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Power generation capacity planning under budget constraint in developing countries

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  • Afful-Dadzie, Anthony
  • Afful-Dadzie, Eric
  • Awudu, Iddrisu
  • Banuro, Joseph Kwaku

Abstract

This paper presents a novel multi-period stochastic optimization model for studying long-term power generation capacity planning in developing countries. A stylized model is developed to achieve three objectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation types in the face of budget constraint, (2) to help decision makers appreciate the consequences of capacity expansion decisions on level of unserved electricity demand and its attendant impact on the national economy, and (3) to encourage the habit of periodic savings towards new generation capacity financing. The problem is modeled using a stochastic mixed-integer linear programming (MILP) technique under demand uncertainty. The effectiveness of the model, together with valuable insights derived from considering different levels of budget constraints are demonstrated using Ghana as a case study. The results indicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generation capacity to meet approximately 95% of its annual electricity demand between 2016 and 2035. Additionally, it is observed that as financial constraint becomes tighter, decisions on the mix of new generation capacities tend to be more costly compared to when sufficient funds are available.

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  • Afful-Dadzie, Anthony & Afful-Dadzie, Eric & Awudu, Iddrisu & Banuro, Joseph Kwaku, 2017. "Power generation capacity planning under budget constraint in developing countries," Applied Energy, Elsevier, vol. 188(C), pages 71-82.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:71-82
    DOI: 10.1016/j.apenergy.2016.11.090
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    2. Bissiri, M. & Moura, P. & Figueiredo, N.C. & Silva, P.P., 2020. "Towards a renewables-based future for West African States: A review of power systems planning approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. Andrzej Wędzik & Tomasz Siewierski & Michał Szypowski, 2019. "The Use of Black-Box Optimization Method for Determination of the Bus Connection Capacity in Electric Power Grid," Energies, MDPI, vol. 13(1), pages 1-21, December.
    4. Thushara, De Silva M. & Hornberger, George M. & Baroud, Hiba, 2019. "Decision analysis to support the choice of a future power generation pathway for Sri Lanka," Applied Energy, Elsevier, vol. 240(C), pages 680-697.
    5. Moret, Stefano & Babonneau, Frédéric & Bierlaire, Michel & Maréchal, François, 2020. "Overcapacity in European power systems: Analysis and robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    6. Tang, Bao-Jun & Li, Ru & Li, Xiao-Yi & Chen, Hao, 2017. "An optimal production planning model of coal-fired power industry in China: Considering the process of closing down inefficient units and developing CCS technologies," Applied Energy, Elsevier, vol. 206(C), pages 519-530.
    7. Constantino Dário Justo & José Eduardo Tafula & Pedro Moura, 2022. "Planning Sustainable Energy Systems in the Southern African Development Community: A Review of Power Systems Planning Approaches," Energies, MDPI, vol. 15(21), pages 1-28, October.
    8. Carlos Roberto de Sousa Costa & Paula Ferreira, 2023. "A Review on the Internalization of Externalities in Electricity Generation Expansion Planning," Energies, MDPI, vol. 16(4), pages 1-19, February.
    9. Neelawela, U.D. & Selvanathan, E.A. & Wagner, L.D., 2019. "Global measure of electricity security: A composite index approach," Energy Economics, Elsevier, vol. 81(C), pages 433-453.
    10. Afful-Dadzie, Anthony & Mallett, Alexandra & Afful-Dadzie, Eric, 2020. "The challenge of energy transition in the Global South: The case of electricity generation planning in Ghana," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    11. 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).

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