CN-121983974-A - Multi-objective coordination control optimization method, system, equipment and medium for intelligent soft switch of power distribution network

CN121983974ACN 121983974 ACN121983974 ACN 121983974ACN-121983974-A

Abstract

The invention discloses a power distribution network intelligent soft switch multi-target coordination control optimization method, a system, equipment and a medium, which belong to the technical field of operation optimization and control of power distribution networks and comprise the steps of collecting real-time operation data of SOP port connection nodes, identifying operation scenes, establishing an objective function system of SOP multi-target coordination control, formulating physical constraints and operation constraints met by SOP control optimization, solving a multi-objective function by adopting an improved non-dominant ordering genetic algorithm, outputting a Pareto optimal solution set, selecting an optimal control scheme according to the Pareto optimal solution set, generating control instructions of all ports of SOP according to the optimal control scheme, executing according to the control instructions, and evaluating effects. The invention realizes comprehensive coordination optimization of multiple control targets, builds an intelligent control mechanism of scene self-adaption, and remarkably improves the multi-target optimization solving efficiency.

Inventors

LI QINGSHENG
YANG JIERUI
WANG ZHUOYUE
WANG LINBO
YAN WEN
Luo Zhongke
Fan Junqiu
LONG JIAHUAN
LI ZHEN
ZHANG ZHAOFENG
ZHANG YU
Liao Zewei
ZHANG YAN
LIU JINSEN
CHEN JULONG

Assignees

贵州电网有限责任公司

Dates

Publication Date: 20260505
Application Date: 20251210

Claims (10)

1. The intelligent soft switch multi-target coordination control optimization method for the power distribution network is characterized by comprising the following steps of, Collecting real-time operation data of SOP port connection nodes, defining scene characteristic parameters to identify operation scenes, and dividing the operation scenes of the power distribution network; According to the identified operation scene, an objective function system of SOP multi-objective coordination control is established, and the priority weight of an objective function is dynamically adjusted; formulating physical constraints and operation constraints which are satisfied by the control optimization of the SOP, wherein the constraint conditions define the feasible regions of the control variables; Solving a multi-objective function by adopting an improved non-dominant sorting genetic algorithm, and outputting a Pareto optimal solution set, wherein each optimal solution corresponds to a group of SOP control parameters; Selecting a control scheme of the current scene according to the Pareto optimal solution set, performing sensitivity analysis, and selecting an optimal control scheme; According to the optimal control scheme, control instructions of all ports of the SOP are generated, effect evaluation is carried out according to execution of the control instructions, and whether self-adaptive adjustment of optimization algorithm parameters is needed is judged.
2. The intelligent soft switch multi-target coordination control optimization method of the power distribution network of claim 1, wherein the identifying operation scene comprises the steps of collecting real-time operation data of all port connection nodes of the SOP through a power distribution network monitoring device, defining operation state vectors of each port, and forming a state vector sequence; defining scene characteristic parameters, identifying a current operation scene, and calculating unbalance degree, voltage deviation index and harmonic pollution level of active power of each port; Based on scene characteristic parameters, a scene recognition decision tree is constructed, and different power distribution network operation scenes are divided.
3. The intelligent soft-switching multi-target coordination control optimization method of the power distribution network according to claim 2, wherein the objective function system of the SOP multi-target coordination control comprises the steps of establishing the objective function system of the SOP multi-target coordination control according to the identified operation scene, and considering the optimization targets to comprise a first target, a second target, a third target and a fourth target; According to the identified scene type, dynamically adjusting the priority weight of each objective function, and determining a weight vector by adopting a hierarchical analysis method; And obtaining a weight vector by solving and normalizing a feature vector corresponding to the maximum feature value of the judgment matrix.
4. The intelligent soft switch multi-target coordination control optimization method of the power distribution network of claim 3, wherein the constraint conditions comprise physical constraint and operation constraint, namely power balance constraint, reactive power constraint, capacity constraint, node voltage constraint and line current constraint of all ports of the SOP; Defining a control variable vector, defining a feasible domain of the control variable by constraint conditions, and adopting normalization processing on the control variable.
5. The intelligent soft-switching multi-objective coordinated control optimization method of power distribution network according to claim 4, wherein solving the multi-objective function comprises solving Pareto optimal solution set by adopting improved non-dominant sorting genetic algorithm, wherein population individuals are control variable vectors The population size is set as The initial population adopts Latin hypercube sampling method in the feasible domain Internal generation, for each individual in the population, calculating objective function values defined by multiple objective functions 、、、 Checking constraint conditions, and processing individuals violating the constraint by adopting a penalty function method; performing non-dominant ranking, layering population individuals according to Pareto dominant relationship, and individuals Overrule individual The condition of (c) is that for all targets k, And at least one object j is present such that The first layer is a non-dominant solution set, and the second layer is a non-dominant solution set after the first layer is removed, and the crowding distance of each individual is calculated: In the formula, For the crowded distance of the i-th individual, And To order the target values of the adjacent individuals before and after the ith individual according to the kth objective function value, And Maximum and minimum values for the target; Selecting according to non-dominant level and crowding distance, giving priority to individuals with low level and giving priority to crowding distance in the same level, and generating parent population by adopting binary tournament selection strategy; performing a crossover operation using an analog binary crossover to generate children: In the formula, And For the progeny of the individual, And As the parent of the individual to be treated, Is a crossover parameter; Performing a mutation operation using polynomial mutation: In the formula, In order to obtain the individual after the mutation, Is variation disturbance and obeys polynomial distribution; Merging parent and offspring populations, performing non-dominant ranking and crowding distance calculation again, and selecting the optimal The individual enters the next generation, and after the algorithm converges, the Pareto optimal solution set is output Wherein M is the number of Pareto optimal solutions, and each optimal solution corresponds to a set of SOP control parameters.
6. The intelligent soft switch multi-objective coordination control optimization method of power distribution network of claim 5, wherein selecting the optimal control scheme comprises making decisions by adopting a weighted evaluation index method and utilizing determined scene weight vectors Carrying out normalization processing on each objective function value, and calculating the comprehensive evaluation index of each Pareto optimal solution: In the formula, Is the comprehensive evaluation index of the ith Pareto optimal solution, For the second step the weight coefficients determined from the scene, The normalized objective function value; selecting a solution with the minimum comprehensive evaluation index as an optimal control scheme of the current scene: In the formula, Performing sensitivity analysis, calculating stability index of optimal solution under weight disturbance, judging that the selected solution has robustness when the sensitivity index is smaller than threshold value, if the sensitivity index exceeds threshold value, selecting the solution with suboptimal comprehensive evaluation index but lower sensitivity, and determining And transmitting the converted control instruction to SOP equipment.
7. The intelligent soft switch multi-target coordination control optimization method of the power distribution network according to claim 6, wherein the execution of the control instruction comprises the steps of restoring a normalized control variable to an actual physical quantity, smoothing a power reference value by adopting a first-order inertia link, enabling a power electronic converter of the SOP to adopt a voltage source converter structure, realizing power adjustment by controlling the amplitude and phase angle of alternating-current side voltage, and enabling a control equation of an ith port of the VSC to be: In the formula, For the VSC to output a voltage that, For the grid-side voltage, And In order to couple the impedance of the circuit, For the control current, j is an imaginary unit, and the control current is calculated by a power command: In the formula, Is the conjugation of the voltage of the power grid, And The VSC output voltage command obtained by calculation is a smoothed control command Converting into PWM modulation signals to control the on-off of IGBT switching tubes, continuously monitoring the actual power output of each port by the system in a control period, comparing with an instruction value, starting a PI controller to perform closed loop correction when the tracking error exceeds a threshold value, evaluating a control effect after controlling and executing one period according to the corrected instruction, adjusting an optimization algorithm parameter according to an evaluation result, collecting the system state data after control, and calculating an actually achieved objective function value 、、、 And optimizing calculated expected values 、、、 Comparison was performed: In the formula, Calculating the comprehensive control effect index for the actual deviation rate of the kth target: In the formula, For controlling the effect evaluation index When the control effect is not ideal, the parameters of the optimization algorithm are adjusted, and the self-adaptive mechanism is adopted to adjust the crossover probability and the variation probability, so that a parameter self-adaptive closed-loop control mechanism is formed.
8. The intelligent soft switch multi-target coordination control optimization system for the power distribution network is characterized by comprising a data acquisition and scene identification module, a multi-target function modeling module, a constraint condition construction module, a multi-target optimization solving module, a decision selection module and a control execution module, wherein the intelligent soft switch multi-target coordination control optimization method for the power distribution network is applied to any one of claims 1-7; The data acquisition and scene identification module acquires real-time operation data of SOP port connection nodes, defines scene characteristic parameters to identify operation scenes, and divides the operation scenes of the power distribution network; The multi-objective function modeling module establishes an objective function system of SOP multi-objective coordination control according to the identified operation scene, and dynamically adjusts the priority weight of the objective function; the constraint condition construction module is used for formulating physical constraints and operation constraints which are met by the control optimization of the SOP, and the constraint conditions define the feasible domains of the control variables; The multi-objective optimization solving module adopts an improved non-dominant sorting genetic algorithm to solve a multi-objective function, outputs a Pareto optimal solution set, and each optimal solution corresponds to a group of SOP control parameters; The decision selection module is used for selecting a control scheme of the current scene according to the Pareto optimal solution set, performing sensitivity analysis and selecting an optimal control scheme; and the control execution module generates control instructions of all ports of the SOP according to the optimal control scheme, performs effect evaluation according to the control instructions, and judges whether the optimization algorithm parameters need to be adjusted in a self-adaptive mode.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the power distribution network intelligent soft switch multi-objective coordination control optimization method according to any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the power distribution network intelligent soft switch multi-objective coordinated control optimization method according to any of claims 1 to 7.

Description

Multi-objective coordination control optimization method, system, equipment and medium for intelligent soft switch of power distribution network Technical Field The invention relates to the technical field of operation optimization and control of power distribution networks, in particular to a power distribution network intelligent soft switch multi-target coordination control optimization method, a system, equipment and a medium. Background With the intelligent development of the distribution network, an intelligent Soft Open Point (SOP) as a novel distribution device based on the power electronic technology has obvious advantages in aspects of power flow regulation, fault support, power quality improvement and the like. However, the existing SOP control method mainly has the following technical defects: The control objective is single, and the traditional SOP control strategy is usually optimized for a single objective, such as only focusing on active power flow equalization or only focusing on voltage quality improvement, so that multiple functional requirements cannot be coordinated. In actual power distribution network operation, SOP needs to meet multiple requirements such as power flow control, voltage support, harmonic suppression and network loss reduction. The existing SOP control method generally adopts fixed control parameters set off-line, and cannot be adjusted in a self-adaptive manner according to the real-time operation scene of the power distribution network. The running state of the power distribution network shows a remarkable change characteristic along with time, namely, the load distribution difference between the daytime and the nighttime is obvious, the fluctuation of the distributed photovoltaic output in sunny days and rainy days is large, and the control requirements in normal running and fault supporting modes are completely different. The fixed control parameters are difficult to accommodate for such dynamic changes, resulting in poor performance of the SOP under certain operating scenarios. The multi-objective optimization solution has low efficiency and poor instantaneity, and the multi-objective coordination control of SOP is essentially a high-dimensional nonlinear optimization problem, and relates to a plurality of mutually coupled control variables and constraint conditions. When the conventional optimization algorithm is used for solving the problems, the problems of large calculation amount and low convergence speed exist, and the requirement of real-time control of the power distribution network is difficult to meet. Disclosure of Invention The present invention has been made in view of the above-described problems. Therefore, the invention aims to realize coordination optimization of multiple control targets, construct a scene self-adaptive control mechanism and improve the solution efficiency of the multiple target optimization. In order to solve the technical problems, the invention provides a technical scheme that the intelligent soft switch multi-objective coordination control optimization method for the power distribution network comprises the following steps of, The method comprises the steps of collecting real-time operation data of SOP port connection nodes, defining scene characteristic parameter identification operation scenes, dividing the operation scenes of a power distribution network, establishing an objective function system for SOP multi-objective coordinated control according to the identified operation scenes, dynamically adjusting priority weights of objective functions, formulating physical constraints and operation constraints met by SOP control optimization, defining feasible regions of control variables by constraint conditions, solving the multi-objective function by adopting an improved non-dominant ordering genetic algorithm, outputting a Pareto optimal solution set, wherein each optimal solution corresponds to a group of SOP control parameters, selecting a control scheme of a current scene according to the Pareto optimal solution set, performing sensitivity analysis, selecting an optimal control scheme, generating control instructions of each SOP port according to the optimal control scheme, performing effect evaluation according to the control instructions, and judging whether self-adaptive adjustment of optimization algorithm parameters is needed. The intelligent soft switch multi-target coordination control optimization method for the power distribution network comprises the steps of acquiring real-time operation data of all port connection nodes of the SOP through a power distribution network monitoring device, defining an operation state vector of each port, and forming a state vector sequence; defining scene characteristic parameters, identifying a current operation scene, and calculating unbalance degree, voltage deviation index and harmonic pollution level of active power of each port; Based on scene characteristic parameters, a sce