Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming - Ben Hmida, Jalel; Chambers, Terrence; Lee, Jim (Electric power systems research, 2019)
•Proposed a novel Hybrid Modified Imperialist Competitive Algorithm and Sequential Quadratic Programming.•Studied the uncertainties of solar power and wind energy.•Solved different OPF problems including wind and solar generators.•A comparative study proved the effectiveness of the HMICA-SQP in providing better solutions.•A statistical performance evaluation proved the validity and robustness of the proposed method. The objective of this paper was to develop and solve different constrained optimal power flow (OPF) problems for hybrid power systems containing renewable energy sources like wind energy and solar power. OPF seeks to optimize the transmission of electric power by finding a steady state operating point that minimizes gas emission and cost of generated electric power without disturbing network power flow, operating limits and system constraints. The OPF problem was solved using a hybrid modified imperialist competitive algorithm and sequential quadratic programming, (HMICA-SQP). Efficacy of the modified imperialist competitive algorithm (MICA) is further enhanced by the process of hybridization with the sequential quadratic programming (SQP) for rapid local refinement and improved precision of the solution. The potential and effectiveness of the proposed metaheuristic were presented and assessed using three benchmark test systems IEEE 30, IEEE 57 and IEEE 118-bus power systems that incorporates several solar and wind energy sources. Comparison of simulation results with recently published optimization approaches solutions showed that the suggested paradigm is more efficient, robust and provides the lowest cost of generated electric power while keeping low emissions.