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Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning

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  • Pang, Kexin
  • Zhou, Jian
  • Tsianikas, Stamatis
  • Coit, David W.
  • Ma, Yizhong

Abstract

Microgrid plays an increasingly important role to enhance power resilience and environmental protection regarding greenhouse gas emission reduction through the widespread applications of distributed and renewable energy. Because of the steady growth of load demand, the strict power resilience requirements and the pressing need of carbon emission reduction, microgrid expansion planning considering those factors has become a currently topical topic. In this study, a new framework for long-term microgrid expansion planning, in which a microgrid serves as a backup power system in the event of main grid outages from the perspectives of economy, resilience and greenhouse gas emission, is proposed. Deep reinforcement learning method is used to solve this dynamic and stochastic optimization problem by taking into account various uncertainties and constraints for the long-range planning. Case studies of 20-year microgrid expansion planning using actual data are conducted. The simulation results demonstrate the effectiveness of the proposed framework on reducing greenhouse gas emissions and total cost including economic losses resulting from power grid outages, investment and operating cost of microgrid entities. In addition, the impact of customer load demand and microgrid entities price on optimal planning policies is discussed. The results demonstrate that microgrid expansion planning can be effectively adapted to different levels of load demand and different scenarios of price changes under the proposed framework. This work is helpful for decision makers to implement cost-effective and power resilient microgrid expansion planning with greenhouse gas emission reduction benefits in the long term.

Suggested Citation

  • Pang, Kexin & Zhou, Jian & Tsianikas, Stamatis & Coit, David W. & Ma, Yizhong, 2024. "Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123009267
    DOI: 10.1016/j.rser.2023.114068
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    References listed on IDEAS

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    1. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Tsianikas, Stamatis & Yousefi, Nooshin & Zhou, Jian & Rodgers, Mark D. & Coit, David, 2021. "A storage expansion planning framework using reinforcement learning and simulation-based optimization," Applied Energy, Elsevier, vol. 290(C).
    3. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    4. Hemmati, Reza & Saboori, Hedayat & Siano, Pierluigi, 2017. "Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems," Energy, Elsevier, vol. 134(C), pages 699-708.
    5. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    6. Zhou, Jian & Tsianikas, Stamatis & Birnie, Dunbar P. & Coit, David W., 2019. "Economic and resilience benefit analysis of incorporating battery storage to photovoltaic array generation," Renewable Energy, Elsevier, vol. 135(C), pages 652-662.
    7. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    8. Feijoo, Felipe & Das, Tapas K., 2015. "Emissions control via carbon policies and microgrid generation: A bilevel model and Pareto analysis," Energy, Elsevier, vol. 90(P2), pages 1545-1555.
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