基于信息间隙决策的含碳捕集虚拟电厂经济调度优化模型

Economic Dispatching Optimization Model of CCPP Virtual Power Plant Based on IGDT

针对可再生能源的“消纳难”以及出力具有随机性的问题,首先建立了包含风光机组、电转气设备、碳捕集电厂、储碳设备、需求响应集成的虚拟电厂系统结构,分析了碳循环利用与可再生能源消纳过程。然后,以系统运行成本最小化为目标,构建了虚拟电厂确定型调度模型;为进一步考虑可再生能源出力的区间不确定性,基于信息间隙决策理论构建了风险规避和机会寻求2种策略下的调度优化模型。最后,借助混沌粒子群算法进行算例求解分析。分析结果表明,碳捕集与电转气技术协同作用下,虚拟电厂的运行成本降低了16.53%,碳排放量减少了54.75%;优化后的调度模型在有效解决可再生能源机组出力不确定性问题的同时,也满足了决策制定者的风险偏好,实现了风险管控与资本管理的均衡。

Aiming at the problem of “difficulty in absorption” of renewable energy and the randomness of output, firstly, this paper establishes the virtual power plant system structure including wind turbines,photovoltaic units, power-to-gas equipment, carbon capture power plants, carbon storage equipment, and demand response. The process of carbon recycling and renewable energy consumption is analyzed. Then,a deterministic dispatching model of virtual power plants is constructed with the goal of minimizing the system operating cost. In order to further consider the interval uncertainty of renewable energy output, a dispatching optimization model is constructed under two strategies of risk avoidance and opportunity seeking based on the information gap decision theory. Finally, the chaotic particle swarm algorithm is used to solve and analyze the calculation examples. The results show that under the synergistic effect of carbon capture and power-to-gas, the operating cost of the virtual power plant is reduced by 16.53%, and the carbon emissions are reduced by 54.75%. The dispatching model optimized by the information gap theory not only effectively solves the problem of the uncertainty of renewable energy unit output, but also satisfies the risk preference of decision makers, and achieves a balance between risk control and capital management.