Stochastic optimal power flow analysis of power systems with wind/PV/TCSC using a developed Runge Kutta optimizer

Recently, renewable energy resources such as wind turbines (WTs) and photovoltaic (PV) systems are wildly installed in electrical systems. However, the main challenge that related to these resources is the optimal planning and operation of the power system due to their stochastic natural and intermittency. However, OPF solution in case of considering the uncertainties of RERs and optimal integration of FACTS device became more complex and not easy task. Consequently, this paper proposes a modified version of the RUNge Kutta optimizer (MRUN) for solving the stochastic OPF problem with optimal integration of the WTs and PV systems along with Thyristor controlled series compensator (TCSC). The Modified RUNge Kutta optimizer (MRUN) is based on Cauchy mutation and the quasi-oppositional based learning techniques to render the RUN optimization tech-nique to be more robust and powerful. The objective functions of the current optimization problem are the total expected power losses (TEPL), the total expected voltage deviations (TEVD) and the total expected voltage stability (TEVS). The proposed algorithm is tested on IEEE 57-bus system. After the RERs and TCSCs integration under uncertainty state, the reduction in the TEPL and TEVD are 24.75 %, and 14.74 % while the TEVS is enhanced by 18.33 % compared to based case (without RERs and TCSCs). In addition to that the proposed MRUN is superior for solving the OPF and assigning the optimal sites and sizes of the RERs and the TCSCs compared to the conventional RUN.