

[0231] Exemplarily, when constructing the network constraints of the independent optimization operation model of the distribution operator, a three-phase distribution network including 1 slack node and M three-phase PQ nodes is considered, and its network constraints are constructed.

[0232] S5. Substitute the basic parameters of the distribution network and the first solution result into the independent optimization operation model of the distribution operator to obtain the second solution result.

[0233] In this embodiment, S5 specifically includes:

[0234] Substitute the basic parameters of the distribution network and the optimal charging strategy of each electric vehicle aggregator during independent optimization operation into the independent optimization operation model of the distribution operator to obtain the second solution;

[0235] The second solution result includes the optimal operation strategy when the distribution operator independently optimizes the operation and the optimal operation cost when the distribution operator independently optimizes the operation.

[0236] For example, the optimal operation strategy when the distribution operator independently optimizes the operation can be expressed as y * , the optimal operating cost of the distribution operator when operating independently can be expressed as C D* .

[0237] S6. Construct a cooperative operation model between electric vehicle aggregators and distribution operators based on generalized Nash bargaining considering three-phase imbalance management.

[0238] In this embodiment, S6 specifically includes:

[0239] S61. Construct a collaborative framework based on broad inclusion bargaining;

[0240] The collaborative cooperation framework based on broad inclusion of bargaining is:

[0241]

[0242]

[0243]

[0244] Among them, φ n The financial compensation provided by the distribution operator to the electric vehicle aggregator n for participating in the three-phase imbalance management, represents the optimal operating cost of EV aggregator n when operating independently, C D* represents the optimal operating cost when the distribution operator operates independently;

[0245] S62. Calculate the flexibility contribution index, the cooperation dependence index and the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index;

[0246] The calculation formula of flexibility contribution index is:

[0247]

[0248]

[0249]

[0250] in, is the flexibility contribution index, k1 and k2 are weighting coefficients, is the unit charging energy deviation of EV aggregator n between independent operation and cooperative operation modes, is the unit charging target deviation of EV aggregator n between independent operation and cooperative operation modes, T is the number of simulation periods, are the optimal charging power of EV aggregator n in time period t under independent operation and cooperative operation modes respectively;

[0251] The calculation formula of cooperation dependence index is:

[0252]

[0253]

[0254]

[0255] in, denote the cooperation dependence index of distribution operators, the cooperation dependence index of all EV aggregators, and the cooperation dependence index of EV aggregator n, respectively. and represents the optimal cost of the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator;

[0256] The calculation formula of the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index is:

[0257]

[0258]

[0259] Among them, α represents the bargaining power parameter of the distribution operator, β n represents the bargaining power parameter of each EV aggregator, k3 and k4 are weighting coefficients;

[0260] S63. Equivalent decomposition is performed on the collaborative cooperation framework based on broad inclusion bargaining, and the flexibility contribution index, cooperation dependence index and bargaining power parameters based on the flexibility contribution index and cooperation dependence index are used as constraints to obtain the cooperative operation model of electric vehicle aggregators and distribution operators;

[0261] The cooperation model between electric vehicle aggregators and distribution operators is:

[0262]

[0263]

[0264]

[0265]

[0266] (3-4)-(3-11)(3-16)

[0267] in, and represents the optimal cost between the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator.

[0268] In this embodiment, under the cooperative operation mode, the distribution operator negotiates with the electric vehicle aggregator to jointly decide on the deployment and switching strategy y of the phase-changing switch and the charging strategy x of the electric vehicle aggregator. At this time, the electric vehicle aggregator will deviate from its profit-maximizing charging strategy to assist the distribution operator in alleviating the three-phase imbalance governance. This enables the distribution operator to minimize its reliance on phase-changing switches, thereby directly reducing the costs associated with phase-changing switches. Therefore, as a beneficiary, the distribution operator should provide corresponding economic compensation to the electric vehicle aggregator. Therefore, it is necessary to construct a cooperative operation model between electric vehicle aggregators and distribution operators to reach a mutually beneficial negotiation agreement between the distribution operator and the electric vehicle aggregator and ensure fair profit distribution.

[0269] Among them, (3-1) is the generalized Nash product that maximizes the profit obtained by each individual after participating in the cooperation. (3-2) ensures that the costs of distribution operators and electric vehicle aggregators in the cooperative operation mode should be less than or equal to the costs in independent operation. (3-3) provides a feasible set of electric vehicle aggregators and distribution operators.

[0270] In the calculation of the flexibility contribution index, in order to evaluate the different contributions of electric vehicle aggregators, a flexibility contribution index is proposed (3-4)-(3-6) reflect the bargaining power of electric vehicle aggregators. In order to meet the needs of cooperation, electric vehicle aggregators provide higher flexibility. Meaning a relatively low flexibility contribution.

[0271] In the generalized Nash bargaining problem, the flexibility contribution index alone is not sufficient to determine the bargaining power of the participants. First, it cannot reflect the bargaining power of distribution operators. Second, it ignores the preference of electric vehicle aggregators to participate in cooperation. Specifically, some electric vehicle aggregators may have changed the charging power more frequently in multiple time periods to provide greater flexibility, but they only shifted the load from low electricity price periods to slightly higher price periods, resulting in smaller additional charging costs. In contrast, some electric vehicle aggregators may provide less flexibility, but they shifted the load from low electricity price periods to very high electricity price periods, resulting in a significant increase in charging costs. In this case, the second type of electric vehicle aggregators have made greater cost sacrifices for cooperation and require distribution operators to provide higher compensation to avoid them choosing not to participate in cooperation. Therefore, in order to better evaluate the bargaining power, the cooperation dependence index of distribution operators, all electric vehicle aggregators, and electric vehicle aggregator n is further introduced, using A higher cooperation dependence index indicates that participants gain greater relative benefits from cooperation, making them more inclined to promote cooperation; conversely, the lower the index, the lower the motivation for cooperation.

[0272] In the calculation process of the bargaining power parameters based on the flexibility contribution index and the cooperation dependence index, the following principles are followed: 1) The higher the flexibility contribution index, the greater the bargaining power; 2) The higher the cooperation dependence index, the smaller the bargaining power; 3) The bargaining power parameters of all participants are positive, and their sum is equal to 1.

[0273] In S63, the collaborative cooperation framework based on generalized inclusion bargaining is equivalently decomposed into two tractable sub-problems that are optimized and solved sequentially, namely the social welfare maximization sub-problem and the payment bargaining sub-problem.

[0274] Among them, the model of the social welfare maximization subproblem can be shown as (3-12)-(3-13), and the model of the payment bargaining subproblem can be shown as (3-14)-(3-16).

[0275] Furthermore, in order to eliminate the nonlinearity of the payment bargaining subproblem, (3-14) can be equivalently converted into logarithmic form, and then the closed-form analytical solution of the payment bargaining subproblem is derived as follows:

[0276]

[0277] in, and represents the optimal cost between the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator.

[0278] S7. Substitute the basic parameters of the distribution network, the first solution result and the second solution result into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain the third solution result, and determine the third solution result as the three-phase distribution network vehicle-grid coordination plan.

[0279] In this embodiment, further, in S7, the basic parameters of the distribution network, the first solution result and the second solution result are substituted into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain a third solution result, which specifically includes:

[0280] S71. Substitute the basic parameters of the distribution network into (3-12) and (3-13) in the cooperative operation model of the electric vehicle aggregator and the distribution operator to solve them, and obtain the optimal charging strategy of the electric vehicle aggregator, the optimal phase-changing switch deployment and switching strategy of the distribution operator, and the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode;

[0281] S72. Substitute the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode, the optimal operating cost of the electric vehicle aggregator when operating independently in the first solution result, and the optimal operating cost of the distribution operator when operating independently in the second solution result into (3-14), (3-15) and (3-16) in the cooperative operation model of the electric vehicle aggregator and the distribution operator for solution to obtain the third solution.

[0282] For example, the third solution may include the incentive fee φ that the distribution operator should pay to the electric vehicle aggregator n. n It is used as a vehicle-grid coordination solution for three-phase distribution networks to strengthen the cooperation between distribution operators and electric vehicle aggregators, thereby achieving coordinated and optimized operation of the vehicle-grid.

[0283] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A three-phase distribution network vehicle-grid coordinated operation method based on generalized Nash bargaining, characterized in that: The method comprises: S1. Obtain basic parameters of the distribution network and vehicle network; The basic parameters of the distribution network vehicle network include basic optimization parameters, technical parameters of electric vehicle aggregators, charging demand forecast data of electric vehicle aggregators, basic technical data of the distribution system and distribution system operation forecast data; S2. Build an independent optimization operation model for electric vehicle aggregators; S3, substituting the basic parameters of the distribution network into the independent optimization operation model of the electric vehicle aggregator, solving the independent optimization operation model of the electric vehicle aggregator, and obtaining a first solution result; S4. Construct an independent optimization operation model for distribution operators considering three-phase imbalance management; S5, substituting the basic parameters of the distribution network and the first solution result into the independent optimization operation model of the distribution operator to obtain a second solution result; S6. Construct a cooperative operation model between electric vehicle aggregators and distribution operators based on generalized Nash bargaining considering three-phase imbalance management; S7. Substitute the basic parameters of the distribution network and the vehicle network, the first solution result and the second solution result into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain a third solution result, and determine the third solution result as the three-phase distribution network and the vehicle network coordinated operation plan.

2. The method according to claim 1, characterized in that: The S2 specifically includes: S21, constructing the objective function of the independent optimization operation model of the electric vehicle aggregator; The objective function of the independent optimization operation model of the electric vehicle aggregator is: in, represents the total cost of an EV aggregator operating independently and optimally, is the charging cost of EV aggregator n, is the equivalent dissatisfaction cost of EV aggregator n when it fails to charge to the expected energy level, a t and b t is the dynamic pricing coefficient of time period t, x n,t is the charging power of aggregator n in time period t, Δt is the duration of each time period, and λ anx is the electric vehicle dissatisfaction coefficient, δ n is the difference between the expected charging amount and the actual charging amount of aggregator n; S22, constructing constraint conditions for the independent optimization operation model of the electric vehicle aggregator; The constraints of the independent optimization operation model of the electric vehicle aggregator include individual electric vehicle charging constraints and electric vehicle aggregator charging constraints; The individual charging constraints of the electric vehicle include a maximum charging power constraint of the electric vehicle in the t-th simulation period, a minimum cumulative charging amount constraint of the electric vehicle in the t-th simulation period, and a maximum cumulative charging amount constraint of the electric vehicle in the t-th simulation period; The maximum charging power constraint of the electric vehicle in the tth simulation period is: in, is the maximum charging power of electric vehicle i determined by the battery technology; and The time when electric vehicle i connects to and leaves the grid; The minimum cumulative charge capacity constraint of the electric vehicle in the tth simulation period is: in, and are the initial charging power of electric vehicle i at the time of arrival and the expected charging power at the time of departure, η is the charging efficiency of the electric vehicle, is the maximum allowable deviation between the expected charging power and the actual charging power of electric vehicle i; The maximum value constraint of the accumulated charge of the electric vehicle in the tth simulation period is: Where t is the tth simulation period, and are the initial charging capacity of electric vehicle i at the time of arrival and the expected charging capacity at the time of departure, The moment when electric vehicle i is connected to the grid; The electric vehicle aggregator charging constraints include the electric vehicle aggregator's charging power constraints in the t-th simulation period, the electric vehicle aggregator's cumulative charging amount constraints in the t-th simulation period, and the electric vehicle aggregator's charging amount deviation constraints; The charging power constraint of the electric vehicle aggregator in the tth simulation period is: in, is the maximum charging power of EV aggregator n determined by the capacity limit of charging stations, is a collection of electric vehicles managed by aggregator n; The cumulative charging capacity constraint of the electric vehicle aggregator in the tth simulation period is: Among them, ∑ τ≤t ηx n,t Δt is the cumulative charging amount of the EV aggregator in the tth simulation period; The electric vehicle aggregator charging capacity deviation constraint is: Among them, δ n is the deviation between the expected charging capacity and the actual charging capacity of the electric vehicle aggregator n; S23, using the constraint conditions of the independent optimization operation model of the electric vehicle aggregator to constrain the objective function of the independent optimization operation model of the electric vehicle aggregator, to obtain the independent optimization operation model of the electric vehicle aggregator; The independent optimization operation model of the electric vehicle aggregator is: in, is the feasible set of electric vehicle aggregators n, x n =[x n,1 ,…,x n,T ]Strategies for electric vehicle aggregators n.

3. The method according to claim 2, characterized in that In S3, the first solution result includes the optimal charging strategy when each electric vehicle aggregator independently optimizes the operation and the optimal operating cost when the electric vehicle aggregator independently optimizes the operation.

4. The method according to claim 3, characterized in that The S4 specifically includes: S41. Constructing the objective function of the independent optimization operation model of the distribution operator; The objective function of the independent optimization operation model of the distribution operator is: my C D =C loss +C inv +C ope (2-1) Among them, C D is the total cost of the distribution operator in the independent optimization operation model, C loss is the transmission loss cost, C inv is the daily equivalent investment cost of the phase-changing switch, C ope is the operating cost of the phase-changing switch, λ loss and They are the unit cost of network loss and the network loss in time period t, C PSD is the purchase cost of a single phase-changing switch, is the number of phase-changing switches installed at node m, r is the discount rate of cash flow, y is the service life of the phase-changing switch, C SWH is the switching cost of a single switching of the commutation switch, N SWH is the total number of switching times of the phase-changing switch in one day; S42, construct network constraints for the independent optimization operation model of distribution operators; The network constraints of the distribution operator's independent optimization operation model include node voltage and injected active power and reactive power constraints, network loss constraints during the distribution network period, and other constraints for independent optimization by the distribution operator; The node voltage and the injected active power and reactive power constraints are: in, is the voltage amplitude of all PQ nodes in time period t, Indicates the voltage amplitude of the three phases A, B, and C of bus m in time period t, is the voltage phasor of all PQ nodes when there is no load connected in the distribution system, and are the injected active power and reactive power of all PQ nodes in Y-connection and triangle connection in period t, and denote the complex power injection of Y-connection and triangle connection of all PQ nodes in period t, E Y and E Δ is the corresponding coefficient matrix; The network loss constraint condition for the distribution network period is: Among them, a is the coefficient vector, F Y and F Δ is the corresponding coefficient matrix, Respectively represent the horizontal quantities whose elements in 3D and 3M dimensions are all 1; The distribution operator independently optimizes other constraints including the phase-changing switch quantity maintenance constraint, the phase-changing switch maximum installation quantity constraint, the phase-changing switch total switching times constraint, the phase-changing switch switching load constraint, the active load constraint, the node voltage constraint and the three-phase imbalance constraint; The phase-changing switch quantity maintenance constraint condition is: Among them, X∈{A, B, C} is the three-phase phase, is an integer decision variable, which indicates the number of commutation switches of phase X connected to node m in period t, t=0 indicates the initial time before the simulation starts, is the set of simulation time periods; The maximum installation quantity constraint condition of the phase-changing switches is: Among them, N PSD+ The maximum number of phase-changing switches that can be installed in the distribution network is limited; The total switching times constraint condition of the phase-changing switch is: in, is an integer decision variable, representing the number of commutation switches of phase X connected to node m in time period t; The switching load constraint condition when the phase-changing switch is switched is: in, are the total active load and total reactive load of phase X at node m in period t, and are the basic active load and reactive load of phase X at node m in period t, respectively; Δp is the load that can be switched by a single phase-changing switch on average each time; is a binary variable, which is 1 when the EV aggregator n is connected to the X phase of the node m, otherwise it is 0, ξ is a constant related to the EV power factor, x n,t represents the optimal charging strategy of the nth EV aggregator when operating independently in time period t; The active load constraint condition is: in, is the active load; The node voltage constraint condition is: Among them, v - and v + are the voltage minimum and maximum vectors respectively; The three-phase unbalance constraint condition is: Among them, U m,t is the maximum three-phase voltage deviation of node m during period t, which is used to measure the unbalanced degree of the three-phase voltage at the node. + is the maximum limit of imbalance, and are the voltage amplitude of node m in phase X at time period t, and the average voltage amplitude of node m at time period t, respectively; S43, constraining the objective function of the independent optimization operation model of the distribution operator using the network constraint conditions of the independent optimization operation model of the distribution operator, and obtaining the independent optimization operation model of the distribution operator considering the three-phase imbalance control; The independent optimization operation model of the distribution operator considering three-phase unbalance management is: in, is the feasible set of distribution operators, represents the decision variable.

5. The method according to claim 4, characterized in that The S5 specifically includes: Substituting the basic parameters of the distribution network and the optimal charging strategy of each electric vehicle aggregator during independent optimization operation into the independent optimization operation model of the distribution operator to obtain the second solution result; The second solution result includes the optimal operation strategy when the distribution operator independently optimizes the operation and the optimal operation cost when the distribution operator independently optimizes the operation.

6. The method according to claim 5, characterized in that The S6 specifically includes: S61. Construct a collaborative framework based on broad inclusion bargaining; The collaborative cooperation framework based on broad inclusion bargaining is: Among them, φ n The financial compensation provided by the distribution operator to the electric vehicle aggregator n for participating in the three-phase imbalance management, represents the optimal operating cost of EV aggregator n when operating independently, C D* represents the optimal operating cost when the distribution operator operates independently; S62. Calculate the flexibility contribution index, the cooperation dependence index and the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index; The calculation formula of the flexibility contribution index is: in, is the flexibility contribution index, k1 and k2 are weighting coefficients, is the unit charging energy deviation of EV aggregator n between independent operation and cooperative operation modes, is the unit charging target deviation of EV aggregator n between independent operation and cooperative operation modes, T is the number of simulation periods, are the optimal charging power of EV aggregator n in time period t under independent operation and cooperative operation modes respectively; The calculation formula of the cooperation dependence index is: Among them, k D ,κ E , denote the cooperation dependence index of distribution operators, the cooperation dependence index of all electric vehicle aggregators, and the cooperation dependence index of electric vehicle aggregators, respectively. and represents the optimal cost of the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator; The calculation formula of the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index is: Among them, α represents the bargaining power parameter of the distribution operator, β n represents the bargaining power parameter of each EV aggregator, k3 and k4 are weighting coefficients; S63, performing equivalent decomposition on the collaborative cooperation framework based on broad inclusion bargaining, and taking the flexibility contribution index, the cooperation dependence index and the bargaining power parameter based on the flexibility contribution index and the cooperation dependence index as constraints, to obtain the cooperative operation model of the electric vehicle aggregator and the distribution operator; The cooperative operation model between electric vehicle aggregators and distribution operators is as follows: (3-4)-(3-11)(3-16) in, and represents the optimal cost between the distribution operator and the EV aggregator n under the cooperative operation mode before the distribution operator provides financial compensation to the EV aggregator.

7. The method according to claim 6, characterized in that In S7, the basic parameters of the distribution network, the first solution result and the second solution result are substituted into the cooperative operation model of the electric vehicle aggregator and the distribution operator to obtain a third solution result, which specifically includes: S71. Substitute the basic parameters of the distribution network into (3-12) and (3-13) in the cooperative operation model of the electric vehicle aggregator and the distribution operator to solve, and obtain the optimal charging strategy of the electric vehicle aggregator, the optimal phase-changing switch deployment and switching strategy of the distribution operator, and the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode; S72. Substitute the optimal cost of the distribution operator and the electric vehicle aggregator in the cooperative operation mode, the optimal operating cost of the electric vehicle aggregator when operating independently in the first solution result, and the optimal operating cost of the distribution operator when operating independently in the second solution result into (3-14), (3-15) and (3-16) in the cooperative operation model of the electric vehicle aggregator and the distribution operator for solution to obtain the third solution.

Images