Power grid supply and demand balance regulation and control method based on improved multi-target particle swarm optimization

Abstract

The invention provides a power grid supply and demand balance regulation and control method based on an improved multi-objective particle swarm optimization algorithm, and the method comprises the steps: taking the capacity of the power of each line of a power distribution network in each line as a constraint, taking the minimum cost, the minimum loss and the minimum voltage deviation as objective functions, and constructing a multi-optimization objective function; a multi-optimization objective function is solved based on improved multi-objective particle swarm optimization, and a power grid supply and demand balance regulation and control scheme is obtained; and performing supply-demand balance regulation and control on the power grid by adopting the power grid supply-demand balance regulation and control scheme. According to the method, three objectives of loss reduction, voltage deviation and minimum cost are taken as a total objective of optimization, and an optimal solution is obtained by adopting an improved multi-objective particle swarm optimization algorithm, so that the capacity of the distributed generator set can be optimized, and the electricity consumption in the peak period can be effectively managed, thereby realizing supply and demand balance of a power grid, reducing line congestion, and improving the reliability of the power grid. The power grid stability and operation efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand response communication network, and in particular to a power grid supply and demand balance control method based on an improved multi-objective particle swarm algorithm. Background Art

[0002] In view of the sudden increase in user electricity demand in recent years and the continuous advancement of the construction of new power systems, it is difficult to meet the needs of stable operation of the system by only considering the regulation resources on the grid side. It is easy to cause voltage drop, increased loss, system failure and even large-scale power outages, thereby damaging the reliability of the power grid. In order to improve the problem of difficulty in using electricity during peak hours, distributed generation (DG) is adopted in many places, but it depends on the specific planning and power generation capacity, as well as the nonlinear constraints brought by the energy storage system, which can both alleviate line congestion and aggravate line congestion. At the same time, demand response guides users to modify or reduce customers' electricity consumption during peak hours by changing electricity prices or issuing incentives, thereby reducing the possibility of imbalance between supply and demand in the power grid.

[0003] Therefore, effectively regulating the supply and demand balance of the power grid is crucial to ensuring the reliability of the grid, reducing costs and improving overall efficiency. Summary of the invention

[0004] In order to solve the problem that as the construction of new power systems continues to advance, only considering the regulation resources on the grid side can no longer meet the needs of stable system operation, which is likely to lead to voltage drop, increased loss, system failure and even large-scale power outages, the present invention proposes a grid supply and demand balance control method based on the IMOPSO algorithm, including:

[0005] Taking the power capacity of each line in the distribution network as the constraint, and the minimum cost, minimum loss and minimum voltage deviation as the objective function, a multi-optimization objective function is constructed;

[0006] Based on the improved multi-objective particle swarm optimization, multiple optimization objective functions are solved to obtain the power grid supply and demand balance control plan;

[0007] The power grid supply and demand balance control scheme is adopted to perform supply and demand balance control on the power grid.

[0008] Optionally, solving multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control scheme includes:

[0009] Based on the multi-optimization objective function, the relevant parameters of the multi-objective particle swarm are improved, the swarm and the network are initialized, a group of particles are generated from the candidate solutions, and the local individual optimum is found according to the current position and speed of each particle. The relevant parameters include at least the swarm size, the number of iterations and the particle dimension;

[0010] Select all non-dominated individuals from the local individual optimum, construct the Pareto optimal solution set, and select the global optimal solution according to the preset optimization target weight;

[0011] The load power in each time period corresponding to the global optimal solution is used as a power grid supply and demand balance control solution.

[0012] Optionally, the method of selecting and improving relevant parameters of a multi-objective particle swarm based on a multi-optimization objective function, initializing the population and the network, generating a group of particles from candidate solutions, and finding a local individual optimum according to the current position and speed of each particle includes:

[0013] Step 1: Initialize the number of iterations t, and continue to initialize the population and the network;

[0014] Step 2: Update the speed of each particle using the update formula according to its position, current speed, optimal global position and randomly generated weight factor;

[0015] Step 3: Use the multi-optimization objective function as the fitness function to calculate the fitness of each particle. If the fitness of the particle is greater than the fitness of the particle in the last iteration, update the position and speed of the particle and proceed to step 4.

[0016] Step 4: Determine whether the number of iterations is greater than the maximum number of iterations, if not, set t=t+1 and return to step 2, otherwise proceed to step 5;

[0017] Step 5: Take the position and velocity of each particle after iteration as the individual local optimal solution.

[0018] Optionally, the update formula is calculated as follows:

[0019]

[0020] In the formula, represents the update speed of the ith particle in the t+1 dimension, V t i represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, represents the local optimal solution of the i-th particle in dimension t, represents the global optimal solution of the i-th particle in dimension t, and i represents the sequence number of the distributed generation equipment participating in the demand response event.

[0021] Optionally, the step of extracting all non-dominated individuals from the local individual optima, constructing a Pareto optimal solution set, and selecting a global optimal solution according to a preset optimization target weight includes:

[0022] Select all non-dominated individuals from the local individual optimum and construct the Pareto optimal solution set;

[0023] Adopting the adaptive grid method, we traverse all individuals in the Pareto optimal solution set and find the minimum and maximum values ​​of all individuals on each objective function.

[0024] Set the parameter n according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and the density of the grids is calculated.

[0025] For a grid k with more than one particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k;

[0026] All non-dominated individuals are selected and put into a set R to construct a Pareto non-split set, and the global optimal solution is selected according to the preset weights of each objective function.

[0027] Optionally, the multi-optimization objective function is as shown in the following formula:

[0028]

[0029] In the formula, Cost is the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response during the regulation period. and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DR Respectively represent the number of distributed generation equipment and the number of demand response events; P LOSS (k, k+1) is the loss between bus k and k+1; n is the total number of lines; k is the line number; P T,LOSS is the total line loss, ΔV D is the voltage deviation, V k is the voltage of the kth line, I represents the serial number of the distributed generation equipment participating in the demand response event, and j represents the serial number of the distributed generation equipment.

[0030] Optionally, the constraints include: voltage constraints, current constraints and power constraints.

[0031] On the other hand, the present invention also provides a power grid supply and demand balance control system based on an improved multi-objective particle swarm algorithm, comprising:

[0032] An objective function construction module is used to construct a multi-optimization objective function with the power of each line of the distribution network at the capacity of each line as a constraint and the minimum cost, minimum loss and minimum voltage deviation as the objective function;

[0033] A solution module is used to solve multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control plan;

[0034] The control module is used to use the power grid supply and demand balance control scheme to perform supply and demand balance control on the power grid.

[0035] Optionally, the solution module includes:

[0036] A local optimum determination submodule is used to select and improve the relevant parameters of the multi-objective particle swarm based on the multi-optimization objective function, initialize the swarm and the network, generate a group of particles from the candidate solutions, and find the local individual optimum according to the current position and speed of each particle. The relevant parameters include at least the swarm size, the number of iterations and the particle dimension.

[0037] The global optimal determination submodule is used to extract all non-dominated individuals from the local individual optimal, construct the Pareto optimal solution set, and select the global optimal solution according to the preset optimization target weight;

[0038] The scheme determination submodule is used to use the load power in each time period corresponding to the global optimal solution as a power grid supply and demand balance control scheme.

[0039] Optionally, the local optimum determination submodule is specifically used to:

[0040] Step 1: Initialize the number of iterations t, and continue to initialize the population and the network;

[0041] Step 2: Update the speed of each particle using the update formula according to its position, current speed, optimal global position and randomly generated weight factor;

[0042] Step 3: Use the multi-optimization objective function as the fitness function to calculate the fitness of each particle. If the fitness of the particle is greater than the fitness of the particle in the last iteration, update the position and speed of the particle and proceed to step 4.

[0043] Step 4: Determine whether the number of iterations is greater than the maximum number of iterations, if not, set t=t+1 and return to step 2, otherwise proceed to step 5;

[0044] Step 5: Take the position and velocity of each particle after iteration as the individual local optimal solution.

[0045] Optionally, the update formula is calculated as follows:

[0046]

[0047] In the formula, represents the update speed of the ith particle in the t+1 dimension, V t i represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, represents the local optimal solution of the i-th particle in dimension t, represents the global optimal solution of the i-th particle in dimension t, and i represents the sequence number of the distributed generation equipment participating in the demand response event.

[0048] Optionally, the global optimum determination submodule is specifically used for:

[0049] Select all non-dominated individuals from the local individual optimum and construct the Pareto optimal solution set;

[0050] Adopting the adaptive grid method, we traverse all individuals in the Pareto optimal solution set and find the minimum and maximum values ​​of all individuals on each objective function.

[0051] Set the parameter n according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and the density of the grids is calculated.

[0052] For a grid k with more than one particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k;

[0053] All non-dominated individuals are selected and put into a set R to construct a Pareto non-split set, and the global optimal solution is selected according to the preset weights of each objective function.

[0054] Optionally, the multi-optimization objective function is as shown in the following formula:

[0055]

[0056] In the formula, Cost is the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response during the regulation period. and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DRRespectively represent the number of distributed generation equipment and the number of demand response events; P LOSS (k, k+1) is the loss between bus k and k+1; n is the total number of lines; k is the line number; P T,LOSS is the total line loss, ΔV D is the voltage deviation, V k is the voltage of the kth line, I represents the serial number of the distributed generation equipment participating in the demand response, and j represents the serial number of the distributed generation equipment.

[0057] Optionally, the constraints include: voltage constraints, current constraints and power constraints.

[0058] In another aspect, the present application further provides a computing device, comprising: at least one processor and a memory;

[0059] The memory is used to store one or more programs;

[0060] When the one or more programs are executed by the at least one processor, a power grid supply and demand balance control method and system based on an improved multi-objective particle swarm algorithm as described above is implemented.

[0061] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements a power grid supply and demand balancing control method and system based on an improved multi-objective particle swarm algorithm as described above.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention provides a method for balancing supply and demand of power grid based on improved multi-objective particle swarm algorithm, including: taking the power of each line of the distribution network in the capacity of each line as a constraint, taking minimum cost, minimum loss and minimum voltage deviation as objective functions, constructing a multi-optimization objective function; solving the multi-optimization objective function based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control scheme; and using the power grid supply and demand balance control scheme to balance supply and demand of the power grid. The present invention takes the three objectives of reducing loss, voltage deviation and minimum cost as the overall optimization objectives, and adopts the improved multi-objective particle swarm optimization (IMOPSO) algorithm to obtain the optimal solution, reduce line congestion, and improve the stability and operation efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A power grid supply and demand balance control method and system flow chart based on an improved multi-objective particle swarm algorithm of the present invention;

[0065] Figure 2 It is a flow chart of a power grid supply and demand balance control method based on IMOPSO algorithm in an embodiment of the present invention;

[0066] Figure 3 It is the principle diagram of the adaptive grid method of the present invention;

[0067] Figure 4 The figure is a schematic diagram of the structure of a computer device of the present invention. DETAILED DESCRIPTION

[0068] The possibility of imbalance between power supply and demand increases, which can easily lead to voltage drop, increased loss, system failure and even large-scale power outages caused by line congestion. The present invention provides a power grid supply and demand balance control method based on improved multi-objective particle swarm optimization (IMOPSO), which takes the three goals of reducing loss, voltage deviation and minimum cost as the overall optimization goal. Considering the uncertainty of the output of distributed generators and the inestimability of user participation in demand response, in order to obtain the optimal solution, the improved multi-objective particle swarm optimization (IMOPSO) algorithm is adopted, which can optimize the production capacity of distributed generators and effectively manage peak power consumption, thereby achieving supply and demand balance of the power grid and improving the power consumption experience of power users.

[0069] Based on the effectiveness of distributed generation and demand response in shaving peaks and filling valleys in the power grid, the IMOPSO algorithm is used to optimize the power production of distributed units, and power users are guided to participate in demand response during peak hours to manage power consumption, thereby reducing line congestion and improving power grid stability and operating efficiency.

[0070] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0071] Embodiment 1:

[0072] A power grid supply and demand balance control method based on improved multi-objective particle swarm algorithm, such as Figure 1 As shown, including:

[0073] Step S1: Taking the power capacity of each line of the distribution network as a constraint, and taking the minimum cost, minimum loss and minimum voltage deviation as the objective function, a multi-optimization objective function is constructed;

[0074] Step S2: solving multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control plan;

[0075] Step S3: adopt the power grid supply and demand balance control scheme to perform supply and demand balance control on the power grid.

[0076] According to the present invention, a power grid supply and demand balance control method based on an improved multi-objective particle swarm optimization algorithm (IMOPSO) is provided, and the method comprises the following steps:

[0077] Step S1: Taking the power capacity of each line of the distribution network as a constraint, and taking the minimum cost, minimum loss and minimum voltage deviation as the objective function, a multi-optimization objective function is constructed, which specifically includes:

[0078] Clarify the parameters required for the scenario, process the existing data, determine the multi-optimization objective function and clarify the constraints.

[0079] The specific method of step S1 is:

[0080] Clarify the parameters required for the scenario, process the existing data, and obtain the initial loss value and bus voltage by running the reverse forward load flow, and estimate the VDI value, while setting the rated power range.

[0081] Determine a variety of optimization objectives. The goal of the optimization function is to minimize the total cost of purchasing electricity while ensuring that the power of each line in the distribution network is within the capacity limit of each line.

[0082] (1) Minimum cost target: that is, during the regulation period, the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response is minimized. The specific expression is:

[0083]

[0084] In the formula, Cost min is the sum of the production capacity costs of distributed generators participating in demand response, and They represent the increase and decrease of power generation of the jth distributed unit during the regulation period; C DG represents the cost of distributed generation; P DR,I represents the amount of electricity transferred in the event of the i-th participant in demand response; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DR They represent the number of distributed generation equipment and the number of distributed generation equipment participating in demand response respectively; j is the serial number of the distributed unit, and I represents the serial number of the distributed generation equipment participating in demand response.

[0085] (2) Loss minimization goal: minimize the loss between busbars k and k+1 in the distribution system, thereby improving the overall operation efficiency of the power grid. The specific expression is as follows:

[0086]

[0087] Where P LOSS,k , Q LOSS,k Respectively represent the active and reactive power loss of the kth line, R k 、V k , Pk , Q k , X k They represent the resistance, voltage, active power, reactive power and leakage reactance of the kth line respectively; Y k1 , Y k2 Respectively represent the equivalent capacitance to ground at both ends of the line, V k+1 is the voltage of the k+1th line, P LOSS (k, k+1) represents the loss between busbars k and k+1, P T,LOSS is the sum of the losses between busbars k and k+1 in the power distribution system, n is the total number of lines, and k is the line number.

[0088] (3) Voltage deviation minimization goal: When the voltage at a specific node deviates from the acceptable range, it will cause power quality problems and reduce efficiency. Therefore, the voltage deviation must be minimized to ensure safe and stable operation of the power grid. The specific expression is:

[0089]

[0090] Set the corresponding constraints, specifically voltage constraints, current constraints, and power constraints:

[0091] V min ≤|V k |≤V max (5)

[0092] |I k.k+1 |≤|I k.k+1.max | (6)

[0093] P Grid +P DG -P DR =P Load (7)

[0094] 0≤|P k |≤P max (8)

[0095] Where ΔV D is the voltage deviation, V min is the minimum voltage, V max is the maximum voltage, I k.k+1 is the current between adjacent nodes, I k.k+1.max is the maximum value of the current between adjacent nodes, P Grid is the power of the distribution network, P DG is the distributed generation power, P DR is the power of the demand response event, P Load is the actual power consumption, P k is the active power of the kth line, P max is the maximum active power.

[0096] Step S2: Solve the multi-optimization objective functions based on the improved multi-objective particle swarm optimization to obtain the power grid supply and demand balance control plan, which specifically includes:

[0097] Step S21 selects the control parameters of IMOPSO, initializes the population and the network, generates a group of particles from the candidate solutions, and finds the local individual optimal P according to the current position and speed of each particle. best The candidate solution here refers to a set of initial particles that may be solutions in the algorithm, that is, the load power in each time period in the possible optimal scheduling solution.

[0098] Preferably, the specific method of step S2 is:

[0099] The IMOPSO algorithm is used to solve the problem of power grid supply and demand balance regulation. First, the population and adaptive network are initialized. A group of candidate solution particles that have evolved through exploration and development are used. The particle updates its own speed according to the best position found by each particle. The goal is to find a set of optimal non-dominated solutions. Finally, the global optimal solution is selected according to the preset weights, which is the optimal candidate result. The speed of each particle is based on the current speed, the best personal position, and the optimal position. Best global position And the randomly generated weight factor (ω) is updated with the formula:

[0100]

[0101] Where V t i represents the update speed of the i-th particle in the t dimension, ω is the weight factor, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, For the best personal position, is the best global position.

[0102] Relying on the position and speed of each particle after each iteration, the local optimal solution pbest is obtained. The position and speed of each individual are constantly changing during the iterative evolution process. Therefore, it is necessary to constantly compare whether this evolution is better than the previous one. If the individual can completely dominate pbest, update pbest. If it is non-dominated, randomly select and update pbest.

[0103] Step S22: select all non-dominated individuals, construct the Pareto optimal solution set, and select the global optimal solution g according to the preset optimization target weight. bes t; find the best candidate solution through repeated iterations until the convergence condition is met.

[0104] Preferably, the specific method of step S22 is:

[0105] The REP set is maintained to prevent overflow. When the number of particles in the REP set exceeds the specified size, the redundant individuals need to be deleted to maintain a stable set size.

[0106] Adopt the adaptive grid method, traverse all individuals in the set REP, and find the minimum value min and the maximum value max of all individuals on each objective function. Then set the parameter n according to the actual situation, and the number of grids drawn in the coordinate system is n. 2 , all individuals are divided into different grids, and then the density of the grid is calculated. The smaller the density of the grid, the greater the probability that the individual will be selected. The particles in the grid with small density are sparse, and selecting the grid with small density can better ensure the diversity of the population obtained in the end. For the grid k with more than 1 particle, the number of particles to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k.

[0107] Construct a Pareto non-split set and find the global optimal solution. After each iteration of all individuals, a global optimal solution gbest needs to be selected from them. In multi-objective, non-dominated individuals are the best in the current group, and there are usually more than one non-dominated individuals. Therefore, first select all non-dominated individuals and put them into a set R to construct a Pareto non-split set. Then select the global optimal solution according to the preset weights of each objective function, and iterate in turn to find the optimal candidate solution.

[0108] In order to verify the effect of the improved IMOPSO algorithm adopted in the present invention, this embodiment adopts the MOPSO algorithm, the NSGA2 algorithm and the improved IMOPSO algorithm to calculate the GD mean value and standard deviation in the test functions ZDT1, ZDT2 and ZDT3, respectively, as shown in Table 1.

[0109] Table 1. GD value comparison

[0110]

[0111]

[0112] Generally, the smaller the GD value, the higher the convergence of the algorithm. As can be seen from Table 4, in the test functions ZDT1, ZDT2, and ZDT3, the GD average and standard deviation of the IMOPSO algorithm improved based on MOPSO are lower than those of the MOPSO algorithm and the NSGA2 algorithm. Therefore, it is shown that the IMOPSO algorithm is not only superior to the traditional MOPSO and NSGA2 algorithms in terms of convergence, but also has better stability in convergence ability than the MOPSO and NSGA2 algorithms.

[0113] Table 2. IGD value comparison

[0114]

[0115] The smaller the IGD value, the better the overall performance of the algorithm. As can be seen from Table 5, on each test function, the IGD average and standard deviation of the IMOPSO algorithm are lower than those of the MOPSO algorithm and the NSGA2 algorithm. This proves that the IMOPSO algorithm has better overall performance and higher stability.

[0116] Example 2

[0117] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Figure 2 As shown, this embodiment provides a power grid supply and demand balance control method based on an improved multi-objective particle swarm algorithm, comprising the following steps:

[0118] Step S1: Taking the power capacity of each line in the distribution network as a constraint, and taking the minimum cost, minimum loss and minimum voltage deviation as the objective function, a multi-optimization objective function is constructed, including:

[0119] S10 clarifies the parameters required for the scenario, processes the existing data, and obtains the initial loss value and bus voltage by running the reverse forward load flow, and estimates the value of VDI, while setting the rated power range.

[0120] S20 determines a variety of optimization objectives, and the goal of the optimization function is to minimize the total cost of purchasing electricity while ensuring that the power of each line in the distribution network is within the capacity limit of each line.

[0121] (1) Minimum cost target: that is, during the regulation period, the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response is minimized. The specific expression is:

[0122]

[0123] In the formula, and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR Represents the cost of electricity conversion after participating in demand response. N DG With N DR They represent the number of distributed generation equipment and the number of distributed generation equipment participating in demand response respectively, and I represents the serial number of the distributed generation equipment participating in demand response.

[0124] (2) Loss minimization goal: minimize the loss between busbars k and k+1 in the distribution system, thereby improving the overall operation efficiency of the power grid. The specific expression is as follows:

[0125]

[0126] Where P LOSS,k , Q LOSS,k Respectively represent the active and reactive power loss of the kth line, R k 、V k , P k , Q k , X k They represent the resistance, voltage, active power, reactive power and leakage reactance of the kth line respectively; Y k1 , Y k2 Respectively represent the equivalent capacitance to ground at both ends of the line, V k+1 is the voltage of the k+1th line, P LOSS (k, k+1) represents the loss between busbars k and k+1, P T,LOSS is the sum of the losses between busbars k and k+1 in the power distribution system, n is the total number of lines, and k is the line number.

[0127] (3) Voltage deviation minimization goal: When the voltage at a specific node deviates from the acceptable range, it will cause power quality problems and reduce efficiency. Therefore, the voltage deviation must be minimized to ensure safe and stable operation of the power grid. The specific expression is:

[0128]

[0129] Set the corresponding constraints, specifically voltage constraints, current constraints, and power constraints:

[0130] V min ≤|V k |≤V max (5)

[0131] |I k.k+1 |≤|I k.k+1.max | (6)

[0132] P Grid +P DG -P DR =P Load (7)

[0133] 0≤|P k |≤P max (8)

[0134] Step S2: Solving multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control plan, including:

[0135] S30 population,network initialization;

[0136] S40 updates particle speed and position to determine the local individual optimal P best ;

[0137] S50 obtains the pareto non-fragmentation set REP;

[0138] S60 RPE set maintenance;

[0139] S70 selects the global optimal solution gbest according to the preset target weight;

[0140] S80 determines whether the convergence condition is met, if so, proceeds to S90, otherwise, iterates i=i+1 and returns to S40;

[0141] S90 obtains the final optimization result.

[0142] The MOPSO algorithm is used to solve the problem of power grid supply and demand balance regulation. First, the population and adaptive network are initialized. A group of candidate solution particles that have evolved through exploration and development are used to update their own speed according to the best position found by each particle. The goal is to find a set of optimal non-dominated solutions. Finally, the global optimal solution is selected according to the preset weights, which is the optimal candidate result. The speed of each particle is based on the current speed, the best personal position, and the optimal position. Best global position And the randomly generated weight factor (ω) is updated with the formula:

[0143]

[0144] Where V t i represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, Represents the current position of the i-th particle in dimension t.

[0145] Relying on the position and speed of each particle after each iteration, the local optimal solution pbest is obtained. The position and speed of each individual are constantly changing during the iterative evolution process. Therefore, it is necessary to constantly compare whether this evolution is better than the previous one. If the individual can completely dominate pbest, update pbest. If it is non-dominated, randomly select and update pbest.

[0146] The REP set is maintained to prevent overflow. When the number of particles in the REP set exceeds the specified size, the redundant individuals need to be deleted to maintain a stable set size.

[0147] This embodiment provides an adaptive grid such as Figure 3As shown in the figure, the adaptive grid method is adopted. In the figure, f1 and f2 represent any two of the three objective functions of minimum loss, minimum cost and minimum voltage deviation. All individuals in the set REP are traversed, and the minimum value min and the maximum value max of all individuals are found on each objective function. Then the parameter n is set according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and then the density of the grid is calculated. The smaller the density of the grid, the greater the probability that the individual is selected. The particles in the grid with low density are sparse. Selecting a grid with low density can better ensure the diversity of the population finally obtained. For a grid k with more than 1 particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k.

[0148] Construct a Pareto non-split set and find the global optimal solution. After each iteration of all individuals, a global optimal solution gbest needs to be selected from them. In multi-objective, non-dominated individuals are the best in the current group, and there are usually more than one non-dominated individuals. Therefore, first select all non-dominated individuals and put them into a set R to construct a Pareto non-split set. Then select the global optimal solution according to the preset weights of each objective function, and iterate in turn to find the optimal candidate solution.

[0149] Based on the improved multi-objective optimization algorithm, precise regulation of the power grid supply and demand balance is achieved by setting specific constraints and multi-dimensional objective functions.

[0150] Example 3

[0151] On the other hand, the present invention also provides a power grid supply and demand balance control system based on an improved multi-objective particle swarm algorithm, comprising:

[0152] An objective function construction module is used to construct a multi-optimization objective function with the power of each line of the distribution network at the capacity of each line as a constraint and the minimum cost, minimum loss and minimum voltage deviation as the objective function;

[0153] A solution module is used to solve multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control plan;

[0154] The control module is used to use the power grid supply and demand balance control scheme to perform supply and demand balance control on the power grid.

[0155] Optionally, the solution module includes:

[0156] A local optimum determination submodule is used to select and improve the relevant parameters of the multi-objective particle swarm based on the multi-optimization objective function, initialize the swarm and the network, generate a group of particles from the candidate solutions, and find the local individual optimum according to the current position and speed of each particle. The relevant parameters include at least the swarm size, the number of iterations and the particle dimension.

[0157] The global optimal determination submodule is used to extract all non-dominated individuals from the local individual optimal, construct the Pareto optimal solution set, and select the global optimal solution according to the preset optimization target weight;

[0158] The scheme determination submodule is used to use the load power in each time period corresponding to the global optimal solution as a power grid supply and demand balance control scheme.

[0159] Optionally, the local optimum determination submodule is specifically used to:

[0160] Step 1: Initialize the number of iterations t, and continue to initialize the population and the network;

[0161] Step 2: Update the speed of each particle using the update formula according to its position, current speed, optimal global position and randomly generated weight factor;

[0162] Step 3: Use the multi-optimization objective function as the fitness function to calculate the fitness of each particle. If the fitness of the particle is greater than the fitness of the particle in the last iteration, update the position and speed of the particle and proceed to step 4.

[0163] Step 4: Determine whether the number of iterations is greater than the maximum number of iterations, if not, set t=t+1 and return to step 2, otherwise proceed to step 5;

[0164] Step 5: Take the position and velocity of each particle after iteration as the individual local optimal solution.

[0165] Optionally, the update formula is calculated as follows:

[0166]

[0167] In the formula, represents the update speed of the ith particle in the t+1 dimension, V t i represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, represents the local optimal solution of the i-th particle in dimension t, represents the global optimal solution of the i-th particle in dimension t, and i represents the sequence number of the distributed generation equipment participating in the demand response event.

[0168] Optionally, the global optimum determination submodule is specifically used for:

[0169] Select all non-dominated individuals from the local individual optimum and construct the Pareto optimal solution set;

[0170] Adopting the adaptive grid method, we traverse all individuals in the Pareto optimal solution set and find the minimum and maximum values ​​of all individuals on each objective function.

[0171] Set the parameter n according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and the density of the grids is calculated.

[0172] For a grid k with more than one particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k;

[0173] All non-dominated individuals are selected and put into a set R to construct a Pareto non-split set, and the global optimal solution is selected according to the preset weights of each objective function.

[0174] Optionally, the multi-optimization objective function is as shown in the following formula:

[0175]

[0176] In the formula, Cost is the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response during the regulation period. and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DR Respectively represent the number of distributed generation equipment and the number of distributed generation equipment participating in demand response; P LOSS (k, k+1) is the loss between bus k and k+1; n is the total number of lines; k is the line number; P T,LOSS is the total line loss, ΔV D is the voltage deviation, V k is the voltage of the kth line.

[0177] Optionally, the constraints include: voltage constraints, current constraints and power constraints.

[0178] Example 4

[0179] like Figure 4 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0180] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a power grid supply and demand balancing control method based on an improved multi-objective particle swarm algorithm in the above embodiment.

[0181] Example 5

[0182] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a power grid supply and demand balance control method based on an improved multi-objective particle swarm algorithm in the above embodiment.

[0183] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0185] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0187] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A power grid supply and demand balance control method based on an improved multi-objective particle swarm algorithm, characterized in that: include: Taking the power capacity of each line in the distribution network as the constraint, and the minimum cost, minimum loss and minimum voltage deviation as the objective function, a multi-optimization objective function is constructed; Based on the improved multi-objective particle swarm optimization, multiple optimization objective functions are solved to obtain the power grid supply and demand balance control plan; The power grid supply and demand balance control scheme is adopted to perform supply and demand balance control on the power grid.

2. The method according to claim 1, characterized in that The method of solving multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control scheme includes: Based on the multi-optimization objective function, the relevant parameters of the multi-objective particle swarm are improved, the swarm and the network are initialized, a group of particles are generated from the candidate solutions, and the local individual optimum is found according to the current position and speed of each particle. The relevant parameters include at least the swarm size, the number of iterations and the particle dimension; Select all non-dominated individuals from the local individual optimum, construct the Pareto optimal solution set, and select the global optimal solution according to the preset optimization target weight; The load power in each time period corresponding to the global optimal solution is used as a power grid supply and demand balance control solution.

3. The method according to claim 2, characterized in that The method of improving the relevant parameters of the multi-objective particle swarm based on multi-optimization objective functions, initializing the population and the network, generating a group of particles from the candidate solutions, and finding the local individual optimum according to the current position and speed of each particle includes: Step 1: Initialize the number of iterations t, and continue to initialize the population and the network; Step 2: Update the speed of each particle using the update formula according to its position, current speed, optimal global position and randomly generated weight factor; Step 3: Use the multi-optimization objective function as the fitness function to calculate the fitness of each particle. If the fitness of the particle is greater than the fitness of the particle in the last iteration, update the position and speed of the particle and proceed to step 4. Step 4: Determine whether the number of iterations is greater than the maximum number of iterations, if not, set t=t+1 and return to step 2, otherwise proceed to step 5; Step 5: Take the position and velocity of each particle after iteration as the individual local optimal solution.

4. The method according to claim 3, characterized in that The update formula is calculated as follows: In the formula, represents the update speed of the i-th particle in the t+1 dimension, represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, represents the local optimal solution of the i-th particle in dimension t, represents the global optimal solution of the i-th particle in dimension t, and i represents the sequence number of the distributed generation equipment participating in the demand response event.

5. The method according to claim 2, characterized in that The method of extracting all non-dominated individuals from the local individual optimum, constructing a Pareto optimal solution set, and selecting a global optimal solution according to a preset optimization target weight includes: Select all non-dominated individuals from the local individual optimum and construct the Pareto optimal solution set; Adopting the adaptive grid method, we traverse all individuals in the Pareto optimal solution set and find the minimum and maximum values ​​of all individuals on each objective function. Set the parameter n according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and the density of the grids is calculated. For a grid k with more than one particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k; All non-dominated individuals are selected and put into a set R to construct a Pareto non-split set, and the global optimal solution is selected according to the preset weights of each objective function.

6. The method according to claim 1, characterized in that The multi-optimization objective function is shown in the following formula: In the formula, Cost is the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response during the regulation period. and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DR Respectively represent the number of distributed generation equipment and the number of demand response events; P LOSS (k, k+1) is the loss between bus k and k+1; n is the total number of lines; k is the line number; P T,LOSS is the total line loss, ΔV D is the voltage deviation, V k is the voltage of the kth line, I represents the serial number of the distributed generation equipment participating in the demand response, and j represents the serial number of the distributed generation equipment.

7. The method according to claim 1, characterized in that The constraints include: voltage constraints, current constraints and power constraints.

8. A power grid supply and demand balance control system based on an improved multi-objective particle swarm algorithm, characterized in that: include: An objective function construction module is used to construct a multi-optimization objective function with the power of each line of the distribution network at the capacity of each line as a constraint and the minimum cost, minimum loss and minimum voltage deviation as the objective function; A solution module is used to solve multiple optimization objective functions based on improved multi-objective particle swarm optimization to obtain a power grid supply and demand balance control plan; The control module is used to use the power grid supply and demand balance control scheme to perform supply and demand balance control on the power grid.

9. The system according to claim 8, characterized in that The solution module comprises: A local optimum determination submodule is used to select and improve the relevant parameters of the multi-objective particle swarm based on the multi-optimization objective function, initialize the swarm and the network, generate a group of particles from the candidate solutions, and find the local individual optimum according to the current position and speed of each particle. The relevant parameters include at least the swarm size, the number of iterations and the particle dimension. The global optimal determination submodule is used to extract all non-dominated individuals from the local individual optimal, construct the Pareto optimal solution set, and select the global optimal solution according to the preset optimization target weight; The scheme determination submodule is used to use the load power in each time period corresponding to the global optimal solution as a power grid supply and demand balance control scheme.

10. The system according to claim 9, characterized in that The local optimum determination submodule is specifically used for: Step 1: Initialize the number of iterations t, and continue to initialize the population and the network; Step 2: Update the speed of each particle using the update formula according to its position, current speed, optimal global position and randomly generated weight factor; Step 3: Use the multi-optimization objective function as the fitness function to calculate the fitness of each particle. If the fitness of the particle is greater than the fitness of the particle in the last iteration, update the position and speed of the particle and proceed to step 4. Step 4: Determine whether the number of iterations is greater than the maximum number of iterations, if not, set t=t+1 and return to step 2, otherwise proceed to step 5; Step 5: Take the position and velocity of each particle after iteration as the individual local optimal solution.

11. The system according to claim 10, characterized in that The update formula is calculated as follows: In the formula, represents the update speed of the i-th particle in the t+1 dimension, represents the update speed of the i-th particle in the t dimension, ω is the weight coefficient, C1 and C2 represent learning parameters, R1 and R2 represent random numbers between 0 and 1, represents the current position of the i-th particle in dimension t, represents the local optimal solution of the i-th particle in dimension t, represents the global optimal solution of the i-th particle in dimension t, and i represents the sequence number of the distributed generation equipment participating in the demand response event.

12. The system according to claim 9, characterized in that The global optimum determination submodule is specifically used for: Select all non-dominated individuals from the local individual optimum and construct the Pareto optimal solution set; Adopting the adaptive grid method, we traverse all individuals in the Pareto optimal solution set and find the minimum and maximum values ​​of all individuals on each objective function. Set the parameter n according to the actual situation. The number of grids drawn in the coordinate system is n2. All individuals are divided into different grids, and the density of the grids is calculated. For a grid k with more than one particle, the number of particles PN to be deleted in the grid is calculated according to the congestion level, and then PN particles are randomly deleted in the grid k; All non-dominated individuals are selected and put into a set R to construct a Pareto non-split set, and the global optimal solution is selected according to the preset weights of each objective function.

13. The system of claim 8, wherein: The multi-optimization objective function is shown in the following formula: In the formula, Cost is the sum of the production cost of distributed generators and the cost of guiding them to participate in demand response during the regulation period. and They represent the increase and decrease of power generation of distributed units during the regulation period; C DG represents the cost of distributed generation; P DR,I Indicates the amount of electricity transferred in response to demand; C DR represents the cost of electricity conversion after participating in demand response; N DG With N DR Respectively represent the number of distributed generation equipment and the number of demand response events; P LOSS (k, k+1) is the loss between bus k and k+1; n is the total number of lines; k is the line number; P T,LOSS is the total line loss, ΔV D is the voltage deviation, V k is the voltage of the kth line, I represents the serial number of the distributed generation equipment participating in the demand response, and j represents the serial number of the distributed generation equipment.

14. The system of claim 8, wherein: The constraints include: voltage constraints, current constraints and power constraints.

15. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a power grid supply and demand balancing control method based on an improved multi-objective particle swarm algorithm as described in any one of claims 1 to 7 is implemented.

16. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a power grid supply and demand balance control method based on an improved multi-objective particle swarm algorithm as described in any one of claims 1 to 7 is implemented.

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