基于推广超分位数的多随机因素两阶段联合优化调度

Two-stage joint optimal dispatch of multiple uncertainties based on generalized alpha quantile method

风电、光电出力的不确定性,碳排放权价格和电价的波动等多重随机因素为电力系统生产和调度带来了新的问题,通过价格机制引导电动汽车可以促进削峰填谷,但静态分时电价可能产生新的高峰。针对上述背景,提出了基于动态分时电价的考虑多种随机因素的两阶段优化模型。先建立峰谷时段划分的优化模型,用模糊C均值聚类算法对问题求解;在此基础上,建立考虑风电、光电、碳排放权价格和电价随机性的联合调度模型,并将α超分位数方法改进推广以求解多重随机因素。将模型运用到算例中,并分析相关因素的影响,结果不仅证明了模型的合理性,也为调度决策提供了参考。

Wind power, photovoltaic power output uncertainty, carbon emissions price and the price of multiple random factors have brought new problems to the power system of production and operation, through the price mechanism to guide the electric vehicle can promote the peak, but the static time-of-use price may produce a new high peak. In view of the above background, this paper proposes a two-stage optimization model based on dynamic time-sharing price. The optimization model is established before the partition of peak and valley time to solve the problem with fuzzy C means clustering algorithm; on this basis, the establishment of wind power, photovoltaic, carbon emission permits the joint scheduling model with stochastic price and the method of the alpha hyper number is improved to solve the multiple ran-dom factors. The model is applied to a numerical example, and the influence of the relevant factors is analyzed. The re-sult not only proves the rationality of the model, but also provides a reference for the scheduling decision.