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Novel methodology for microgrids in isolated communities: Electricity cost-coverage trade-off with 3-stage technology mix, dispatch & configuration optimizations

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  • Bustos, Cristian
  • Watts, David

Abstract

Around the world, 1.1billion people are severely affected by their lack of access to electricity. Other vulnerable communities receive low quality access, or face expensive prices that force them to restrict their consumption because of suboptimal technology choices made by their suppliers, which are sometimes forced by local regulation. Microgrids, properly sized and managed, may represent the best option to overcome these dilemmas, offering a tailor made supply. Today’s standard methodologies to design isolated microgrids optimize the cost of supply as well as the cost of the energy not served with an exogenous per unit value for the lost load. They do not include community’s restrictions, such as willingness-to-pay, consumption level, budget constraints or its particular (endogenous) value of lost load. We developed a novel methodology that offers a range of microgrid designs to an isolated community, where each of them is optimal for a particular consumption pattern and value of lost load, from which the community may choose the one that best suits their needs. For this purpose, a Pareto optimal cost-coverage trade-off was constructed for an isolated community in northern Chile. A three-stage optimization was done: capacity (Genetic Algorithm), operation (robust optimization and mixed integer linear programming) and configuration (DC or AC). Diesel, gas, PV, wind and storages were modeled and 176 designs were found in total. More expensive microgrids (and with a larger electricity coverage) have hybrid mixes (conventional and renewable) and have an almost linear total cost from 298 to 249USD/MWh for ENS from 0% to 28%. Lower quality microgrids are fully renewable, providing a very cheap but unreliable supply. The direct impact of lower-cost/limited supply microgrids offered here is the improvement of the quality of life of millions of vulnerable people, but it requires adjustments in the country’s public policies of electrification programs.

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  • Bustos, Cristian & Watts, David, 2017. "Novel methodology for microgrids in isolated communities: Electricity cost-coverage trade-off with 3-stage technology mix, dispatch & configuration optimizations," Applied Energy, Elsevier, vol. 195(C), pages 204-221.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:204-221
    DOI: 10.1016/j.apenergy.2017.02.024
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    3. Castillo-Calzadilla, T. & Cuesta, M.A. & Olivares-Rodriguez, C. & Macarulla, A.M. & Legarda, J. & Borges, C.E., 2022. "Is it feasible a massive deployment of low voltage direct current microgrids renewable-based? A technical and social sight," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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    5. Bustos, Cristian & Watts, David & Olivares, Daniel, 2019. "The evolution over time of Distributed Energy Resource’s penetration: A robust framework to assess the future impact of prosumage under different tariff designs," Applied Energy, Elsevier, vol. 256(C).
    6. Kate Anderson & Nicholas D. Laws & Spencer Marr & Lars Lisell & Tony Jimenez & Tria Case & Xiangkun Li & Dag Lohmann & Dylan Cutler, 2018. "Quantifying and Monetizing Renewable Energy Resiliency," Sustainability, MDPI, vol. 10(4), pages 1-13, March.
    7. Shakya, Bhupendra & Bruce, Anna & MacGill, Iain, 2019. "Survey based characterisation of energy services for improved design and operation of standalone microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 493-503.
    8. Lu, Tianguang & Ai, Qian & Wang, Zhaoyu, 2018. "Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids," Applied Energy, Elsevier, vol. 212(C), pages 1462-1475.
    9. Konečná, Eva & Teng, Sin Yong & Máša, Vítězslav, 2020. "New insights into the potential of the gas microturbine in microgrids and industrial applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    10. Roozbeh Ghasemi & Martin Wosnik & Diane L. Foster & Weiwei Mo, 2023. "Multi-Objective Decision-Making for an Island Microgrid in the Gulf of Maine," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    11. Swaminathan, Siddharth & Pavlak, Gregory S. & Freihaut, James, 2020. "Sizing and dispatch of an islanded microgrid with energy flexible buildings," Applied Energy, Elsevier, vol. 276(C).
    12. Vossos, Vagelis & Gerber, Daniel & Bennani, Youness & Brown, Richard & Marnay, Chris, 2018. "Techno-economic analysis of DC power distribution in commercial buildings," Applied Energy, Elsevier, vol. 230(C), pages 663-678.
    13. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    14. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    15. 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.
    16. Hirwa, Jusse & Zolan, Alexander & Becker, William & Flamand, Tülay & Newman, Alexandra, 2023. "Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital," Applied Energy, Elsevier, vol. 348(C).

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