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考虑多类型需求响应的多能源虚拟电厂优化调度
Optimal Scheduling of Multi-Energy Virtual Power Plants Considering Multi-Type Demand Response
将需求响应纳入虚拟电厂中，可以增强虚拟电厂的灵活性和经济性，但需求响应存在不确定性，给调度运行带来了困难；另外，多类型需求响应应用在虚拟电厂中的研究也较少。针对上述问题，提出考虑效益系数的激励型需求响应模型，在激励补偿的基础上增加了效益系数，降低激励型需求响应不理想情况下的激励补偿；提出考虑用户满意度的替代性需求响应模型，以反映用户满意度对替代型需求响应的影响；最后，构建多类型需求响应的多能源虚拟电厂模型，同时考虑3种需求响应，来达到更好的优化效果。算例结果表明：考虑效益系数的激励型需求响应模型能提升系统的经济性；考虑用户满意度的替代性需求响应模型能更准确地反映用户的潜在负荷变化，提高需求响应的精确度；多类型需求响应参与虚拟电厂的优化调度，整体效果最佳。
Integrating demand response into virtual power plants can enhance their flexibility and economic efficiency，but the inherent uncertainty of demand response poses challenges for scheduling and operation. Moreover，research on applying multiple demand response types in virtual power plants remains limited. To address these issues，this paper proposes an incentive-based demand response model incorporating a benefit coefficient that adjusts incentive compensation to reduce payouts under unsatisfactory demand response performance，as well as a replaceable-based demand response model considering customer satisfaction to better reflect its influence on replaceable demand response. Finally，a multi-energy virtual power plants model integrating multiple demand response types is established，considering three demand response strategies to achieve superior optimization. Case studies demonstrate that the incentive-based demand response model considering the benefit-coefficient can improve economic efficiency of the system，and the replaceable-based demand response model considering customer satisfaction can more accurately capture potential load variations to enhance demand response precision，and the coordinated participation of multiple demand response types in virtual power plant scheduling yields optimal overall performance.
需求响应 / 不确定性 / 效益系数 / 用户满意度 / 虚拟电厂 / 优化调度
demand response / uncertainty / efficiency coefficient / customer satisfaction / virtual power plant / optimization scheduling
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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. |
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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.
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