目的 为应对虚拟电厂(virtual power plant，VPP)在参与电能量和需求响应市场时所面临的新能源出力和负荷不确定性问题，提出一种考虑多重不确定性的鲁棒优化调度策略，旨在降低鲁棒优化的保守性并提高VPP的经济效益。 方法 构建基于条件风险价值(conditional value at risk，CVaR)的多面体不确定性集，在此基础上，考虑风电、光伏出力和负荷的不确定性，建立VPP参与电能量和需求响应市场策略的日前两阶段鲁棒优化模型。基于行列生成(column-and-constraint generation，C&CG)算法和拉格朗日对偶理论，将所建模型分为可利用求解器求解的主问题和子问题。最后，利用蒙特卡罗方法生成大量风电、光伏和负荷数据，对所提策略进行仿真分析，并与其他方案的优化结果进行对比。 结果 所提策略采用基于CVaR的多面体不确定性集，能够充分利用历史数据，相比于采用传统不确定性集的方案，VPP的总成本降低了约2%。 结论 所提策略可以显著降低鲁棒优化结果的保守性，并在多重不确定性条件下提升VPP参与市场的经济性。

Objectives To address the uncertainties of renewable energy output and load faced by virtual power plant (VPP) when participating in electric energy and demand response markets, a robust optimal scheduling strategy considering multiple uncertainties was proposed to reduce the conservativeness of robust optimization and improve the economic benefits of VPP. Methods A polyhedral uncertainty set based on conditional value at risk (CVaR) was constructed. On this basis, considering the uncertainties of wind power, photovoltaic output and load, a day-ahead two-stage robust optimization model of VPP participating in electric energy and demand response markets was established. Then, using a column-and-constraint generation (C&CG) algorithm and Lagrangian dual theory, the model was divided into a master problem and a sub-problem that can be solved by a solver. Finally, Monte Carlo method was used to generate a large number of wind power, photovoltaic and load data. The proposed strategy was simulated and analyzed, and compared with the optimization results of other schemes. Results The proposed strategy adopting a polyhedral uncertainty set based on CVaR can make full use of historical data. Compared with the scheme using traditional uncertainty set, the total cost of VPP is reduced by about 2%. Conclusions The proposed strategy can significantly reduce the conservativeness of robust optimization results and enhance the economy of VPP participation in the market under multiple uncertainties.

需求响应作为电力系统的重要调节手段,可显著提升系统灵活性和经济性。利用价格弹性构建了包含价格与激励措施的需求响应模型,并在此基础上考虑需求响应的不确定性,以综合能源系统经济性和环保性为优化目标,构建了综合能源系统多目标优化调度模型。利用E约束法将多目标优化模型转化为单目标优化模型,得到Pareto最优解集,运用模糊决策法从中选取最优方案。基于实际案例进行测算,结果表明价格型与激励型需求响应手段的结合能够实现削峰填谷,有效降低系统的运行成本和碳排放量。

As an important regulation means of power system, demand response can significantly improve system flexibility and economy. Based on the time-of-use electricity price mechanism, the demand response model including price and incentive measures is constructed by using the price elasticity matrix, and on this basis, the uncertainty of demand response is considered, and the multi-objective optimization scheduling model of the integrated energy system is constructed by taking the economy and environmental protection of the integrated energy system as the optimization objectives. The constraint method is used to transform the multi-objective optimization model into the single-objective optimization model, Pareto optimal solution set is obtained, and the fuzzy decision method is used to select the optimal scheme. Based on the actual case, the calculation results show that the combination of price and incentive demand response means can achieve peak load cutting and effectively reduce the operating cost and carbon emissions of the system.