Power distribution network topology optimization configuration method and system for novel power system

Abstract

The invention discloses a novel power system-oriented power distribution network topology optimization configuration method and system. The method comprises the steps of obtaining current power grid topology data of a target power distribution network and supply and demand state data of a period of time in the future; the power grid topological data comprises a global topological graph and a current topological structure; the supply and demand state data comprises power supply data and load data; performing iterative optimization on the topological structure of the target power distribution network by adopting an optimization algorithm based on a particle swarm to obtain a new topological structure; wherein in the iterative optimization process, a fitness function is constructed by taking minimization of power grid network loss and node voltage deviation as targets, a population is initialized according to a current topological structure, the fitness of particles is calculated according to the supply and demand state and the fitness function, and particle position updating is performed according to the global topological graph under radial constraints.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution network operation optimization, in particular to a power distribution network topology optimization configuration method and system for a new power system. BACKGROUND

[0002] With the development of the new power system into a new stage of deep low carbonization, digitization and intelligentization, the power distribution network, as an important hub connecting power sources and loads, is facing profound changes in its operation mode and structural form. Especially under the background of large-scale access of new loads and power sources such as distributed energy (such as photovoltaic, wind power), electric vehicles and energy storage systems, the operation environment of the power distribution network is becoming increasingly complex, and the supply and demand relationship is becoming more dynamic and nonlinear. This not only puts forward higher requirements for the reliability, flexibility and control capacity of the power distribution network, but also exposes the shortcomings of traditional power distribution network topology configuration methods in adaptability and intelligence.

[0003] Traditional power distribution network topology configuration methods are often based on fixed structures and historical operation data for planning and design, and use static optimization methods. This method has certain applicability in the background of traditional centralized power supply, but in the face of increasingly frequent fluctuations in new energy and rapid changes in load, it has been difficult to meet the actual needs of flexible scheduling and real-time response. With the diversification of load consumption behavior and the significant increase in the uncertainty of the location and output of distributed resources, the static topology structure has been unable to effectively match the dynamic supply and demand conditions, thereby possibly leading to problems such as decreased operation efficiency, increased network loss, and frequent voltage out-of-limit. SUMMARY

[0004] In view of this, the embodiments of the present application provide a power distribution network topology optimization configuration method and system for a new power system.

[0005] According to one aspect of the present application, a power distribution network topology optimization configuration method for a new power system is provided, the method comprising:

[0006] obtaining current power grid topology data and supply and demand state data for a future period of time of a target power distribution network; the power grid topology data includes a global topology graph and a current topology structure; the supply and demand state data includes power source data and load data;

[0007] using a particle swarm-based optimization algorithm to iteratively optimize the topology structure of the target power distribution network to obtain a new topology structure; wherein, in the iterative optimization process, a fitness function is constructed with the goal of minimizing power grid network loss and node voltage deviation, and the population is initialized according to the current topology structure, the fitness of the particles is calculated according to the supply and demand state and the fitness function, and the particle position is updated according to the global topology graph under radial constraints.

[0008] In the embodiment of the present application, optionally, the adopting the particle swarm optimization algorithm to iteratively optimize the topology structure of the target power distribution network comprises:

[0009] Step one, randomly generating the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; the position of each particle is represented by a binary vector of the topology structure of the target power distribution network, wherein 1 represents the corresponding switch being open and 0 represents being closed;

[0010] Step two, performing power flow calculation based on the particle position and the supply and demand state to obtain power flow and node voltage, and substituting the calculation result into the fitness function to calculate the fitness of the particle;

[0011] Step three, updating the individual optimal position and the global optimal position of each particle according to the fitness of each particle; wherein for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the position of the current particle is updated to the individual optimal position; if the fitness of the current particle is less than the fitness of the global optimal position, the position of the current particle is updated to the global optimal position;

[0012] Step four, updating the iteration number; if the iteration number reaches the maximum iteration number, the optimization process is ended and the global optimal position or the individual optimal position with the minimum fitness is output; otherwise, step five is entered;

[0013] Step five, perturbing the global optimal position to obtain a plurality of new solutions; calculating the fitness of the plurality of new solutions and selecting the new solution with the lowest fitness as a target new solution;

[0014] Step six, determining whether the target new solution is taken as the global optimal position according to the simulated annealing acceptance criterion;

[0015] Step seven, updating the speed of each particle according to the global optimal position and the individual optimal position of each particle;

[0016] Step eight, updating the position of each particle by using the binary method under the radial network constraint according to the speed of each particle and the global topology graph; returning to step two.

[0017] In the embodiment of the present application, optionally, the fitness function is represented as:

[0018]

[0019] Wherein, f is the fitness to be solved; L1 is the network loss generated by the particle position; L c is the initial network loss of the power grid; L2 is the node voltage deviation generated by the particle position; L v is the initial node voltage deviation; and α and β represent a weight coefficient respectively; Ik,p is the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval length of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

[0020] In the embodiments of the present application, the global optimal position is optionally disturbed to obtain a plurality of new solutions, comprising:

[0021] Step A1, in each component of the global optimal position, a position component equal to 1 is randomly selected and set to 0 to obtain a disturbed state;

[0022] Step A2, according to the disturbed state, the opening and closing states of the switches in the global topology graph are controlled to obtain a disturbed topology structure; according to the disturbed topology structure, a target loop switch set is determined, which represents a set of switches that will form a loop after being closed in the current topology structure;

[0023] Step A3, a switch in the target loop switch set is randomly selected to be opened, and the component of the corresponding position in the disturbed state is set to 1 to obtain a new solution;

[0024] Step A4, steps A1 to A3 are repeated multiple times to obtain a plurality of new solutions.

[0025] In the embodiments of the present application, it is optionally determined whether the target new solution is taken as the global optimal position according to the simulated annealing acceptance criterion, comprising:

[0026] Step B1, the fitness difference value Δf = f new -f gbest is calculated; wherein f new and f gbest are the fitness of the target new solution and the global optimal position, respectively;

[0027] Step B2, if Δf < 0, the target new solution is accepted as the global optimal position; otherwise, the subsequent steps are continued;

[0028] Step B3, the acceptance probability is calculated: wherein T is a preset temperature parameter in the simulated annealing algorithm;

[0029] Step B4, a random number r between 0 and 1 is generated; if r < p, the target new solution is accepted as the global optimal position, otherwise it is not accepted;

[0030] Step B5, the temperature T is updated as T ← λT (0 < λ < 1) for the next calculation of the acceptance probability.

[0031] In the embodiment of the present application, the velocity of each particle is updated according to the global optimal position and the individual optimal position of each particle, comprising:

[0032]

[0033] wherein w t is the inertia weight at iteration round t; w max and w min are the maximum and minimum values of the inertia weight respectively; MNOI is the maximum number of iterations; is the component of the velocity of the i-th particle in the d-th dimension at iteration round t; pbest i,d is the component of the individual optimal position of the i-th particle in the d-th dimension; gbest d is the component of the global optimal position in the d-th dimension; c1 and c2 are learning factors respectively; r1 and r2 are random numbers between 0 and 1; is the component of the updated particle velocity in the d-th dimension.

[0034] In the embodiment of the present application, the position of the particle is updated by using a binary method under the radial network constraint according to the velocity of each particle and the global topology graph, comprising:

[0035] Step C1, setting all components of the position of the target particle to 0, obtaining a loop switch set and a non-loop switch set according to the global topology graph, and assigning the velocity of the target particle to a temporary velocity vector; the target particle is any particle in the population;

[0036] Step C2, setting the velocity component corresponding to the non-loop switch in the temporary velocity vector to infinity;

[0037] Step C3, setting the position component corresponding to the minimum velocity component in the temporary velocity vector to 1;

[0038] Step C4, disconnecting the switch corresponding to the position component of 1 in the global topology graph to obtain a new topology structure; updating the loop switch set and the non-loop switch set according to the new topology structure;

[0039] Step C5, if the loop switch set is not empty, returning to step C2; otherwise, completing the position update of the target particle.

[0040] According to another aspect of the present application, a power distribution network topology optimization configuration system for a new power system is provided, comprising:

[0041] A state monitoring module is configured to acquire current power grid topology data and future power supply and demand state data of the target power distribution network; the power grid topology data includes a global topology graph and a current topology structure; the power supply and demand state data includes power supply data and load data;

[0042] An optimization configuration module is configured to perform iterative optimization on the topology structure of the target power distribution network by using a particle swarm-based optimization algorithm to obtain a new topology structure; in the iterative optimization process, a fitness function is constructed with the objective of minimizing power grid network loss and node voltage deviation, a population is initialized according to the current topology structure, the fitness of a particle is calculated according to the power supply and demand state and the fitness function, and the position of the particle is updated according to the global topology graph under radial constraints.

[0043] In the embodiments of the present application, the optimization configuration module can include:

[0044] An initialization module is configured to randomly generate the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; and each particle position is represented by a binary vector of the topology structure of the target power distribution network, wherein 1 represents an open switch and 0 represents a closed switch.

[0045] A fitness calculation module is configured to perform power flow calculation based on the position of the particle and the power supply and demand state to obtain power flow and node voltage, and to calculate the fitness of the particle by substituting the calculation results into the fitness function.

[0046] A solution updating module is configured to update the individual optimal position and the global optimal position of each particle according to the fitness of each particle; for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the position of the current particle is updated to the individual optimal position; and if the fitness of the current particle is less than the fitness of the global optimal position, the position of the current particle is updated to the global optimal position.

[0047] A flow control module is configured to update the number of iterations; if the number of iterations reaches a maximum number of iterations, the optimization process is ended, and the global optimal position or the individual optimal position with the minimum fitness is output; otherwise, the process proceeds to a disturbance module.

[0048] The disturbance module is configured to disturb the global optimal position to obtain a plurality of new solutions, calculate the fitness of the plurality of new solutions, and select a new solution with the lowest fitness as a target new solution.

[0049] A local escape module is configured to determine whether to take the target new solution as the global optimal position according to a simulated annealing acceptance criterion.

[0050] A speed updating module is configured to update the speed of each particle according to the global optimal position and the individual optimal position of each particle.

[0051] a position updating module, configured to update the position of each particle by using a binary method under radial network constraints according to the velocity of each particle and the global topology graph; and return the fitness calculation module.

[0052] In the embodiment of the present application, the fitness function is expressed as:

[0053]

[0054] wherein f is the fitness to be solved; L1 is the network loss caused by the position of the particle; L c is the initial network loss; L2 is the node voltage deviation caused by the position of the particle; L v is the initial node voltage deviation; a and b respectively represent a weight coefficient; I k,p is the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval duration of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

[0055] In the embodiment of the present application, the perturbation module is further configured to:

[0056] Step A1, in each component of the global optimal position, a position component equal to 1 is randomly selected and set to 0 to obtain a perturbation state;

[0057] Step A2, according to the perturbation state, the opening and closing states of the switches in the global topology graph are controlled to obtain a perturbed topology structure; according to the perturbed topology structure, a target loop switch set is determined, which represents a set of switches that will form a loop after being closed in the current topology structure;

[0058] Step A3, a switch in the target loop switch set is randomly selected to be opened, and the component of the corresponding position in the perturbation state is set to 1 to obtain a new solution;

[0059] Step A4, steps A1 to A3 are repeated multiple times to obtain multiple new solutions.

[0060] In the embodiment of the present application, the local jump-out module is further configured to:

[0061] Step B1, a fitness difference value Δf = f new -f gbest is calculated; wherein f new and f gbest are the fitness of the target new solution and the global optimal position respectively.

[0062] Step B2, if Δf < 0, accept the new solution of the target as the global optimal position; otherwise, continue the following steps;

[0063] Step B3, calculate the acceptance probability: wherein, T is a preset temperature parameter in the simulated annealing algorithm;

[0064] Step B4, generate a random number r between 0 and 1; if r < p, accept the new solution of the target as the global optimal position, otherwise, do not accept;

[0065] Step B5, update the temperature T <- λT (0 < λ < 1) for the next calculation of the acceptance probability.

[0066] In the embodiment of the present application, optionally, the speed updating module is further used for:

[0067]

[0068] wherein, w t is the inertia weight at the iteration round t; w max and w min are the maximum value and the minimum value of the inertia weight respectively; MNOI is the maximum number of iterations; is the component of the speed of the i-th particle in the d-th dimension at the iteration round t; pbest i,d is the component of the individual optimal position of the i-th particle in the d-th dimension; gbest d is the component of the global optimal position in the d-th dimension; c1 and c2 are learning factors respectively; r1 and r2 are random numbers between 0 and 1; is the component of the updated particle speed in the d-th dimension.

[0069] In the embodiment of the present application, optionally, the position updating module is further used for:

[0070] Step C1, set all components of the position of the target particle to 0, obtain the loop switch set and the non-loop switch set according to the global topology graph, and assign the speed of the target particle to the temporary speed vector; the target particle is any particle in the population;

[0071] Step C2, set the speed component corresponding to the non-loop switch in the temporary speed vector to infinity;

[0072] Step C3, set the position component corresponding to the minimum speed component in the temporary speed vector to 1;

[0073] Step C4, in the global topology graph, turn off the switch corresponding to the position component 1, and obtain a new topology structure; according to the new topology structure, update the loop switch set and the non-loop switch set;

[0074] Step C5, if the loop switch set is not empty, return to step C2; otherwise, complete the position update of the target particle.

[0075] According to another aspect of the present application, a storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the power distribution network topology optimization configuration method for new power systems.

[0076] According to another aspect of the present application, a computer device is provided, which includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, and the processor executes the program to implement the power distribution network topology optimization configuration method for new power systems.

[0077] Through the above technical solutions, the power distribution network topology optimization configuration method and system for new power systems provided by the embodiments of the present application can obtain the topology and supply-demand state data of the power distribution network in real time, perform topology optimization by using an improved particle swarm optimization algorithm, make the power distribution network dynamically reconfigure the network structure according to the load and power supply state, better adapt to the power grid fluctuation caused by new energy access and load change, reduce the power grid operation loss, improve the node voltage quality, enhance the flexibility and stability of the power grid, solve the problem that the traditional static topology cannot cope with dynamic supply-demand changes, and promote the development of the power distribution network to low carbonization and intelligence.

[0078] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0079] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0080] Figure 1 Fig. 1 shows a flowchart of a power distribution network topology optimization configuration method for new power systems provided by an embodiment of the present application;

[0081] Figure 2 Fig. 2 shows a flowchart of another power distribution network topology optimization configuration method for new power systems provided by an embodiment of the present application;

[0082] Figure 3 Fig. 2 shows a flow diagram of another power distribution network topology optimization configuration method for a new power system provided by an embodiment of the present application;

[0083] Figure 4 Fig. 3 shows a flow diagram of still another power distribution network topology optimization configuration method for a new power system provided by an embodiment of the present application;

[0084] Figure 5 Fig. 4 shows a structural diagram of a power distribution network topology optimization configuration device for a new power system provided by an embodiment of the present application;

[0085] Figure 6 Fig. 5 shows a structural diagram of another power distribution network topology optimization configuration device for a new power system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0086] Hereinafter, the present application will be described in detail with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0087] In the present embodiment, a power distribution network topology optimization configuration method for a new power system is provided, as shown in Figure 1 The method comprises the following steps.

[0088] obtaining current power grid topology data and future supply-demand state data of a target power distribution network; the power grid topology data comprises a global topology graph and a current topology structure; the supply-demand state data comprises power supply data and load data;

[0089] performing iterative optimization on the topology structure of the target power distribution network by using a particle swarm-based optimization algorithm to obtain a new topology structure; wherein, in the iterative optimization process, a fitness function is constructed with the objective of minimizing power grid network loss and node voltage deviation, and a population is initialized according to the current topology structure, the fitness of particles is calculated according to the supply-demand state and the fitness function, and the position of the particles is updated according to the global topology graph under radial constraints.

[0090] By applying the technical solution of the present embodiment, by obtaining the topology and supply-demand state data of the power distribution network in real time, the topology is optimized by using an improved particle swarm optimization algorithm, so that the power distribution network can dynamically reconstruct the network structure according to the load and power supply state, thereby better adapting to the power grid fluctuations caused by new energy access and load changes, reducing power grid operation loss, improving node voltage quality, enhancing the flexibility and stability of the power grid, solving the problem that the traditional static topology cannot cope with dynamic supply-demand changes, and promoting the development of the power distribution network towards low carbonization and intelligentization.

[0091] In the present embodiment of the present application, optionally,Figure 2 As shown, the iteration optimization of the topology structure of the target power distribution network by using the particle swarm optimization algorithm comprises:

[0092] Step one, randomly generate the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; the position of each particle is represented by a binary vector of the topology structure of the target power distribution network, wherein 1 represents the opening of the corresponding switch and 0 represents the closing;

[0093] Step two, perform power flow calculation based on the particle position and the supply and demand state to obtain power flow and node voltage, and substitute the calculation results into the fitness function to calculate the fitness of the particle;

[0094] Step three, update the individual optimal position and the global optimal position of each particle according to the fitness of each particle; wherein for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the current particle position is updated to the individual optimal position; if the fitness of the current particle is less than the fitness of the global optimal position, the current particle position is updated to the global optimal position;

[0095] Step four, update the iteration number; if the iteration number reaches the maximum iteration number, end the optimization process and output the global optimal position or the individual optimal position with the minimum fitness; otherwise, go to step five;

[0096] Step five, disturb the global optimal position to obtain a plurality of new solutions; calculate the fitness of the plurality of new solutions and select the new solution with the lowest fitness as the target new solution;

[0097] Step six, determine whether to take the target new solution as the global optimal position according to the simulated annealing acceptance criterion;

[0098] Step seven, update the speed of each particle according to the global optimal position and the individual optimal position of each particle;

[0099] Step eight, update the position of each particle by using the binary method under the radial network constraint according to the speed of each particle and the global topology graph; return to step two.

[0100] In this embodiment, the topology of the power distribution network is iteratively optimized by a particle swarm-based optimization algorithm to minimize the network loss and the node voltage deviation, thereby obtaining a new and better topology. The specific steps are as follows: First, obtain the current power grid topology data of the target power distribution network (including the global topology graph and the current topology structure) and the supply and demand state data for a future period of time (including power supply data and load data). Then, use the particle swarm-based optimization algorithm, take the current topology structure as the initial position of a particle in the population, and randomly generate the initial position and speed of other particles. Each particle position is represented by a binary vector, where 1 represents the corresponding switch being open and 0 represents the corresponding switch being closed. Based on the particle position and the supply and demand state, perform power flow calculation to obtain the power flow and node voltage, and then substitute them into the fitness function to calculate the fitness of the particle. The fitness function aims to minimize the network loss and the node voltage deviation. According to the fitness of each particle, update the individual optimal position and the global optimal position of each particle. If the fitness of the current particle is better than that of its individual optimal position or global optimal position, update accordingly. Update the iteration count, if the maximum iteration count is reached, end the optimization process and output the optimal position; otherwise, perturb the global optimal position to obtain multiple new solutions, and select the new solution with the lowest fitness as the target new solution. According to the simulated annealing acceptance criterion, decide whether to take the target new solution as the global optimal position to avoid falling into a local optimal solution. Update the particle speed according to the global optimal position and the individual optimal position of each particle, and then update the particle position under the radial network constraint using the binary method. Return to step two and continue the iterative optimization process until the end condition is met. Each particle position represents a solution and is a binary vector, indicating the topology of the power distribution network, where 1 represents the corresponding switch being open and 0 represents the corresponding switch being closed. The smaller the fitness value, the better the solution. Both the particle position and the particle speed are vectors, and each element in the vector is called a component. Adding the current topology structure as part of the initial solution helps the algorithm converge faster to a practical solution. Perturbing around the global optimal position and selecting solutions through the simulated annealing mechanism helps to jump out of the local optimum and enhance the global search ability.

[0101] In the embodiments of the present application, the fitness function is represented as:

[0102]

[0103] where f is the fitness to be solved; L1 is the network loss generated by the particle position; L c is the initial network loss; L2 is the node voltage deviation generated by the particle position; L v is the initial node voltage deviation; α and β represent weight coefficients, respectively; I k,pis the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval length of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

[0104] In this embodiment, the fitness function uses network loss to reflect energy efficiency, and uses node voltage deviation to reflect voltage quality, comprehensively evaluates the power distribution network topology corresponding to the particle position from two dimensions, and realizes double optimization of energy consumption and voltage quality. Wherein, the weight coefficients a and b can be set to 0.75 and 0.25 respectively. The interval length of the time period refers to dividing a period of time in the future into multiple time periods. For example, by predicting the power generation data and load data in the future 1 hour, the supply and demand state data in the future 1 hour is obtained, and the 1 hour is evenly divided into 4 time periods of 15 minutes for power flow simulation calculation, to obtain the equivalent state of each time period. According to the equivalent state, the network loss and voltage deviation are calculated.

[0105] In the embodiments of the present application, optionally, as shown in Figure 3 the global optimal position is disturbed to obtain a plurality of new solutions, including:

[0106] Step A1, in each component of the global optimal position, a position component equal to 1 is randomly selected and set to 0 to obtain a disturbed state;

[0107] Step A2, according to the disturbed state, the opening and closing state of the switch in the global topology graph is controlled to obtain a disturbed topology structure; according to the disturbed topology structure, a target loop switch set is determined, the target loop switch set represents a set of switches that will form a loop after being closed in the current topology structure;

[0108] Step A3, a switch in the target loop switch set is randomly selected to be opened, and the component of the corresponding position in the disturbed state is set to 1 to obtain a new solution;

[0109] Step A4, steps A1 to A3 are repeated multiple times to obtain a plurality of new solutions.

[0110] In this embodiment, the global optimal position is disturbed to obtain multiple new solutions, including: step one, in each component of the global optimal position, a position component equal to 1 is randomly selected and set to 0 to obtain a disturbed state. That is, a disconnected switch is randomly selected to be closed. Step two, according to the disturbed state, the opening and closing states of the switches in the global topology graph are controlled to obtain a disturbed topology structure; according to the disturbed topology structure, a target loop switch set is determined. After closing the disconnected switch, a loop is formed, and the switches of the branches on the loop are combined into the target loop switch set. Step three, a switch in the target loop switch set is randomly selected to be disconnected, and the component of the corresponding position in the disturbed state is set to 1 to obtain a new solution. That is, a switch on the loop (different from the switch closed in the foregoing step) is randomly selected to be disconnected to obtain a power grid topology structure satisfying the radial constraint. Step four, steps one to three are repeated multiple times to obtain multiple new solutions. Each time a switch is selected to be closed, the same switch as before can be avoided. When the same switch is randomly selected again, it can be reselected by means of a historical selection record table.

[0111] By closing the switch to form a new loop and then disconnecting the switch, structural disturbance rather than pure random disturbance is introduced, which can more effectively explore the neighborhood space and avoid the algorithm from falling into a local optimum. By using the information of the global topology graph and the target loop switch set, it is ensured that the topology structure reconfigured after disturbance is a legal radial structure, and it is ensured that each new solution meets the operation constraint of the distribution network. The way of changing the position component and reconfiguring the network topology makes the new solution have directionality and structural rationality, and has higher optimization efficiency than blind variation.

[0112] In the embodiments of the present application, whether the target new solution is taken as the global optimal position is determined according to the simulated annealing acceptance criterion, including:

[0113] Step B1, calculate the fitness difference value: Δf = f new -f gbest ; wherein f new and f gbest are the fitness of the target new solution and the global optimal position respectively;

[0114] Step B2, if Δf < 0, the target new solution is accepted as the global optimal position; otherwise, the subsequent steps are continued;

[0115] Step B3, calculate the acceptance probability: wherein T is a preset temperature parameter in the simulated annealing algorithm;

[0116] Step B4, generate a random number r between 0 and 1; if r < p, the target new solution is accepted as the global optimal position, otherwise it is not accepted;

[0117] Step B5, update temperature T←λT(0<λ<1) for next calculation of acceptance probability.

[0118] In this embodiment, the criterion allows accepting a suboptimal solution with a probability, helping to jump out of local optimum and enhancing global search ability.

[0119] In the embodiments of the present application, the speed of each particle is updated according to the global optimal position and the individual optimal position of each particle, comprising:

[0120]

[0121] wherein w t is the inertia weight at iteration round t; w max and w min are the maximum and minimum values of the inertia weight respectively; MNOI is the maximum number of iterations; is the component of the speed of the i-th particle in the d-th dimension at iteration round t; pbest i,d is the component of the individual optimal position of the i-th particle in the d-th dimension; gbest d is the component of the global optimal position in the d-th dimension; c1 and c2 are learning factors respectively; r1 and r2 are random numbers between 0 and 1; is the component of the updated particle speed in the d-th dimension.

[0122] In this embodiment, the inertia weight decreases with the number of iterations, which helps to enhance the global search ability (explore a wider space) in the early stage and strengthen the local search ability (fine adjustment of the convergent solution) in the later stage, improving the ability of the algorithm to jump out of local optimum in the early stage of search and quickly converge in the later stage.

[0123] In the embodiments of the present application, as shown in Figure 4 , the position of each particle is updated by using a binary method under the radial network constraint according to the speed of each particle and the global topology graph, comprising:

[0124] Step C1, setting all components of the position of a target particle to 0, obtaining a loop switch set and a non-loop switch set according to the global topology graph, and assigning the speed of the target particle to a temporary speed vector; the target particle is any particle in the population;

[0125] Step C2, setting the speed component corresponding to the non-loop switch in the temporary speed vector to infinity;

[0126] Step C3, setting the position component corresponding to the smallest speed component in the temporary speed vector to 1;

[0127] Step C4, in the global topology graph, turn off the switch corresponding to the position component 1 to obtain a new topology structure; update the loop switch set and the non-loop switch set according to the new topology structure;

[0128] Step C5, if the loop switch set is not empty, return to step C2; otherwise, complete the position update of the target particle.

[0129] In this embodiment, the loop switch refers to the switch of the branch located on the loop. The non-loop switch refers to the switch of the branch not located on the loop. The speed of the non-loop switch is set to be infinite, which effectively avoids the component violating the topology constraint from being selected, turns off one loop switch at a time, and dynamically adjusts the turn-off position by updating the loop switch set, gradually constructs a legal radial topology structure, avoids generating a ring network or an island structure, and ensures physical feasibility. By ensuring that the particle position at each time is a feasible and practical solution, the search for invalid solutions is avoided, and the operation efficiency is improved. The particle speed value is used as the selection basis, and the smaller the speed is, the more preferred the particle is selected, which reflects that the particle is more confident in turning off the switch at this position, thereby realizing the consistency of the position update and the particle motion intention, and improving the solution quality.

[0130] Further, as Figure 1 a specific implementation of the method, the embodiment of the present application provides a power distribution network topology optimization configuration system for a new type of power system, as Figure 5 shown, the system comprises:

[0131] a state monitoring module, configured to acquire current power grid topology data and future supply-demand state data of a target power distribution network; the power grid topology data comprises a global topology graph and a current topology structure; the supply-demand state data comprises power supply data and load data;

[0132] an optimization configuration module, configured to perform iterative optimization on the topology structure of the target power distribution network by using a particle swarm-based optimization algorithm to obtain a new topology structure; wherein, in the iterative optimization process, a fitness function is constructed with the objective of minimizing power grid network loss and node voltage deviation, a population is initialized according to the current topology structure, the fitness of a particle is calculated according to the supply-demand state and the fitness function, and the position of the particle is updated under radial constraints according to the global topology graph.

[0133] In the embodiment of the present application, the optimization configuration module can comprise: Figure 6 as shown, the optimization configuration module comprises:

[0134] an initialization module, configured to randomly generate the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; the position of each particle is represented by a binary vector, wherein 1 represents turning off the corresponding switch and 0 represents closing;

[0135] a fitness calculation module, configured to perform power flow calculation based on the particle position and the supply-demand state to obtain power flow and node voltage, and to calculate the fitness of the particle by substituting the calculation result into a fitness function;

[0136] an optimal solution updating module, configured to update the individual optimal position and the global optimal position of each particle according to the fitness of each particle; for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the current particle position is updated to the individual optimal position; if the fitness of the current particle is less than the fitness of the global optimal position, the current particle position is updated to the global optimal position;

[0137] a flow control module, configured to update the iteration number; if the iteration number reaches a maximum iteration number, the optimization process is ended, and the global optimal position or the individual optimal position with the minimum fitness is output; otherwise, the process enters the disturbance module;

[0138] the disturbance module, configured to disturb the global optimal position to obtain a plurality of new solutions, to calculate the fitness of the plurality of new solutions, and to select a new solution with the lowest fitness as a target new solution;

[0139] the local escape module, configured to determine whether the target new solution is taken as the global optimal position according to a simulated annealing acceptance criterion;

[0140] a speed updating module, configured to update the speed of each particle according to the global optimal position and the individual optimal position of each particle;

[0141] a position updating module, configured to update the position of each particle by using a binary method under radial network constraint according to the speed of each particle and the global topology; and return to the fitness calculation module.

[0142] In the embodiments of the present application, the fitness function is expressed as:

[0143]

[0144] wherein f is the fitness to be solved; L1 is the network loss generated by the particle position; L c is the initial network loss of the power grid; L2 is the node voltage deviation generated by the particle position; L v is the initial node voltage deviation; a and b respectively represent a weight coefficient; I k,p is the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval duration of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

[0145] Optionally, in the embodiment of the present application, the perturbation module is further configured to:

[0146] Step A1, in each component of the global optimal position, a position component equal to 1 is randomly selected and set to 0 to obtain a perturbation state;

[0147] Step A2, according to the perturbation state, the opening and closing states of the switches in the global topology graph are controlled to obtain a perturbed topology structure; according to the perturbed topology structure, a target loop switch set is determined, which represents a set of switches that will form a loop after being closed in the current topology structure;

[0148] Step A3, a switch in the target loop switch set is randomly selected to be opened, and the component at the corresponding position in the perturbation state is set to 1 to obtain a new solution;

[0149] Step A4, steps A1 to A3 are repeated multiple times to obtain multiple new solutions.

[0150] Optionally, in the embodiment of the present application, the local jump-out module is further configured to:

[0151] Step B1, a fitness difference value is calculated: Δf = f new -f gbest ; wherein f new and f gbest are the fitness of the target new solution and the global optimal position, respectively;

[0152] Step B2, if Δf < 0, the target new solution is accepted as the global optimal position; otherwise, the subsequent steps are continued;

[0153] Step B3, the acceptance probability is calculated: wherein T is a preset temperature parameter in the simulated annealing algorithm;

[0154] Step B4, a random number r between 0 and 1 is generated; if r < p, the target new solution is accepted as the global optimal position; otherwise, it is not accepted;

[0155] Step B5, the temperature T is updated as T ← λT (0 < λ < 1) for the next calculation of the acceptance probability.

[0156] Optionally, in the embodiment of the present application, the speed updating module is further configured to:

[0157]

[0158] wherein w t is the inertia weight at iteration round t; w max and w minare the maximum and minimum of the inertia weight, respectively; MNOI is the maximum number of iterations; is the component of the velocity of the i-th particle in the d-th dimension at iteration round t; pbest i,d is the component of the individual best position of the i-th particle in the d-th dimension; gbest d is the component of the global best position in the d-th dimension; c1 and c2 are learning factors, respectively; r1 and r2 are random numbers between 0 and 1; is the component of the updated velocity of the particle in the d-th dimension.

[0159] In the embodiments of the present application, the position updating module is further configured to:

[0160] Step C1, setting all components of the position of a target particle to 0, obtaining a loop switch set and a non-loop switch set according to the global topology graph, and assigning the velocity of the target particle to a temporary velocity vector; the target particle is any particle in the population;

[0161] Step C2, setting the velocity component corresponding to the non-loop switch in the temporary velocity vector to infinity;

[0162] Step C3, setting the position component corresponding to the minimum velocity component in the temporary velocity vector to 1;

[0163] Step C4, in the global topology graph, disconnecting the switch corresponding to the position component of 1 to obtain a new topology structure; updating the loop switch set and the non-loop switch set according to the new topology structure;

[0164] Step C5, if the loop switch set is not empty, returning to Step C2; otherwise, completing the position updating of the target particle.

[0165] It should be noted that other corresponding descriptions of the various functional units involved in the power distribution network topology optimization configuration device for the new power system provided in the embodiments of the present application can be referred to the corresponding descriptions in the Figures 1 to 4 method, which will not be repeated here.

[0166] The embodiments of the present application also provide a computer device, which can be a personal computer, a server, a network device, etc. The computer device comprises a bus, a processor, a memory and a communication interface, and can further comprise an input / output interface and a display device. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store location information. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the steps in the method embodiments.

[0167] Those skilled in the art can understand that the structure of the computer device described above is only part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can comprise more or fewer components, or combine certain components, or have a different component arrangement.

[0168] In one embodiment, a computer readable storage medium is provided, which can be non-volatile or volatile, and has stored thereon a computer program. The computer program is executed by the processor to implement the steps in the method embodiments described above.

[0169] In one embodiment, a computer program product is provided, which comprises a computer program. The computer program is executed by the processor to implement the steps in the method embodiments described above.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0172] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0173] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A power distribution network topology optimization configuration method for a new power system, characterized by, The method comprises: obtaining current power grid topology data and future supply-demand state data of a target power distribution network; the power grid topology data comprises a global topology graph and a current topology structure; the supply-demand state data comprises power supply data and load data; iterative optimization of the topology structure of the target power distribution network is performed by using a particle swarm optimization algorithm, so as to obtain a new topology structure; in the iterative optimization process, a fitness function is constructed with the aim of minimizing power grid network loss and node voltage deviation, a population is initialized according to the current topology structure, the fitness of particles is calculated according to the supply-demand state and the fitness function, and the position of the particles is updated according to the global topology graph under radial constraints.

2. The power distribution network topology optimization configuration method for new power systems according to claim 1, characterized in that, The iterative optimization of the topology structure of the target power distribution network by using the particle swarm optimization algorithm comprises: Step 1: randomly generating the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; the position of each particle is represented by a binary vector, wherein 1 represents an open switch and 0 represents a closed switch; Step 2: performing power flow calculation based on the position of the particles and the supply-demand state, obtaining power flow and node voltage, and substituting the calculation results into the fitness function to calculate the fitness of the particles; Step 3: updating the individual optimal position and the global optimal position of each particle according to the fitness of each particle; for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the position of the current particle is updated to the individual optimal position; if the fitness of the current particle is less than the fitness of the global optimal position, the position of the current particle is updated to the global optimal position; Step 4: updating the iteration number; if the iteration number reaches the maximum iteration number, the optimization process is ended and the global optimal position or the individual optimal position with the minimum fitness is output; otherwise, Step 5 is entered; Step 5: perturbing the global optimal position to obtain multiple new solutions; calculating the fitness of the multiple new solutions and selecting the new solution with the lowest fitness as a target new solution; Step 6: determining whether the target new solution is taken as the global optimal position according to a simulated annealing acceptance criterion; Step 7: updating the speed of each particle according to the global optimal position and the individual optimal position of each particle; Step 8: updating the position of each particle by using a binary method under radial network constraints according to the speed of each particle and the global topology graph; returning to Step 2.

3. The power distribution network topology optimization configuration method for new power systems according to claim 2, characterized in that, The fitness function is expressed as: wherein f is the fitness to be solved; L1 is the power grid network loss generated by the particle position; L c is the initial power grid network loss; L2 is the node voltage deviation generated by the particle position; L v is the initial node voltage deviation; a and b respectively represent a weight coefficient; I k,p is the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval duration of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

4. The power distribution network topology optimization configuration method for new power systems according to claim 2, characterized in that, The perturbation of the global optimal position to obtain multiple new solutions comprises: Step A1: randomly selecting a position component equal to 1 in each component of the global optimal position and setting it to 0 to obtain a perturbed state; Step A2: controlling the open-close state of the switches in the global topology graph according to the perturbed state to obtain a perturbed topology structure; determining a target loop switch set according to the perturbed topology structure, wherein the target loop switch set represents a set of switches that will form a loop after being closed in the current topology structure; Step A3, randomly select one switch in the target loop switch set to be off, set the component in the corresponding position in the perturbed state to 1, and obtain a new solution; Step A4, repeat steps A1 to A3 multiple times to obtain multiple new solutions.

5. The power distribution network topology optimization configuration method for new power systems according to claim 2, characterized in that, According to the simulated annealing acceptance criterion, it is determined whether the target new solution is accepted as the global optimal position, including: Step B1, calculate fitness difference: Δf = f new -f gbest ; where f new and f gbest are the fitness of the target new solution and the global optimum position, respectively. Step B2, if Δf < 0, the target new solution is accepted as the global optimal position; otherwise, the subsequent steps are continued; Step B3, calculate the acceptance probability: where T is a preset temperature parameter in the simulated annealing algorithm; Step B4, a random number r between 0 and 1 is generated; if r < p, the target new solution is accepted as the global optimal position; otherwise, it is not accepted; Step B5, the temperature T is updated to λT (0 < λ < 1) for the next calculation of the acceptance probability.

6. The power distribution network topology optimization configuration method for new power systems according to claim 2, characterized in that, According to the global optimal position and the individual optimal position of each particle, the speed of each particle is updated, including: where w t is the inertia weight at iteration round t; w max and w min are the maximum and minimum values of the inertia weight, respectively; MNOI is the maximum number of iterations; is the component of the velocity of the ith particle in the dth dimension at iteration round t; pbest i,d is the component of the individual best position of the ith particle in the dth dimension; gbest d is the component of the global best position in the dth dimension; c1 and c2 are learning factors; r1 and r2 are random numbers between [0, 1]; is the component of the updated particle velocity in the dth dimension.

7. The power distribution network topology optimization configuration method for new power systems according to claim 2, characterized in that, According to the speed of each particle and the global topology graph, the position of the particle is updated under the radial network constraint by using a binary method, including: Step C1, set all components of the position of the target particle to 0, and obtain the loop switch set and the non-loop switch set according to the global topology graph; and the speed of the target particle is assigned to the temporary speed vector; the target particle is any particle in the population; Step C2, set the speed component corresponding to the non-loop switch in the temporary speed vector to infinity; Step C3, set the position component corresponding to the smallest speed component in the temporary speed vector to 1; Step C4, in the global topology graph, the switch corresponding to the position component of 1 is turned off to obtain a new topology structure; according to the new topology structure, the loop switch set and the non-loop switch set are updated; Step C5, if the loop switch set is not empty, return to step C2; otherwise, the position update of the target particle is completed.

8. A power distribution network topology optimization configuration system for a new power system, characterized by, The system comprises: A state monitoring module is configured to acquire current power grid topology data and future supply-demand state data of a target power distribution network; the power grid topology data comprises a global topology graph and a current topology structure; the supply-demand state data comprises power supply data and load data; An optimal configuration module is configured to use a particle swarm optimization algorithm to iteratively optimize the topology structure of the target power distribution network to obtain a new topology structure; wherein, in the iterative optimization process, a fitness function is constructed with the objective of minimizing power grid network loss and node voltage deviation, a population is initialized according to the current topology structure, the fitness of a particle is calculated according to the supply-demand state and the fitness function, and the position of the particle is updated according to the global topology graph under radial constraint.

9. The power distribution network topology optimization configuration system for new-type power system according to claim 8, characterized in that, The optimal configuration module comprises: An initialization module is configured to randomly generate the initial position and speed of each particle in the population, wherein the current topology structure is taken as the initial position of a particle in the population; the position of each particle is represented by a binary vector, wherein 1 represents that the corresponding switch is off and 0 represents that the switch is closed; A fitness calculation module is configured to perform power flow calculation based on the particle position and the supply-demand state to obtain power flow and node voltage, and to calculate the fitness of the particle by substituting the calculation results into the fitness function. The optimal solution updating module is configured to update the individual optimal position and the global optimal position of each particle according to the fitness of each particle; for any particle, if the fitness of the current particle is less than the fitness of the individual optimal position, the current particle position is updated to the individual optimal position; if the fitness of the current particle is less than the fitness of the global optimal position, the current particle position is updated to the global optimal position; The flow control module is configured to update the iteration number; if the iteration number reaches the maximum iteration number, the optimization process is ended, and the global optimal position or the individual optimal position with the minimum fitness is output; otherwise, the disturbance module is entered; The disturbance module is configured to disturb the global optimal position to obtain a plurality of new solutions; the fitness of the plurality of new solutions is calculated, and the new solution with the lowest fitness is selected as a target new solution; The local escape module is configured to determine whether the target new solution is taken as the global optimal position according to a simulated annealing acceptance criterion; The speed updating module is configured to update the speed of each particle according to the global optimal position and the individual optimal position of each particle; The position updating module is configured to update the position of each particle by using a binary method under radial network constraints according to the speed of each particle and the global topology graph; and the fitness calculation module is returned.

10. The power distribution network topology optimization configuration system for new-type power system according to claim 9, characterized in that, The fitness function is expressed as: wherein f is the fitness value to be solved; L1 is the power grid network loss generated by the particle position; L c is the initial power grid network loss; L2 is the node voltage deviation generated by the particle position; L v is the initial node voltage deviation; a and b represent a weight coefficient respectively; I k,p is the current flowing through the kth branch in the pth time period; R k is the resistance of the kth branch; Δt p is the interval duration of the pth time period; V i,p is the voltage of the ith node in the pth time period; V i,ref is the reference voltage of the ith node.

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