Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus
13 voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’
14 constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources
15 on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar
16 PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed
17 using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of
18 simulation and statistical results of EO with other optimization techniques showed that EO is more effective
19 and superior.