多形态冷热负荷平抑风电不确定性的鲁棒优化方法

Research on robust optimization method for stabilizing wind power uncertainty based on multi-type cooling and heating loads

风电不确定性所引起的弃风和功率波动,成为限制风电大规模并网的关键因素。针对冷热负荷响应速度快且聚合容量大的特点,分析了包含分散式冷热负荷和集群式冷热负荷的多形态冷热负荷模型。为研究多形态冷热负荷平抑风电不确定性的鲁棒优化方法,以消纳风电和平抑风电功率波动为目标,对分散式冷热负荷功率和集群式冷热负荷功率进行优化,建立多形态冷热负荷两阶段鲁棒优化模型,并利用CCG算法对模型求解。最后,以IEEE39节点系统建立仿真模型,对鲁棒优化方法进行仿真分析。结果表明,多形态冷热负荷两阶段鲁棒优化方法能够有效平抑风电不确定性。

Wind curtailment and fluctuation caused by wind power uncertainty have become a key factor to limit the large-scale grid connection of wind power.Due to the fast response and large polymerization capacity of cooling and heating loads(CHLs),a multi-type CHLs model including decentralized CHLs and clustered CHLs was studied.In order to study using the multi-type CHLs to stabilize the wind power uncertainty,the wind power accommodations and fluctuations are taken as the objective function,a two-stage robust optimization model of CHLs is established by optimizing decentralized CHLs power and clustered CHLs power.The model was solved by CCG algorithm.Finally,the simulation model is established by IEEE39 node system,and the robust optimization method is simulated.The results show that the two-stage robust optimization method of multi-type CHLs can effectively suppress wind power uncertainty.