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Vol. 4 No. 2 (2025): May 2025 Issue

Optimal power load flow considering stochastic wind and solar power

Kenneth Liau Chin Ern
School of Electrical and Electronics Engineering, Swinburne University of Technology, Sarawak, Kuching, Malaysia
Jalal Tavalaei
School of Electrical and Electronics Engineering, Swinburne University of Technology, Sarawak, Kuching, Malaysia

Corresponding Author(s) : Jalal Tavalaei

jtavalaei@swinburne.edu.my

Future Energy, Vol. 4 No. 2 (2025): May 2025 Issue

Abstract

Optimal power flow (OPF) is a popular model in the study of power systems that aims to achieve optimization and operational stability while minimizing costs. Multiple research documents depict the various methods and algorithms to further explore system configurations that would achieve OPF or multi-objective OPF. In relation to power systems, many power plants rely on non-renewable sources such as fossil fuels to generate power to be supplied to cities, towns, and rural areas, but these sources are steadily decreasing in their availability and are no longer suitable for sustainability. Renewable sources (solar, wind, and hydro) have been around for decades and have been found to be suitable alternatives for power plants, but due to their stochastic nature, the power needed via this method of generation is inconsistent. The relationship between the types of sources and OPF can be seen via costs, emissions, and power since the costs required for generation and distribution are dependent on their source, be they renewable or non-renewable, including the possibility of a decrease in emissions from these power plants. This research is expected to show that OPF can be achieved using renewable sources within power systems, even with their stochastic behaviors.

Keywords

Optimal power load flow Stochastic renewable power Power systems Multi-objective genetic algorithm (MOGA) WSCC 9-bus test system
Chin Ern, Kenneth Liau, and Jalal Tavalaei. 2025. “Optimal Power Load Flow Considering Stochastic Wind and Solar Power”. Future Energy 4 (2):35-40. https://fupubco.com/fuen/article/view/293.
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