Stackelberg game-theoretic model for low carbon energy market scheduling
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 c...
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doaj-2ed1e30ea5784b36b39a61e18d1d7c5b2021-04-02T11:48:32ZengWileyIET Smart Grid2515-29472019-08-0110.1049/iet-stg.2018.0109IET-STG.2018.0109Stackelberg game-theoretic model for low carbon energy market schedulingWeiqi Hua0Dan Li1Hongjian Sun2Peter Matthews3Durham UniversityDurham UniversityDurham UniversityDurham UniversityExcessive 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.https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0109profitabilitygame theoryrenewable energy sourcesoptimisationpower marketsair pollution controlpower generation economicsgovernment policiesenvironmental economicspricingsustainable developmentstackelberg game-theoretic modellow carbon energy market schedulingexcessive carbon emissionsappropriate market-based low carbon policyenergy sectoroptimal low carbon policy designlow-carbon generating sourcescarbon priceenergy consumptioncarbon reductionsoptimal policy decisionsoptimal generationconsumption decisionscarbon mitigationrenewable energy sources |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weiqi Hua Dan Li Hongjian Sun Peter Matthews |
spellingShingle |
Weiqi Hua Dan Li Hongjian Sun Peter Matthews Stackelberg game-theoretic model for low carbon energy market scheduling IET Smart Grid profitability game theory renewable energy sources optimisation power markets air pollution control power generation economics government policies environmental economics pricing sustainable development stackelberg game-theoretic model low carbon energy market scheduling excessive carbon emissions appropriate market-based low carbon policy energy sector optimal low carbon policy design low-carbon generating sources carbon price energy consumption carbon reductions optimal policy decisions optimal generation consumption decisions carbon mitigation renewable energy sources |
author_facet |
Weiqi Hua Dan Li Hongjian Sun Peter Matthews |
author_sort |
Weiqi Hua |
title |
Stackelberg game-theoretic model for low carbon energy market scheduling |
title_short |
Stackelberg game-theoretic model for low carbon energy market scheduling |
title_full |
Stackelberg game-theoretic model for low carbon energy market scheduling |
title_fullStr |
Stackelberg game-theoretic model for low carbon energy market scheduling |
title_full_unstemmed |
Stackelberg game-theoretic model for low carbon energy market scheduling |
title_sort |
stackelberg game-theoretic model for low carbon energy market scheduling |
publisher |
Wiley |
series |
IET Smart Grid |
issn |
2515-2947 |
publishDate |
2019-08-01 |
description |
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. |
topic |
profitability game theory renewable energy sources optimisation power markets air pollution control power generation economics government policies environmental economics pricing sustainable development stackelberg game-theoretic model low carbon energy market scheduling excessive carbon emissions appropriate market-based low carbon policy energy sector optimal low carbon policy design low-carbon generating sources carbon price energy consumption carbon reductions optimal policy decisions optimal generation consumption decisions carbon mitigation renewable energy sources |
url |
https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0109 |
work_keys_str_mv |
AT weiqihua stackelberggametheoreticmodelforlowcarbonenergymarketscheduling AT danli stackelberggametheoreticmodelforlowcarbonenergymarketscheduling AT hongjiansun stackelberggametheoreticmodelforlowcarbonenergymarketscheduling AT petermatthews stackelberggametheoreticmodelforlowcarbonenergymarketscheduling |
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