Unit Commitment Optimization With Wind Farm Based Chance-constrained Programming

The research of Unit Commitment Optimization is a very practical application of engineering problem. Rationalized Unit Commitment Optimization can reduce energy consumption and improve economic efficiency. With China’s economy rapid development, the seriously society environmental pollution, and wind energy has been vigorously developed and utilized as a renewable clean energy. The problem of Unit Commitment with wind farms is a discrete, non-convex, high-dimensional nonlinear problem, and because of the random nature of wind power output, increasing the difficulty of Unit Commitment with wind farms. Wind energy has not only brought good economic and environmental benefits, but also brought new challenges to the safety and stability operation of power system. So Unit Commitment with wind farms has become a research topic needed to solve.First, the relevant characteristics of wind power is introduced in the paper;The consumption characteristics of quadratic curves is also studied; Several factors about the start and stop unit cost of consumption should be considered and the feasibility of chance constrained programming related issues in wind power are analyzed. Secondly, cultural algorithm is a new swarm intelligence algorithm which is studied in this paper.Chaos cultural particle swarm optimization, which is framework of cultural algorithm integrate chaos particle swarm optimization algorithm, is proposed. The double evolutionary mechanism of cultural algorithm reflects more accurately the mechanisms of evolution. Simulation to verify the feasibility of the algorithm proposed in this article by typical test functions. Thirdly, a mathematical model of Unit Commitment Optimization in the power system is build, Unit Commitment Optimization is divided into two processes, one is the Unit Commitment status determination using Chaos particle swarm optimization, and the other is the use of Chaos cultural particle swarm optimization algorithm to load distribution. Through10machine system example, the use of the proposed algorithm and improved measures to solve Unit Commitment problem, verify the effectiveness of the proposed algorithm for solving Unit Commitment optimization.Finally, under the energy saving and lower emissions, Unit Commitment optimization problem with wind farms connected to the grid is considered in this paper. The mathematical model of Unit Commitment containing wind farms based Chance-constrained programming is planned. Multi-objective problems consisting of coal consumption and emission of polluting gases is considered. Chance-constrained programming is verified by Monte Carlo stochastic modeling techniques. Chance constraints to request a different confidence level of profit and risk advantages of coordinated programs is analyzed, and for solving unit commitment optimization with wind power provides a new way of thinking.