摘要
针对负荷的不确定性,并充分结合其时序性特征,提出考虑负荷不确定性和时段优化的动态无功优化模型。该模型充分考虑负荷预测误差的概率模型,采用拉丁超立方抽样技术和场景削减技术获得负荷样本及其概率,且对负荷样本期望值采用统计学指标归一化处理形成综合负荷趋势序列;同时,为克服动态无功优化过程中的"维数灾"问题,设计了基于特征趋势的自适应方法进行时段划分;最后计及场景概率,以网损最小为目标,将电压越限和发电机无功出力越限计入罚函数,建立动态无功优化模型并采用基于精英保存策略的遗传算法进行求解。算例仿真结果验证了所提模型的正确性和有效性。
Considering the uncertainties and the feature trend of predicted day-ahead load consumption,a reactive power optimization model considering uncertainties of loads and time-based optimization was presented. Probabilistic model of load consumption are discussed,Latin hypercube sampling and scenarios reduction are run to obtain the load samplings. Statistical indicators are utilized to consider the feature trend of load time series,the normalization method is used to formation comprehensive feature trend. In order to overcome ＂dimension disaster＂in solving the dynamic reactive power optimization problems,time-based optimization method is raised to divide the load time series. At last,counting the probability of load samplings,taking the power loss as objective function,the proposed model is formulated,which set the limits of bus voltage and reactive power of generators as penalty function. The model can be solved by the genetic algorithm. Case simulation illustrated the effectiveness and validity of proposed model and method.
出处
《科学技术与工程》
北大核心
2016年第15期92-97,共6页
Science Technology and Engineering
关键词
不确定性
时段划分
无功优化
遗传算法
load uncertainties
time-based optimization
reactive power optimization
genetic algorithm
