Optimization and system dynamics-based simulation for electricity generation-mix planning and ghg emissions mitigation

This paper presents a stochastic linear programming model for the optimal electricity generation-mix problem to meet a specified CO2 emission target. The objective function minimizes the weighted sum of two terms: the electricity generation cost and the Green House Gas (GHG) emissions cost. This paper contributes to the literature by three folds: (1) considers the uncertainty in the grid demand; (2) applies sample average approximation approach to solve the proposed stochastic model; and (3) tests the proposed generation mix plan in the operational level under stochastic grid demand patterns using system dynamics approach. The mathematical model is solved optimally, and the simulation model is developed using a case study based on the electricity sector 2030 plan in South Africa. The proposed models are solved, and results show that the proposed approach offers generation-mix plans that could achieve the CO2 emissions reduction target. Results are reported for two generation-mix plans under two weighting scenarios; a minimum cost driven generation-mix and a minimum CO2 driven generation-mix. The former lowers cost by around 6.45% while the latter lowers CO2 emissions by around 4.41%. Both scenarios could eliminate CO2 daily emissions, compared to CO2 daily limits, by around 18% and 22%, respectively.