Constrained-Optimal Power Flow with Wind Turbine and Thyristor-Controlled Series Compensator Using DEPSO Algorithm.

Recently, the dependency on renewable energy sources (RES) has increased. This is due to several reasons, including reducing environmental harm and lowering production costs. The RES in nature is variable and intermittent, such as the wind turbine (WT), which depends on the wind speed, so several challenges are faced in integrating (RES) with power systems. This article used the optimal power flow (OPF) to solve these challenges with a hybrid optimization algorithm, combining the conventional differential evolution (DE) and particle swarm optimization (PSO) this hybrid method called differential evaluation-particle swarm optimization (DEPSO), with thyristor-controlled series compensators (TCSC). This approach gives better results than conventional algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and artificial bee colony (ABC). The constraints of the power system such as the reactive power of the generator or voltage of the load bus are retained in their limits. The objective functions (OF) discussed here are active power losses (MW), voltage deviation (VD) (p.u.), and thermal generation fuel cost ($/h). The DEPSO reduced the active power losses by 49.98%, voltage deviation by 91.02%, and generation cost by 11.36%. This article uses the reactance of the TCSC and the wind turbine bus magnitude voltage as control variables in OPF. This paper has tested the approach with an IEEE 30 bus with two WT farms. [ABSTRACT FROM AUTHOR]