Hua, Weiqi and Li, Dan and Sun, Hongjian and Matthews, Peter (2020) 'Stackelberg game-theoretic model for low carbon energy market scheduling.', IET smart grid., 3 (1). pp. 31-41.
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.
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|Publisher Web site:||https://doi.org/10.1049/iet-stg.2018.0109|
|Publisher statement:||This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)|
|Date accepted:||28 August 2019|
|Date deposited:||03 September 2019|
|Date of first online publication:||29 August 2019|
|Date first made open access:||18 February 2020|
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