Stackelberg game-theoretic model for low carbon energy market scheduling - Weiqi Hua; Dan Li; Hongjian Sun; Peter Matthews (IET Smart Grid, 2019)
Excessive carbon emissions have posed a threat to sustainable development. An appropriate market-based low carbon policy becomes the essence of regulating strategy for reducing carbon emissions in the energy sector. This study proposes a Stackelberg game-theoretic model to determine an optimal low carbon policy design in energy market. To encourage fuel switching to low-carbon generating sources, the effects of varying carbon price on generator's profit are evaluated. Meanwhile, to reduce carbon emissions caused by energy consumption, carbon tracing and billing incentive methods for consumers are proposed. The efficiency of low carbon policy is ensured through maximising social welfare and the overall carbon reductions from economic and environmental perspectives. A bi-level multiobjective optimisation immune algorithm is designed to dynamically find optimal policy decisions in the leader level, and optimal generation and consumption decisions in the followers level. Case studies demonstrate that the designed model leads to better carbon mitigation and social welfare in the energy market. The proposed methodology can save up to 26.41% of carbon emissions from the consumption side for the UK power sector and promote 31.45% of more electricity generation from renewable energy sources.