Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas

Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaics, and decentralized wind power, this study designed 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 were applied to construct a nearly-zero carbon optimal operation model. In this model, the maximum revenue and minimum carbon emissions were selected as the initial objectives, which were converted into one maximum satisfaction objective. Three uncertainty variables, namely, wind power, photovoltaic power, and user’s load, were described using the IGDT. Secondly, to optimize the distribution of the cooperative operation revenue for the entities in GPW-VPP, a Nash negotiation-based benefit allocation strategy is established considering the multidimensional contribution factors of risks, benefits, and carbon emissions. Finally, the Lankao Rural Energy Revolution Pilot program in China was selected as the case study, the results showed: (1) GPW-VPP can aggregate and utilize different types of distributed energy sources such as rural wind power plants (WPPs) and photovoltaic power generation (PVs) to realize the electricity–carbon–electricity cycle effect. (2) The proposed operation optimization model can measure the uncertainty risk and formulate an optimal plan considering the above dual objectives. When the deviation coefficient of the predicted objectives is 0.5, the uncertainty degree is 0.142, and the cost of the decision plan is less than the expected cost of decision maker. Compared with the maximum revenue objective, the operation revenue and carbon emissions reduced by 4.6% and 35.76% under the comprehensive optimization objective, respectively, (3) The proposed benefit distribution strategy can be used to formulate a better benefit distribution plan that meets the comprehensive contributions of multiple entities. Affected by the risk of output uncertainty, the benefit proportion of WPP and PV increased, but it was 1.64% lower than that in the traditional distribution plan. Affected by carbon emissions, the benefit proportion of biomass power generation decreased, but it was 0.57% higher than that in the traditional distribution plan. Overall, the proposed operation optimization model and benefit distribution strategy can balance the interest requirements of different entities and promote the optimal aggregation and utilization of rural distributed energy resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.