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Improving rural electricity system planning: An agent-based model for stakeholder engagement and decision making

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  • Alfaro, Jose F.
  • Miller, Shelie
  • Johnson, Jeremiah X.
  • Riolo, Rick R.

Abstract

Energy planners in regions with low rates of electrification face complex and high-risk challenges in selecting appropriate generating technologies and grid centralization. To better inform such processes, we present an Agent-Based Model (ABM) that facilitates engagement with stakeholders. This approach evaluates long-term plans using the cost of delivered electricity, resource mix, jobs and economic stimulus created within communities, and decentralized generation mix of the system, with results provided in a spatially-resolved format. This approach complements existing electricity planning methods (e.g., Integrated Resource Planning) by offering novel evaluation criteria based on typical stakeholder preferences.

Suggested Citation

  • Alfaro, Jose F. & Miller, Shelie & Johnson, Jeremiah X. & Riolo, Rick R., 2017. "Improving rural electricity system planning: An agent-based model for stakeholder engagement and decision making," Energy Policy, Elsevier, vol. 101(C), pages 317-331.
    • Handle: RePEc:eee:enepol:v:101:y:2017:i:c:p:317-331
    DOI: 10.1016/j.enpol.2016.10.020
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    Cited by:

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    2. Walzberg, Julien & Dandres, Thomas & Merveille, Nicolas & Cheriet, Mohamed & Samson, Réjean, 2019. "Assessing behavioural change with agent-based life cycle assessment: Application to smart homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 365-376.
    3. T. Chamarande & S. Mathy & B. Hingray, 2022. "The least cost design of 100% solar power microgrids in Africa: sensitivity to meteorological and economic drivers and possibility for simple pre-sizing rules," Post-Print hal-03740059, HAL.
    4. al Irsyad, M. Indra & Halog, Anthony & Nepal, Rabindra, 2018. "Estimating the impacts of financing support policies towards photovoltaic market in Indonesia: A social-energy-economy-environment (SE3) model simulation," Working Papers 2018-09, University of Tasmania, Tasmanian School of Business and Economics.
    5. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    6. B. Domenech & L. Ferrer-Martí & R. Pastor, 2022. "Multicriteria analysis of renewable-based electrification projects in developing countries," Annals of Operations Research, Springer, vol. 312(2), pages 1375-1401, May.
    7. Li, Kuanhong & Wang, Linping & Wang, Lianhui, 2024. "Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction," Applied Energy, Elsevier, vol. 365(C).
    8. Nock, Destenie & Levin, Todd & Baker, Erin, 2020. "Changing the policy paradigm: A benefit maximization approach to electricity planning in developing countries," Applied Energy, Elsevier, vol. 264(C).
    9. Duran, Asligul Serasu & Sahinyazan, Feyza G., 2021. "An analysis of renewable mini-grid projects for rural electrification," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    10. Edgar Ojeda Camargo & John E. Candelo-Becerra & Alcides Santander Mercado, 2019. "Lexicographic Multi-objective Optimisation of Hybrid Power Generation Systems for Communities in Non-Interconnected Zones," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 205-217.
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