In the electricity market environment, a power supply company or a retail company needs to predict the electricity prices in various markets so as to develop an optimal purchasing strategy. Theoretically, the higher-order moments of the profit function associated with the retail company concerned, especially the third moment or the so-called skewness are not negligible in the decision-making process; or in other word, in building an optimal purchasing strategy for a retail company it is necessary to reasonably account for the skewness of its profit function, and hence it is demanding to study the expression form and application approach of the skewness. Moreover, the electricity purchasing procedure is dynamic in nature due to uncertain load demands. Given this background, a skewness-VaR (value at risk) based dynamic model is presented for developing the optimal electricity purchasing strategy in multiple markets by quantifying the risk with the VaR and skewness. An auto reverse model is utilized to estimate the load demand. By employing this model, a retail company can reasonably allocate the electricity amount to be purchased among various markets dynamically so as to make a compromise between maximizing the profit and minimizing the risk associated. The developed method provides retail companies a new tool for assisting decision-making and risk management in the dynamic electricity purchasing process. Finally, a sample example is served for demonstrating the essential features of the developed model and method.%在存在多个不同类型市场的环境下,供电公司或电力零售公司(简称零售公司)需提前对各市场的电价进行预测以构造最优购电策略.理论上,零售公司只有合理计及购电收益函数的3阶矩(即偏度)才可能得到最优购电策略,因此有必要研究偏度的具体表达形式和应用方法.由于负荷需求具有不确定性,购电过程就具有动态特征.在此背景下,以风险价值(VaR)指标量化零售公司风险,在计及偏度的情况下,构建了零售公司动态购电组合模型,其中,购电量用自回归模型进行模拟.基于该模型,零售公司可以将购电量在多个时段、不同市场中进行合理分配,以兼顾期望利润最大化和风险最小化,从而为零售公司的动态购电决策与风险评估提供了新途径.最后,用算例说明了所述方法的基本特征.
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