Multi-dimensional energy management based on an optimal power flow model using an improved quasi-reflection jellyfish optimization algorithm

Abstract

This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives.

KEYWORDS:

• Optimal power flow
• jellyfish optimization
• quasi-reflection learning
• fuel costs
• emissions

Acknowledgement

The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project TURSP 2020/122, Taif University, Taif, Saudi Arabia.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available within the article and the associated supplementary file.

Additional information

Funding

This work was supported by Taif University Researchers Supporting Project TURSP 2020/122, Taif University, Taif, Saudi Arabia.