Study Of The Unit Commitment Problem Based On Improved Particle Swarm Optimization

Unit commitment(UC)problem is an important part of the economic dispatch in the electric power system,and it can bring significant economic and social benefits through reasonable arrangement of unit start and stop and output plan.In the view of mathematics,the UC problem is a high-dimensional,non-convex,discrete,nonlinear NP-difficult combination optimization problem.With the deepening of the reform of the electricity market and the new energy large-scale grid-connected,the objective function and the constraint condition in the unit commitment problem model are more complicated,and the problem solving becomes more and more difficult.Therefore,improving and exploring the new optimization algorithm in the unit commitment problem is significance.In this paper,the particle swarm optimization algorithm for solving the unit commitment problem is studied and improved.The main contents include:Firstly,the mathematic model of the commitment of two types of units is established,including the mathematic model of the deterministic unit commitment and the mathematical model of the commitment of the uncertainty unit.The solution of the sub-problem of the economic distribution of the load is expounded,and the process of calculating the economic allocation of the load is introduced.Secondly,the basic principle of the standard particle swarm algorithm is introduced,and its convergence behavior is briefly analyzed.Based on the analysis of median change probability and inertia weight in the discrete binary particle swarm optimization algorithm,an improved binary particle swarm optimization algorithm with chaotic incremental inertia weight is proposed,which can have both the early global search ability and the latter part of the local search ability.In the particle velocity,the difference between the individual and the population is introduced in the updating formula,and an improved integer particle swarm optimization algorithm is proposed.Thirdly,two solution flows based on the improved binary particle swarm optimization algorithm and the improved integer particle swarm optimization algorithm were designed.In the process of solving the binary particle swarm algorithm based on the improved binary particle swarm optimization algorithm,the binary coding of the particle is carried out,and the heuristic repair strategy is used to deal with the reserve constraint and the minimum on/off time constraint.In the process of solving the improved integer particle swarm algorithm,Integer coding can effectively reduce the size of matrix coding.Finally,the improved particle swarm algorithm is proposed to solve the problem of unit commitment of 10?100 machine system.By comparing the results of the solution with the results of the existing literature to verify the effective particle swarm optimization algorithm to solve the unit commitment problem.The results show that the improved particle swarm optimization algorithm can solve the problem of unit commitment quickly and efficiently.With the increase of the scale of the unit,the solution time is approximately linearly.The improved binary particle swarm is suitable for solving the smaller system of the unit,Particle swarm optimization is more effective in solving large-scale unit commitment problem.