Power generation capacity planning under budget constraint in developing countries
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Date
2017
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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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Keywords
Generation capacity planning, Unserved demand, Stochastic optimization, Scenario generation, Budget constraint