Research On Related Technology Of Optimal Power Flow Based On Artificial Bee Colony Algorithm

With the grid-connected of large scale distributed generation and rapid development of flexible loads such as electrical vehicles, the modem power system is becoming more and more complex. Optimal power flow which is consider as an analytical tool of power system planning, economic dispatch and market transaction can effectively solve the problem of complex power system. The essence of optimal power flow is a multi-dimensional nonlinear optimization problem with discrete and continuous variables. Select the appropriate methods directly determines the effectiveness and superiority of optimal power flow solutions.As a novel intelligent optimization algorithm, artificial bee colony(ABC) algorithm has certain advantages in dealing with nonlinear, multi-constrained, multi-variable, discontinuous, non-convex optimization problems, and has been widely used in the field of artificial neural network, image recognition, voice recognition etc. However, ABC algorithm which is similar to other intelligent optimization algorithms has some problems to be solved in the initial research stage, such as improving the convergence speed and calculation accuracy. Therefore, the paper takes the research on related technology of optimal power flow based on artificial bee colony algorithm as the research topic. The aim of this paper is to provide a new method for the single objective and multi-objective OPF problems and provide more analysis and decision-making information for power system problems which considering the OPF as a calculation tool through innovation research on artificial bee colony algorithm. The main research contents of the dissertation are as follows:(1) Each phase mathematical model of ABC algorithm is analyzed. The optimization performance of ABC algorithm is simulated by several standard numerical test functions. The results demonstrated that ABC algorithm has a high convergence property and can effectively deal with the numerical optimization problems.(2) ABC algorithm has better optimization ability when dealing with the problem of low dimension, however, it is easily fall into local optimal when dealing with the problem of high dimension. For overcoming the disadvantage, a chaos differential ABC (IABC) algorithm is proposed. In the IABC algorithm, the mutation and crossover operations of differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity.(3) The classical OPF model is analyzed and the objective functions from economy, environmental and power quality are established, such as total fuel cost of generating units, atmospheric pollutant emissions, active power losses and voltage deviations. According to the multi-objective, a fuzzy multi-objective OPF model is established by the fuzzy satisfaction-maximizing method. Proposed a solving method for the fuzzy multi-objective OPF model based on IABC algorithm. The simulation results demonstrated the effectiveness, reliability and superiority of IABC algorithm, and the operation scheme obtained by the proposed method can improve the economy, environmental and voltage level of power system.(4) For obtaining the true Pareto fronts, an improved multi-objective ABC algorithm is studied and proposed. The algorithm used mutation and crossover operations to generation new feasible solution, utilized fast non-dominated sorting to acquire the dominated information and update external archive, adopted the information of objective values and crowding distance to calculate the probability value of feasible solution chosen by onlooker bee, calculated the crowding distance to control the size of external archive, used the Pareto front of external archive as reference nectar of ABC algorithm. Proposed a method for solving the multi-objective OPF model based on multi-objective ABC algorithm. The method used multi-objective ABC algorithm to obtain the Pareto fronts, utilized K-means method to cluster the Pareto fronts and used fuzzy set theory to make decision. Simulation results demonstrated that the proposed multi-objective ABC algorithm can effectively obtain the Pareto fronts, and the model can provide more reliable and excellent operation decision-making program.(5) For solving the OPF of power system with uncertain factors such as wind generation and power load, the paper established a multi-objective probability optimal power flow (POPF) considering wind generation and load random variation. The FPOPF model considered the expectation and standard deviation value of fuel cost as optimization objectives and made the expectation of constraint condition violation adding to objective values. Two kinds of methods are proposed to solve the POPF model, one is utilizing the fuzzy satisfaction-maximizing method to process two objectives and using an improved ABC algorithm based on Latin hypercube sampling to obtain the optimal solution. Another one is using the multi-objective ABC algorithm to obtain the Pareto fronts of POPF problem. Simulation results of modified IEEE 30-bus test system demonstrated that the effectiveness and superiority of the proposed two kinds of methods, and the established POPF model can effectively deal with OPF problem including the random variables of wind generation and load.