Distribution area distributed optimization method and device, computer equipment and storage medium

Abstract

The invention relates to a transformer area distributed optimization method and device, computer equipment and a storage medium, and the method comprises the steps: initializing a near-end atom coordination parameter, and updating the system parameter information in each transformer area according to the source load prediction information of the transformer area; according to the near-end atom coordination parameters and the system parameter information, a distributed optimization model of each transformer area is established; converting the zone area distributed optimization model into an atomization zone area optimization model according to a target constraint condition, and solving the atomization zone area optimization model; judging whether the current iteration error of the atomization zone area optimization model is smaller than preset convergence precision or not; and if the current iteration error is smaller than the preset convergence precision, outputting a solving result of the atomization zone area optimization model in the current iteration. According to the method, the distribution area distributed optimization model is constructed, distribution area distributed optimization operation can be realized on the basis of near-end atom coordination solution, and the economic operation problem of the flexible interconnection distribution area is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power grid distribution optimization, and in particular to a method, device, computer equipment and storage medium for distributed optimization of a substation. Background Art

[0002] There are many substations in the distribution network and they are widely distributed. With the access of a high proportion of distributed power sources, the scale of the substations is larger and more complex, with more redundant parameters. Currently, the commonly used substation optimization methods are mainly divided into centralized methods and distributed methods. The centralized method collects various system data through the upper distribution network, and issues the optimal dispatching strategy after unified calculation and processing. The centralized method needs to collect and store a large amount of data information, which has a heavy communication burden and high operating costs.

[0003] With the development of flexible distribution network, the types and number of new power electronic devices are increasing, and the data storage capacity and computing performance of the upper distribution network are facing more severe tests. Considering the speed and bandwidth limitations of communication between the upper distribution network and the substation, it is difficult for the upper distribution network and the substation to carry out a large amount of data transmission and information exchange in time, resulting in the difficulty of updating the specific status parameter information of the substation to the upper distribution network in time, which makes it difficult for centralized strategies to be calculated and issued in time. Summary of the invention

[0004] Based on this, it is necessary to provide a distributed optimization method, device, computer equipment and storage medium for the substation area that can effectively reduce the data transmission volume, reduce the communication burden, and accurately calculate the global optimal result in response to the above technical problems.

[0005] In a first aspect, the present application provides a distributed optimization method for a station area, comprising:

[0006] Initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source and load prediction information;

[0007] According to the proximal atomic coordination parameters and the system parameter information, a distributed optimization model for each substation is established, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0008] The distributed optimization model of the station area is converted into an atomized station area optimization model according to the target constraint conditions, and the atomized station area optimization model is solved;

[0009] Determining whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy;

[0010] If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

[0011] In one embodiment, the method further comprises:

[0012] If the iteration error is greater than or equal to the preset convergence accuracy, the proximal atomic coordination parameters are updated and the number of iterations is accumulated, and the atomization station optimization model is solved according to the updated proximal atomic coordination parameters until the iteration error of the atomization station optimization model is less than the preset convergence accuracy, and the solution result of the atomization station optimization model in the current iteration is output.

[0013] In one embodiment, before initializing the proximal atomic coordination parameters, the method further includes:

[0014] Obtaining system parameter information of the target substation and source load prediction information of the substation;

[0015] Calculate the own variables of the substation area, the own variables of the intelligent soft switch, and the boundary variables of the substation area and the intelligent soft switch area according to the system parameter information;

[0016] Determine, according to the own variables and the boundary variables, a constraint matrix within the substation area, a constraint matrix within the intelligent soft switch area, and a coordination matrix between the substation area and the intelligent soft switch area;

[0017] The initializing of the proximal atomic coordination parameters includes:

[0018] The proximal atomic coordination parameters are initialized according to the own variables, the boundary variables, the constraint matrix and the coordination matrix.

[0019] In one embodiment, the updating of system parameter information in each substation according to the substation source load prediction information includes:

[0020] The distributed power source information and load fluctuation coefficient of each substation in the current optimization period are updated according to the substation source and load prediction information.

[0021] In one embodiment, before converting the distributed optimization model of the metro area into an atomized metro area optimization model according to the target constraint condition and solving the atomized metro area optimization model, the method further includes:

[0022] Set target constraints, where the target constraints include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power supply constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

[0023] In one embodiment, the distributed optimization model of the station area is converted into an atomic station area optimization model according to the target constraint condition, and the atomic station area optimization model is solved, and further includes:

[0024] In the process of solving the atomic station optimization model, each adjacent area is made to exchange boundary information, and the boundary information includes boundary variables of the station area and the intelligent soft switch area.

[0025] In one embodiment, after outputting the solution result of the atomization stage optimization model at the current iteration, the method further includes:

[0026] When the target substation runs to the next optimization period, the system parameter information in each substation is updated based on the substation source load prediction information, and the subsequent steps of establishing a distributed optimization model for each substation are performed based on the proximal atomic coordination parameters and the system parameter information until a new solution result is output.

[0027] In a second aspect, the present application also provides a distributed optimization device for a substation area, including:

[0028] Initialization module, used to initialize the proximal atomic coordination parameters and update the system parameter information in each substation according to the substation source load prediction information;

[0029] A model building module, used to establish a distributed optimization model for each substation according to the proximal atomic coordination parameters and the system parameter information, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0030] A solution module, used for converting the distributed optimization model of the station area into an atomized station area optimization model according to the target constraint conditions, and solving the atomized station area optimization model;

[0031] A judgment module, used to judge whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy;

[0032] The output module is used to output the solution result of the atomization stage optimization model in the current iteration if the current iteration error is less than the preset convergence accuracy.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distributed optimization method of the substation area described in the first aspect are implemented.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the distributed optimization method for substations described in the first aspect are implemented.

[0035] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the distributed optimization method of the substation area described in the first aspect are implemented.

[0036] In summary, the present application proposes a distributed optimization method, device, computer equipment and storage medium for substations, including: initializing proximal atomic coordination parameters, and updating system parameter information in each substation according to the substation source-load prediction information; establishing a distributed optimization model for each substation according to the proximal atomic coordination parameters and the system parameter information; converting the substation distributed optimization model into an atomized substation optimization model according to the target constraint conditions, and solving the atomized substation optimization model; determining whether the current iteration error of the atomized substation optimization model is less than the preset convergence accuracy; if the current iteration error is less than the preset convergence accuracy, outputting the solution result of the atomized substation optimization model in the current iteration. The present application can realize distributed optimization operation of substations and solve the economic operation problem of flexible interconnected substations by constructing a distributed optimization model for substations based on proximal atomic coordination solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A diagram of an application environment of a distributed optimization method for a substation in an embodiment;

[0038] Figure 2 A schematic diagram of a process flow of a distributed optimization method for a substation in an embodiment;

[0039] Figure 3 A schematic diagram of a flow chart of a distributed optimization method for a substation in another embodiment;

[0040] Figure 4 A schematic diagram of a flow chart of a distributed optimization method for a substation in another embodiment;

[0041] Figure 5 A schematic diagram of a flow chart of a distributed optimization method for a substation in another embodiment;

[0042] Figure 6 is a load fluctuation curve of a distribution network in an embodiment;

[0043] Figure 7 A distributed power supply fluctuation curve of a power distribution network in one embodiment;

[0044] Figure 8A comparison of the maximum voltage values ​​in each time period under different schemes in an embodiment;

[0045] Fig. 9 is the active power value of the intelligent soft switch port in each time period of scheme 2 in an embodiment;

[0046] Fig.10 is the active power value of the intelligent soft switch port in each time period of scheme 3 in an embodiment;

[0047] Fig.11 is the reactive power value of the intelligent soft switch port in each time period of scheme 2 in an embodiment;

[0048] Fig.12 is the reactive power value of the intelligent soft switch port in each time period of scheme 3 in an embodiment;

[0049] Fig.13 It is a structural block diagram of a distributed optimization device in a substation area in an embodiment;

[0050] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The distributed optimization method for the substation area provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The distribution network in this embodiment includes 4 substations, 1 four-port intelligent soft switch and 44 voltage control nodes, wherein the 4 substations are respectively substation 1, substation 2, substation 3 and substation 4, wherein each substation is connected to the power grid through a corresponding intelligent soft switch. It should be noted that the substation distributed optimization method provided in this embodiment can be adaptively applied to various distribution networks including substations and intelligent soft switches. Figure 1 For illustrative purposes only.

[0053] As shown in the background technology section, if a centralized optimization method is used to process the optimization of the existing flexible interconnected distribution network substations, it will cause a huge communication burden, huge data transmission volume, and extremely high operating costs. This embodiment provides a distributed optimization method for substations, which can utilize the advantages of distributed methods such as small data transmission volume, small communication burden, and low computing and storage requirements to achieve the corresponding optimization results locally in the substation and intelligent soft switch, and the computing structure can achieve the global optimal result while realizing the distributed operation of the substation.

[0054] In one embodiment, Figure 2 As shown, a distributed optimization method for a station area is provided, and the method is applied to Figure 1 The distribution network in the example is used to illustrate, including the following steps:

[0055] S201, initializing proximal atomic coordination parameters, and updating system parameter information in each substation area according to substation area source load prediction information.

[0056] In this embodiment, the proximal atomic coordination parameters include the values ​​of each station's own variables, boundary variables, primal Lagrange multipliers, and dual Lagrange multipliers.

[0057] By initializing the proximal atomic coordination parameters, we can obtain the initial values ​​including the initial values ​​of each station's own variables, the initial values ​​of boundary variables, the initial values ​​of the original Lagrange multipliers and the initial values ​​of the dual Lagrange multipliers.

[0058] Before initializing the proximal atomic coordination parameters, this embodiment also obtains the system basic parameter information of the target substation and the substation source and load prediction information. The target substation is the substation to be optimized in the distribution network.

[0059] Preferably, the distributed optimization method for substations proposed in this embodiment can be applied to flexible interconnected substations.

[0060] After obtaining the substation source and load forecast information, before building the distributed optimization model of each substation, it is necessary to determine whether the system parameter information of the current optimization period is updated based on the substation source and load forecast information. If the system parameter information is updated, the basic system parameter information of each substation must be updated immediately based on the substation source and load forecast information.

[0061] It should be noted that this embodiment ensures the accuracy and timeliness of the subsequent distributed optimization output results of the substations by updating the system parameter information in real time.

[0062] S202, establishing a distributed optimization model for each substation based on the proximal atomic coordination parameters and system parameter information, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost.

[0063] In this embodiment, after the initialization process of the proximal atomic coordination parameters and the update process of the system parameter information are completed, the distributed optimization model of each substation can be constructed based on the proximal atomic coordination parameters and the system parameter information.

[0064] It should be noted that in the actual application process, the objective function of the distributed optimization model of each substation is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost. The constraints considered include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

[0065] S203, converting the distributed optimization model of the substation into an atomized substation optimization model according to the target constraint conditions, and solving the atomized substation optimization model.

[0066] In this embodiment, the target constraints include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

[0067] After obtaining the distributed optimization model of the substation, the distributed optimization model of the substation is transformed into an atomic substation optimization model through equality constraints and inequality constraints, which is specifically expressed as:

[0068]

[0069]

[0070] in, Indicates the area The atomic objective function, Indicates the area The matrix of independent variables and boundary variables; The equation constraint part includes the power flow constraint of the substation, the power balance constraint of the intelligent soft switch, the loss constraint of the intelligent soft switch and the output constraint of the distributed power source; It is the inequality constraint part, including intelligent soft switch capacity constraint, intelligent soft switch power upper and lower limit constraint, safe operation constraint, distributed power supply power upper and lower limit constraint, Indicates the area Inequality constraint matrix for own variables and boundary variables, represents the constant column vector in the inequality constraint matrix; It is the inter-regional coordination constraint part, including the intelligent soft switch power balance constraint between the substation area and the intelligent soft switch area; Indicates the number of stations.

[0071] After transforming the distributed optimization model of the substation into the atomic substation optimization model, the original Lagrange multiplier is used , , the objective function of the atomization station optimization model is converted into a Lagrangian function, which is specifically expressed as:

[0072]

[0073]

[0074] in, Indicates the area All variables in the coordination matrix between the station area and the intelligent soft switch area The corresponding column in .

[0075] For the above Lagrangian function, the following relationship exists: = = = , thus the Lagrangian function can be expressed as in the form of.

[0076] exist On the basis of increase Regularization penalty term, the final Lagrangian function is:

[0077]

[0078] in, and To consider the regularized dual Lagrange multiplier; The result of the previous iteration.

[0079] The Lagrangian function of the atomic substation optimization model is solved by a mathematical solver to obtain the solution result of the current iteration. The solution result of the atomic substation optimization model includes the voltage of each node in the substation, the operation strategy of the intelligent soft switch and the operation cost of the substation.

[0080] S204, determining whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy.

[0081] In this embodiment, it is determined that the atomization stage optimization model is in the current Whether the iteration error in the iteration process meets the preset convergence accuracy , judge the Whether the error of the iteration meets the preset convergence accuracy The basis is as follows:

[0082]

[0083]

[0084] in, and Taiwan The connected intelligent soft switch port The active power of the first iteration and the The active power of the iteration; and Taiwan The connected intelligent soft switch port The reactive power of the first iteration and the The reactive power of the iteration.

[0085] In this embodiment, the iteration error of the atomization station optimization model includes the difference in reactive power and active power between the current iteration process and the previous iteration process.

[0086] It should be noted that the preset convergence accuracy in this embodiment is Adaptive configuration can be performed according to the needs of actual application scenarios.

[0087] S205: If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

[0088] In this embodiment, if the current iteration error meets the preset convergence accuracy , the distributed optimization calculation results of the atomic substation optimization model in the current iteration can be directly output. The distributed optimization calculation results include the voltage of each node in the substation, the operation strategy of the intelligent soft switch, and the operation cost of the substation, and continue until the next optimization period.

[0089] In summary, this embodiment provides a distributed optimization method for substations. Aiming at the economic operation problem of flexible interconnected substations, a distributed optimization model of flexible interconnected substations based on proximal atomic coordination is established. The objective function is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost. The substation flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power supply constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas are comprehensively considered. Finally, a distributed optimization strategy for flexible interconnected substations based on proximal atomic coordination is determined, which can reduce the communication burden while accurately obtaining the global optimal strategy.

[0090] In one embodiment, if Figure 3 As shown, the distributed optimization method for the substation area also includes:

[0091] S206, if the iteration error is greater than or equal to the preset convergence accuracy, update the proximal atomic coordination parameters and accumulate the number of iterations, solve the atomization station optimization model according to the updated proximal atomic coordination parameters, until the iteration error of the atomization station optimization model is less than the preset convergence accuracy, and output the solution result of the atomization station optimization model in the current iteration.

[0092] In this embodiment, if the current iteration error does not meet the preset convergence accuracy , which is greater than the preset convergence accuracy , the cumulative number of update iterations is required ,make The proximal atomic coordination parameters are updated, and the steps S203-S204 are executed again until the condition of step S205 is met, and a solution result that meets the real-time iteration number is output.

[0093] In this embodiment, the proximal atomic coordination parameters are updated by:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] in, For the updated area The matrix of independent variables and boundary variables; , is the updated original Lagrange multiplier; , is the updated dual Lagrange multiplier.

[0100] In one embodiment, Figure 4 As shown, before initializing the proximal atomic coordination parameters, the method further includes:

[0101] S401, obtaining system parameter information of a target substation and substation source and load prediction information.

[0102] In this embodiment, the system parameter information of the target substation includes basic system parameter information such as distribution network topology connection relationship, line resistance parameters, line reactance parameters, node active load parameters, node reactive load parameters, distributed power source access location and capacity, intelligent soft switch access location and capacity.

[0103] S402, calculating the own variables of the substation area, the own variables of the intelligent soft switch, and the boundary variables of the substation area and the intelligent soft switch area according to the system parameter information;

[0104] In this embodiment, according to the basic parameter information of the system, the calculation method of obtaining the independent variables of the substation area, the independent variables of the intelligent soft switch area, and the boundary variables of the substation area and the intelligent soft switch area is specifically expressed as follows:

[0105] The specific calculation formulas for the independent variables of the substation area include:

[0106]

[0107]

[0108] in, for Time zone 's own variable set; for Distributed power injection node Active power of for Distributed power injection node Reactive power; for Time period node Active load; for Time period node Reactive load; for Time period node The square of the voltage; for Time Route Up from the node Flow to Node Active power of for Time Route Up from the node Flow to Node Reactive power; for Time Route Up from the node Flow to Node The square of the current; For Taiwan Nodes within the area gather; For Taiwan The collection of lines within; It is a collection of areas in the Taiwan area.

[0109] The specific calculation formulas of the independent variables in the intelligent soft switching area include:

[0110]

[0111] in, for Intelligent soft switching area 's own variable set; for Time intelligent soft switch port Active power at for Time intelligent soft switch port Reactive power at for Time intelligent soft switch port Active power loss at for Time intelligent soft switch port The converter capacity at It is a collection of intelligent soft switch ports; It is a collection of intelligent soft switching areas.

[0112] The specific calculation formulas for the boundary variables of the station area and the intelligent soft switch area include:

[0113]

[0114] in, for Time zone and intelligent soft switching area The set of boundary variables; Respectively represent the area The connected intelligent soft switch port is Active power and reactive power of the time period.

[0115] S403, determining the constraint matrix in the substation area, the constraint matrix in the intelligent soft switch area, and the coordination matrix between the substation area and the intelligent soft switch area according to the own variables and the boundary variables;

[0116] In this embodiment, after the calculation model in S402 calculates the independent variables and boundary variables, the constraint matrix within the substation area, the constraint matrix within the intelligent soft switch area, and the coordination matrix between the substation area and the intelligent soft switch area can be further determined based on the independent variables and boundary variables.

[0117] The specific calculation method of the constraint matrix in the substation area includes:

[0118]

[0119]

[0120] in, Represents the constraint matrix in the station area; Representation Matrix The dimension of each line There are 3 variables ; Each node There are 5 variables ; Indicates the area The number of nodes in Indicates the area Number of lines within Indicates the area The number of boundary variables.

[0121] The specific calculation method of the constraint matrix in the intelligent soft switching area includes:

[0122]

[0123]

[0124] in, represents the constraint matrix in the intelligent soft switching region; Representation Matrix Each intelligent soft switch port has four variables ; Indicates the number of ports of the intelligent soft switch.

[0125] The specific calculation method of the coordination matrix between the substation area and the intelligent soft switch area includes:

[0126]

[0127]

[0128]

[0129] in, Represents the coordination matrix between the substation area and the intelligent soft switch area; Representation Matrix No. Row, No. Elements of a column; Indicates the area The boundary variables in the matrix The corresponding row in Indicates the area the number of boundary variables; express The dimension of Indicates the number of all the independent variables and boundary variables in the area; Indicates the number of stations.

[0130] Initialize proximal atomic coordination parameters, including:

[0131] The proximal atomic coordination parameters are initialized according to the own variables, boundary variables, constraint matrix and coordination matrix.

[0132] In this embodiment, the specific implementation of initializing the proximal atomic coordination parameters includes:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] in, For Taiwan The matrix of independent variables and boundary variables; for Dimensional matrix; For Taiwan the number of own variables and boundary variables; is the step length; For Taiwan The original Lagrange multiplier in the region and the primitive Lagrange multiplier between regions The over-relaxation coefficient of For Taiwan The dual Lagrange multiplier in the region Dual Lagrange multipliers between regions The over-relaxation coefficient of is the initial value of the matrix of regional independent variables and boundary variables; is the initial value of the original Lagrange multiplier; is the initial value of the dual Lagrange multiplier.

[0139] In one embodiment, updating the system parameter information in each area according to the area source load prediction information includes:

[0140] Update the distributed power source information and load fluctuation coefficient of each substation during the current optimization period based on the substation source and load forecast information.

[0141] In this embodiment, in each optimization period, the system parameter information in each area needs to be updated according to the area source load prediction information in the corresponding period. In actual application, the system parameter information that mainly needs to be adjusted based on the optimization period includes distributed power supply information and load fluctuation coefficient.

[0142] In one embodiment, before converting the distributed optimization model of the metro area into an atomized metro area optimization model according to the target constraint condition and solving the atomized metro area optimization model, the method further includes:

[0143] Set target constraints, where the target constraints include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

[0144] In this embodiment, when establishing the distributed optimization model of each substation, the objective function is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost, and the constraints considered are the substation flow constraint, the intelligent soft switch constraint, the safe operation constraint, the distributed power supply constraint, and the intelligent soft switch power balance constraint between the substation area and the intelligent soft switch area.

[0145] The objective function is set to minimize the sum of the area network loss cost and the intelligent soft switch loss cost. The specific function is expressed as follows:

[0146]

[0147] in, represents the objective function of the station area; Indicates the unit electricity price; Indicates the total number of time periods; Indicates line The resistance value; Indicates the duration of each scheduling period.

[0148] The power flow constraint condition of the substation can be expressed as:

[0149]

[0150]

[0151]

[0152] in, Indicates line The reactance value; and express Time period node Injected active and reactive power; express Time period node The square of the voltage at .

[0153] The intelligent soft switching constraints can be expressed as:

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] in, Intelligent soft switch port The loss coefficient at ; and They are Time intelligent soft switch port The minimum and maximum active power at ; and They are Time intelligent soft switch port The minimum and maximum reactive power at .

[0160] It should be noted that the intelligent soft switch constraints include the intelligent soft switch power balance constraints , intelligent soft switching loss constraints , Intelligent soft switch capacity constraint , intelligent soft switching power upper and lower limit constraints and .

[0161] The safe operation constraints can be expressed as:

[0162]

[0163]

[0164] in, For Node The square of the voltage at ; and They are Time period node Minimum and maximum voltage values ​​at ; For line The square of the current, ; for Time Route The maximum current at .

[0165] The distributed generation constraints can be expressed as:

[0166]

[0167]

[0168]

[0169] in, To connect to the node Distributed power sources at Active power output during the time period; To connect to the node Distributed power sources at The predicted value of the fluctuation curve of active power output in each period; To connect to the node Distributed power sources at Reactive power output during the time period; , To connect to the node Distributed power sources at The upper and lower limits of reactive power output during a period; For Node The capacity of distributed power sources; It is the set of nodes connected to the distributed power source.

[0170] Distributed generation constraints include distributed generation output constraints , upper and lower limits of distributed generation power , Distributed Generation Capacity Constraints .

[0171] The power balance constraint condition of the intelligent soft switch between the substation area and the intelligent soft switch area can be expressed as:

[0172]

[0173]

[0174] in, Indicates the area Active power of the connected intelligent soft switch port, Indicates the area Reactive power of the connected smart soft switch port.

[0175] In one embodiment, the distributed optimization model of the station area is converted into an atomic station area optimization model according to the target constraint condition, and the atomic station area optimization model is solved, and the following steps are further included:

[0176] In the process of solving the atomic station optimization model, each adjacent area is made to exchange boundary information, and the boundary information includes boundary variables of the station area and the intelligent soft switching area.

[0177] In this embodiment, the distributed optimization model of the substation area can realize the distributed optimization operation of the substation area and effectively solve the economic operation problem of the flexible interconnected substation area based on the proximal atomic coordinated solution and boundary information interaction and parameter iterative update.

[0178] In one embodiment, after outputting the solution result of the atomization stage optimization model at the current iteration, the method further includes:

[0179] When the target substation runs to the next optimization period, the system parameter information in each substation is updated according to the substation source and load prediction information, and the subsequent steps of establishing the distributed optimization model of each substation are executed according to the proximal atomic coordination parameters and system parameter information until a new solution result is output.

[0180] In this embodiment, the optimization process of the target substation is divided into multiple optimization periods for execution respectively. When the target substation runs to each optimization period, it is necessary to update the system parameter information in each substation according to the substation source load prediction information, and then re-execute steps S202-S206.

[0181] It should be noted that after the distributed optimization model of each substation is re-established, the iteration of the distributed optimization model of each substation will be reset to 0, and a certain number of iterations will be accumulated according to the needs of the actual application scenario until the global optimal result is obtained.

[0182] In a more detailed embodiment, the complete execution flow of the distributed optimization method of the substation area proposed by the present invention is as follows: Figure 5 The improved actual area calculation example is used to verify the area distribution optimization method proposed in the present invention. Its network topology is shown in Figure 1 This example includes 4 feeders, with a rated voltage of 0.4kV, a total active load of 194.02kW, and a total reactive load of 179.51kvar. Among them, the active load of area 1 is 70.30kW, and the reactive load is 71.40kvar, the active load of area 2 is 82.70kW, and the reactive load is 81.60kvar, the active load of area 3 is 22.51kW, and the reactive load is 11.81kvar, and the active load of area 4 is 18.51kW, and the reactive load is 14.70kvar. Areas 1 and 2 are set as industrial loads, and areas 3 and 4 are set as residential loads. The load fluctuation curve of the area is shown in the figure. Figure 6 As shown; assume that 2 photovoltaic and 2 wind turbines are connected to the substation, the photovoltaic capacity is 100kWp, the wind turbine capacity is 100kVA, and the distributed power supply fluctuation curve is as follows Figure 7 As shown; intelligent soft switch capacity 200kVA; set convergence accuracy , the current number of iterations , over-relaxation coefficient , , step length .

[0183] In order to verify the effectiveness of the proposed method, the following three comparison schemes are set up:

[0184] Solution 1: Without considering the intelligent soft switch, the reactive output of the distributed power source is set to 20% of the active output to obtain the initial operating state of the substation.

[0185] Option 2: Consider using intelligent soft switches to achieve flexible interconnection of substations, controllable reactive output of distributed power sources, and adopt a centralized optimization method.

[0186] Solution 3: Considering the use of intelligent soft switches to achieve flexible interconnection of substations and controllable reactive power output of distributed power sources, the distributed optimization method for substations provided in this embodiment is adopted.

[0187] The comparison of system operation optimization effects is shown in Table 1. In the initial operation state of the substation without intelligent soft switches, although the loss cost of the intelligent soft switch is saved during the operation of the substation, the network loss is significantly increased, and expensive network loss costs need to be paid. The overall operation economy of the system is poor.

[0188] By adding flexible interconnected areas with intelligent soft switches, network losses are greatly reduced. Even if the loss cost of the intelligent soft switches is increased, the overall operating economy of the system is still greatly improved.

[0189] Compared with Scheme 1, the distributed optimization strategy of Scheme 3 effectively reduces the network loss cost of the system; compared with Scheme 2, the strategy obtained in Scheme 3 has basically the same optimization results as the centralized strategy, which proves the effectiveness of the method proposed in this invention.

[0190] Comparison of the maximum voltage values ​​in each period under different schemes Figure 8 As shown in the figure, it can be seen that in the state without intelligent soft switches, the voltage fluctuation in the substation is quite severe. After the substation realizes flexible interconnection through intelligent soft switches, the voltage fluctuation is effectively alleviated. Figure 9-12 The active power and reactive power values ​​of the intelligent soft switch port in each period under Scheme 2 and Scheme 3 are listed.

[0191] Table 2 lists the maximum calculation time for a single iteration of Scheme 3. The maximum time for a single iteration of each substation is 0.9529s; the maximum time for iterative convergence in each time period is 433.6077s, and the calculation time meets the calculation time requirements for optimizing the scheduling duration.

[0192] Table 1 Comparison of system optimization effects under different schemes

[0193]

[0194] Table 2 Maximum calculation time of a single iteration in each substation in the whole period

[0195]

[0196] To sum up, the distributed optimization method of the substation provided in this embodiment, taking into account the access of intelligent soft switches, can effectively realize the distributed optimization operation of the flexible interconnected substation, improve the power mutual assistance capacity of the substation, solve the economic operation problem of the flexible interconnected substation and the voltage over-limit problem of the substation under the access of distributed power sources.

[0197] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0198] Based on the same inventive concept, the embodiment of the present application also provides a distributed area optimization device for implementing the above-mentioned distributed area optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the distributed area optimization device provided below can refer to the limitations of the distributed area optimization method above, and will not be repeated here.

[0199] In one embodiment, Fig.13 As shown, a distributed optimization device 1300 for a substation is provided, comprising: an initialization module 1310, a model building module 1320, a solution module 1330, a judgment module 1340 and an output module 1350, wherein:

[0200] Initialization module 1310, used to initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source load prediction information;

[0201] The model building module 1320 is used to establish a distributed optimization model for each substation according to the proximal atomic coordination parameters and the system parameter information, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0202] A solution module 1330, configured to transform the distributed optimization model of the station area into an atomized station area optimization model according to target constraints, and solve the atomized station area optimization model;

[0203] A judgment module 1340 is used to judge whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy;

[0204] The output module 1350 is used to output the solution result of the atomization stage optimization model in the current iteration if the current iteration error is less than a preset convergence accuracy.

[0205] In one embodiment, the output module 1350 is specifically used to update the proximal atomic coordination parameters and accumulate the number of iterations if the iteration error is greater than or equal to the preset convergence accuracy, and solve the atomization station optimization model according to the updated proximal atomic coordination parameters until the iteration error of the atomization station optimization model is less than the preset convergence accuracy, and output the solution result of the atomization station optimization model in the current iteration.

[0206] In one embodiment, the initialization module 1310 is specifically used to obtain system parameter information of the target substation and the source and load prediction information of the substation; calculate the independent variables of the substation area, the independent variables of the intelligent soft switch, and the boundary variables of the substation area and the intelligent soft switch area according to the system parameter information; determine the constraint matrix within the substation area, the constraint matrix within the intelligent soft switch area, and the coordination matrix between the substation area and the intelligent soft switch area according to the independent variables and the boundary variables; initialize the proximal atomic coordination parameters according to the independent variables, the boundary variables, the constraint matrix and the coordination matrix.

[0207] In one of the embodiments, the output module 1350 is specifically used to update the distributed power source information and load fluctuation coefficient of each substation in the current optimization period according to the substation source-load prediction information.

[0208] In one embodiment, the model building module 1320 is specifically used to set target constraints, wherein the target constraints include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power supply constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

[0209] In one embodiment, the solution module 1330 is specifically used to enable each adjacent area to exchange boundary information during the solution process of the atomized stage optimization model, and the boundary information includes boundary variables of the stage area and the intelligent soft switch area.

[0210] In one embodiment, the distributed optimization device for a substation area further includes:

[0211] The loop module is used to re-update the system parameter information in each substation according to the substation source load prediction information when the target substation runs to the next optimization period, and execute the subsequent steps of establishing a distributed optimization model for each substation according to the proximal atomic coordination parameters and the system parameter information until a new solution result is output.

[0212] In summary, this embodiment provides a distributed optimization device for a substation. Aiming at the economic operation problem of a flexible interconnected substation, a distributed optimization model of a flexible interconnected substation based on proximal atomic coordination is established. The objective function is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost. The substation power flow constraint, intelligent soft switch constraint, safe operation constraint, distributed power supply constraint, and intelligent soft switch power balance constraint between the substation area and the intelligent soft switch area are comprehensively considered to finally determine the distributed optimization strategy of the flexible interconnected substation based on proximal atomic coordination. In addition, this application solves based on proximal atomic coordination and performs boundary information interaction and parameter iterative update to realize distributed optimization operation of the substation and solve the economic operation problem of the flexible interconnected substation.

[0213] Each module in the above-mentioned distributed optimization device for the substation area can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0214] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.14As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a distributed optimization method for a station area is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0215] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0216] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0217] Initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source and load prediction information;

[0218] According to the proximal atomic coordination parameters and system parameter information, a distributed optimization model for each substation is established, where the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0219] According to the target constraints, the distributed optimization model of the substation is transformed into an atomic substation optimization model, and the atomic substation optimization model is solved;

[0220] Determine whether the current iteration error of the atomization station optimization model is less than the preset convergence accuracy;

[0221] If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

[0222] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0223] Initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source and load prediction information;

[0224] According to the proximal atomic coordination parameters and system parameter information, a distributed optimization model for each substation is established, where the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0225] According to the target constraints, the distributed optimization model of the substation is transformed into an atomic substation optimization model, and the atomic substation optimization model is solved;

[0226] Determine whether the current iteration error of the atomization station optimization model is less than the preset convergence accuracy;

[0227] If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

[0228] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0229] Initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source and load prediction information;

[0230] According to the proximal atomic coordination parameters and system parameter information, a distributed optimization model for each substation is established, where the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost;

[0231] According to the target constraints, the distributed optimization model of the substation is transformed into an atomic substation optimization model, and the atomic substation optimization model is solved;

[0232] Determine whether the current iteration error of the atomization station optimization model is less than the preset convergence accuracy;

[0233] If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

[0234] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0235] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0236] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distributed optimization method for a station area, characterized in that: The method comprises: Initialize the near-end atomic coordination parameters and update the system parameter information in each area according to the area source and load prediction information; According to the proximal atomic coordination parameters and the system parameter information, a distributed optimization model for each substation is established, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost; The distributed optimization model of the station area is converted into an atomized station area optimization model according to the target constraint conditions, and the atomized station area optimization model is solved; Determining whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy; If the current iteration error is less than the preset convergence accuracy, the solution result of the atomization stage optimization model in the current iteration is output.

2. The method according to claim 1, characterized in that The method further comprises: If the iteration error is greater than or equal to the preset convergence accuracy, the proximal atomic coordination parameters are updated and the number of iterations is accumulated, and the atomization station optimization model is solved according to the updated proximal atomic coordination parameters until the iteration error of the atomization station optimization model is less than the preset convergence accuracy, and the solution result of the atomization station optimization model in the current iteration is output.

3. The method according to claim 1, characterized in that Before initializing the proximal atomic coordination parameters, the method further includes: Obtaining system parameter information of the target substation and source load prediction information of the substation; Calculate the own variables of the substation area, the own variables of the intelligent soft switch, and the boundary variables of the substation area and the intelligent soft switch area according to the system parameter information; Determine, according to the own variables and the boundary variables, a constraint matrix within the substation area, a constraint matrix within the intelligent soft switch area, and a coordination matrix between the substation area and the intelligent soft switch area; The initializing of the proximal atomic coordination parameters includes: The proximal atomic coordination parameters are initialized according to the own variables, the boundary variables, the constraint matrix and the coordination matrix.

4. The method according to claim 3, characterized in that The updating of system parameter information in each substation according to the substation source load prediction information includes: The distributed power source information and load fluctuation coefficient of each substation in the current optimization period are updated according to the substation source and load prediction information.

5. The method according to claim 1, characterized in that Before converting the distributed optimization model of the metro area into an atomized metro area optimization model according to the target constraint condition and solving the atomized metro area optimization model, the method further includes: Set target constraints, where the target constraints include substation power flow constraints, intelligent soft switch constraints, safe operation constraints, distributed power supply constraints, and intelligent soft switch power balance constraints between substation areas and intelligent soft switch areas.

6. The method according to claim 1, characterized in that The distributed optimization model of the station area is converted into an atomized station area optimization model according to the target constraint condition, and the atomized station area optimization model is solved, and further includes: In the process of solving the atomic station optimization model, each adjacent area is made to exchange boundary information, and the boundary information includes boundary variables of the station area and the intelligent soft switch area.

7. The method according to any one of claims 1 to 6, characterized in that: After outputting the solution result of the atomization stage optimization model at the current iteration, the method further includes: When the target substation runs to the next optimization period, the system parameter information in each substation is updated based on the substation source load prediction information, and the subsequent steps of establishing a distributed optimization model for each substation are performed based on the proximal atomic coordination parameters and the system parameter information until a new solution result is output.

8. A distributed optimization device for a substation, characterized in that: The device comprises: Initialization module, used to initialize the proximal atomic coordination parameters and update the system parameter information in each substation according to the substation source load prediction information; A model building module, used to establish a distributed optimization model for each substation according to the proximal atomic coordination parameters and the system parameter information, wherein the objective function of the distributed optimization model is set to minimize the sum of the substation network loss cost and the intelligent soft switch loss cost; A solution module, used for converting the distributed optimization model of the station area into an atomized station area optimization model according to the target constraint conditions, and solving the atomized station area optimization model; A judgment module, used to judge whether the current iteration error of the atomization stage optimization model is less than a preset convergence accuracy; The output module is used to output the solution result of the atomization stage optimization model in the current iteration if the current iteration error is less than the preset convergence accuracy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed optimization method for stations according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed optimization method for a station area according to any one of claims 1 to 7 are implemented.

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