A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response

Abstract Power-to-gas (P2G) provides a new means for accommodating abandoned new energy that will support the optimal operation of virtual power plants (VPPs) in the future. In this study, a novel structure of a P2G-based virtual power plant (GVPP) is designed. A flexible risk aversion model for GVPP operation is proposed with two objectives—maximum operation profit and minimum operation risk. In the model, the conditional value at risk method and robust optimization theory are utilized to reflect uncertainty risks. To solve the multi-objective model, a solution algorithm was constructed for iteratively obtaining the optimal weight coefficient for transforming the multi-objective model into a single-objective model based on a payoff table and a rough set method. Four simulation cases were set for comparative analysis based on a nine-node energy hub system. The results show the following outcomes: (1) GVPP realizes the complementary utilization of distributed energy and forms a power-gas-power recycling mode, (2) the proposed model can provide an effective decision-making tool for different risk-attitude decision makers by setting a reasonable confidence level and robust coefficient, (3) P2G and price-based demand response (PBDR) improve the grid-connected space of clean energy, in particular, PBDR improves the flexibility of system operation and reduces operation risk, and (4) if the maximum emission trade allowance (META) is considered, P2G preferentially converts carbon dioxide into methane when the META is lower, while clean energy is preferentially used to satisfy load demand and the methane produced by P2G can be sold to the natural gas network when the META is higher. Overall, the proposed optimal decision model achieves the maximum utilization of clean energy to obtain higher economic benefits while rationally controlling operation risks; hence, providing reliable support for decision makers.

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