风火联合系统的多目标随机优化调度方法的研究

摘要

Due to the randomness of wind farms power,a multi-objective stochastic chance con-strained programming model of wind-thermo combined system considering unit commitment is presented,minimizing both the fuel cost and emission of polluted gas of thermal generators as ob-jective functions and expressing constraining conditions by probability.In order to avoid frequent starting and shutting of generating set or the system capacity redundancy,a heuristic search tactic combined with the priority list of generating set start and stop is applied to determine the unit commitment states.The multi-objective particle swarm optimization algorithm based on Pareto combined with fuzzy logic evaluation method and stochastic simulation technique is adopted to carry out the economic load distribution for the given generating set.The calculation examples of 10 generating sets wind-thermo combined system are used to verify the feasibility and effective-ness of the suggested dispatching method.The optimized dispatching results indicate:① This model is able to make full use of clean energy,to lower the operation cost of the system ,to reduce a-mount of emission of polluted gas and to improve the comprehensive benefits of power system operation;②This algorithm is of high calculation accuracy and fast speed so as to avoid the disadvantages of the basic particle swarm optimization algorithm that is easy to fall into local optimum.%考虑到风电场出力的随机性，以常规机组燃煤费用最小、污染气体排放量最小为目标函数，约束条件以概率的形式表示，构建考虑机组组合的风火联合系统的多目标随机机会约束规划模型。结合机组启停优先顺序表，采用启发式搜索策略确定机组组合状态，以避免机组频繁启停或系统容量冗余。采用基于 Pareto 的多目标粒子群优化算法，结合随机模拟技术和模糊逻辑评价法，对既定机组进行经济负荷分配。以含10机的风火联合系统为算例，验证了所提调度方法的可行性、有效性。优化调度结果表明：①该模型能够充分利用清洁能源，降低系统运行成本，减少污染气体排放量，提高电力系统运行的综合效益；②该算法计算精度高，速度快，避免了基本粒子群优化算法易陷入局部最优的缺点。