Alternating current and direct current hybrid transformer area decentration reactive power optimization method based on Nash equilibrium

Abstract

The invention relates to the field of power grid optimization, and provides a Nash equilibrium-based decentralized reactive power optimization method for an alternating current / direct current hybrid power distribution area, which comprises the following steps of: S1, dividing the alternating current / direct current hybrid power distribution area into a plurality of independent optimized areas; s2, constructing a target function and related constraints of each region to form a decentralized voltage reactive power optimization model; s3, independently solving the decentralized voltage reactive power optimization model of each region by adopting a Nash equilibrium algorithm to obtain an optimal solution of each region; and S4, according to the Nash equilibrium solution of the reactive power optimization model obtained in the step S3, distributing reactive power output of distributed energy sources and reactive power sources in the alternating current and direct current hybrid power distribution area, and realizing the minimum active power loss of the alternating current and direct current hybrid power distribution area. According to the method, the reactive power source share in the distribution area is optimized, so that the utilization efficiency of reactive power sources such as distributed renewable energy sources and capacitors is improved, and the privacy security of users in each area is reliably protected.

Description

Technical Field

[0001] The present application relates to the field of power grid optimization, and specifically to a decentralized reactive power optimization method for an AC / DC hybrid substation based on Nash equilibrium. Background Art

[0002] In the current low-voltage distribution substations, due to the rapid increase in the penetration rate of new DC loads, distributed energy (such as wind power, photovoltaics, etc.) and energy storage equipment, the degree of power electronics and AC / DC hybrid connection in low-voltage distribution networks is increasing. The hybrid distribution substations with flexible AC / DC interconnection have the advantages of both AC and DC distribution lines. On the other hand, due to the large capacity and high proportion of distributed energy access, the randomness and volatility of its output have aggravated the power loss of the distribution substation operation, limiting the transformation of the new distribution network with renewable energy as the main body to a safe, controllable, flexible and efficient development trend.

[0003] In the existing technology, integrating AC / DC hybrid distribution stations into a low-voltage active distribution network is one of the main solutions to improve the benefits of distributed energy: the ends of the AC stations are interconnected through DC lines and intelligent soft switches (soft openpoint, SOP), photovoltaics, flexible DC loads, reactive power sources, etc. are connected to the DC lines, and the advantages of distributed photovoltaic output are fully utilized through interconnection channels. In recent years, research on AC / DC flexible hybrid distribution stations has mainly focused on control schemes for AC / DC voltage, power flow, etc. and distribution station operation strategies. The control scheme emphasizes the instantaneous operating conditions of system voltage and frequency, while the main goal of the operation strategy is the optimal operation of distributed energy at the hourly level.

[0004] Since voltage-reactive power optimization is one of the main problems of the distribution system, it aims to determine the optimal allocation ratio of various reactive power sources such as distributed energy and reactive compensators, so as to optimize specific objective functions while meeting network constraints. In order to reduce the power loss of the hybrid substation network, voltage-reactive power optimization methods are often adopted. The current common method is mainly a fully cooperative centralized optimization method, that is, to optimize and coordinate the overall performance indicators of the AC / DC hybrid substation, such as minimizing the total operating cost of the overall system of the existing distribution substation. It should be noted that although the global optimum is obtained through a fully cooperative centralized model, it may not meet the requirements of the target optimization for a specific AC or substation line. From the perspective of the solution, the centralized method needs to collect all the data of the entire substation line in the control center for model solution, and its communication and calculation burden is very high; in addition, the user load of the AC / DC distribution line may be managed by different operators, and its privacy cannot be protected in a centralized way. Therefore, it should be considered to adopt an optimization method that can effectively deal with the above decentralized problems. In summary, it is necessary to adopt a decentralized voltage-reactive power optimization method for the AC / DC hybrid substation to achieve good optimization results. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to propose a decentralized reactive power optimization method for AC / DC hybrid substations based on Nash equilibrium, which is used to coordinate the allocation ratio of relevant reactive power sources in multiple AC / DC hybrid low-voltage distribution substations. The proposed decentralized coordinated voltage reactive power optimization method can effectively reduce the power loss of the network while protecting the privacy of AC / DC line participants, providing support for the development of new AC / DC distribution networks.

[0006] To achieve the above object, the solution adopted by the present invention is a decentralized reactive power optimization method for AC / DC hybrid substations based on Nash equilibrium, which includes the following steps:

[0007] S1. Divide the AC / DC hybrid distribution station area into N independently optimized areas;

[0008] S2. Construct the objective function and related constraints of each region to form a decentralized voltage and reactive power optimization model;

[0009] S21, minimizing the active power loss of each line in the divided area as the objective function of each area;

[0010] Objective function of the communication area:

[0011] Objective function in the DC region:

[0012] Where i is the area number of the AC / DC hybrid distribution station area, and the numbers of the AC area and the DC area are arranged in the same order, i = 1, 2, ..., N; A i Indicates that area i is the communication area, D i Indicates that region i is a DC region; represents the objective function when region i is an AC region, represents the objective function when region i is a DC region, P i ls is the active power loss of area i;

[0013] S22, determining the optimization constraints of the communication area;

[0014] When area i is an AC area, that is, The optimization constraints for region i are:

[0015]

[0016] P i ls =r i l i =P i Ι-P i Ⅱ (7)

[0017]

[0018] Where k is the node number of the line belonging to area i. The node numbers are uniformly sorted in the AC / DC hybrid distribution substation area. K is the set of node numbers in area i, k∈K; and is the active and reactive power load at node k; and The active and reactive output of distributed energy accessed by node k; Represents the reactive output of reactive power sources such as capacitors connected to node k; and is the active power and reactive power of node k in the front-end line flowing to the AC area; and is the active power and reactive power flowing from node k in the AC area to the back-end line; Used to identify whether node k is a sending node or a receiving node. When node k is a sending node is 1 when node k is a receiving node is -1; v k is the square value of the voltage at node k; r i and x i is the resistance and reactance of the line in area i; P i Ι and is the active and reactive power input to the head end of the line in area i; i is the square value of the line current in region i; ||||2 represents the 2-norm of the matrix, P i Ⅱ and is the active and reactive power output at the end of the line in area i; P i ls and is the active and reactive power loss of the line in area i; v k and are the lower and upper limits of the square value of the voltage at node k; is the upper limit of the square value of the line current in area i; m k 、n k , α k , β k are all predefined parameters that limit the reactive power output of distributed energy resources connected to node k; is the upper limit of the active output of distributed energy connected to node k; and The lower and upper limits of reactive power output of reactive power sources such as capacitors connected to k;

[0019] S23, determining the optimization constraints of the DC region;

[0020] When region i is a DC region, that is, The optimization constraints for region i are:

[0021]

[0022] P i ls =r i l i =P i Ι -P i Ⅱ (18)

[0023]

[0024] in, is the active power of node k in the front-end line flowing to the DC region; is the active power flowing from node k in the DC region to the back-end line;

[0025] S3. Use the Nash equilibrium algorithm to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the optimal solution for each region;

[0026] S4. According to the Nash equilibrium solution of the reactive power optimization model obtained in step S3, the reactive power output of distributed energy in the AC / DC hybrid distribution station area is calculated. and reactive power output of reactive power source Distribution is carried out to minimize the active power loss in the AC / DC hybrid distribution station area.

[0027] Preferably, step S1 specifically comprises:

[0028] Independent areas that need to be optimized are divided into AC and DC hybrid distribution substations, including AC areas and DC areas, where each AC substation line connected to the distribution network bus at the head end and the SOP at the end is divided into an independent AC area; each DC line connected to the SOP at both ends is divided into an independent DC area; the number of areas obtained after division is N, where N is a positive integer.

[0029] Preferably, step S3 specifically includes the following steps:

[0030] S31, describing the decentralized voltage-reactive power optimization model in step S2 using a boundary information allocation method;

[0031] S32, based on the boundary variables received in each region The Nash equilibrium algorithm is used to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the independent variables and And the new boundary variables and And based on the new boundary variables obtained in each area, update the boundary variables received by other areas in, is the boundary variable of other regions received by the communication region i, is the boundary variable of other regions received by DC region i, is the independent variable of the optimization constraint in the optimization model of AC region i, is the independent variable of the optimization constraint in the DC region i optimization model, is the boundary variable sent by communication area i to other areas, is the boundary variable sent by DC region i to other regions;

[0032] S33, determine whether the objective function solved in step S32 meets the set iteration stop criteria: if it does, it means that each region can achieve the optimal objective function according to the Nash equilibrium solution, and the iteration stops; if it does not, return to step S32, and update the boundary variables received by each region according to the updated boundary variables received by each region. Continue to solve for the variables.

[0033] Preferably, step S31 uses a boundary information allocation method to describe the decentralized voltage-reactive power optimization model in step S2, specifically:

[0034] (a) When region i is an AC region, that is, The decentralized voltage-reactive power optimization model of region i is:

[0035]

[0036] Among them, formula (22) is the objective function of the optimization model of communication area i; formula (23) represents all non-equality constraints in the optimization constraints of the optimization model of communication area i; formula (24) represents all equality constraints in the optimization constraints of the optimization model of communication area i; is the independent variable of the optimization constraint in the optimization model of communication area i, including l i , P i ls , These 7 variables; is the boundary variable sent by communication area i to other areas, including the v k These 4 variables; is the boundary variable of other regions received by the communication region i, is a known value, based on the boundary variables of other regions Can get the communication area i P i Ι and The “boundary variables of other regions” here include boundary variables sent by other DC regions and other AC regions;

[0037] (b) When region i is a DC region, that is, The decentralized voltage-reactive power optimization model of DC region i is:

[0038]

[0039] Among them, formula (25) is the objective function of the optimization model of DC region i; formula (26) represents all non-equality constraints in the optimization constraints of the optimization model of DC region i; formula (27) represents all equality constraints in the optimization constraints of the optimization model of DC region i; is the independent variable of the optimization constraint in the DC region i optimization model, including the DC region i l i , P i ls These 4 variables; is the boundary variable sent by DC region i to other regions, including the and v k These 2 variables; is the boundary variable of other regions received by DC region i, is a known value, based on the boundary variables of other regions It can be obtained that the DC region i and P i Ι .

[0040] Preferably, the iteration stopping criterion in step S33 is:

[0041]

[0042] Where, e represents the e-th iteration; are the values ​​of the objective function in the AC region obtained at the e-th iteration and the (e-1)-th iteration, respectively; are the values ​​of the objective function in the DC region obtained at the e-th iteration and the (e-1)-th iteration respectively; It is a pre-set judgment threshold. When all regions meet the iteration stop criteria after the e-th iteration, it is considered that the set iteration stop criteria are met.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The AC / DC hybrid distribution station area is divided into independent optimization areas, and the voltage and reactive power optimization model of each area is established. By solving the voltage and reactive power optimization model of each area in the AC / DC hybrid distribution station area, the distributed power supply and reactive power supply share in the distribution station area are optimized, and the utilization efficiency of distributed power supply and reactive power supply such as capacitors is improved.

[0045] 2. The voltage and reactive power optimization model of each area in the AC / DC hybrid substation is solved by a decentralized optimization solution method based on the Nash equilibrium criterion. Only the regional boundary variable data can be shared, which can reduce the communication and computing burden while ensuring the privacy of participating customers.

[0046] 3. Through reactive power optimization, the active power loss of the AC / DC hybrid substation network is effectively reduced, and the efficiency of substation operation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a decentralized reactive power optimization method for AC / DC hybrid substations based on Nash equilibrium of the present invention;

[0048] Figure 2 A detailed flow chart of the invention for solving the decentralized voltage-reactive power optimization model in the AC / DC hybrid substation area;

[0049] Figure 3 A topological diagram of dividing an AC / DC hybrid substation area into independent optimization areas in an embodiment of the present invention;

[0050] Figure 4 The comparison result of reactive power share utilization rate in each area before and after the decentralized voltage reactive power optimization method is adopted in the embodiment of the present invention;

[0051] Figure 5 The comparison results of active power loss calculation of each region before and after the decentralized voltage and reactive power optimization method is adopted in the embodiment of the present invention;

[0052] Figure 6 It is a calculation result of the stopping standard of the objective function iteration after each optimization solution of a certain communication area in the embodiment of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention.

[0054] The embodiment of the present invention provides a decentralized voltage and reactive power optimization method based on Nash equilibrium in AC / DC hybrid substations, which is mainly used to coordinate the allocation ratio of relevant reactive power sources in multiple AC / DC hybrid low-voltage distribution substations. Figure 1 As shown, the specific steps include:

[0055] S1. Divide the AC / DC hybrid distribution station area into multiple independently optimized areas.

[0056] In the AC / DC hybrid distribution substation, separate areas that need to be optimized are divided, including AC areas and DC areas, where: each AC substation line connected to the distribution network bus at the head end and the SOP at the end is divided into an independent AC area; each DC line connected to the SOP at both ends is divided into an independent DC area. The specific number of areas N obtained after division is determined according to the actual structure of the AC / DC hybrid distribution substation, and N is a positive integer.

[0057] S2. Construct the objective function and related constraints of each region to form a decentralized voltage-reactive power optimization model.

[0058] In the power grid, active power loss can be achieved by changing the line voltage, and the line voltage can be achieved by changing the reactive power output. Therefore, the active power loss can be reduced through voltage and reactive power optimization to achieve optimization of the power grid.

[0059] For an AC / DC hybrid power distribution substation containing multiple independent areas, if the active power loss of each area is minimized, then the active power loss of the AC / DC hybrid power distribution substation is minimized. Therefore, an objective function and related constraints are constructed for each area to form a voltage-reactive power optimization model, which specifically includes the following steps:

[0060] S21. Minimizing the active power loss of each line in the divided area is taken as the objective function of each area.

[0061] Objective function of the communication area:

[0062] Objective function in the DC region:

[0063] Where i is the area number of the AC / DC hybrid distribution station area, and the numbers of the AC area and the DC area are arranged in the same order, i = 1, 2, ..., N; A i Indicates that area i is the communication area, D i Indicates that region i is a DC region; represents the objective function when region i is the communication region, f Di represents the objective function when region i is a DC region, P i ls is the active power loss in area i.

[0064] S22. Determine optimization constraints of the communication area.

[0065] When area i is an AC area, that is, The optimization constraints for region i are:

[0066]

[0067] P i ls =r i l i =P i Ι -P i Ⅱ (7)

[0068]

[0069] Where k is the node number of the line belonging to area i. The node numbers are uniformly sorted in the AC / DC hybrid distribution substation area. K is the set of node numbers in area i, k∈K; and is the active and reactive power load at node k; and The active and reactive output of distributed energy accessed by node k; Represents the reactive output of reactive power sources such as capacitors connected to node k; and is the active power and reactive power of node k in the front-end line flowing to the AC area; and is the active power and reactive power flowing from node k in the AC area to the back-end line; Used to identify whether node k is a sending node or a receiving node. When node k is a sending node is 1 when node k is a receiving node is -1; v k is the square value of the voltage at node k; r i and x i is the resistance and reactance of the line in area i; P i Ι and is the active and reactive power input to the head end of the line in area i; i is the square value of the line current in region i; ||||2 represents the 2-norm of the matrix, P i Ⅱ and is the active and reactive power output at the end of the line in area i; P i ls and is the active and reactive power loss of the line in area i; v k and are the lower and upper limits of the square value of the voltage at node k; is the upper limit of the square value of the line current in area i; m k 、n k , α k , β k are all predefined parameters that limit the reactive power output of distributed energy resources connected to node k; is the upper limit of the active output of distributed energy connected to node k; and The lower and upper limits of reactive power output of reactive power sources such as capacitors connected to k.

[0070] It can be seen that in the optimization constraints of the AC area, formulas (3)-(8) are obtained based on the circuit flow calculation principle, and formulas (9)-(14) are set according to the circuit operation safety requirements or distributed energy operation requirements.

[0071] S23. Determine the optimization constraints of the DC region.

[0072] When region i is a DC region, that is, The optimization constraints for region i are:

[0073]

[0074] P i ls =r i l i =P i Ι -P i Ⅱ (18)

[0075]

[0076] in, is the active power of node k in the front-end line flowing to the DC region; is the active power flowing from node k in the DC region to the back-end line.

[0077] It can be seen that in the optimization constraints of the DC area, formulas (15)-(18) are obtained based on the circuit power flow calculation principle, and formulas (19)-(21) are set according to the circuit operation safety requirements or distributed energy operation requirements.

[0078] S3. Use the Nash equilibrium algorithm to independently solve the decentralized voltage-reactive power optimization model for each region and obtain the optimal solution for each region, such as Figure 2 As shown, the specific steps include:

[0079] S31. Use the partitioning and assignment of boundary information (PABI) method to describe the decentralized voltage-reactive power optimization model in step 2:

[0080] (a) When region i is an AC region, that is, The decentralized voltage-reactive power optimization model of region i is:

[0081]

[0082] Among them, formula (22) is the objective function of the optimization model of communication area i; formula (23) represents all non-equality constraints in the optimization constraints of the optimization model of communication area i; formula (24) represents all equality constraints in the optimization constraints of the optimization model of communication area i; is the independent variable of the optimization constraint in the optimization model of communication area i, including l i , P i ls , These 7 variables; is the boundary variable sent by communication area i to other areas, including the v k These 4 variables; is the boundary variable of other regions received by the communication region i, is a known value, based on the boundary variables of other regions Can get the communication area i P i Ι and The “boundary variables of other regions” here include boundary variables sent by other DC regions and other AC regions.

[0083] (b) When region i is a DC region, that is, The decentralized voltage-reactive power optimization model of DC region i is:

[0084]

[0085] Among them, formula (25) is the objective function of the optimization model of DC region i; formula (26) represents all non-equality constraints in the optimization constraints of the optimization model of DC region i; formula (27) represents all equality constraints in the optimization constraints of the optimization model of DC region i; is the independent variable of the optimization constraint in the DC region i optimization model, including the DC region i l i , P i ls These 4 variables; is the boundary variable sent by DC region i to other regions, including the and v k These 2 variables; is the boundary variable of other regions received by DC region i, is a known value, based on the boundary variables of other regions It can be obtained that the DC region i and P i Ι .

[0086] S32, based on the boundary variables received in each region The Nash equilibrium algorithm is used to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the independent variables and And the new boundary variables and And based on the new boundary variables obtained in each area, update the boundary variables received by other areas At initialization, each region initially receives the bounds variable The initial value can be set to 0 or the circuit rated value, and the reactive output of reactive power sources such as distributed power sources and capacitors at each node is set to 0.

[0087] S33, determine whether the objective function solved in step S32 meets the set iteration stop criteria: if it does, it means that each region can achieve the optimal objective function according to the Nash equilibrium solution, and the iteration stops; if it does not, return to step S32, and update the boundary variables received by each region according to the updated boundary variables received by each region. Continue to solve for the variables.

[0088] In this embodiment, the iteration stopping criterion is:

[0089]

[0090] Where, e represents the e-th iteration; are the values ​​of the objective function in the AC region obtained at the e-th iteration and the (e-1)-th iteration, respectively; are the values ​​of the objective function in the DC region obtained at the e-th iteration and the (e-1)-th iteration respectively; is a pre-set judgment threshold. When all regions meet the iteration stop criteria after the e-th iteration, it is considered that the set iteration stop criteria are met.

[0091] S4. According to the Nash equilibrium solution of the reactive power optimization model obtained in step S3, the reactive power output of distributed energy in the AC / DC hybrid distribution station area is calculated. and reactive power output of reactive power source Distribution is carried out to optimize the AC / DC hybrid distribution station area and minimize the active power loss in the AC / DC hybrid distribution station area.

[0092] This embodiment provides a specific application example to illustrate the whole process of using the method of the present invention to perform decentralized voltage and reactive power optimization of AC / DC hybrid substations based on Nash equilibrium. The object of this embodiment is to connect multiple AC distribution line terminals with DC lines through flexible interconnection devices SOP to form an AC / DC hybrid substation. The independent optimization area of ​​the AC / DC hybrid substation is divided as follows: Figure 3 The load, renewable energy output and reactive power capacity of each node are shown in the following table.

[0093]

[0094]

[0095] In the table, node k Reactive power capacity is

[0096] Specifically, the method of this embodiment includes the following steps:

[0097] S1. The AC / DC hybrid distribution station area is divided into multiple independently optimized areas:

[0098] Each AC line connected to the distribution network bus at the head end and the SOP at the end is divided into independent optimization areas, namely A1, A2, and A3; the DC line connected to the SOP at both ends is also divided into independent optimization areas, namely D1 and D2; Figure 3 shown.

[0099] S2. Construct the objective function and related constraints of each region to form a decentralized voltage-reactive power optimization model.

[0100] Considering the loss of network active power, determine the voltage and reactive power optimization model of each independent AC and DC area, specifically: Construct the objective function of each area A1, A2, A3, D1, and D2: Minimize the network active power loss Add constraints to form a voltage and reactive power optimization model. The lower limit of the node voltage in the AC area is 399V and the upper limit is 361V, the lower limit of the node voltage in the DC area is 393.75V, and the upper limit is 356.25V; the upper limit of the line current in the AC area is 300A, and the upper limit of the line current in the DC area is 500A.

[0101] S3. Using the Nash equilibrium algorithm to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the optimal solution for each region, specifically including the following steps:

[0102] The PABI method is used to describe the decentralized voltage and reactive power optimization model in the AC and DC areas, and the model in the AC area is determined: Model for the DC region:

[0103] Initialize the boundary variables exchanged between AC and DC areas, and replace the boundary variables received by the area at the beginning with The initial quantities are all set to the voltage rated values, and the reactive outputs of reactive power sources such as distributed power sources and capacitors at each node are all set to 0; then the voltage and reactive power optimization models of regions A1, A2, A3, D1, and D2 are solved independently, and the independent variables and boundary variables of each region obtained by solving the model are updated, and then the boundary variables are exchanged between regions to complete the first iteration.

[0104] After each iteration, it is determined whether the objective function after solution meets the set iteration stop criteria. The judgment threshold set in this embodiment is If the stopping criteria are met, the Nash equilibrium solution is obtained in each region, the objective function is optimal, and the iteration stops; if the criteria are not met, the iteration continues. As an example, Figure 6 It is the calculation result of the stopping criterion of the objective function iteration after each optimization solution of a certain communication area in the embodiment.

[0105] S4. After the iteration, the Nash equilibrium solution of the reactive power optimization model obtained in step 3 is and Optimize the allocation of reactive power output and reactive power share of distributed generation in AC / DC hybrid substations. Figure 4 The figure shows the comparison results of reactive power share utilization rate of each region before and after the decentralized voltage reactive power optimization method is adopted in the embodiment; after the optimization is completed by the method of the present invention, the total network loss power of regions A1, A2, A3, D1, and D2 is reduced by 59.4% compared with that before the optimization, and the average reactive power share utilization rate of the entire hybrid substation area is increased by 26.4%. Figure 5 The figure is a comparison result of active power loss calculation in each area before and after the decentralized voltage-reactive power optimization method is adopted in the embodiment.

[0106] The decentralized voltage-reactive power optimization method of the AC / DC hybrid substation based on Nash equilibrium applied in the embodiment of the present invention solves the voltage-reactive power optimization model of each area in the AC / DC hybrid substation, optimizes the allocation of distributed power sources and reactive power shares in the substation, improves the utilization efficiency of distributed power sources and reactive power sources such as capacitors, effectively reduces the active power loss of the AC / DC hybrid substation network, and improves the efficiency of the substation operation; and because only the regional boundary variable data is shared, it can reduce the communication and computing burden while ensuring the privacy information of participating customers.

[0107] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A decentralized reactive power optimization method for AC / DC hybrid substations based on Nash equilibrium, characterized in that: It includes the following steps: S1. Divide the AC / DC hybrid distribution station area into N independently optimized areas; S2. Construct the objective function and related constraints of each region to form a decentralized voltage and reactive power optimization model; S21, minimizing the active power loss of each line in the divided area as the objective function of each area; Objective function of the communication area: Objective function in the DC region: Where i is the area number of the AC / DC hybrid distribution station area, and the numbers of the AC area and the DC area are arranged in the same order, i = 1, 2, ..., N; A i Indicates that area i is the communication area, D i Indicates that region i is a DC region; represents the objective function when region i is an AC region, represents the objective function when region i is a DC region, is the active power loss of area i; S22, determining the optimization constraints of the communication area; When area i is an AC area, that is, The optimization constraints for region i are: Where k is the node number of the line belonging to area i. The node numbers are uniformly sorted in the AC / DC hybrid distribution substation area. K is the set of node numbers in area i, k∈K; and is the active and reactive power load at node k; and The active and reactive output of distributed energy accessed by node k; Represents the reactive output of reactive power sources such as capacitors connected to node k; and is the active power and reactive power of node k in the front-end line flowing to the AC area; and is the active power and reactive power flowing from node k in the AC area to the back-end line; Used to identify whether node k is a sending node or a receiving node. When node k is a sending node is 1 when node k is a receiving node is -1; v k is the square value of the voltage at node k; r i and x i is the resistance and reactance of the line in area i; and is the active and reactive power input to the head end of the line in area i; i is the square value of the line current in region i; ||‖‖2 represents the 2-norm of the matrix, and is the active and reactive power output at the end of the line in area i; and is the active and reactive power loss of the line in area i; v k and are the lower and upper limits of the square value of the voltage at node k; is the upper limit of the square value of the line current in area i; m k 、n k , α k , β k are all predefined parameters that limit the reactive power output of distributed energy resources connected to node k; is the upper limit of the active output of distributed energy connected to node k; and The lower and upper limits of reactive power output of reactive power sources such as capacitors connected to k; S23, determining optimization constraints of the DC region; When region i is a DC region, that is, The optimization constraints for region i are: in, is the active power of node k in the front-end line flowing to the DC region; is the active power flowing from node k in the DC region to the back-end line; S3. Use the Nash equilibrium algorithm to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the optimal solution for each region; S4. According to the Nash equilibrium solution of the reactive power optimization model obtained in step S3, the reactive power output of distributed energy in the AC / DC hybrid distribution station area is calculated. and reactive power output of reactive power source Distribution is carried out to minimize the active power loss in the AC / DC hybrid distribution station area.

2. The method for decentralized reactive power optimization in AC / DC hybrid substations based on Nash equilibrium according to claim 1 is characterized in that: Step S1 is specifically as follows: Independent areas that need to be optimized are divided into AC and DC hybrid distribution substations, including AC areas and DC areas, where each AC substation line connected to the distribution network bus at the head end and the SOP at the end is divided into an independent AC area; each DC line connected to the SOP at both ends is divided into an independent DC area; the number of areas obtained after division is N, where N is a positive integer.

3. The method for decentralized reactive power optimization in AC / DC hybrid substations based on Nash equilibrium according to claim 1 is characterized in that: Specifically in step S3 The following steps are involved: S31, describing the decentralized voltage-reactive power optimization model in step S2 using a boundary information allocation method; S32, based on the boundary variables received in each area The Nash equilibrium algorithm is used to independently solve the decentralized voltage-reactive power optimization model of each region to obtain the independent variables and And the new boundary variables and And based on the new boundary variables obtained in each area, update the boundary variables received by other areas in, is the boundary variable of other regions received by the communication region i, is the boundary variable of other regions received by DC region i, is the independent variable of the optimization constraint in the optimization model of AC region i, is the independent variable of the optimization constraint in the DC region i optimization model, is the boundary variable sent by communication area i to other areas, is the boundary variable sent by DC region i to other regions; S33, determine whether the objective function solved in step S32 meets the set iteration stop criteria: if it does, it means that each region can achieve the optimal objective function according to the Nash equilibrium solution, and the iteration stops; if it does not, return to step S32, and update the boundary variables received by each region according to the updated boundary variables received by each region. Continue to solve for the variables.

4. The method for decentralized reactive power optimization in AC / DC hybrid substations based on Nash equilibrium according to claim 3 is characterized in that: Step S31 uses the boundary information allocation method to describe the decentralized voltage-reactive power optimization model in step S2, specifically: (a) When region i is an AC region, that is, The decentralized voltage-reactive power optimization model of region i is: Among them, formula (22) is the objective function of the optimization model of communication area i; formula (23) represents all non-equality constraints in the optimization constraints of the optimization model of communication area i; formula (24) represents all equality constraints in the optimization constraints of the optimization model of communication area i; is the independent variable of the optimization constraint in the optimization model of communication area i, including l i , These 7 variables; is the boundary variable sent by communication area i to other areas, including the v k These 4 variables; is the boundary variable of other regions received by the communication region i, is a known value, based on the boundary variables of other regions Can get the communication area i and (b) When region i is a DC region, that is, The decentralized voltage-reactive power optimization model of DC region i is: Among them, formula (25) is the objective function of the optimization model of DC region i; formula (26) represents all non-equality constraints in the optimization constraints of the optimization model of DC region i; formula (27) represents all equality constraints in the optimization constraints of the optimization model of DC region i; is the independent variable of the optimization constraint in the DC region i optimization model, including the DC region i l i , These 4 variables; is the boundary variable sent by DC region i to other regions, including the and v k These 2 variables; is the boundary variable of other regions received by DC region i, is a known value, based on the boundary variables of other regions It can be obtained that the DC region i and 5. The method for decentralized reactive power optimization in AC / DC hybrid substations based on Nash equilibrium according to claim 3 is characterized in that: The iteration stopping criterion in step S33 is: Where, e represents the e-th iteration; are the values ​​of the objective function in the AC region obtained at the e-th iteration and the (e-1)-th iteration, respectively; are the values ​​of the objective function in the DC region obtained at the e-th iteration and the (e-1)-th iteration respectively; It is a pre-set judgment threshold. When all regions meet the iteration stop criteria after the e-th iteration, it is considered that the set iteration stop criteria are met.

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