计及碳捕集和电转气的农村虚拟电厂多目标随机调度优化模型

Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas

针对农村地区存在大量生物质秸秆、垃圾、屋顶光伏、分散式风电等分布式能源,设计了集成燃气碳捕集设备(gas-power plant carbon capture, GPPCC)、电转气设备(power-to-gas, P2G)及垃圾发电(waste incineration power, WI)的农村虚拟电厂(GPPCC-P2G-WI-based virtual power plant, GPW-VPP)。然后,引入信息间隙理论(information gap decision theory, IGDT)和模糊满意度方法构造GPW-VPP近零碳运营优化模型。其中,选择最大化运营收益和最小化碳排放量作为优化目标,并利用模糊满意度理论转化为综合满意度最优化目标。再然后,利用IGDT描述风电、光伏发电和用户负荷3个不确定性变量影响,用于构造GPW-VPP随机调度优化模型。最后,选择中国兰考能源革命试点为对象开展实例分析验证所提模型的有效性,结果表明所提运营优化模型能在兼顾不同主体利益诉求的同时推进农村分布式能源的最优聚合利用,有利于实现整体能源结构清洁低碳转型。

Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaic power, and decentralized wind power, this paper designs a novel structure of a virtual power plant connected with gas-power plant carbon capture(GPPCC), power-to-gas(P2G), and waste incineration power(WI), namely, a GPW-VPP. Then, the information gap decision theory(IGDT) and fuzzy satisfaction method are applied to construct a nearly zero-carbon optimal operation model. In this model, maximizing revenue and minimizing carbon emissions are selected as the initial goals of the GPW-VPP operation, which are converted into one maximum satisfaction goal. Three uncertainty variables, namely, wind power, photovoltaic power, and user load, are described using the IGDT. Finally, the Lankao Rural Energy Revolution Pilot program in China is selected as the case study to verify the effectiveness of the proposed model. The results show that the proposed operation optimization model and benefit distribution strategy can take into account the interests of different subjects, and at the same time, promote the optimal aggregation and utilization of rural distributed energy, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.