Abstract:
The Ghanaian electric power system, like most Sub-Saharan African countries, is bedevilled with the problem of inadequate generation of electric power amidst growing demand for electricity. Governments over the years have tried to tackle the issue of inadequate generation capacity and supply of electricity in Ghana to meet the increasing demand for electric power. Yet electricity supply in Ghana remains erratic and inconsistent. The purpose of this study was to develop a long-term (20-years) electricity generation expansion plan for Ghana’s electricity sub-sector that takes into account important attributes specially related to Ghana, such as budget constraint. The study employs multi-period stochastic mixed-integer linear programming (MILP) to model and solve the problem of determining the technology type, timing and number of units of generators to add to the existing capacity under uncertain demand taking into account budget constraint. Secondary data was used to estimate all the model parameters. Periodic electricity demand scenarios were obtained by assuming that the uncertain demand follows a triangular distribution with a minimum increase of 1%, the most likely increase of 7% and a maximum increase of 15% over the immediate past year’s electricity demand. The proposed multi-period stochastic MILP model was run for two cases: without budget constraint which depicts the case where there are sufficient funds to undertake an expansion plan and the budget constraint case, where the expansion plan is faced with lack of funds. The imposition of budget constraint is a departure from the typical generation capacity expansion models found in the literature and helps explain generation expansion pattern in Ghana. The expected values of the objective function and the generation expansion plans considering no budget constraint and budget constraints were optimized in order to draw analogy. It is observed that the presence of budget constraint sometimes forces the decision maker to take decisions that might be sub-optimal compared to when sufficient funds are available.
