Reactive Power Compensation Optimization Method and Device for Distribution Network with Distributed Generation

Abstract

The present invention provides a reactive power compensation optimization method and device for a distribution network with distributed power sources. The method includes: establishing a power model of the distributed power sources, wherein the parameters of the power model include the reactive power of the distributed power sources; establishing a multi-objective function of the reactive power compensation optimization model for the distribution network; improving the simulated annealing algorithm and fusing the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm; using the power model of the distributed power sources as a constraint, and solving the multi-objective function through the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters for the distribution network. Thus, by using the particle swarm algorithm fused with the improved simulated annealing algorithm, multi-objective optimization solution is performed on the reactive power compensation optimization model for the distribution network, and the reactive power regulation ability of the distributed power sources is considered during the optimization process, improving the optimization ability and the optimization effect, so as to ensure the economic operation and the safety and reliability of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power compensation in distribution networks, and specifically relates to a method for optimizing reactive power compensation in a distribution network with distributed power sources and a device for optimizing reactive power compensation in a distribution network with distributed power sources. Background Art

[0002] New energy represented by distributed power sources has received extensive attention and rapid development. However, the increasing penetration rate of distributed power sources in the distribution network makes reactive power optimization in the distribution network more and more difficult, and reactive power compensation optimization is an important means to improve the operating economy of the distribution network and ensure safe and reliable operation. Therefore, studying reactive power optimization in a distribution network with distributed power sources has important theoretical and practical value.

[0003] Conventional algorithms for reactive power compensation optimization include nonlinear programming method, dynamic programming method, etc. However, they have too high requirements for the accuracy of the optimization model, too large computational amount, and it is difficult to handle uncertainty problems. Therefore, relevant scholars have applied intelligent optimization algorithms to reactive power optimization. In reference [1], a genetic intelligent algorithm was applied to reactive power optimization in a distribution network with distributed power sources. In reference [2], an improved differential grey wolf fusion algorithm was applied to reactive power compensation optimization in a distribution network. The above references did not consider the reactive power regulation ability of distributed power sources when performing reactive power compensation optimization in the distribution network, and there are situations where the optimization results of the solution algorithms for the reactive power compensation optimization model are not ideal, resulting in low optimization ability and poor optimization effect.

[0004] Reference [1], Zhao Kun, Geng Guangfei. Reactive Power Optimization of Distribution Network Based on Improved Genetic Algorithm [J]. Power System Protection and Control, 2011, 39(5): 57-62.

[0005] Reference [2], Zhang Tao, Yu Li, Yao Jianfeng, etc. Reactive Power Optimization of Distribution Network Based on Improved Multi-Objective Differential Grey Wolf Algorithm [J]. Information and Control, 2020, 49(1): 78-86. Summary of the Invention

[0006] In order to solve the problem that the reactive power regulation ability of distributed power sources is not considered when performing reactive power compensation optimization in related technologies, and there are problems with the optimization results of the solution algorithms for the reactive power compensation optimization model, the following technical solutions are proposed in the present invention.

[0007] An embodiment of the first aspect of the present invention provides a reactive power compensation optimization method for a distribution network with distributed power sources, including the following steps: establishing a power model of the distributed power sources, where the parameters of the power model of the distributed power sources include the reactive power of the distributed power sources; establishing a multi-objective function of the reactive power compensation optimization model of the distribution network; improving the simulated annealing algorithm and fusing the particle swarm optimization algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm; solving the multi-objective function through the particle swarm fusion algorithm with the power model of the distributed power sources as a constraint to obtain the reactive power compensation optimization parameters of the distribution network; and performing reactive power compensation optimization on the distribution network according to the reactive power compensation optimization parameters.

[0008] In addition, the reactive power compensation optimization method for a distribution network with distributed power sources according to the above embodiment of the present invention may further have the following additional technical features.

[0009] According to an embodiment of the present invention, establishing the power model of the distributed power sources includes: establishing a power model of distributed wind turbines and a power model of distributed photovoltaics.

[0010] According to an embodiment of the present invention, the multi-objective function of the reactive power compensation optimization model is:

[0011] min F = min(f1, f2)

[0012]

[0013]

[0014] where F is a two-dimensional function of f1 and f2, f1 is the cost function of the distribution network, f2 is the voltage difference function of the distribution network, c P represents the cost per unit active power loss, P loss is the total active power loss of the distribution network per unit time, E TAP , E C are respectively the cost of on-load tap-changer regulation action and the cost of reactive power compensation capacitor switching, E DG,k is the regulation cost of the kth distributed power source connected to the grid, M is the number of distributed power sources connected to the grid, N is the number of distribution network nodes, U j , U js , U jmax , U jmin are respectively the actual voltage, standard voltage, upper limit voltage and lower limit voltage of the jth distribution network node.

[0015] According to an embodiment of the present invention, the total active power loss of the distribution network per unit time, the tap-changing operation cost of the on-load tap-changer transformer, the switching cost of the reactive power compensation capacitor, and the regulation cost of the k-th distributed power source are calculated respectively according to the following formulas:

[0016]

[0017]

[0018]

[0019]

[0020] where U i is the actual voltage of the i-th distribution network node, g ij , δ ij are the conductance and phase angle difference between node i and node j respectively, E e c , L TAP are the installation cost and service life of the on-load tap-changer transformer respectively, △T TAP , r are the number of tap adjustments of the on-load tap-changer transformer and the maximum number of allowable tap adjustments respectively, L ssp , L C are the equipment cost and service life of the reactive power compensation capacitor respectively, △Q C,t is the reactive power change amount of the reactive power compensation capacitor at time t, F DG,k , Q DG,kt are the cost of the k-th distributed power source per unit reactive power output and the reactive power output value at time t respectively.

[0021] According to an embodiment of the present invention, the simulated annealing algorithm is improved, including: making the amplitude of the annealing temperature decrease proportional to the number of accepted states of the solution generated at the annealing temperature; recording the optimal solution in the optimization process of the simulated annealing algorithm to avoid missing the optimal solution; when the annealing temperature drops to a preset value, gradually reduce the perturbation.

[0022] According to an embodiment of the present invention, the multi-objective function is solved by the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters of the distribution network, including: solving the multi-objective function by the particle swarm fusion algorithm to obtain the Pareto optimal solution set; selecting a set of solutions from the Pareto optimal solution set as the reactive power compensation optimization parameters of the distribution network through the fuzzy satisfaction degree algorithm.

[0023] According to an embodiment of the present invention, solving the multi-objective function by the particle swarm fusion algorithm to obtain a Pareto optimal solution set includes: calculating the first personal extreme value and the first population extreme value of particles at the initial temperature; updating the velocity and position of the particles according to the first personal extreme value and the first population extreme value to obtain a new state; calculating the second personal extreme value and the second population extreme value of the particles in the new state; comparing the magnitude relationship between the first personal extreme value and the second personal extreme value, accepting the second personal extreme value according to the comparison result, and recording the current optimal solution; when the number of iterations does not reach the maximum number of iterations, updating the velocity and position of the particles, and returning to the step of calculating the second personal extreme value and the second population extreme value of the particles in the new state until the number of iterations reaches the maximum number of iterations; after the number of iterations reaches the maximum number of iterations, outputting the current optimal solution, and determining whether the current optimal solution satisfies the iteration termination condition; if the current optimal solution satisfies the iteration termination condition, taking the current optimal solution as the global optimal solution and outputting the global optimal solution; if the current optimal solution does not satisfy the iteration termination condition, updating the initial temperature, and returning to the step of calculating the first personal extreme value and the first population extreme value of particles at the initial temperature until the global optimal solution is output.

[0024] According to an embodiment of the present invention, accepting the second personal extreme value according to the comparison result includes: when the first personal extreme value is less than or equal to the second personal extreme value, accepting the second personal extreme value according to the Metropolis criterion; when the first personal extreme value is greater than the second personal extreme value, directly accepting the second personal extreme value.

[0025] According to an embodiment of the present invention, after updating the initial temperature, it further includes: if the updated initial temperature drops to a preset value, reducing the perturbation.

[0026] An embodiment of the second aspect of the present invention provides a reactive power compensation optimization device for a distribution network with distributed power sources, including: a first establishment module for establishing a power model of the distributed power source, where the parameters of the power model of the distributed power source include the reactive power of the distributed power source; a second establishment module for establishing a multi-objective function of the reactive power compensation optimization model of the distribution network; a fusion module for improving the simulated annealing algorithm and fusing the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm; a solution module for solving the multi-objective function by the particle swarm fusion algorithm with the power model of the distributed power source as a constraint to obtain the reactive power compensation optimization parameters of the distribution network; and an optimization module for optimizing the reactive power compensation of the distribution network according to the reactive power compensation optimization parameters.

[0027] In the technical solution of the embodiment of the present invention, first establish a power model of a distributed power source, where the parameters of the power model include the reactive power of the distributed power source, establish a multi-objective function of a reactive power compensation optimization model for a distribution network, improve the simulated annealing algorithm, and fuse the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm. Then, with the power model of the distributed power source as a constraint, solve the multi-objective function through the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters for the distribution network, and perform reactive power compensation optimization on the distribution network according to the reactive power compensation optimization parameters. Thus, by using the particle swarm algorithm integrated with the improved simulated annealing algorithm, perform multi-objective optimization solution on the reactive power compensation optimization model of the distribution network, and consider the reactive power regulation ability of the distributed power source during the optimization process, improve the optimization ability, and improve the optimization effect, so as to ensure the economic operation and safety and reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of a reactive power compensation optimization method for a distribution network with a distributed power source according to an embodiment of the present invention.

[0029] Figure 2 It is a flowchart of solving a multi-objective function according to an example of the present invention.

[0030] Figure 3 It is a block diagram of a reactive power compensation optimization device for a distribution network with a distributed power source according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Figure 1 It is a flowchart of a reactive power compensation optimization method for a distribution network with a distributed power source according to an embodiment of the present invention.

[0033] As Figure 1 shown, the reactive power compensation optimization method for a distribution network with a distributed power source includes the following steps S1 to S5.

[0034] S1. Establish a power model of a distributed power source, where the parameters of the power model of the distributed power source include the reactive power of the distributed power source.

[0035] Specifically, when it is necessary to optimize the reactive power compensation of a distribution network with distributed power sources (such as wind turbine power sources, solar photovoltaic power sources, natural gas power sources, etc.), first, a power model of the distributed power source can be established based on the influencing factors of its reactive power regulation ability. The parameters of this power model include the reactive power of the distributed power source. Furthermore, this power model can be used to characterize the reactive power regulation ability of the distributed power source.

[0036] S2. Establish a multi-objective function for the reactive power compensation optimization model of the distribution network.

[0037] Specifically, when performing reactive power optimization of the distribution network, the reactive power regulation ability of the distributed power source is taken into account, and at the same time, the economy, safety, and reliability of the distribution network operation are considered. Among them, the economic objective is to minimize the operation cost of the distribution network, and the safety objective is to minimize the node voltage deviation of the distribution network.

[0038] That is to say, on the premise of considering the reactive power regulation ability of the distributed power source, a multi-objective function of the reactive power compensation optimization model of the distribution network is established with the economic objective and the safety objective as the optimization objectives, and this multi-objective function is solved to obtain a reactive power compensation scheme with the lowest operation cost and the highest safety and reliability.

[0039] S3. Improve the simulated annealing algorithm and fuse the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm.

[0040] Specifically, to further enhance the optimization ability of the simulated annealing algorithm, the existing simulated annealing algorithm is improved, and the probability mutation ability of the improved simulated annealing algorithm is introduced into the existing particle swarm algorithm to obtain a particle swarm fusion algorithm.

[0041] It should be noted that the particle swarm algorithm has poor optimization ability and is prone to falling into local extreme points during optimization calculations. In the embodiments of the present invention, the probability jump ability of the simulated annealing algorithm is applied to the particle swarm algorithm, that is, an annealing process is incorporated into the particle swarm algorithm (when the annealing temperature decreases, the number of iterations increases, and the probability of accepting non-optimal solutions decreases). Each particle has to go through the annealing process, thereby improving the optimization ability of the entire population.

[0042] S4. With the power model of the distributed power source as a constraint, solve the multi-objective function through the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters of the distribution network.

[0043] Specifically, the particle swarm fusion algorithm is used to optimize and solve the multi-objective function to obtain the reactive power compensation optimization parameters. When finding the optimal solution, the power model of the distributed power source is used as a certain constraint. When the active power output is determined, the reactive power that the power source can output or absorb has its boundary value. Solving the multi-objective function to obtain the reactive power compensation optimization parameters of the distribution network, which can make the distribution network operate safely and reliably while meeting the lowest operating cost, meeting both economy and safety and reliability.

[0044] S5. Optimize the reactive power compensation of the distribution network according to the reactive power compensation optimization parameters.

[0045] Specifically, after obtaining the reactive power compensation optimization parameters, optimize the reactive power compensation of the distribution network according to the reactive power compensation optimization parameters, realize the reactive power compensation optimization of the distribution network, and improve the power supply quality.

[0046] In the embodiment of the present invention, the probability mutation ability of the simulated annealing algorithm is introduced into the particle swarm algorithm, and each particle has to go through the annealing process during the optimization calculation, so as to improve the optimization ability of the entire population, and can solve the disadvantages of poor optimization ability and easy to fall into local extreme points of the particle swarm algorithm during the optimization calculation.

[0047] Compared with the genetic intelligent algorithm and the differential grey wolf fusion algorithm used in the related technology, on the one hand, the embodiment of the present invention establishes a multi-objective function of the reactive power compensation optimization model of the distribution network, improves the existing simulated annealing algorithm, and integrates it into the particle swarm algorithm to obtain the particle swarm fusion algorithm. Furthermore, the multi-objective function is optimized and solved by the particle swarm fusion algorithm, so as to improve the optimization ability and avoid the disadvantages of poor optimization ability and easy to fall into local extreme points of the particle swarm algorithm during the optimization calculation; on the other hand, the reactive power of the distributed power source is considered during the solution process, so as to improve the reactive power compensation optimization effect.

[0048] Therefore, the reactive power compensation optimization method for the distribution network with distributed power sources in the embodiment of the present invention uses the particle swarm algorithm integrated with the improved simulated annealing algorithm to perform multi-objective optimization and solution on the reactive power compensation optimization model of the distribution network, and considers the reactive power regulation ability of the distributed power source during the optimization process, improves the optimization ability, and improves the optimization effect, so as to improve the operation economy of the distribution network and ensure the safety and reliability of the distribution network.

[0049] In an embodiment of the present invention, establishing the power model of the distributed power source may include: establishing the power model of the distributed wind turbine and the power model of the distributed photovoltaic.

[0050] Specifically, when distributed wind turbines and distributed photovoltaic systems are included in the distribution network, a power model of the distributed wind turbine can be established based on the power influencing factors of the distributed wind turbine, and a power model of the distributed photovoltaic system can be established based on the power influencing factors of the distributed photovoltaic system.

[0051] Specifically, establishing the power model of the distributed wind turbine may include the following steps:

[0052] The wind speed probability density function f(v) presents the shape of a Weibull distribution curve, and its expression is:

[0053]

[0054] In the formula: K is the shape parameter of the Weibull distribution (dimensionless), C is the scale parameter of the Weibull distribution (m / s), and v is the wind speed (m / s).

[0055] The active power P output by the distributed wind turbine w The relationship with the wind speed v can be expressed as:

[0056]

[0057] In the formula: P r Represents the rated value of the active power output by the distributed wind turbine, v r Represents the rated wind speed of the distributed wind turbine, v ci 、v co Represent the cut-in wind speed and cut-out wind speed respectively, and k1 and k2 represent the slope and intercept of the output characteristic function when the wind speed is between the cut-in wind speed and the rated wind speed.

[0058] The active power P output by the distributed wind turbine w And the reactive power Q w Are mainly determined by the stator winding current and the current on the rotor side of the converter. Therefore, the power model of the distributed wind turbine can be expressed as:

[0059]

[0060] Among them, P w Is the active power output by the distributed wind turbine, Q w Is the reactive power output by the distributed wind turbine, U s 、I s Are the voltage and winding current on the stator side of the distributed wind turbine respectively, s is the magnitude of the slip ratio, X s 、X m Are the leakage reactance and magnetizing reactance respectively, and I r Represents the magnitude of the current on the rotor side of the converter (the speed of the wind turbine rotor is variable, and the converter is added to the rotor side to change the excitation to make the stator side output a constant-frequency power).

[0061] Establishing the power model of distributed photovoltaic may include the following steps:

[0062] The magnitude of the active power output P of distributed photovoltaic PV is mainly determined by the solar irradiance r, and the irradiance is a random variable subject to the Beta distribution. Therefore, the probability density function f(P PV ) is expressed as:

[0063]

[0064] where: α and β are the shape parameter and size parameter of the Beta distribution respectively, Γ represents the gamma function, and P M is the maximum power output value of the photovoltaic.

[0065] The output power of distributed photovoltaic is mainly related to the irradiance and the ambient temperature. Therefore, the output characteristic equation of the photovoltaic can be expressed as:

[0066]

[0067] where: I, I ph , I0 represent the output current, photocurrent, and dark saturation current of the photovoltaic cell respectively, U represents the output voltage of the photovoltaic cell, R s , R sh represent the series resistance and parallel resistance of the photovoltaic cell respectively, q represents the electron charge, A represents the diode ideality factor, K represents the Boltzmann constant, and T is the ambient temperature of the photovoltaic cell.

[0068] The magnitude of the reactive power regulation ability of distributed photovoltaic is related to the total capacity of the grid-connected inverter (distributed photovoltaic is connected to the distribution network, and the inverter is used to convert DC to AC), and according to the relationship between its active power and reactive power, the power model of distributed photovoltaic can be obtained as:

[0069]

[0070] where, |Q| max (t) represents the maximum value of the reactive power output of distributed photovoltaic, S max represents the maximum value of the inverter capacity, and P act (t) represents the active power output of distributed photovoltaic.

[0071] Thus, the relationship expression between the active power and reactive power of distributed wind turbines and distributed photovoltaic can be obtained through the above steps, that is, the power models of distributed wind turbines and distributed photovoltaic can be obtained, which can be used as certain constraints in the optimization solution.

[0072] In an embodiment of the present invention, the multi-objective function of the reactive power compensation optimization model is as follows:

[0073] min F = min(f1, f2) (7)

[0074]

[0075]

[0076] where F is a two-dimensional function of f1 and f2, f1 is the cost function of the distribution network, f2 is the voltage difference function of the distribution network, c P represents the cost per unit active power loss, P loss is the total active power loss of the distribution network per unit time, E TAP and E C are the tap-changing operation cost of the on-load tap-changer and the switching cost of the reactive power compensation capacitor respectively, E DG,k is the regulation cost of the k-th distributed power source connected to the grid, M is the total number of distributed power sources connected to the grid, N is the number of nodes in the distribution network, U j and U js and U jmax and U jmin are the actual voltage, standard voltage, upper limit voltage and lower limit voltage of the j-th distribution network node respectively.

[0077] Furthermore, the total active power loss, tap-changing operation cost of the on-load tap-changer, switching cost of the reactive power compensation capacitor and regulation cost of the k-th distributed power source of the distribution network are calculated according to the following formulas respectively:

[0078]

[0079]

[0080]

[0081]

[0082] where U i is the actual voltage of the i-th distribution network node, g ij and δ ij are the conductance and phase angle difference between node i and node j respectively, E e c and L TAP are the installation cost and service life of the on-load tap-changer respectively, △T TAP and r are the number of tap-changes of the on-load tap-changer and the maximum number of allowable tap-changes respectively, L ssp and L C are the equipment cost and service life of the reactive power compensation capacitor respectively, △QC,t is the reactive power change of the reactive power compensation capacitor at time t, in F DG,k , Q DG,kt are respectively the cost of the k-th distributed power source for outputting unit reactive power and the reactive power output value at time t.

[0083] Specifically, the total active power loss P of the distribution network per unit time is calculated by formula (10) loss , the on-load tap-changing transformer tap-changing operation cost E is calculated by formula (11) TAP , the reactive power compensation capacitor switching cost E is calculated by formula (12) C , the regulation cost E of the k-th distributed power source is calculated by formula (13) DG,k , and then, the calculation results are substituted into formula (8) for calculation to obtain the cost of the distribution network. The actual voltages of all nodes in the distribution network are detected, the standard voltages, upper limit voltages and lower limit voltages of all nodes are obtained, and each voltage value is substituted into formula (9) for calculation to obtain the voltage difference of the distribution network. The cost and voltage difference of the distribution network are substituted into formula (7), and the particle swarm fusion algorithm is used to optimize formula (7) to obtain the reactive power compensation optimization parameters of the distribution network.

[0084] Among them, in formula (13), when calculating the reactive power output value Q DG,kt of the k-th distributed power source, the power model of the distributed power source established in step S1 is required. In addition, when optimizing and solving the multi-objective function of the reactive power compensation optimization model, the power model of the distributed power source is used as a certain constraint condition. For example, when the distributed power source includes a distributed wind turbine and a distributed photovoltaic, the reactive power output value of the wind turbine is calculated by formula (3), and the reactive power output value of the photovoltaic is calculated by formula (6), and formula (3) and formula (6) are used as constraint conditions during the optimization and solution.

[0085] In an embodiment of the present invention, improving the simulated annealing algorithm may include: making the amplitude of the annealing temperature decrease proportional to the number of times the solution generated at the annealing temperature is accepted; recording the optimal solution during the optimization process of the simulated annealing algorithm to avoid missing the optimal solution; when the annealing temperature drops to a preset value, gradually reduce the perturbation.

[0086] Among them, the preset value can be specifically set according to the actual problem and is set manually in advance.

[0087] Specifically, the particle swarm optimization algorithm has poor optimization ability and is prone to falling into local extreme points during optimization calculations. Therefore, in the embodiments of the present invention, the probability jump ability of the simulated annealing algorithm is applied to the particle swarm optimization algorithm, and each particle has to go through an annealing process, thereby improving the optimization ability of the entire population. To further enhance the optimization ability of the simulated annealing algorithm, the improvement of the simulated annealing algorithm is mainly as follows: the amplitude of temperature drop is proportional to the number of accepted states at that temperature; the optimal solution during the optimization process is recorded to avoid missing the optimal solution; when the annealing temperature drops to a preset value, the perturbation should be gradually reduced to accelerate the convergence speed.

[0088] The probability mutation ability of the improved simulated annealing algorithm is applied to the particle swarm optimization algorithm to obtain the particle swarm fusion algorithm. The inertia weight coefficient of the particle swarm fusion algorithm is:

[0089]

[0090] where: w max and w min are the maximum and minimum values of the inertia weight respectively, and f, f avg and f min are the fitness value of the current particle, the average fitness value of the population, and the minimum fitness value respectively.

[0091] In an embodiment of the present invention, the multi-objective function is solved by the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters of the distribution network, which may include: solving the multi-objective function by the particle swarm fusion algorithm to obtain the Pareto optimal solution set; selecting a set of solutions from the Pareto optimal solution set as the reactive power compensation optimization parameters of the distribution network through the fuzzy satisfaction degree algorithm.

[0092] In one example, the multi-objective function is solved by the particle swarm fusion algorithm to obtain the Pareto optimal solution set, including: calculating the first personal extreme value and the first population extreme value of the particles at the initial temperature; updating the velocity and position of the particles according to the first personal extreme value and the first population extreme value to obtain a new state; calculating the second personal extreme value and the second population extreme value of the particles in the new state; comparing the magnitude relationship between the first personal extreme value and the second personal extreme value, accepting the second personal extreme value according to the comparison result, and recording the current optimal solution; when the number of iterations does not reach the maximum number of iterations, updating the velocity and position of the particles, and returning to the step of calculating the second personal extreme value and the second population extreme value of the particles in the new state until the number of iterations reaches the maximum number of iterations: after the number of iterations reaches the maximum number of iterations, outputting the current optimal solution, and determining whether the current optimal solution meets the iteration termination condition; if the current optimal solution meets the iteration termination condition, taking the current optimal solution as the global optimal solution and outputting the global optimal solution; if the current optimal solution does not meet the iteration termination condition, updating the initial temperature, and returning to the step of calculating the first personal extreme value and the first population extreme value of the particles at the initial temperature until the global optimal solution is output.

[0093] Further, accepting the second personal extreme value according to the comparison result may include: when the first personal extreme value is less than or equal to the second personal extreme value, accepting the second personal extreme value according to the Metropolis criterion; when the first personal extreme value is greater than the second personal extreme value, directly accepting the second personal extreme value.

[0094] Even further, after updating the initial temperature, it may further include: if the updated initial temperature drops to a preset value, reducing the perturbation.

[0095] Specifically, as Figure 2 shown, the multi-objective function can be solved by the particle swarm fusion algorithm according to the following steps to obtain the Pareto optimal solution set:

[0096] Step 1, calculate the first personal extreme value P best1 and the first population extreme value g best1 of the particles at the initial temperature;

[0097] Step 2, update the velocity and position of the particles according to the first personal extreme value P best1 and the first population extreme value g best1 to obtain a new state;

[0098] Step 3, calculate the second personal extreme value P best2 and the second population extreme value g best2 of the particles in the new state;

[0099] Step 4, if P best1 is less than or equal to P best2 then accept the new value P according to the Metropolis criterionbest2 and record the current optimal solution; if P best1 is greater than P best2 then directly accept the new value P best2 and record the current optimal solution;

[0100] Step 5, update the velocity and position of the particle when the number of iterations has not reached the maximum number of iterations, and return to Step 2 until the maximum number of iterations is reached;

[0101] Step 6, output the current optimal solution after the number of iterations reaches the maximum number of iterations, and determine whether the current optimal solution satisfies the iteration termination condition;

[0102] Step 7, if the current optimal solution satisfies the termination condition, then use the current optimal solution as the global optimal solution and output the global optimal solution, and the solution is completed;

[0103] Step 8, if the current optimal solution does not satisfy the termination condition, then update the initial temperature and execute Step 9;

[0104] Step 9, if the updated initial temperature drops to the preset value, then reduce the perturbation, otherwise do nothing;

[0105] Step 10, return to Step 1 until the global optimal solution is output.

[0106] What is obtained after solving the multi-objective problem is the Pareto optimal solution set. It is necessary to select a suitable set of solutions from the Pareto optimal solution set as the final solution of the optimization problem, so as to obtain the reactive power compensation optimization parameters. The embodiment of the present invention adopts a decision-making method of fuzzy satisfaction degree when selecting the final solution, and the corresponding expression is:

[0107]

[0108]

[0109] where μ represents the satisfaction degree of the Pareto optimal solution set, and the closer its value is to 1, the better, f i 、f imax 、f imin are respectively the function value of the objective function i, its maximum value and minimum value.

[0110] In summary, for the reactive power compensation optimization method of the distribution network with distributed power sources according to the embodiments of the present invention, by introducing the probability mutation ability of the simulated annealing algorithm, each particle has to go through the annealing process during the optimization calculation, thereby improving the optimization ability of the entire population, and solving the shortcomings of poor optimization ability and easy entrapment in local extreme points of the particle swarm algorithm during the optimization calculation; when performing reactive power compensation optimization of the distribution network, the reactive power regulation ability of the distributed power sources is considered, thereby optimizing the reactive power compensation and improving the reactive power compensation effect; the reactive power optimization compensation is comprehensively carried out considering the operation economy and safety reliability of the distribution network, improving the operation economy of the distribution network and ensuring the safe and reliable operation of the distribution network.

[0111] Corresponding to the reactive power compensation optimization method of the distribution network with distributed power sources in the above embodiments, the present invention also proposes a reactive power compensation optimization device for a distribution network with distributed power sources.

[0112] Figure 3 It is a block diagram of the reactive power compensation optimization device for the distribution network with distributed power sources according to the embodiments of the present invention.

[0113] As Figure 3 shown, the reactive power compensation optimization device for the distribution network with distributed power sources includes a first establishment module 10, a second establishment module 20, a fusion module 30, a solution module 40, and an optimization module 50.

[0114] Among them, the first establishment module 10 is used to establish the power model of the distributed power source, and among the parameters of the power model of the distributed power source, the reactive power of the distributed power source is included; the second establishment module 20 is used to establish the multi-objective function of the reactive power compensation optimization model of the distribution network; the fusion module 30 is used to improve the simulated annealing algorithm and fuse the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm; the solution module 40 is used to solve the multi-objective function through the particle swarm fusion algorithm with the power model of the distributed power source as a constraint to obtain the reactive power compensation optimization parameters of the distribution network; the optimization module 50 is used to perform reactive power compensation optimization on the distribution network according to the reactive power compensation optimization parameters.

[0115] It should be noted that the specific implementation manner and implementation principle of the reactive power compensation optimization device for the distribution network with distributed power sources can refer to the specific implementation manner of the reactive power compensation optimization method of the distribution network with distributed power sources above. To avoid redundancy, it will not be elaborated here in detail.

[0116] The reactive power compensation optimization device for a distribution network with distributed power sources according to an embodiment of the present invention adopts a particle swarm algorithm integrated with an improved simulated annealing algorithm to perform multi-objective optimization and solution on the reactive power compensation optimization model of the distribution network, and considers the reactive power regulation ability of distributed power sources during the optimization process, improving the optimization ability and the optimization effect, thereby improving the operation economy of the distribution network and ensuring the safety and reliability of the distribution network.

[0117] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0118] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0119] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present invention.

[0120] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0121] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment. In addition, in each of the various embodiments of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-described integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0122] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A reactive power compensation optimization method for a distribution network with distributed power sources, characterized in that, The steps include: Establish the power model of the distributed power source, where the parameters of the power model of the distributed power source include the reactive power of the distributed power source; Establish the multi-objective function of the reactive power compensation optimization model of the distribution network; Improve the simulated annealing algorithm and fuse the particle swarm algorithm with the improved simulated annealing algorithm to obtain the particle swarm fusion algorithm; Taking the power model of the distributed power source as a constraint, solve the multi-objective function through the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters of the distribution network; Optimize the reactive power compensation of the distribution network according to the reactive power compensation optimization parameters, The multi-objective function of the reactive power compensation optimization model is: ， ， ， Among them, F is f 1 and f 2 a two-dimensional function of f 1 is the cost function of the distribution network, f 2 is the voltage difference function of the distribution network, c P represents the cost per unit active power loss, P loss is the total active power loss of the distribution network per unit time, E TAP , E C are respectively the cost of on-load tap-changer operation and the cost of reactive power compensation capacitor switching, E DG,k is the regulation cost of the k th distributed power source connected to the grid, M is the number of distributed power sources connected to the grid, N is the number of distribution network nodes, U j , U js , U jmax , U jmin are respectively the actual voltage, standard voltage, upper limit voltage and lower limit voltage of the j th distribution network node, Improving the simulated annealing algorithm includes: Making the amplitude of the annealing temperature decrease proportional to the number of times the solution generated at the annealing temperature is accepted; Recording the optimal solution during the optimization process of the simulated annealing algorithm to avoid missing the optimal solution; When the annealing temperature drops to a preset value, gradually reduce the perturbation.

2. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 1, wherein, Establishing the power model of the distributed power source includes: Establish the power model of the distributed wind turbine and the power model of the distributed photovoltaic.

3. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 1, characterized in that Calculate the total active power loss of the distribution network per unit time, the tap-changing operation cost of the on-load tap-changer transformer, the switching cost of the reactive power compensation capacitor, and the regulation cost of the k th distributed power source according to the following formulas respectively: ， ， ， ， Among them, U i is the actual voltage of the i th distribution network node, g ij , δ ij are respectively the conductance and phase angle difference between node i and node j . E e c , L TAP are respectively the installation cost and service life of the on-load tap-changer transformer. △ T TAP , r are respectively the number of tap adjustments of the on-load tap-changer transformer and the maximum number of allowed tap adjustments. L ssp , L C are respectively the equipment cost and service life of the reactive power compensation capacitor. △ Q C,t is the reactive power change of the reactive power compensation capacitor at time t . F DG,k , Q DG,kt are respectively the cost of the k th distributed power source for outputting unit reactive power and the reactive power output value at time t .

4. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 1, characterized in that, Solving the multi-objective function through the particle swarm fusion algorithm to obtain the reactive power compensation optimization parameters of the distribution network, including: Solving the multi-objective function through the particle swarm fusion algorithm to obtain the Pareto optimal solution set; Selecting a set of solutions from the Pareto optimal solution set as the reactive power compensation optimization parameters of the distribution network through the fuzzy satisfaction degree algorithm.

5. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 4, characterized in that, Solving the multi-objective function through the particle swarm fusion algorithm to obtain the Pareto optimal solution set, including: Calculating the first personal extreme value and the first population extreme value of the particle at the initial temperature; Updating the velocity and position of the particle according to the first personal extreme value and the first population extreme value to obtain a new state; Calculating the second personal extreme value and the second population extreme value of the particle in the new state; Comparing the magnitude relationship between the first personal extreme value and the second personal extreme value, accepting the second personal extreme value according to the comparison result, and recording the current optimal solution; When the number of iterations does not reach the maximum number of iterations, update the velocity and position of the particle and return to the step of calculating the second personal extreme value and the second population extreme value of the particle in the new state until the number of iterations reaches the maximum number of iterations; After the number of iterations reaches the maximum number of iterations, output the current optimal solution and determine whether the current optimal solution satisfies the iteration termination condition; If the current optimal solution satisfies the iteration termination condition, take the current optimal solution as the global optimal solution and output the global optimal solution; If the current optimal solution does not satisfy the iteration termination condition, update the initial temperature and return to the step of calculating the first personal extreme value and the first population extreme value of the particle at the initial temperature until the global optimal solution is output.

6. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 5, characterized in that, Accepting the second personal extreme value according to the comparison result, including: When the first personal extreme value is less than or equal to the second personal extreme value, accepting the second personal extreme value according to the Metropolis criterion; When the first personal extreme value is greater than the second personal extreme value, directly accepting the second personal extreme value.

7. The reactive power compensation optimization method for a distribution network with distributed power sources according to claim 5, characterized in that, After updating the initial temperature, it further includes: If the updated initial temperature drops to a preset value, the disturbance is reduced.

8. A reactive power compensation optimization device for a distribution network with distributed power sources, which is based on the reactive power compensation optimization method for a distribution network with distributed power sources according to any one of claims 1-7, characterized in that, It includes: A first establishment module for establishing a power model of the distributed power source, wherein the reactive power of the distributed power source is included in the parameters of the power model of the distributed power source; A second establishment module for establishing a multi-objective function of the reactive power compensation optimization model of the distribution network; A fusion module for improving the simulated annealing algorithm and fusing the particle swarm algorithm with the improved simulated annealing algorithm to obtain a particle swarm fusion algorithm; A solution module for solving the multi-objective function by the particle swarm fusion algorithm with the power model of the distributed power source as a constraint to obtain the reactive power compensation optimization parameters of the distribution network; An optimization module for optimizing the reactive power compensation of the distribution network according to the reactive power compensation optimization parameters.

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