Abstract
Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).
Highlights
Electrical utilities always strive to find the best solutions for scheduling generation to lower the cost of production and satisfy safe and reliable operating restrictions and power transmission restrictions
This paper proposes a meta-heuristic optimization technique known as hybrid gradientbased optimizer and moth-flame optimization algorithm (GBO-moth–flame optimization algorithm (MFO)) technique to minimize the generation cost, reduce the power losses, minimize the cost and power losses, and compare with three other techniques (GBO, MFO, SMA, and coulomb–franklin’s algorithm (CFA))
Our study explores an appropriate model of wind power and optimizes the placement of different FACTS devices (SVC, thyristor-controlled series compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS))
Summary
Electrical utilities always strive to find the best solutions for scheduling generation to lower the cost of production and satisfy safe and reliable operating restrictions and power transmission restrictions. In 2015 [22], the authors solved the OPF challenge by employing the modified bacteria foraging algorithm (MBFA) to combine wind and fossil fuel generation units [20], while in 2016 [23], the author integrated wind units into an IEEE 14-bus system, and the OPF issues were handled using quadratic programming. Despite all of this development, the wind speed is unpredictably variable is a major issue.
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