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The Impact of Nature-Inspired Optimization Techniques on Peak and Electricity Cost in Distribution Systems

International Journal of Electrical and Electronics Engineering
© 2023 by SSRG - IJEEE Journal
Volume 10 Issue 10
Year of Publication : 2023
Authors : P. Kanakaraj, L. Ramesh, R. Narayanamoorthi
10.14445/23488379/IJEEE-V10I10P116
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How to Cite?

P. Kanakaraj, L. Ramesh, R. Narayanamoorthi, "The Impact of Nature-Inspired Optimization Techniques on Peak and Electricity Cost in Distribution Systems," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 10, pp. 165-175, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I10P116

Abstract:

In recent years, smart household appliances have led to an increase in residential electricity usage. Peak loads are created by these appliances in residential distribution systems. At peak times, residential distribution power consumption exceeds grid power. The imbalance in power demand results in low voltage in the distribution system, affecting household appliances. Increasing or decreasing grid power demand is necessary to protect these household appliances. The authors implemented renewable energy sources to address this issue, increasing grid power and demand-side management techniques to reduce energy consumption. Despite various research on optimal peak and cost reduction, a lack of different nature-inspired optimization techniques has been evident. The present paper proposes a demand-side load-shifting algorithm for peak load control in a residential building. This multi-objective load-shifting algorithm employs nature-inspired optimization techniques, including MBO, CSO, and AFSO, to reduce the utility’s peak load and the consumer’s electricity cost simultaneously. A comparison is made between the above-mentioned nature-inspired optimization approaches in this paper. Finally, the MBO’s results are superior to other nature-inspired optimization methods.

Keywords:

Peak Load Management (PLM), Demand Side Management (DSM), Load Shifting Algorithm (LSA), Monarch Butterfly Optimization (MBO), Crow Search Optimization (CSO), Artificial Fish Swarm Optimization (AFSO).

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