Power generation capacity planning under budget constraint in developing countries

Abstract

This paper presents a novel multi-period stochastic optimization model for studying long-term powergeneration capacity planning in developing countries. A stylized model is developed to achieve threeobjectives: (1) to serve as a tool for determining optimal mix, size and timing of power generation typesin the face of budget constraint, (2) to help decision makers appreciate the consequences of capacityexpansion decisions on level of unserved electricity demand and its attendant impact on the nationaleconomy, 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 underdemand uncertainty. The effectiveness of the model, together with valuable insights derived from consid-ering different levels of budget constraints are demonstrated using Ghana as a case study. The resultsindicate that at an annual savings equivalent to 0.75% of GDP, Ghana could finance the needed generationcapacity 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 gen-eration capacities tend to be more costly compared to when sufficient funds are available