Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm

This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, opposition-based learning (OBL) has been integrated with teaching learning based optimization (TLBO) to form the opposition teaching learning based optimization (OTLBO). Flexible AC Transmission System (FACTS) devices such as Thyristor controlled series compensator (TCSC) can be very effective for power system security. Numerical results on test systems IEEE 30-Bus with valve point effect is presented and compared with results of other competitive global approaches. The results show that the proposed approach can converge to the optimum solution and obtains the solution with high accuracy.

Download Full-text

An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer

Sustainability ◽

10.3390/su132313382 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13382

Author(s):

Muhammad Riaz ◽

Aamir Hanif ◽

Haris Masood ◽

Muhammad Attique Khan ◽

Kamran Afaq ◽

...

Keyword(s):

Power System ◽

Power Flow ◽

Optimal Power Flow ◽

Fossil Fuel ◽

Renewable Energy Sources ◽

Test System ◽

Total System ◽

Flow Solution ◽

Optimal Power ◽

Generation Cost

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

Download Full-text