Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (CGO) algorithm. Five objective functions are considered, which include minimizing generation costs, emissions, active power loss, voltage deviation, and enhancing voltage profiles. The OPF formulation considers the anticipated electricity production from wind turbines (WT) and photovoltaic (PV) units as dependent variables, while the voltage magnitude at WT and PV buses is treated as a control variable. Probabilistic models based on wind speed and solar irradiance are used to forecast the electrical output of WT and PV units. The proposed OPF methodology and solution method are validated on the IEEE 30-bus test network. This paper introduces and applies four optimization techniques inspired by biological and natural phenomena, namely CGO, Osprey Optimization Algorithm (OOA), RIME Algorithm, and Slime Mould Algorithm (SMA), to address both single-OPF and multi-OPF objective problems in electric power networks. The suggested optimization approaches are tested under different operational scenarios, considering various combinations of FACTS, renewable energy sources (solar PV and wind), and their locations in the network. To predict wind and solar PV power generation, Weibull and lognormal probability density functions are utilized, respectively. The objective function accounts for reserve cost due to overestimation and penalty cost due to underestimation of intermittent solar and wind power. The results demonstrate that the CGO technique is more efficient than other methods in solving OPF instances.