A control parameter optimization method and system of a power distribution system

Abstract

The application provides a control parameter optimization method and system of a power distribution system, and the method comprises the following steps: obtaining operation parameters of the power distribution system and building a network topology structure of the power distribution system; based on the power distribution system, taking the minimum real power loss on the feeder, the minimum bus voltage deviation, the highest bus voltage stability and the minimum real power change as the objective function, combining the constraint conditions to build a multi-objective power distribution system optimization model; taking the transformer tap position, the input capacitor group virtual power, the wind turbine output virtual power and the energy storage system discharge power as the decision variables of the optimization model; adopting the image group search algorithm to solve the optimization model and obtaining the optimal decision variable. The application solves the multi-objective power distribution system optimization model, obtains the optimal control parameter meeting multiple limit conditions, improves the calculation efficiency and calculation accuracy, and realizes the optimal operation control of the power distribution system.

Description

Technical Field

[0001] This invention relates to the field of power grid control, and more particularly to a method, system, and computer-readable storage medium for optimizing control parameters of a power distribution system. Background Technology

[0002] With rapid economic and industrial development, reducing the environmental impact of resources and achieving sustainable resource development has become a major research direction in power grid technology. Therefore, green energy, such as wind power and photovoltaic power, has been widely applied and is gradually being integrated into the power grid for residential or commercial use. However, the intelligent control of green energy distribution systems is quite complex. Its structure includes green energy and energy storage systems. On the one hand, it is necessary to ensure the power supply quality of the distribution system, and on the other hand, to minimize power loss and improve voltage stability. Many factors need to be considered simultaneously. Therefore, how to achieve optimal operation control of green energy distribution systems and determine the optimal control parameters is a pressing technical problem in this field.

[0003] Traditionally, the optimal control parameters for power distribution systems are calculated based on empirical values. However, this approach has several drawbacks. Optimizing control parameters in a power distribution system involves numerous objective functions and constraints, making empirical values ​​unsuitable for solving multi-objective optimization problems. Control parameters set based on empirical values ​​are often inaccurate. Furthermore, setting empirical values ​​requires highly experienced technicians with extensive knowledge of power grid optimization, and the results can vary significantly depending on the technician's skill level. Additionally, the large number of parameters necessitates substantial time for calculation. Therefore, existing methods for optimizing control parameters in power distribution systems suffer from low computational efficiency and inaccurate results. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing control parameters of a green energy power distribution system. This method can solve the technical problems of low computational efficiency and inaccurate calculation results in the existing technology for optimizing control parameters of power distribution systems.

[0005] The first aspect of this invention provides a method for optimizing control parameters of a green energy power distribution system, comprising the following steps:

[0006] S1: Obtain the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes busbars, photovoltaic power generation systems, wind power generation systems, energy storage systems, and capacitor banks;

[0007] S2: Based on the power distribution system, a multi-objective power distribution system optimization model is constructed with the objective functions of minimizing the total actual power loss on the feeder, minimizing the bus voltage deviation, maximizing the bus voltage stability, and minimizing the actual power flow change, combined with the constraints.

[0008] S3: The transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine, and the discharge power of the energy storage system are used as decision variables for the optimization model.

[0009] S4: The optimization model is solved using the elephant group search algorithm to obtain the optimal decision variables, and the optimal decision variables are used as the optimal control parameters of the power distribution system.

[0010] As a further improvement of the present invention, the decision variables are mapped to the elephant herd members of the elephant herd search algorithm, and the algorithm specifically includes the following steps:

[0011] S41: Initialize algorithm parameters: Set parameters α, β, and γ. α is the influence factor of the elephant herd leader on the movement of herd members, β is the influence factor determining the movement of herd members towards the herd center, and γ is the influence factor for the random movement of herd members. β, γ ∈ (0, 1), and the number of elephants n... c and the number of members n in each elephant herd ci The maximum number of iterations is T, the current number of iterations is TI, α = cos(π·TI / 2T), the positions of the members of the object group are randomly initialized, and the objective function of the algorithm is set to f = {f i}, i = 1 to 4 and fitness function AF;

[0012] S42: Calculate the fitness values ​​of the fitness functions of the members in each elephant herd and sort them to obtain the herd members with the best fitness values.

[0013] S43: Optimize the objective function value corresponding to each elephant herd member using the Pareto optimization algorithm to obtain the Pareto optimal solution set, calculate the clustering distance of the solution set, and determine the elephant herd leader as the elephant herd member with the largest value in the result.

[0014] S44: Update the position of each elephant group member, and use the correction algorithm to correct the updated position. Finally, replace the elephant group member with the worst fitness function.

[0015] S45: Calculate the fitness function value of each member of the elephant population after correction, and sort them to obtain the member of the elephant population with the best fitness function;

[0016] S46: The objective function values ​​of each elephant herd member are optimized again using the Pareto optimization algorithm to obtain the Pareto optimal solution set. The clustering distance of the solution set is calculated, and the elephant herd member with the largest value in the result is determined as the herd leader.

[0017] S47: Determine if the number of iterations has reached the maximum value. If not, repeat steps S44-S46 until the stopping condition is met.

[0018] S48: Output the optimal result.

[0019] As a further improvement of the present invention, the objective function is f1 to f4, specifically including:

[0020]

[0021] Where f1 is the daily actual power loss on the feeder, N b R represents the number of busbars. ij P is the feed resistance from bus i to bus j. ih Q represents the actual power flowing out of bus i in the h-th hour. ih V represents the virtual power flowing out of bus i in the h-th hour. ih f1 is the voltage of bus i in hour h, f2 is the daily voltage deviation of bus i, and V ref Here, f3 is the reference voltage, f3 is the reciprocal of the daily bus voltage stability, and X is the reference voltage. ij f4 is the feeder reactance from bus i to bus j, f4 is the daily actual power variation, and P 1,h P represents the actual power injected into the substation in the h-th hour. 1,ref For reference actual power.

[0022] As a further improvement of the present invention, the constraint conditions include: energy conservation: Energy conservation in energy storage systems: E Bk,h =E Bk,h-1 +ρ B, ch P Bk,ch,h -P Bk,dis,h / ρ B,dis k = 1, 2, ... N BESS Transformer joint switching frequency limit: Capacitor bank switching frequency limit: Among them, P jh Q represents the actual power flowing out of bus j in the h-th hour. jh P represents the virtual power of bus j flowing out in the h-th hour. Gjh P represents the actual power injected into the generator of busbar j in hour h. Bj,dis,h P represents the discharge power of the energy storage system at bus j in hour h.Bj,ch,h The charging power of the energy storage system for busbar j in hour h, P Djh For the actual power load demand of busbar j in hour h, Q wjh The virtual power injected into the wind turbine of busbar j in the h-th hour, Q Cjh The virtual power Q injected into the capacitor of bus j in hour h is... Djh For the virtual power load demand of bus j in the hth hour, Tap h For the on-load tap position of the transformer in the h-th hour, C s,h This represents the on / off state of the s-th capacitor in the h-th hour, where 1 represents on, 0 represents off, and N represents the on / off state. s,max E represents the maximum number of switching operations for the s-th capacitor. Bk,h P represents the energy consumption of the k-th energy storage system in the h-th hour. Bk,dis,h P is the discharge power of the k-th energy storage system in the h-th hour. Bk,ch,h The charging power of the k-th energy storage system in the h-th hour, ρ B,ch For the charging efficiency of energy storage systems, ρ B,dis For the discharge efficiency of the energy storage system, N BESS This represents the total number of energy storage systems.

[0023] As a further improvement of the present invention, step S43 includes: the aggregation distance calculation method is as follows: Where u s Let cd be the s-th solution in the Pareto optimal solution set. s For u s The gathering distance, f n (u s+1 ) is u s+1 Input into the nth objective function f n The obtained function value, f n min To minimize the value of the nth objective function, N sol The total number of solutions.

[0024] As a further improvement of the present invention: the updating of the position of each elephant group member specifically involves:

[0025] P(c,j,T+1)=P(c,j,T)(1-α-β)+αP(c,b,T)+βP(c,ct,T)+γr

[0026] Where c is the index of the elephant herd, P(c,j,T+1) is the updated position of member j of herd c, P(c,j,T) is the current position of member j of herd c, P(c,b,T) is the position of the herd leader, P(c,ct,T) is the center position of herd c, and r = (2rand-1)(P max -Pmin ), where rand is a random number between 0 and 1, and P max and P min Let θ be the upper and lower bounds of the positions of the elephant herd members. The correction algorithm is: Q(c,j,T+1)=θP(c,j,T+1)+ω1Q(c,j1,T)+ω2Q(c,j2,T-1)+ω1Q(c,j3,T-2), where Q(c,j,T+1) is the corrected position of elephant member j after the (T+1)th iteration, Q(c,j1,T) is the corrected position of elephant member j1 after the Tth iteration, Q(c,j2,T-1) is the corrected position of elephant member j2 after the (T-1)th iteration, Q(c,j3,T-2) is the corrected position of elephant member j3 after the (T-2)th iteration, θ and ω1~ω3 are correction factors, where θ is a random number between 0 and 1, and j1~j3 are individuals randomly selected from the elephant herd.

[0027] These are the fitness values ​​of the corrected positions of individuals j1 to j3 after the Tth, T-1th, and T-2th iterations, respectively; the replacement of the group members in step S44 is specifically as follows: P(c,wt,T)=μP(c,b,T), where P(c,wt,T) is the position of the group member with the worst fitness function value, and μ is a random number between 0.95 and 1.05.

[0028] As a further improvement of the present invention: step S48 includes: calculating the total attribution function for each solution in the Pareto optimal solution set, and determining the solution with the largest total attribution function value as the optimal solution, wherein: in Let be the attribution function of the nth objective function for the s-th solution. Let be the upper and lower bounds of the nth objective function. Let L be the nth objective function value of the s-th solution. p Let be the total attribution function value of the s-th solution.

[0029] As a further improvement of the present invention: the fitness function in step S41 is: AF = b1f1 + b2f2 + b3f3 + b4f4, where b1 to b4 are scaling factors. 0 < {b1~b4] < 1.

[0030] A second aspect of the present invention provides a control parameter optimization system for a power distribution system, characterized in that it includes a parameter acquisition module, a model construction module, a decision variable determination module, and a model optimal solution module, wherein...

[0031] The parameter acquisition module is used to acquire the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes a bus, a photovoltaic power generation system, a wind power generation system, an energy storage system, and a capacitor bank;

[0032] The model building module is used to construct a multi-objective power distribution system optimization model based on the power distribution system, with the objective functions of minimizing actual power loss on feeders, minimizing bus voltage deviation, maximizing bus voltage stability, and minimizing actual power change, combined with constraints.

[0033] The decision variable determination module is used to take the transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine generator, and the discharge power of the energy storage system as decision variables of the optimization model.

[0034] The model optimal solution module is used to solve the optimization model using the elephant group search algorithm and obtain the optimal decision variables, which are then used as the optimal control parameters of the power distribution system.

[0035] A third aspect of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that can be executed by a processor to implement the control parameter optimization method for a power distribution system as described above.

[0036] Compared with existing technologies, the present invention has the following advantages: By solving a multi-objective power distribution system optimization model, the present invention can quickly obtain the optimal control parameters that satisfy multiple constraints, improving computational efficiency and accuracy, and enabling better optimal operation control of the power distribution system. Furthermore, the Elephant Search Algorithm used in this invention is a highly efficient multi-objective search algorithm. Compared with existing technologies, it has stronger search capabilities and faster convergence speed, and is less prone to getting trapped in local solutions. It provides better guidance for optimizing control parameters in power distribution systems and offers better reference for power distribution system decision-makers. Attached Figure Description

[0037] Figure 1 This is a structural block diagram of a power distribution system provided in Embodiment 1 of the present invention.

[0038] Figure 2 The flowchart illustrates the steps for optimizing control parameters of a power distribution system according to Embodiment 1 of the present invention.

[0039] Figure 3 This is a flowchart of an elephant herd search algorithm provided in Embodiment 1 of the present invention.

[0040] Figure 4This invention provides a control parameter optimization system for a power distribution system, as described in Embodiment 2 of the present invention.

[0041] Figure 5 This is a computer-readable storage medium provided in Embodiment 3 of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to the embodiments.

[0043] Example 1:

[0044] Figure 1 This invention provides a structural block diagram of a power distribution system, which adopts an IEEE 33-Bus and includes 33 busbars. Each feeder has a parallel capacitor bank. The power distribution system also includes a wind turbine (WG), a photovoltaic (PV) generator, and a battery energy storage system (BESS). Parallel capacitor banks are installed on the substation feeders, and the busbar voltage limit is 0.95 pu-1.05 pu.

[0045] like Figure 2 As shown in the figure, this embodiment provides a method for optimizing control parameters of a green energy power distribution system, including the following steps:

[0046] S1: Obtain the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes busbars, photovoltaic power generation systems, wind power generation systems, energy storage systems, and capacitor banks;

[0047] S2: Based on the power distribution system, a multi-objective power distribution system optimization model is constructed with the objective functions of minimizing the total actual power loss on the feeder, minimizing the bus voltage deviation, maximizing the bus voltage stability, and minimizing the actual power flow change, combined with the constraints.

[0048] S3: The transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine, and the discharge power of the energy storage system are used as decision variables for the optimization model.

[0049] S4: The optimization model is solved using the elephant group search algorithm to obtain the optimal decision variables, and the optimal decision variables are used as the optimal control parameters of the power distribution system.

[0050] This invention solves a multi-objective power distribution system optimization model, enabling rapid acquisition of optimal control parameters that satisfy multiple constraints. This improves computational efficiency and accuracy, allowing for better optimal operation control of the power distribution system. Furthermore, the Elephant Search Algorithm used in this invention is a highly efficient multi-objective search algorithm. Compared to existing technologies, it possesses stronger search capabilities and faster convergence speed, and is less prone to getting trapped in local solutions. This provides valuable guidance for optimizing control parameters in power distribution systems, offering better reference for decision-makers.

[0051] As a further improvement of the present invention, the decision variables are mapped to the elephant herd members of the elephant herd search algorithm, and the algorithm specifically includes the following steps:

[0052] S41: Initialize algorithm parameters: Set parameters α, β, and γ. α is the influence factor of the elephant herd leader on the movement of herd members, β is the influence factor determining the movement of herd members towards the herd center, and γ is the influence factor for the random movement of herd members. β, γ ∈ (0, 1), and the number of elephants n... c and the number of members n in each elephant herd ci The maximum number of iterations is T, the current number of iterations is TI, α = cos(π·TI / 2T), the positions of the members of the object group are randomly initialized, and the objective function of the algorithm is set to f = {f i}, i = 1 to 4 and fitness function AF;

[0053] S42: Calculate the fitness values ​​of the fitness functions of the members in each elephant herd and sort them to obtain the herd members with the best fitness values.

[0054] S43: Optimize the objective function value corresponding to each elephant herd member using the Pareto optimization algorithm to obtain the Pareto optimal solution set, calculate the clustering distance of the solution set, and determine the elephant herd leader as the elephant herd member with the largest value in the result.

[0055] S44: Update the position of each elephant group member, and use the correction algorithm to correct the updated position. Finally, replace the elephant group member with the worst fitness function.

[0056] S45: Calculate the fitness function value of each member of the elephant population after correction, and sort them to obtain the member of the elephant population with the best fitness function;

[0057] S46: The objective function values ​​of each elephant herd member are optimized again using the Pareto optimization algorithm to obtain the Pareto optimal solution set. The clustering distance of the solution set is calculated, and the elephant herd member with the largest value in the result is determined as the herd leader.

[0058] S47: Determine if the number of iterations has reached the maximum value. If not, repeat steps S44-S46 until the stopping condition is met.

[0059] S48: Output the optimal result.

[0060] The elephant herd search algorithm is a swarm intelligence optimization algorithm that simulates the social structure and behavior of elephant herds, including clan updates and clan separation operations, to solve multi-objective optimization problems. In the basic elephant herd search algorithm, the clan represents the local search, while male elephants leaving the clan perform the global search, thus balancing the algorithm's global search capability and local search accuracy. However, the original elephant herd search algorithm does not utilize the optimal information of previous generations of individuals to guide the current and subsequent search processes when updating individual positions, which leads to a slower convergence speed. This invention improves the elephant herd search algorithm by fully utilizing the information of previous generations of individuals during position updates and using a correction algorithm to refine the updated positions. This increases communication between different generations of individuals, improves the algorithm's search capability, avoids getting trapped in local optima, and increases the convergence speed.

[0061] As a further improvement of the present invention, the objective function is f1 to f4, specifically including:

[0062]

[0063] Where f1 is the daily actual power loss on the feeder, N b R represents the number of busbars. ij P is the feed resistance from bus i to bus j. ih Q represents the actual power flowing out of bus i in the h-th hour. ih V represents the virtual power flowing out of bus i in the h-th hour. ih f1 is the voltage of bus i in hour h, f2 is the daily voltage deviation of bus i, and V ref Here, f3 is the reference voltage, f3 is the reciprocal of the daily bus voltage stability, and X is the reference voltage. ij f4 is the feeder reactance from bus i to bus j, f4 is the daily actual power variation, and P 1,h P represents the actual power injected into the substation in the h-th hour. 1,ref For reference actual power.

[0064] As a further improvement of the present invention, the constraint conditions include: energy conservation: Energy conservation in energy storage systems: E Bk,h =E Bk,h-1 +ρ B, ch P Bk,ch,h -P Bk,dis,h / ρ B,dis k = 1, 2, ... N BESS Transformer joint switching frequency limit: Capacitor bank switching frequency limit: Among them, P jh Q represents the actual power flowing out of bus j in the h-th hour. jh P represents the virtual power of bus j flowing out in the h-th hour. Gjh P represents the actual power injected into the generator of busbar j in hour h. Bj,dis,h P represents the discharge power of the energy storage system at bus j in hour h. Bj,ch,h The charging power of the energy storage system for busbar j in hour h, P Djh For the actual power load demand of busbar j in hour h, Q wjh The virtual power injected into the wind turbine of busbar j in the h-th hour, Q Cjh The virtual power Q injected into the capacitor of bus j in hour h is... Djh For the virtual power load demand of bus j in the hth hour, Tap h For the on-load tap position of the transformer in the h-th hour, C s,h This represents the on / off state of the s-th capacitor in the h-th hour, where 1 represents on, 0 represents off, and N represents the on / off state. s,max E represents the maximum number of switching operations for the s-th capacitor. Bk,h P represents the energy consumption of the k-th energy storage system in the h-th hour. Bk,dis,h P is the discharge power of the k-th energy storage system in the h-th hour. Bk,ch,h The charging power of the k-th energy storage system in the h-th hour, ρ B,ch For the charging efficiency of energy storage systems, ρ B,dis For the discharge efficiency of the energy storage system, N BESS This represents the total number of energy storage systems.

[0065] As a further improvement of the present invention, step S43 includes: the aggregation distance calculation method is as follows: Where u s Let cd be the s-th solution in the Pareto optimal solution set. s For u s The gathering distance, f n (u s+1 ) is u s+1 Input into the nth objective function f n The obtained function value, f n min To minimize the value of the nth objective function, N sol The total number of solutions.

[0066] In step S43, the objective function values ​​of each group member need to be optimized using the Pareto optimization algorithm to obtain the Pareto optimal solution set. The Pareto optimization algorithm is a commonly used algorithm for solving multi-objective optimization problems. This algorithm is an optimization method based on the Pareto principle, including concepts such as non-dominated relations, Pareto optimal solutions, and Pareto fronts. Pareto proposed the concept of a non-dominated set in multi-objective optimization in 1986. It is defined as follows: if for any two solutions s1 and s2, s1 is better than s2 for all objective functions, then we say that s1 dominates s2. If s1 is not dominated by any other solution, then s1 is called a non-dominated solution, also known as a Pareto optimal solution. When there are multiple objectives, due to conflicts and incomparability between objectives, a solution that is best for one objective may be worst for others. Improving any objective function will inevitably weaken at least one other objective function's solution to a non-dominated solution. Pareto improvement is achieved by making at least one objective better without worsening any other objective. A Pareto optimal state is one in which no further Pareto improvements are possible. The set of optimal solutions to a given objective function is called the Pareto optimal set. The surface formed by this optimal set in space is called the Pareto frontier.

[0067] As a further improvement of the present invention: the updating of the position of each elephant group member specifically involves:

[0068] P(c,j,T+1)=P(c,j,T)(1-α-β)+αP(c,b,T)+βP(c,ct,T)+γr

[0069] Where c is the index of the elephant herd, P(c,j,T+1) is the updated position of member j of herd c, P(c,j,T) is the current position of member j of herd c, P(c,b,T) is the position of the herd leader, P(c,ct,T) is the center position of herd c, and r = (2rand-1)(P max -P min ), where rand is a random number between 0 and 1, and P max and P min The upper and lower bounds are the positions of the elephant herd members; the correction algorithm is as follows:

[0070] Q(c,j,T+1)=θP(c,j,T+1)+ω1Q(c,j1,T)+ω2Q(c,j2,T-1)+ω1Q(c,j3,T-2), where Q(c,j,T+1) is the corrected position of elephant member j after the (T+1)th iteration, Q(c,j1,T) is the corrected position of elephant member j1 after the Tth iteration, Q(c,j2,T-1) is the corrected position of elephant member j2 after the (T-1)th iteration, Q(c,j3,T-2) is the corrected position of elephant member j3 after the (T-2)th iteration, θ and ω1~ω3 are correction factors, where θ is a random number between 0 and 1, and j1~j3 are individuals randomly selected from the elephant population.

[0071] These are the fitness values ​​of the corrected positions of individuals j1 to j3 after the Tth, T-1th, and T-2th iterations, respectively; the replacement of the group members in step S44 is specifically as follows: P(c,wt,T)=μP(c,b,T), where P(c,wt,T) is the position of the group member with the worst fitness function value, and μ is a random number between 0.95 and 1.05.

[0072] As a further improvement of the present invention: step S48 includes: calculating the total attribution function for each solution in the Pareto optimal solution set, and determining the solution with the largest total attribution function value as the optimal solution, wherein: in Let be the attribution function of the nth objective function for the s-th solution. Let be the upper and lower bounds of the nth objective function. Let L be the nth objective function value of the s-th solution. p Let be the total attribution function value of the s-th solution.

[0073] As a further improvement of the present invention: the fitness function in step S41 is: AF = b1f1 + b2f2 + b3f3 + b4f4, where b1 to b4 are scaling factors. 0 < {b1~b4} < 1.

[0074] Example 2:

[0075] A second aspect of the present invention provides a control parameter optimization system for a power distribution system, see [link to relevant documentation]. Figure 4 As shown, it is characterized by including a parameter acquisition module, a model construction module, a decision variable determination module, and a model optimal solution module, wherein,

[0076] The parameter acquisition module is used to acquire the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes a bus, a photovoltaic power generation system, a wind power generation system, an energy storage system, and a capacitor bank;

[0077] The model building module is used to construct a multi-objective power distribution system optimization model based on the power distribution system, with the objective functions of minimizing actual power loss on feeders, minimizing bus voltage deviation, maximizing bus voltage stability, and minimizing actual power change, combined with constraints.

[0078] The decision variable determination module is used to take the transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine generator, and the discharge power of the energy storage system as decision variables of the optimization model.

[0079] The model optimal solution module is used to solve the optimization model using the elephant group search algorithm and obtain the optimal decision variables, which are then used as the optimal control parameters of the power distribution system.

[0080] Example 3:

[0081] A third aspect of the present invention provides a computer-readable storage medium, see [link to relevant documentation]. Figure 5 As shown, the computer-readable storage medium stores a computer program that can be executed by a processor to implement the control parameter optimization method for the power distribution system as described above.

[0082] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing control parameters of a power distribution system, comprising the following steps: S1: Obtain the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes busbars, photovoltaic power generation systems, wind power generation systems, energy storage systems, and capacitor banks; S2: Based on the power distribution system, a multi-objective power distribution system optimization model is constructed with the objective functions of minimizing the actual power loss on the feeder, minimizing the bus voltage deviation, maximizing the bus voltage stability, and minimizing the actual power change, combined with the constraints. S3: The transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine, and the discharge power of the energy storage system are used as decision variables for the optimization model. S4: The optimization model is solved using the elephant group search algorithm to obtain the optimal decision variables, and the optimal decision variables are used as the optimal control parameters of the power distribution system. It also includes mapping the decision variables to elephant herd members of the elephant herd search algorithm, the algorithm specifically including the following steps: S41: Initialize algorithm parameters: Set parameters α, β, and γ. α is the influence factor of the elephant herd leader on the movement of herd members, β is the influence factor determining the movement of herd members towards the herd center, and γ is the influence factor for the random movement of herd members. β, γ ∈ (0, 1), and the number of elephants n... c and the number of members n in each elephant herd ci The maximum number of iterations is T, the current number of iterations is TI, α = cos(π·TI / 2T), the positions of the members of the object group are randomly initialized, and the objective function of the algorithm is set to f = {f i }, i = 1 to 4 and fitness function AF; S42: Calculate the fitness values ​​of the fitness functions of the members in each elephant herd and sort them to obtain the herd members with the best fitness values. S43: Optimize the objective function value corresponding to each elephant herd member using the Pareto optimization algorithm to obtain the Pareto optimal solution set, calculate the clustering distance of the solution set, and determine the elephant herd leader as the elephant herd member with the largest value in the result. S44: Update the position of each elephant group member, and use the correction algorithm to correct the updated position. Finally, replace the elephant group member with the worst fitness function. S45: Calculate the fitness function value of each member of the elephant population after correction, and sort them to obtain the member of the elephant population with the best fitness function; S46: The objective function values ​​of each elephant herd member are optimized again using the Pareto optimization algorithm to obtain the Pareto optimal solution set. The clustering distance of the solution set is calculated, and the elephant herd member with the largest value in the result is determined as the herd leader. S47: Determine if the number of iterations has reached the maximum value. If not, repeat steps S44-S46 until the stopping condition is met. S48: Output the optimal result.

2. The method according to claim 1, characterized in that: The objective function is f1 to f4, specifically including: Where f1 is the daily actual power loss on the feeder, N b R represents the number of busbars. ij P is the feed resistance from bus i to bus j. ih Q represents the actual power flowing out of bus i in the h-th hour. ih V represents the virtual power flowing out of bus i in the h-th hour. ih f1 is the voltage of bus i in hour h, f2 is the daily voltage deviation of bus i, and V ref Here, f3 is the reference voltage, f3 is the reciprocal of the daily bus voltage stability, and X is the reference voltage. ij f4 is the feeder reactance from bus i to bus j, f4 is the daily actual power variation, and P 1,h P represents the actual power injected into the substation in the h-th hour. 1,ref For reference actual power.

3. The method according to claim 2, characterized in that: The constraints include: energy conservation. Energy conservation in energy storage systems: E Bk,h =E Bk,h-1 +ρ B, ch P Bk,ch,h -P Bk,dis,h / ρ B,dis k = 1, 2, ... N BESS Transformer joint switching frequency limit: Capacitor bank switching frequency limit: Among them, P jh Q represents the actual power flowing out of bus j in the h-th hour. jh P represents the virtual power of bus j flowing out in the h-th hour. Gjh P represents the actual power injected into the generator of busbar j in hour h. Bj,dis,h P represents the discharge power of the energy storage system at bus j in hour h. Bj,ch,h The charging power of the energy storage system for busbar j in hour h, P Djh For the actual power load demand of busbar j in hour h, Q wjh The virtual power injected into the wind turbine of busbar j in the h-th hour, Q Cjh The virtual power Q injected into the capacitor of bus j in hour h is... Djh For the virtual power load demand of bus j in the hth hour, Tap h For the on-load tap position of the transformer in the h-th hour, C s,h This represents the on / off state of the s-th capacitor in the h-th hour, where 1 represents on, 0 represents off, and N represents the on / off state. s,max E represents the maximum number of switching operations for the s-th capacitor. Bk,h P represents the energy consumption of the k-th energy storage system in the h-th hour. Bk,dis,h P is the discharge power of the k-th energy storage system in the h-th hour. Bk,ch,h The charging power of the k-th energy storage system in the h-th hour, ρ B,ch For the charging efficiency of energy storage systems, ρ B,dis For the discharge efficiency of the energy storage system, N BESS This represents the total number of energy storage systems.

4. The method according to claim 1, characterized in that: Step S43 includes: The method for calculating the aggregation distance is as follows: Where u s Let cd be the s-th solution in the Pareto optimal solution set. s For u s The gathering distance, f n (u s+1 ) is u s+1 Input into the nth objective function f n The obtained function value, To minimize the value of the nth objective function, N sol The total number of solutions.

5. The method according to claim 4, wherein updating the position of each elephant group member specifically comprises: P(c,j,T+1)=P(c,j,T)(1-α-β)+αP(c,b,T)+βP(c,ct,T)+γr Where c is the index of the elephant herd, P(c,j,T+1) is the updated position of member j of herd c, P(c,j,T) is the current position of member j of herd c, P(c,b,T) is the position of the herd leader, P(c,ct,T) is the center position of herd c, and r = (2rand-1)(P max -P min ), where rand is a random number between 0 and 1, and P max and P min Let θ be the upper and lower bounds of the positions of the elephant herd members. The correction algorithm is: Q(c,j,T+1)=θP(c,j,T+1)+ω1Q(c,j1,T)+ω2Q(c,j2,T-1)+ω3Q(c,j3,T-2), where Q(c,j,T+1) is the corrected position of elephant member j after the (T+1)th iteration, Q(c,j1,T) is the corrected position of elephant member j1 after the Tth iteration, Q(c,j2,T-1) is the corrected position of elephant member j2 after the (T-1)th iteration, Q(c,j3,T-2) is the corrected position of elephant member j3 after the (T-2)th iteration, θ and ω1~ω3 are correction factors, where θ is a random number between 0 and 1, and j1~j3 are individuals randomly selected from the elephant herd. These are the fitness values ​​of the corrected positions of individuals j1 to j3 after the Tth, T-1th, and T-2th iterations, respectively; the replacement of the group members in step S44 is specifically as follows: P(c,wt,T)=μP(c,b,T), where P(c,wt,T) is the position of the group member with the worst fitness function value, and μ is a random number between 0.95 and 1.

05.

6. The method according to claim 5, characterized in that: Step S48 includes: calculating the total attribution function for each solution in the Pareto optimal solution set, and determining the solution with the largest total attribution function value as the optimal solution, wherein: in Let be the attribution function of the nth objective function for the s-th solution. Let be the upper and lower bounds of the nth objective function. Let L be the nth objective function value of the s-th solution. p Let be the total attribution function value of the s-th solution.

7. The method according to claim 2, characterized in that: The fitness function mentioned in step S41 is: AF = b1f1 + b2f2 + b3f3 + b4f4, where b1 to b4 are scaling factors.

8. A control parameter optimization system for a power distribution system, characterized in that, It includes a parameter acquisition module, a model building module, a decision variable determination module, and a model optimal solution module, among which, The parameter acquisition module is used to acquire the operating parameters of the power distribution system and build the network topology of the power distribution system; the power distribution system includes a bus, a photovoltaic power generation system, a wind power generation system, an energy storage system, and a capacitor bank; The model building module is used to construct a multi-objective power distribution system optimization model based on the power distribution system, with the objective functions of minimizing actual power loss on feeders, minimizing bus voltage deviation, maximizing bus voltage stability, and minimizing actual power change, combined with constraints. The decision variable determination module is used to take the transformer tap position, the virtual power of the input capacitor bank, the virtual power output of the wind turbine generator, and the discharge power of the energy storage system as decision variables of the optimization model. The model optimal solution module is used to solve the optimization model using an elephant group search algorithm and obtain the optimal decision variables, which are then used as the optimal control parameters of the power distribution system. It also includes mapping the decision variables to elephant group members of the elephant group search algorithm, comprising the following steps: S41: Initialize algorithm parameters: Set parameters α, β, and γ. α is the influence factor of the elephant herd leader on the movement of herd members, β is the influence factor determining the movement of herd members towards the herd center, and γ is the influence factor for the random movement of herd members. β, γ ∈ (0, 1), and the number of elephants n... c and the number of members n in each elephant herd ci The maximum number of iterations is T, the current number of iterations is TI, α = cos(π·TI / 2T), the positions of the members of the object group are randomly initialized, and the objective function of the algorithm is set to f = {f i }, i = 1 to 4 and fitness function AF; S42: Calculate the fitness values ​​of the fitness functions of the members in each elephant herd and sort them to obtain the herd members with the best fitness values. S43: Optimize the objective function value corresponding to each elephant herd member using the Pareto optimization algorithm to obtain the Pareto optimal solution set, calculate the clustering distance of the solution set, and determine the elephant herd leader as the elephant herd member with the largest value in the result. S44: Update the position of each elephant group member, and use the correction algorithm to correct the updated position. Finally, replace the elephant group member with the worst fitness function. S45: Calculate the fitness function value of each member of the elephant population after correction, and sort them to obtain the member of the elephant population with the best fitness function; S46: The objective function values ​​of each elephant herd member are optimized again using the Pareto optimization algorithm to obtain the Pareto optimal solution set. The clustering distance of the solution set is calculated, and the elephant herd member with the largest value in the result is determined as the herd leader. S47: Determine if the number of iterations has reached the maximum value. If not, repeat steps S44-S46 until the stopping condition is met. S48: Output the optimal result.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that can be executed by a processor to implement the control parameter optimization method for a power distribution system as described in any one of claims 1-7.

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