This will still continue for decades until renewable energy sources become the main energy supplies. In the energy sector, fossil fuels (including coal, gas, and oil) supply around 80% of total energy demand ]. The global population will increase from 7 billion to 9 billion over the next 40 years, with energy demand rising by 50% ]. 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. Case studies demonstrate that the designed model leads to better carbon mitigation and social welfare in the energy market. 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. The efficiency of low carbon policy is ensured through maximising social welfare and the overall carbon reductions from economic and environmental perspectives. Meanwhile, to reduce carbon emissions caused by energy consumption, carbon tracing and billing incentive methods for consumers are proposed. To encourage fuel switching to low-carbon generating sources, the effects of varying carbon price on generator's profit are evaluated.
This study proposes a Stackelberg game-theoretic model to determine an optimal low carbon policy design in energy market. An appropriate market-based low carbon policy becomes the essence of regulating strategy for reducing carbon emissions in the energy sector.
IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
0 kommentar(er)
