DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method, system and device and medium

Abstract

The invention discloses a DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method, system and device and a medium. The method comprises the steps of determining an active power flow distribution condition of a power distribution network after optimization scheduling; determining the negotiation capability of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in cooperation, wherein each voltage regulation auxiliary service participant comprises an electric vehicle charging station, a power distribution network operator and a charging station operator; constructing an asymmetric Nash negotiation model according to the active power flow distribution condition of the power distribution network after optimization scheduling and the negotiation capability of each voltage regulation auxiliary service participant; the asymmetric Nash negotiation model is solved, and the benefit distribution condition of each voltage regulation auxiliary service participant is obtained.According to the method, the system, the equipment and the medium, fair and reasonable scheduling of an electric vehicle charging station, a power distribution network operator and a charging station operator can be achieved.

Description

Technical Field

[0001] The invention belongs to the field of electric vehicle optimization scheduling, and relates to a DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method, system, equipment and medium. Background Art

[0002] As the level of intelligence of the power grid increases, the power balance of the power grid, the maintenance of power quality management, and other needs to ensure the safety, economic operation, and efficient and high-quality services of the power system can be effectively achieved through corresponding auxiliary services. With its high adjustability of distributed access to power grid nodes and the duality of source and load under the V2G architecture, massive electric vehicles have also shown great application potential in providing voltage regulation auxiliary services to the power grid. As a natural aggregate of electric vehicles, electric vehicle charging stations can achieve rapid two-way flow of power between them and the power grid with the help of V2G technology, aggregate the load and storage resources of electric vehicles in the station, participate in the grid voltage regulation auxiliary service, and provide efficient active regulation support. However, the distribution of benefits of voltage regulation auxiliary services involves multiple stakeholders. Existing research on the participation of electric vehicle charging stations in voltage regulation auxiliary services rarely involves discussions on the distribution of service benefits. It is necessary to design a fair and reasonable benefit distribution mechanism to enhance the enthusiasm of all parties to participate in voltage regulation auxiliary services.

[0003] The existing traditional optimization method that considers the economic and reliability of power grid operation is essentially a cooperative process to maximize the overall benefits of the distribution network. How to make the dispatching plan of electric vehicle charging stations (EVCS), distribution network operators (DSO) and charging station operators (CSO) fair and reasonable is the focus of common concern among the three parties. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method, system, device and medium, which can achieve fair and reasonable scheduling of electric vehicle charging stations, distribution network operators and charging station operators.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] In one aspect of the present invention, the DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method of the present invention comprises:

[0007] Determine the active power flow distribution of the distribution network after optimized dispatch;

[0008] Determine the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes electric vehicle charging stations, distribution network operators and charging station operators;

[0009] According to the distribution of active power flow in the distribution network after the optimized dispatch and the negotiation capabilities of each voltage regulation auxiliary service participant, an asymmetric Nash negotiation model is constructed;

[0010] The asymmetric Nash negotiation model is solved to obtain the scheduling information of each voltage regulation auxiliary service participant, and the operation of each voltage regulation auxiliary service participant is controlled according to the scheduling information of each voltage regulation auxiliary service participant.

[0011] The DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method of the present invention is further improved in that:

[0012] Furthermore, the process of determining the active power flow distribution of the distribution network after the optimized dispatch is as follows:

[0013] The objective function of DSO side is constructed with the goal of minimizing network loss and voltage deviation in the region;

[0014] Based on the DSO side objective function, the total objective function of voltage regulation auxiliary service is constructed by considering the charging and discharging loss cost of EVCS cluster electric vehicles.

[0015] The total objective function of the voltage regulation auxiliary service is solved to obtain the active power flow distribution of the distribution network after optimized scheduling.

[0016] Furthermore, the overall objective function of the voltage regulation auxiliary service is:

[0017]

[0018] Among them, F DSO =ω1P DSO +ω2V DSO , P DSO is the network loss in the distribution network area managed by DSO, V DSO is the voltage deviation of the distribution network managed by DSO, ω1 and ω2 are P DSO and V DSO The proportionality coefficient, N is the loss cost of EVCS cluster electric vehicles considering charging and discharging losses. CSO is the number of EVCS within the jurisdiction of the CSO.

[0019] Furthermore, the constraints in the process of solving the overall objective function of the voltage regulation auxiliary service include radial distribution network constraints and energy storage system constraints in the EVCS, and the radial distribution network constraints include power flow balance constraints and safety constraints.

[0020] Furthermore, the negotiation power of each voltage regulation auxiliary service participant κ i for:

[0021]

[0022] in, The total active energy provided by voltage regulation auxiliary service participant i when participating in the distribution network optimization dispatch, E S,max The maximum value of active energy provided by the voltage regulation auxiliary service participants. is the total active energy received by voltage regulation auxiliary service participant i when participating in distribution network optimization dispatch, E R,max The maximum value of active energy received by the voltage regulation auxiliary service participant. The total amount of electric energy that voltage regulation auxiliary service participant i interacts with all other entities.

[0023] Furthermore, the asymmetric Nash negotiation model is:

[0024]

[0025] in, is the operating cost of voltage regulation auxiliary service participant i before participating in voltage regulation auxiliary service optimization, The operating cost of the voltage regulation auxiliary service participant i after participating in the voltage regulation auxiliary optimization, The negotiation interaction cost of voltage regulation auxiliary service participant i.

[0026] Furthermore, the process of solving the asymmetric Nash negotiation model is:

[0027] The asymmetric Nash negotiation model is solved based on the ADMM algorithm.

[0028] In a second aspect of the present invention, the DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling system of the present invention comprises:

[0029] The first determination module is used to determine the active power flow distribution of the distribution network after optimized scheduling;

[0030] The second determination module is used to determine the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes an electric vehicle charging station, a distribution network operator and a charging station operator;

[0031] A construction module is used to construct an asymmetric Nash negotiation model according to the distribution of active power flow of the distribution network after the optimized scheduling and the negotiation capabilities of each voltage regulation auxiliary service participant;

[0032] The solution module is used to solve the asymmetric Nash negotiation model, obtain the scheduling information of each voltage regulation auxiliary service participant, and control the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant.

[0033] In a third aspect of the present invention, the computer device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method when executing the computer program.

[0034] In a fourth aspect of the present invention, the computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method are implemented.

[0035] The present invention has the following beneficial effects:

[0036] The DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method, system, device and medium described in the present invention, during specific operation, uses the emerging subject distribution network operator as the operation and management party of the distribution network, introduces the charging station operator as the management and guidance link of the charging station, and quantifies the negotiation ability of each auxiliary service participant in the asymmetric Nash negotiation according to the contribution of each participant in the cooperation, and obtains the scheduling information of each voltage regulation auxiliary service participant, and controls the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant, thereby protecting the interests of each participant, improving the enthusiasm of each participant to participate in the auxiliary service, and solving the centralization and privacy security risks in the process of interest distribution of each participant. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 It is a structural diagram of the DSO-CSO-EVCS system in the present invention;

[0040] Figure 3 It is a solution flow chart based on ADMM algorithm;

[0041] Figure 4 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0043] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0044] like Figure 1 As shown, the DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method of the present invention comprises the following steps:

[0045] 1) Load the scenario of electric vehicle charging station (EVCS) participating in the auxiliary service of distribution network voltage regulation, and obtain the basic data of distribution network operator (DSO), charging station operator (CSO) and electric vehicle charging station;

[0046] In step 1), if Figure 2 As shown in the figure, DSO is responsible for the management of the distribution network and is the publisher of voltage regulation auxiliary services; EVCS is the actual participant of voltage regulation auxiliary services and is guided and managed by CSO; CSO is the information exchange platform and guide of EVCS and plays a bridging role between EVCS and DSO. The basic data on the distribution network operator side, charging station operator side and electric vehicle charging station side include: line complex impedance, types of dispatchable vehicles in the charging station, number of vehicles, rated charging and discharging power, rated capacity of energy storage system in the charging station, and charging and discharging loss coefficient in the station.

[0047] 2) Combining voltage regulation with loss reduction, taking the minimum network loss and voltage deviation in the region as the goal, constructing the DSO side objective function, and then considering the charging and discharging loss cost of EVCS cluster electric vehicles, constructing the total objective function of voltage regulation auxiliary service, using Yalmip / CPLEX to solve the total objective function of voltage regulation auxiliary service, and obtaining the active power flow distribution of the distribution network after optimized scheduling;

[0048] The objective function of the DSO side in step 2) is:

[0049] F DSO =ω1P DSO +ω2V DSO

[0050] Among them, P DSO V is the network loss in the distribution network area managed by DSO; DSO is the voltage deviation of the distribution network managed by DSO; ω1 and ω2 are PDSO and V DSO The proportionality factor.

[0051] The overall objective function of the voltage regulation auxiliary service in step 2) is:

[0052]

[0053] in, N is the loss cost of EVCS cluster electric vehicles considering charging and discharging losses. CSO is the number of EVCS within the jurisdiction of the CSO.

[0054] The constraints in the solution process in step 2) include:

[0055] The radial distribution network constraints include power flow balance constraints and safety constraints, namely:

[0056]

[0057] Among them, P j , Q j are the active and reactive injected powers of node j respectively; P jk , Q jk are respectively the active and reactive power flows of branch jk; P ij , Q ij are respectively the active and reactive power flows of branch ij; G j , B j are the equivalent conductance and inductance of node j respectively. i 、V j are the voltage amplitudes of nodes i and j respectively; and They are the upper and lower limits of current and voltage under safety constraints respectively.

[0058] The constraints of the energy storage system in EVCS are:

[0059]

[0060] in, is the energy storage system capacity of charging station j; and are the charging and discharging power of the energy storage system of charging station j respectively; η ESS,ch and η ESS,ch are the charging and discharging efficiency of the energy storage system respectively; and The upper and lower limits of charging and discharging power of the energy storage system of charging station j; and are the upper and lower limits of the energy storage system power of charging station j; and 0-1 Boolean variables representing the charging and discharging states of the energy storage system, indicating that charging and discharging cannot be performed at the same time; the initial power of the energy storage system in each scheduling cycle must remain consistent.

[0061] In the optimized dispatch of voltage regulation auxiliary services, each EVCS needs to meet the charging needs of electric vehicles in the station, namely:

[0062]

[0063] Among them, η EV The charging and discharging efficiency of electric vehicles; and are the charging and discharging power of cluster electric vehicles respectively; To meet the charging needs of cluster electric vehicles.

[0064] Step 3) determining the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of the voltage regulation auxiliary service participant in the cooperation;

[0065] The negotiation capacity of each voltage regulation auxiliary service participant in step 3) is:

[0066]

[0067] in, The total active energy provided by voltage regulation auxiliary service participant i when participating in the distribution network optimization dispatch; E S,max The maximum value of active energy provided to the participants of voltage regulation auxiliary service; E is the total active energy received by voltage regulation auxiliary service participant i when participating in distribution network optimization dispatch; R,max The maximum value of active electric energy received by the voltage regulation auxiliary service participant; The total amount of electric energy that voltage regulation auxiliary service participant i interacts with all other entities.

[0068] 4) Taking the maximization of the Nash product of the added value of all participating entities as the goal, an asymmetric Nash negotiation model is established, and the objective function is transformed accordingly, and auxiliary variables are introduced to decouple the coupled income settlement equation constraints among DSO, CSO, and EVCS;

[0069] The asymmetric Nash negotiation model in step 4) is:

[0070]

[0071] in, is the operating cost of voltage regulation auxiliary service participant i before participating in voltage regulation auxiliary service optimization; The operating cost of voltage regulation auxiliary service participant i after participating in voltage regulation auxiliary optimization; The negotiation interaction cost of voltage regulation auxiliary service participant i.

[0072] The transformation operation of the objective function in step 4) is to first take a logarithmic transformation of the original objective function, transform the multiplication problem of the sub-problem into an addition problem, and then take the opposite number, transform the maximum value problem into the minimum value problem, and the new objective function is:

[0073]

[0074] Introducing auxiliary variables Decouple the revenue settlement equation constraints between DSO, CSO and EVCS, and obtain the new constraint formula: Among them, the auxiliary variable The transfer income that the voltage regulation auxiliary service participant j expects to achieve with the entity i.

[0075] Step 5), based on the ADMM algorithm principle, the augmented Lagrangian function of the objective function is decomposed to obtain the augmented Lagrangian function of each voltage regulation auxiliary service participant;

[0076] The augmented Lagrangian function of each voltage regulation auxiliary service participant in step 5) is:

[0077]

[0078] Among them, u ij and i are the Lagrange multiplier and penalty coefficient of voltage regulation auxiliary service participant i respectively.

[0079] 6) The objective function is solved by using the ADMM algorithm until the convergence accuracy or the maximum number of iterations is reached, and the benefit distribution of each auxiliary service entity is obtained.

[0080] The solution steps of the ADMM algorithm in step 6) refer to Figure 3 , specifically:

[0081] 61) Initialization parameters: Set the maximum number of iterations k max , convergence accuracy ξ, penalty parameter ρ i =1, initialize the number of iterations k = 0, Initialize the profit transfer strategy of DSO, CSO and EVCS Lagrange multiplier u i =0;

[0082] 62) Update settlement strategy and exchange revenue transfer information:

[0083] 621) For the voltage regulation auxiliary service participant i, receive the income transfer strategy of other subjects j in the kth iteration And calculate its k+1th profit strategy in, for:

[0084]

[0085] 622) For auxiliary service participant j, which is different from i, receive the benefit transfer strategy of subject i for the k+1th time And calculate its k+1th profit transfer strategy in, for:

[0086]

[0087] Repeat step 621) and step 622) until the DSO, CSO and EVCS have completed the k+1th revenue transfer strategy update;

[0088] 63) Update the Lagrange multiplier:

[0089]

[0090] 64) Update the number of iterations k = k + 1;

[0091] 65) Determine whether the algorithm converges. The convergence criterion is:

[0092]

[0093] When the convergence criterion is met, or the maximum number of iterations k is exceeded max When , the iteration terminates, otherwise, jump back to step 621);

[0094] 66)End.

[0095] Embodiment 2

[0096] refer to Figure 4 The DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling system of the present invention comprises:

[0097] The first determination module is used to determine the active power flow distribution of the distribution network after optimized scheduling;

[0098] The second determination module is used to determine the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes an electric vehicle charging station, a distribution network operator and a charging station operator;

[0099] A construction module is used to construct an asymmetric Nash negotiation model according to the distribution of active power flow of the distribution network after the optimized scheduling and the negotiation capabilities of each voltage regulation auxiliary service participant;

[0100] The solution module is used to solve the asymmetric Nash negotiation model, obtain the scheduling information of each voltage regulation auxiliary service participant, and control the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant.

[0101] As an implementation mode of the present invention, the process of determining the active power flow distribution of the distribution network after optimized scheduling is as follows:

[0102] The objective function of DSO side is constructed with the goal of minimizing network loss and voltage deviation in the region;

[0103] Based on the DSO side objective function, the total objective function of voltage regulation auxiliary service is constructed by considering the charging and discharging loss cost of EVCS cluster electric vehicles.

[0104] The total objective function of the voltage regulation auxiliary service is solved to obtain the active power flow distribution of the distribution network after optimized scheduling.

[0105] As an implementation mode of the present invention, the overall objective function of the voltage regulation auxiliary service is:

[0106]

[0107] Among them, F DSO =ω1P DSO +ω2V DSO , P DSO is the network loss in the distribution network area managed by DSO, V DSO is the voltage deviation of the distribution network managed by DSO, ω1 and ω2 are P DSO and V DSO The proportionality coefficient, N is the loss cost of EVCS cluster electric vehicles considering charging and discharging losses. CSO is the number of EVCS within the jurisdiction of the CSO.

[0108] As an implementation mode of the present invention, the constraints in the process of solving the total objective function of the voltage regulation auxiliary service include radial distribution network constraints and energy storage system constraints in the EVCS, and the radial distribution network constraints include power flow balance constraints and safety constraints.

[0109] As an implementation method of the present invention, the negotiation capability κ of each voltage regulation auxiliary service participant i for:

[0110]

[0111] in, The total active energy provided by voltage regulation auxiliary service participant i when participating in the distribution network optimization dispatch, ES,max The maximum value of active energy provided by the voltage regulation auxiliary service participants. is the total active energy received by voltage regulation auxiliary service participant i when participating in distribution network optimization dispatch, E R,max The maximum value of active energy received by the voltage regulation auxiliary service participant. The total amount of electric energy that voltage regulation auxiliary service participant i interacts with all other entities.

[0112] As an implementation mode of the present invention, the asymmetric Nash negotiation model is:

[0113]

[0114] in, is the operating cost of voltage regulation auxiliary service participant i before participating in voltage regulation auxiliary service optimization, The operating cost of the voltage regulation auxiliary service participant i after participating in the voltage regulation auxiliary optimization, The negotiation interaction cost of voltage regulation auxiliary service participant i.

[0115] As an implementation mode of the present invention, the process of solving the asymmetric Nash negotiation model is:

[0116] The asymmetric Nash negotiation model is solved based on the ADMM algorithm.

[0117] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0118] Embodiment 3

[0119] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method when executing the computer program, including: determining the active power flow distribution of the distribution network after optimized scheduling; determining the negotiation capability of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes an electric vehicle charging station, a distribution network operator and a charging station operator; constructing an asymmetric Nash negotiation model according to the active power flow distribution of the distribution network after the optimized scheduling and the negotiation capability of each voltage regulation auxiliary service participant; solving the asymmetric Nash negotiation model to obtain the scheduling information of each voltage regulation auxiliary service participant, and controlling the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, network interface, and memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0120] Embodiment 4

[0121] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the DSO-CSO-EVCS asymmetric Nash negotiation auxiliary service scheduling method are implemented, specifically, including: determining the distribution of active power flow of the distribution network after optimized scheduling; determining the negotiation ability of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, and each voltage regulation auxiliary service participant includes an electric vehicle charging station, a distribution network operator and a charging station operator; constructing an asymmetric Nash negotiation model according to the distribution of active power flow of the distribution network after the optimized scheduling and the negotiation ability of each voltage regulation auxiliary service participant; solving the asymmetric Nash negotiation model to obtain the scheduling information of each voltage regulation auxiliary service participant, and controlling the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include a random access memory (RAM) and / or a cache memory, etc. The non-volatile memory may include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, and the like.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method, characterized in that: include: Determine the active power flow distribution of the distribution network after optimized dispatch; Determine the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes electric vehicle charging stations, distribution network operators and charging station operators; According to the distribution of active power flow in the distribution network after the optimized dispatch and the negotiation capabilities of each voltage regulation auxiliary service participant, an asymmetric Nash negotiation model is constructed; The asymmetric Nash negotiation model is solved to obtain the scheduling information of each voltage regulation auxiliary service participant, and the operation of each voltage regulation auxiliary service participant is controlled according to the scheduling information of each voltage regulation auxiliary service participant.

2. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The process of determining the active power flow distribution of the distribution network after optimized dispatch is as follows: The objective function of DSO side is constructed with the goal of minimizing network loss and voltage deviation in the region; Based on the DSO side objective function, the total objective function of the voltage regulation auxiliary service is constructed by considering the charging and discharging loss cost of the EVCS cluster electric vehicles; The total objective function of the voltage regulation auxiliary service is solved to obtain the active power flow distribution of the distribution network after optimized scheduling.

3. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The overall objective function of the voltage regulation auxiliary service is: Among them, F DSO =ω1P DSO +ω2V DSO , P DSO is the network loss in the distribution network area managed by DSO, V DSO is the voltage deviation of the distribution network managed by DSO, ω1 and ω2 are P DSO and V DSO The proportionality coefficient, N is the loss cost of EVCS cluster electric vehicles considering charging and discharging losses. CSO is the number of EVCS within the jurisdiction of the CSO.

4. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The constraints in the process of solving the total objective function of the voltage regulation auxiliary service include radial distribution network constraints and energy storage system constraints in the EVCS, and the radial distribution network constraints include power flow balance constraints and safety constraints.

5. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The negotiation capability of each voltage regulation auxiliary service participant κ i for: in, The total active energy provided by voltage regulation auxiliary service participant i when participating in the distribution network optimization dispatch, E S,max The maximum value of active energy provided by the voltage regulation auxiliary service participants. is the total active energy received by voltage regulation auxiliary service participant i when participating in distribution network optimization dispatch, E R,max The maximum value of active energy received by the voltage regulation auxiliary service participant. The total amount of electric energy that voltage regulation auxiliary service participant i interacts with all other entities.

6. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The asymmetric Nash negotiation model is: in, is the operating cost of voltage regulation auxiliary service participant i before participating in voltage regulation auxiliary service optimization, The operating cost of the voltage regulation auxiliary service participant i after participating in the voltage regulation auxiliary optimization, The negotiation interaction cost of voltage regulation auxiliary service participant i.

7. The DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method according to claim 1, characterized in that: The process of solving the asymmetric Nash negotiation model is: solving the asymmetric Nash negotiation model based on the ADMM algorithm.

8. A DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling system, characterized in that: include: The first determination module is used to determine the active power flow distribution of the distribution network after optimized scheduling; The second determination module is used to determine the negotiation capacity of each voltage regulation auxiliary service participant according to the contribution of each voltage regulation auxiliary service participant in the cooperation, wherein each voltage regulation auxiliary service participant includes an electric vehicle charging station, a distribution network operator and a charging station operator; A construction module is used to construct an asymmetric Nash negotiation model according to the distribution of active power flow of the distribution network after the optimized scheduling and the negotiation capabilities of each voltage regulation auxiliary service participant; The solution module is used to solve the asymmetric Nash negotiation model, obtain the scheduling information of each voltage regulation auxiliary service participant, and control the operation of each voltage regulation auxiliary service participant according to the scheduling information of each voltage regulation auxiliary service participant.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the DSO-CSO-EVCS asymmetric Nash negotiation assisted service scheduling method as described in any one of claims 1 to 7 are implemented.

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