Optimal power flow in power systems with renewable energy resources uncertainty including geothermal power plants

Abstract

This article proposes an innovative application of the Catch Fish Optimization method (CFOA) to effectively handle the complex Probabilistic Optimal Power Flow (POPF) optimization problem in modern power grids. The growing penetration of stochastic renewable energy sources, photovoltaic (PV) and wind energy, plus the presence of geothermal generation, causes uncertainties. The classical Optimal Power Flow (OPF) can’t address such uncertainties. This paper introduces the capabilities of the CFOA in addressing such uncertainties. The target is to determine optimal design variables considering the probabilistic models of generation. The introduced algorithm has been investigated on IEEE 30- and 118-bus networks. Moreover, these systems are modified to include PV, wind, and geothermal units. Both fixed and dynamic load profiles are included in the study. The simulation results for the 30-bus system show a reduction in total daily fuel costs of approximately 9.64% when compared with the no-renewables baseline. For the larger 118-bus system, the daily fuel cost reduction was even more significant, at approximately 15.91%. The results obtained using the CFOA are compared with those from other well-established algorithms. The comparative analysis confirms the greater CFOA performance in terms of the convergence speed besides the robustness. This analysis affirms the effectiveness of the introduced optimization techniques in tackling the POPF problem. The current research paves the way for further investigation into the application and enhancement of the CFOA for various power system optimization problems.