摘要
以一定时期内期望发电效益最大化为目标,采用马尔可夫链对梯级水电站机组未来调度时段的预想故障及上网电价进行概率预测,构建了一种新的梯级水电站短期概率优化调度的模型,并且采用服从正态分布的负荷波动来分析时变负荷对优化调度的影响。该模型全面考虑了梯级水电站蓄水量、弃水量、水位、发电引用流量等约束条件,实现了机组运行状态概率预测与优化调度决策的密切结合。利用微分进化算法鲁棒性强、搜索效率高的特点,与蒙特卡洛方法对模型进行求解。以一梯级水电站系统为例进行计算分析,表明所提出的模型合理和有效。
Aiming at attaining the maxim return of power generation in a specific period,this paper establishes a novel probabilistic optimal scheduling model of cascade hydroelectric stations by forecasting probabilities of contingency states and electricity price in the next dispatching period by means of Markov chain.In addition,the influences of time-varying load are analyzed by adopting the load subjected to normal distribution.The detailed representation of cascade hydroelectric stations,which comprise water volume,water discharge,water head and water inflow,is considered in this paper.The forecasting probability of the unit state and optimal scheduling decision-making are close combined in this model.On account of the advantages of differential evolution such as searching efficiency and robustness,this paper applies the algorithm and monte carlo simulation to a cascade hydro plants system.The results demonstrate the rationality and validity of the proposed model.
出处
《江苏电机工程》
2011年第2期5-10,共6页
Jiangsu Electrical Engineering
关键词
梯级水电站
概率优化调度
马尔科夫链
微分进化算法
蒙特卡洛方法
cascade hydroelectric stations
probabilistic optimal scheduling
Markov chain
differential evolution algorithm
monte carlo simulation
