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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Main Authors: Weiqi Hua, Dan Li, Hongjian Sun, Peter Matthews
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:IET Smart Grid
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0109
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spelling 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
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AT danli stackelberggametheoreticmodelforlowcarbonenergymarketscheduling
AT hongjiansun stackelberggametheoreticmodelforlowcarbonenergymarketscheduling
AT petermatthews stackelberggametheoreticmodelforlowcarbonenergymarketscheduling
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