Multi-body power distribution network line loss optimization method and device

Abstract

The invention relates to the technical field of power distribution network line loss optimization, and particularly provides a multi-body power distribution network line loss optimization method and device, and the method comprises the steps: solving a pre-constructed multi-body power distribution network line loss optimization model through employing a snake vulture secretary bird algorithm, and obtaining an optimization result; based on the optimization result, obtaining a multi-main-body power distribution network line loss optimization scheme; wherein the optimization result comprises the grid-connected capacity and the grid-connected position of the distributed power supply. According to the technical scheme provided by the invention, a strategy of avoiding natural enemies of a secretary bird is introduced, the algorithm can be effectively prevented from falling into a local optimal solution, and the robustness and the flexibility are relatively high.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network line loss optimization, and in particular to a multi-agent power distribution network line loss optimization method and device. Background Art

[0002] As global climate change and environmental problems become increasingly severe, accelerating the development of new energy and promoting energy transformation have become the primary goals. The use of clean energy, especially the promotion of distributed power sources, has brought revolutionary changes to the power system. The large-scale access of multiple entities such as distributed power sources has not only improved the flexibility and reliability of the power system, but also changed the traditional distribution network structure and power flow distribution, which in turn caused changes in the line loss of the distribution network. Therefore, it is particularly important to optimize the line loss of the distribution network containing multiple entities such as distributed power sources.

[0003] At present, mathematical optimization algorithms and intelligent optimization algorithms are mainly used to optimize the line loss of distribution networks. Common mathematical optimization algorithms include linear programming and quadratic programming. Through Taylor expansion, the nonlinear problem of distribution network planning is transformed into a linear problem, and then the optimal solution is obtained through linear programming and other methods. However, the mathematical analysis method requires a lot of calculations in the solution process, so it is gradually replaced by intelligent optimization algorithms. Most intelligent optimization algorithms are derived from some natural phenomena. Common methods include particle swarm algorithms, genetic algorithms, and hybrid algorithms. For example, the particle swarm algorithm simulates the activity patterns of bird groups. Each individual in the population continuously adjusts its position and speed according to the local optimal and overall optimal solutions, and iterates multiple times to the optimal. The genetic algorithm is derived from the biological evolution process, and solves the optimization problem by simulating the mechanisms of inheritance, mutation, hybridization, and natural selection in the biological evolution process.

[0004] However, the above distribution network line loss optimization algorithm has problems such as high computational complexity and easy to fall into local optimality. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present invention proposes a method and device for optimizing line losses in a multi-agent distribution network.

[0006] In a first aspect, a multi-agent distribution network line loss optimization method is provided, and the multi-agent distribution network line loss optimization method comprises:

[0007] The secretary bird algorithm is used to solve the pre-built multi-agent distribution network line loss optimization model and obtain the optimization results.

[0008] Based on the optimization results, a multi-agent distribution network line loss optimization scheme is obtained;

[0009] Wherein, the optimization result includes: the grid-connected capacity and grid-connected location of the distributed power source.

[0010] Preferably, the pre-constructed multi-agent distribution network line loss optimization model includes: a first objective function of minimizing active line loss, a second objective function of minimizing comprehensive grid vulnerability, a third objective function of optimizing distribution network economy, and constraints.

[0011] Furthermore, the first objective function f 1 as follows:

[0012]

[0013] In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

[0014] Furthermore, the second objective function f 2 as follows:

[0015]

[0016] In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω 1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω 2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows:

[0017]

[0018] The distribution network vulnerability balance degree at time t is as follows:

[0019]

[0020] In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,tIt is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

[0021] Furthermore, the third objective function f 3 as follows:

[0022] f 3 =minC=CI+CM+CD

[0023] In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

[0024] Furthermore, the initial investment cost is as follows:

[0025]

[0026] The maintenance costs are as follows:

[0027]

[0028] The recovery costs are as follows:

[0029]

[0030] In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,i is the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

[0031] Furthermore, the constraints are as follows:

[0032]

[0033] In the above formula, P Gi is the active power injected by the generator at node i, P DGi is the active power of distributed generation on node i, P Li The active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ijis the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

[0034] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird predation phase, if the current algorithm iteration number is less than one third of the total iteration number, the position of the individual is updated according to the following formula:

[0035] X 11 =X i +(X random1 -X random2 )·R1

[0036] X 1 =max(X 11 ,lb)

[0037] X 1 =min(X 11 ,ub)

[0038] If the current algorithm iteration number is between one-third and two-thirds of the total iteration number, the individual position is updated as follows:

[0039]

[0040] If the current algorithm iteration number is greater than two-thirds of the total iteration number, the individual position is updated as follows:

[0041] X 1 =Bast p +CF·X i ·RL

[0042] In the above formula, X1 is the updated position of the secretary bird during the predation phase, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, RL is the Levy flight vector, and t is the current number of iterations.

[0043] Furthermore, the attenuation factor is as follows:

[0044]

[0045] The Levy flight vector is as follows:

[0046]

[0047] In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, and β is the exponential parameter of Levy flight.

[0048] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird escape phase, the position of the individual is updated according to the following formula:

[0049]

[0050] In the above formula, r is a randomly generated number between 0 and 1, and X 2 The updated position of the individual in the secretary bird escape phase, Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

[0051] In a second aspect, a multi-agent distribution network line loss optimization device is provided, and the multi-agent distribution network line loss optimization device comprises:

[0052] The first analysis module is used to solve the pre-built multi-agent distribution network line loss optimization model by using the secretary bird algorithm to obtain the optimization result;

[0053] A second analysis module is used to obtain a multi-subject distribution network line loss optimization solution based on the optimization result;

[0054] Wherein, the optimization result includes: the grid-connected capacity and grid-connected location of the distributed power source.

[0055] Preferably, the pre-constructed multi-agent distribution network line loss optimization model includes: a first objective function of minimizing active line loss, a second objective function of minimizing comprehensive grid vulnerability, a third objective function of optimizing distribution network economy, and constraints.

[0056] Furthermore, the first objective function f 1 as follows:

[0057]

[0058] In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

[0059] Furthermore, the second objective function f 2 as follows:

[0060]

[0061] In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω 1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω 2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows:

[0062]

[0063] The distribution network vulnerability balance degree at time t is as follows:

[0064]

[0065] In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

[0066] Furthermore, the third objective function f 3 as follows:

[0067] f 3 =minC=CI+CM+CD

[0068] In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

[0069] Furthermore, the initial investment cost is as follows:

[0070]

[0071] The maintenance costs are as follows:

[0072]

[0073] The recovery costs are as follows:

[0074]

[0075] In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,i is the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

[0076] Furthermore, the constraints are as follows:

[0077]

[0078] In the above formula, P Gi is the active power injected by the generator at node i, P DGi is the active power of distributed generation on node i, P LiThe active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ij is the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

[0079] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird predation phase, if the current algorithm iteration number is less than one third of the total iteration number, the position of the individual is updated according to the following formula:

[0080] X 11 =X i +(X random1 -X random2 )·R1

[0081] X 1 =max(X 11 ,lb)

[0082] X 1 =min(X 11 ,ub)

[0083] If the current algorithm iteration number is between one-third and two-thirds of the total iteration number, the individual position is updated as follows:

[0084]

[0085] If the current algorithm iteration number is greater than two-thirds of the total iteration number, the individual position is updated as follows:

[0086] X 1=Bast p +CF·X i ·RL

[0087] In the above formula, X 1 is the updated position of the secretary bird during the predation phase, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, RL is the Levy flight vector, and t is the current number of iterations.

[0088] Furthermore, the attenuation factor is as follows:

[0089]

[0090] The Levy flight vector is as follows:

[0091]

[0092] In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, and β is the exponential parameter of Levy flight.

[0093] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird escape phase, the position of the individual is updated according to the following formula:

[0094]

[0095] In the above formula, r is a randomly generated number between 0 and 1, and X 2 The updated position of the individual in the secretary bird escape phase, Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

[0096] In a third aspect, a computer device is provided, comprising: one or more processors;

[0097] The processor is configured to execute one or more programs;

[0098] When the one or more programs are executed by the one or more processors, the multi-agent distribution network line loss optimization method is implemented.

[0099] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the multi-agent distribution network line loss optimization method is implemented.

[0100] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0101] The present invention provides a multi-agent distribution network line loss optimization method and device, including: using a secretary bird algorithm to solve a pre-constructed multi-agent distribution network line loss optimization model to obtain an optimization result; based on the optimization result, obtaining a multi-agent distribution network line loss optimization scheme; wherein the optimization result includes: the grid-connected capacity and grid-connected position of distributed power sources. The technical solution provided by the present invention introduces the secretary bird's strategy of avoiding natural enemies, which can effectively prevent the algorithm from falling into a local optimal solution and has strong robustness and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 It is a schematic flow chart of the main steps of the multi-agent distribution network line loss optimization method according to an embodiment of the present invention;

[0103] Figure 2 A schematic diagram of the secretary bird algorithm flow of the secretary bird of an embodiment of the present invention. DETAILED DESCRIPTION

[0104] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0105] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0106] As disclosed in the background technology, with the increasing severity of global climate change and environmental problems, accelerating the development of new energy and promoting energy transformation have become the primary goals. The use of clean energy, especially the promotion of distributed power sources, has brought revolutionary changes to the power system. The large-scale access of multiple entities such as distributed power sources has not only improved the flexibility and reliability of the power system, but also changed the traditional distribution network grid structure and power flow distribution, thereby causing changes in the line loss of the distribution network. Therefore, it is particularly important to optimize the line loss of the distribution network containing multiple entities such as distributed power sources.

[0107] At present, mathematical optimization algorithms and intelligent optimization algorithms are mainly used to optimize the line loss of distribution networks. Common mathematical optimization algorithms include linear programming and quadratic programming. Through Taylor expansion, the nonlinear problem of distribution network planning is transformed into a linear problem, and then the optimal solution is obtained through linear programming and other methods. However, the mathematical analysis method requires a lot of calculations in the solution process, so it is gradually replaced by intelligent optimization algorithms. Most intelligent optimization algorithms are derived from some natural phenomena. Common methods include particle swarm algorithms, genetic algorithms, and hybrid algorithms. For example, the particle swarm algorithm simulates the activity patterns of bird groups. Each individual in the population continuously adjusts its position and speed according to the local optimal and overall optimal solutions, and iterates multiple times to the optimal. The genetic algorithm is derived from the biological evolution process, and solves the optimization problem by simulating the mechanisms of inheritance, mutation, hybridization, and natural selection in the biological evolution process.

[0108] However, the above distribution network line loss optimization algorithm has problems such as high computational complexity and easy to fall into local optimality.

[0109] In order to improve the above problems, the present invention provides a multi-agent distribution network line loss optimization method and device, including: using the secretary bird algorithm to solve the pre-constructed multi-agent distribution network line loss optimization model to obtain the optimization result; based on the optimization result, a multi-agent distribution network line loss optimization scheme is obtained; wherein the optimization result includes: the grid-connected capacity and grid-connected position of the distributed power source. The technical solution provided by the present invention introduces the secretary bird's strategy of avoiding natural enemies, which can effectively prevent the algorithm from falling into a local optimal solution and has strong robustness and flexibility.

[0110] The above scheme is described in detail below.

[0111] Example 1

[0112] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a multi-agent distribution network line loss optimization method according to an embodiment of the present invention. Figure 1 As shown, the multi-agent distribution network line loss optimization method in the embodiment of the present invention mainly includes the following steps:

[0113] Step S101: using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model to obtain the optimization result;

[0114] Based on the optimization results, a multi-agent distribution network line loss optimization scheme is obtained;

[0115] Wherein, the optimization result includes: the grid-connected capacity and grid-connected location of the distributed power source.

[0116] In this embodiment, the pre-constructed multi-agent distribution network line loss optimization model includes: a first objective function to minimize active line loss, a second objective function to minimize comprehensive grid vulnerability, a third objective function to optimize the economic efficiency of the distribution network, and constraints.

[0117] In one embodiment, the first objective function f 1 as follows:

[0118]

[0119] In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

[0120] In one embodiment, the second objective function f 2 as follows:

[0121]

[0122] In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω 1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω 2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows:

[0123]

[0124] The distribution network vulnerability balance degree at time t is as follows:

[0125]

[0126] In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

[0127] In one embodiment, the third objective function f 3 as follows:

[0128] f 3 =minC=CI+CM+CD

[0129] In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

[0130] In one embodiment, the initial investment cost is as follows:

[0131]

[0132] The maintenance costs are as follows:

[0133]

[0134] The recovery costs are as follows:

[0135]

[0136] In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,i is the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

[0137] In one embodiment, the constraints are as follows:

[0138]

[0139] In the above formula, PGi is the active power injected by the generator at node i, P DGi is the active power of distributed generation on node i, P Li The active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ij is the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

[0140] In this embodiment, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, the predation behavior of the secretary bird is simulated, and the optimal solution is searched by searching, approaching and attacking prey, and a strategy to avoid natural enemies is introduced to prevent the algorithm from falling into a local optimal solution, such as Figure 2 As shown, the specific implementation steps are as follows:

[0141] Step 1: Problem modeling. The optimization objectives are to minimize the active line loss of the distribution network, minimize the comprehensive vulnerability of the power grid, and optimize the economy. They are uniformly weighted and the constraints are determined.

[0142] Step 2: Input the original data and algorithm parameters of the distribution network system.

[0143] Step 3: First, initialize the population and randomly generate the initial population in the feasible area of ​​the distribution network. Each individual represents a possible distributed power configuration scheme.

[0144] Step 4: Calculate the initial fitness. Call the distribution network multi-objective collaborative optimization scheduling calculation program to calculate the fitness of each numerical individual, and save the fitness of each individual and its access position and access capacity corresponding position.

[0145] Step 5: Set the number of iterations T and find the global optimal solution and its location

[0146] Step 6: Secretary bird predation phase, which is divided into three phases: searching, approaching, and attacking prey according to the number of iterations t of the algorithm.

[0147] Phase 1: Prey search phase. This phase occurs in the early stages of the algorithm, with the number of iterations being less than one-third of the total number of iterations. During this phase, individuals explore the solution space through random walks to find possible high-quality solutions. The corresponding mathematical calculation model is:

[0148] X 11 =X i +(X random1 -X random2 )·R1

[0149] X 1 =max(X 11 ,lb)

[0150] X 1 =min(X 11 ,ub)

[0151] Phase 2: Approaching prey. In this phase, after searching for prey, the number of iterations is between T / 3 and 2T / 3. In this phase, the individual gradually approaches the global optimal solution and explores the surrounding area to find a better solution. The corresponding mathematical calculation model is:

[0152]

[0153] In this stage, the exponential function exp((t / T)^4) is used to control the speed of approaching the prey, and the normal distribution random number RB and the global optimal solution Bast are combined. P With the current individual position X i The difference is used to update the new position X 1 .

[0154] Phase 3: Attack prey phase. This phase is in the later stage of the algorithm, and the number of iterations is greater than or equal to 2T / 3. In this phase, individuals conduct a more precise search for the global optimal solution, using the attenuation factor and Levy flight to update the position in order to find the optimal solution or approximate optimal solution in the solution space. The corresponding mathematical formula is:

[0155] X 1 =Bast p +CF·Xi ·RL

[0156] In the above formula, X 1 is the updated position of the secretary bird during the predation phase, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, and RL is the Levy flight vector.

[0157] In one embodiment, the attenuation factor is as follows:

[0158]

[0159] The Levy flight vector is as follows:

[0160]

[0161] In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, β is the exponential parameter of Levy flight, which usually takes a value between (1,2), and here β = 1.5.

[0162] Step 7: Secretary bird escape phase. Simulate the behavior of secretary birds escaping from natural enemies and update the individual position by randomly perturbing the current position. Secretary birds have two escape strategies, namely, hiding in the environment and escaping from predators. By giving the global optimal position Bast P , a randomly selected individual position X random , and a random number r is used to decide which evasion strategy to adopt. The specific formula is as follows:

[0163]

[0164] In the above formula, r is a randomly generated number between 0 and 1, and X 2 The updated position of the individual in the secretary bird escape phase, Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

[0165] Step 8: Update the global optimal solution and its position. When the number of iterations does not reach the set value, t=t+1, return to step 6. When the number of iterations is equal to T, output the global optimal point and its position.

[0166] Example 2

[0167] Based on the same inventive concept, the present invention also provides a multi-subject distribution network line loss optimization device, the multi-subject distribution network line loss optimization device comprising:

[0168] The first analysis module is used to solve the pre-built multi-agent distribution network line loss optimization model by using the secretary bird algorithm to obtain the optimization result;

[0169] A second analysis module is used to obtain a multi-subject distribution network line loss optimization solution based on the optimization result;

[0170] Wherein, the optimization result includes: the grid-connected capacity and grid-connected location of the distributed power source.

[0171] Preferably, the pre-constructed multi-agent distribution network line loss optimization model includes: a first objective function of minimizing active line loss, a second objective function of minimizing comprehensive grid vulnerability, a third objective function of optimizing distribution network economy, and constraints.

[0172] Furthermore, the first objective function f 1 as follows:

[0173]

[0174] In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

[0175] Furthermore, the second objective function f 2 as follows:

[0176]

[0177] In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω 1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows:

[0178]

[0179] The distribution network vulnerability balance degree at time t is as follows:

[0180]

[0181] In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

[0182] Furthermore, the third objective function f 3 as follows:

[0183] f 3 =minC=CI+CM+CD

[0184] In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

[0185] Furthermore, the initial investment cost is as follows:

[0186]

[0187] The maintenance costs are as follows:

[0188]

[0189] The recovery costs are as follows:

[0190]

[0191] In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,iis the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

[0192] Furthermore, the constraints are as follows:

[0193]

[0194] In the above formula, P Gi is the active power injected by the generator at node i, P DGi is the active power of distributed generation on node i, P Li The active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ij is the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

[0195] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird predation phase, if the current algorithm iteration number is less than one third of the total iteration number, the position of the individual is updated according to the following formula:

[0196] X 11 =X i +(X random1 -X random2 )·R1

[0197] X 1 =max(X 11 ,lb)

[0198] X 1 =min(X 11 ,ub)

[0199] If the current algorithm iteration number is between one-third and two-thirds of the total iteration number, the individual position is updated as follows:

[0200]

[0201] If the current algorithm iteration number is greater than two-thirds of the total iteration number, the individual position is updated as follows:

[0202] X 1 =Bast p +CF·X i ·RL

[0203] In the above formula, X 1 is the updated position of the secretary bird during the predation phase, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, RL is the Levy flight vector, and t is the current number of iterations.

[0204] Furthermore, the attenuation factor is as follows:

[0205]

[0206] The Levy flight vector is as follows:

[0207]

[0208] In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, and β is the exponential parameter of Levy flight.

[0209] Preferably, in the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird escape phase, the position of the individual is updated according to the following formula:

[0210]

[0211] In the above formula, r is a randomly generated number between 0 and 1, and X 2 The updated position of the individual in the secretary bird escape phase, Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

[0212] Example 3

[0213] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a multi-subject distribution network line loss optimization method in the above embodiment.

[0214] Example 4

[0215] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a multi-subject distribution network line loss optimization method in the above embodiment.

[0216] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0218] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-agent distribution network line loss optimization method, characterized in that: The method comprises: The secretary bird algorithm is used to solve the pre-built multi-agent distribution network line loss optimization model and obtain the optimization results. Based on the optimization results, a multi-agent distribution network line loss optimization scheme is obtained; Among them, the optimization results include: the grid-connected capacity and grid-connected location of distributed power sources; the pre-built multi-agent distribution network line loss optimization model includes: the first objective function with minimum active line loss, the second objective function with minimum comprehensive vulnerability of the power grid, the third objective function with optimal economic efficiency of the distribution network, and constraints.

2. The method according to claim 1, characterized in that The first objective function f1 is as follows: In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

3. The method according to claim 1, characterized in that The second objective function f2 is as follows: In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows: The distribution network vulnerability balance degree at time t is as follows: In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

4. The method according to claim 1, characterized in that The third objective function f3 is as follows: f3=minC=CI+CM+CD In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

5. The method according to claim 4, characterized in that The initial investment costs are as follows: The maintenance costs are as follows: The recovery costs are as follows: In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,i is the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

6. The method according to claim 1, characterized in that The constraints are as follows: In the above formula, P Gi is the active power injected by the generator at node i, P DGi is the active power of the distributed generation on node i, P Li The active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ij is the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

7. The method according to claim 1, characterized in that In the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird predation phase, if the current algorithm iteration number is less than one third of the total iteration number, the position of the individual is updated according to the following formula: X 11 =X i +(X random1 -X random2 )·R1 X1=max(X 11 ,lb) X1=min(X 11 ,b) If the current algorithm iteration number is between one-third and two-thirds of the total iteration number, the individual position is updated as follows: If the current algorithm iteration number is greater than two-thirds of the total iteration number, the individual position is updated as follows: X1=Bast p +CF·X i ·RL In the above formula, X1 is the updated position of the individual in the secretary bird predation stage, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, RL is the Levy flight vector, and t is the current number of iterations.

8. The method according to claim 7, characterized in that The attenuation factors are as follows: The Levy flight vector is as follows: In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, and β is the exponential parameter of Levy flight.

9. The method according to claim 1, characterized in that In the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird escape phase, the position of the individual is updated according to the following formula: In the above formula, r is a randomly generated number between 0 and 1, X2 is the updated position of the individual in the secretary bird escape phase, and Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

10. A multi-agent distribution network line loss optimization device, characterized in that: The device comprises: The first analysis module is used to solve the pre-built multi-agent distribution network line loss optimization model by using the secretary bird algorithm to obtain the optimization result; A second analysis module is used to obtain a multi-subject distribution network line loss optimization solution based on the optimization result; The optimization results include: the grid-connected capacity and grid-connected location of the distributed power source; The pre-built multi-agent distribution network line loss optimization model includes: a first objective function with minimum active line loss, a second objective function with minimum comprehensive vulnerability of the power grid, a third objective function with optimal economic efficiency of the distribution network, and constraints.

11. The device according to claim 10, characterized in that The first objective function f1 is as follows: In the above formula, P loss is the line loss, r is the line resistivity, G is the length from the substation to the distributed generation access point, P L is the active power consumed by the load, P g is the active power output of the distributed power supply, Q L is the reactive power consumed by the load, Q g is the reactive power output by the distributed generation, U is the phase voltage on the transmission line, and L is the length of the line segment from the substation to the load.

12. The device according to claim 10, characterized in that The second objective function f2 is as follows: In the above formula, V com is the comprehensive vulnerability of the power grid, T is the optimization period, ω1 is the first weight coefficient, V t ent is the overall vulnerability index of the distribution network at time t, ω2 is the second weight coefficient, V t enq is the distribution network vulnerability balance degree at time t, where the overall distribution network vulnerability index at time t is as follows: The distribution network vulnerability balance degree at time t is as follows: In the above formula, N is the total number of distribution network nodes, V i,t is the vulnerability index of node i at time t, V t,min is the minimum value of the node vulnerability index, V t,max is the maximum value of the node vulnerability index, H i,t It is the ratio of the sum of the vulnerability indices of the first i nodes after the vulnerability indices of each node are sorted from small to large at time t to the sum of the vulnerability indices of all nodes. is the per unit value of the voltage at node i at time t, ΔU max is the maximum voltage offset.

13. The device according to claim 10, characterized in that The third objective function f3 is as follows: f3=minC=CI+CM+CD In the above formula, C is the economic efficiency of the distribution network, CI is the initial investment cost, CM is the maintenance cost, and CD is the recovery cost.

14. The device according to claim 13, characterized in that The initial investment costs are as follows: The maintenance costs are as follows: The recovery costs are as follows: In the above formula, N is the total number of distribution network nodes, r is the discount rate, y is the service life, M is the type of distributed power source, c DG,m,i is the unit capacity cost of the mth type of distributed generation at node i, E DG,m,i is the capacity of the mth type of distributed generation at node i, δ DG,m,i is the ratio of the maintenance cost of the mth type of distributed generation at node i to the initial investment cost, is the average yield of the mth type of distributed generation at node i, D is the number of times the distributed generation replaces components, t∈[1,D].

15. The device according to claim 10, characterized in that The constraints are as follows: In the above formula, P Gi is the active power injected by the generator at node i, P DGi is the active power of the distributed generation on node i, P Li The active power injected into the load of node i, V i is the voltage of the power distribution input node i, V j is the voltage of the power distribution output node j, G ij is the conductance between nodes i and j, θ ij is the phase difference between nodes i and j, B ij is the susceptance between nodes i and j, Q Gi is the reactive power injected by the generator at node i, Q DGi is the reactive power of distributed generation at node i, Q Li is the reactive power injected into the load by node i, N is the total number of distribution network nodes, V imin is the lower limit of the node voltage, V imax is the upper limit of the node voltage, I ijmin is the lower limit of branch current, I ij is the current between branches ij, I ijmax is the upper limit of the branch current, is the lower limit of the active power of the distributed generation on node i, is the upper limit of the active power of the distributed generation on node i, S i is the apparent power of node i, S imax is the apparent power upper limit of node i.

16. The device according to claim 10, characterized in that In the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird predation phase, if the current algorithm iteration number is less than one third of the total iteration number, the position of the individual is updated according to the following formula: X 11 =X i +(X random1 -X random2 )·R1 X1=max(X 11 ,lb) X1=min(X 11 ,b) If the current algorithm iteration number is between one-third and two-thirds of the total iteration number, the individual position is updated as follows: If the current algorithm iteration number is greater than two-thirds of the total iteration number, the individual position is updated as follows: X1=Bast p +CF·X i ·RL In the above formula, X1 is the updated position of the individual in the secretary bird predation stage, X 11 is the middle position, lb is the lower bound of the solution space, ub is the upper bound of the solution space, X i is the current individual position, X random1 is the position of another randomly selected individual, X random2 is the position of another randomly selected individual, R1 is a random number vector between 0 and 1, Bast p is the global optimal position, e is a natural constant, T is the total number of iterations, RB is the first randomly generated normally distributed vector, CF is the attenuation factor, RL is the Levy flight vector, and t is the current number of iterations.

17. The device according to claim 16, characterized in that The attenuation factors are as follows: The Levy flight vector is as follows: In the above formula, t is the current iteration number, u is the random vector of the first standard normal distribution, v is the random vector of the second standard normal distribution, and β is the exponential parameter of Levy flight.

18. The device according to claim 10, characterized in that In the process of using the secretary bird algorithm to solve the pre-built multi-agent distribution network line loss optimization model, when executing the secretary bird escape phase, the position of the individual is updated according to the following formula: In the above formula, r is a randomly generated number between 0 and 1, X2 is the updated position of the individual in the secretary bird escape phase, and Bast p is the global optimal position, t is the current iteration number, T is the total iteration number, RB is the first randomly generated normal distribution vector, X i is the current individual's position, R2 is the second randomly generated normal distribution vector, X random is the position of other individuals randomly selected, and K is a scalar.

19. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the multi-agent distribution network line loss optimization method as described in any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the multi-agent distribution network line loss optimization method as described in any one of claims 1 to 9 is implemented.

Images