Least cost generation expansion planning in the presence of renewable energy sources using correction matrix method with indicators-based discrete water cycle algorithm

Abstract

Generation expansion planning (GEP) is a vital step in power system planning after load forecast. It is a highly constrained, dynamic, combinatorial, and discrete optimization problem. Mathematically, it is modeled as a mixed-integer nonlinear programming problem with high dimensionality and stochastic characteristics. The integration of renewable energy sources makes the GEP problem a complicated task and less reliable due to its intermittent nature. Meta-heuristic approaches are considered as potential solution methodologies to optimize the least cost GEP problem. This paper presents a novel GEP optimization framework to pursue the least cost GEP achieving a certain reliability level according to the forecasted demand for a planning horizon. The proposed GEP optimization framework is a correction matrix method with an indicator-based discrete water cycle algorithm (DWCA-CMMI). In DWCA-CMMI, a new parallel constraint handling approach, called a correction matrix method with indicators (CMMI), has been developed. DWCA-CMMI requires a smaller number of iterations and search agents to minimize the total GEP cost as compared to penalty factor-based metaheuristic approaches. Hence, CMMI enhances the convergence speed of the algorithm, avoids trapping in local optima, and improves both exploration and particularly exploitation. The proposed optimization framework is applied to reliability constrained and emission constrained GEP problems (test systems) from the literature. The proposed framework shows the promising results in terms of least cost and runtime as compared to results given by recent approaches presented in the literature. The applicability of the proposed approach has also been evaluated by applying to a real case study of Pakistan's power system to devise the feasible generation expansion plan.