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Sustainable microgrid design with peer-to-peer energy trading involving government subsidies and uncertainties

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  • Yu, Vincent F.
  • Le, Thi Huynh Anh
  • Gupta, Jatinder N.D.

Abstract

Sustainable microgrid is a feasible approach to handle environmental impacts and satisfy customer demand due to its economic, environmental, and social benefits. Due to the high initial investment cost, the installation rate of microgrids has been limited. Various studies investigated the influence of government subsidies on sustainable microgrid design to increase the installation rate. However, the effect of government subsidies, financial factors, and elasticity coefficient of demand under peer-to-peer energy trading has not been explored in the current sustainable microgrids design literature. To overcome this limitation, this paper investigates the sustainable microgrid design problem to simultaneously consider the effect of government subsidies, peer-to-peer energy trading, time value of money, elasticity coefficient of demand, and uncertainties. The objectives are to maximize total profit and minimize the total environmental cost to satisfy electric energy demand. Fuzzy multi-objective programming is applied to determine the optimal decisions on the number, location, type of renewable energy, the capacity of renewable distributed generation sources, electricity flows, price for selling electricity to demand areas and P2P energy trading, and government subsidy rates. A genetic algorithm and its hybrid versions to include tabu search and simulated annealing are then used to solve the proposed model. Numerical experiments used to evaluate the performance of the proposed model and algorithms show that the proposed genetic algorithm is most effective in maximizing total profit and minimizing environmental cost. Computational results demonstrate that on average, the inclusion of the peer-to-peer trading and government subsidies in the proposed model increases total profit by 13.23% and reduces total environmental cost by 6.29%.

Suggested Citation

  • Yu, Vincent F. & Le, Thi Huynh Anh & Gupta, Jatinder N.D., 2023. "Sustainable microgrid design with peer-to-peer energy trading involving government subsidies and uncertainties," Renewable Energy, Elsevier, vol. 206(C), pages 658-675.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:658-675
    DOI: 10.1016/j.renene.2023.02.003
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    as
    1. Oskar Lecuyer & Philippe Quirion, 2019. "Interaction between CO2 emissions trading and renewable energy subsidies under uncertainty: feed-in tariffs as a safety net against over-allocation," Climate Policy, Taylor & Francis Journals, vol. 19(8), pages 1002-1018, September.
    2. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2018. "Impact of government subsidies on economic feasibility of offshore wind system: Implications for Taiwan energy policies," Applied Energy, Elsevier, vol. 217(C), pages 336-345.
    3. Jung, Seung Hwan & Feng, Tianjun, 2020. "Government subsidies for green technology development under uncertainty," European Journal of Operational Research, Elsevier, vol. 286(2), pages 726-739.
    4. Quashie, Mike & Marnay, Chris & Bouffard, François & Joós, Géza, 2018. "Optimal planning of microgrid power and operating reserve capacity," Applied Energy, Elsevier, vol. 210(C), pages 1229-1236.
    5. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Jamal, Taskin & Dyduch, Joanna & Arif, M.T. & Manoj Kumar, Nallapaneni & Shafiullah, GM & Chopra, Shauhrat S. & Nadarajah, Mithulananthan, 2021. "Envisioning the UN Sustainable Development Goals (SDGs) through the lens of energy sustainability (SDG 7) in the post-COVID-19 world," Applied Energy, Elsevier, vol. 292(C).
    6. Shahbazbegian, Vahid & Hosseini-Motlagh, Seyyed-Mahdi & Haeri, Abdorrahman, 2020. "Integrated forward/reverse logistics thin-film photovoltaic power plant supply chain network design with uncertain data," Applied Energy, Elsevier, vol. 277(C).
    7. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    8. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward blockchain-based renewable energy microgrid design considering default risk and demand uncertainty," Renewable Energy, Elsevier, vol. 163(C), pages 870-881.
    9. Chen, Zhisong & Ivan Su, Shong-Iee, 2019. "Social welfare maximization with the least subsidy: Photovoltaic supply chain equilibrium and coordination with fairness concern," Renewable Energy, Elsevier, vol. 132(C), pages 1332-1347.
    10. Milis, Kevin & Peremans, Herbert & Van Passel, Steven, 2018. "The impact of policy on microgrid economics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3111-3119.
    11. Zahiri, B. & Tavakkoli-Moghaddam, R. & Mohammadi, M. & Jula, P., 2014. "Multi-objective design of an organ transplant network under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 101-124.
    12. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 290(C).
    13. Tsao, Yu-Chung & Thanh, Vo-Van & Chang, Yi-Ying & Wei, Hsi-Hsien, 2021. "COVID-19: Government subsidy models for sustainable energy supply with disruption risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    14. Hagspiel, Verena & Nunes, Cláudia & Oliveira, Carlos & Portela, Manuel, 2021. "Green investment under time-dependent subsidy retraction risk," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    15. Chen, Jen-Yi & Dimitrov, Stanko & Pun, Hubert, 2019. "The impact of government subsidy on supply Chains’ sustainability innovation," Omega, Elsevier, vol. 86(C), pages 42-58.
    16. Peter C. Schuur, 1997. "Classification of Acceptance Criteria for the Simulated Annealing Algorithm," Mathematics of Operations Research, INFORMS, vol. 22(2), pages 266-275, May.
    17. Li, Zhimin & Pan, Yanchun & Yang, Wen & Ma, Jianhua & Zhou, Ming, 2021. "Effects of government subsidies on green technology investment and green marketing coordination of supply chain under the cap-and-trade mechanism," Energy Economics, Elsevier, vol. 101(C).
    18. Mansour-lakouraj, Mohammad & Shahabi, Majid, 2019. "Comprehensive analysis of risk-based energy management for dependent micro-grid under normal and emergency operations," Energy, Elsevier, vol. 171(C), pages 928-943.
    19. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    20. Deng Xu & Yong Long, 2019. "The Impact of Government Subsidy on Renewable Microgrid Investment Considering Double Externalities," Sustainability, MDPI, vol. 11(11), pages 1-15, June.
    21. Li, Yan & Zhang, Peng & Yue, Meng, 2018. "Networked microgrid stability through distributed formal analysis," Applied Energy, Elsevier, vol. 228(C), pages 279-288.
    22. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
    23. Zhang, X.Y. & Huang, G.H. & Zhu, H. & Li, Y.P., 2017. "A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties," Energy, Elsevier, vol. 123(C), pages 664-676.
    24. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    25. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    26. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    27. Pablo Benalcazar & Adam Suski & Jacek Kamiński, 2020. "The Effects of Capital and Energy Subsidies on the Optimal Design of Microgrid Systems," Energies, MDPI, vol. 13(4), pages 1-23, February.
    28. Jeongmeen Suh & Sung-Guk Yoon, 2020. "Maximizing Solar PV Dissemination under Differential Subsidy Policy across Regions," Energies, MDPI, vol. 13(11), pages 1-15, June.
    29. Long, Chao & Wu, Jianzhong & Zhou, Yue & Jenkins, Nick, 2018. "Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid," Applied Energy, Elsevier, vol. 226(C), pages 261-276.
    30. Roslan, M.F. & Hannan, M.A. & Ker, Pin Jern & Uddin, M.N., 2019. "Microgrid control methods toward achieving sustainable energy management," Applied Energy, Elsevier, vol. 240(C), pages 583-607.
    31. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    32. Tang, Chong & Liu, Mingbo & Dai, Yue & Wang, Zhijun & Xie, Min, 2019. "Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    33. Tsao, Yu-Chung & Thanh, Vo-Van, 2019. "A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 124(C), pages 13-39.
    34. Hocine, Amine & Kouaissah, Noureddine & Bettahar, Samir & Benbouziane, Mohamed, 2018. "Optimizing renewable energy portfolios under uncertainty: A multi-segment fuzzy goal programming approach," Renewable Energy, Elsevier, vol. 129(PA), pages 540-552.
    35. Cayir Ervural, Beyzanur & Evren, Ramazan & Delen, Dursun, 2018. "A multi-objective decision-making approach for sustainable energy investment planning," Renewable Energy, Elsevier, vol. 126(C), pages 387-402.
    36. Nelson, James & Johnson, Nathan G. & Fahy, Kelsey & Hansen, Timothy A., 2020. "Statistical development of microgrid resilience during islanding operations," Applied Energy, Elsevier, vol. 279(C).
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