Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Version 1 : Received: 8 March 2021 / Approved: 8 March 2021 / Online: 8 March 2021 (13:56:36 CET)

How to cite: Hossain, M.A.; Sallam, K.M.; Elsayed, S.S.; Chakrabortty, R.K.; Ryan, M.J. Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm. Preprints 2021, 2021030228 (doi: 10.20944/preprints202103.0228.v1). Hossain, M.A.; Sallam, K.M.; Elsayed, S.S.; Chakrabortty, R.K.; Ryan, M.J. Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm. Preprints 2021, 2021030228 (doi: 10.20944/preprints202103.0228.v1).

Abstract

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

Subject Areas

Evolutionary Algorithms; differential evolution; constraints handling techniques; optimal power flow ; renewable energy

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