Applying portfolio theory-based modified ABC to electricity generation mix

Abstract

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy), installed capacity (power) and actual instantaneous power delivery is proposed. Therefore, in this model, the uncertainties of wind power and ramp-up/down constrains of traditional power plants are correctly considered in the investment cost. The numerical results show the great potential of proposed model with lowest risk on generation cost. Also, they are show that MABC approach is successful in portfolio optimization.