An effective quantum artificial rabbits optimizer for energy management in microgrid considering demand response

Abstract

Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an exceptional performance, however there is no single algorithm can solve all engineering problem. So, this paper proposes a modified version of artificial rabbits optimization algorithm, called QARO, by quantum mechanics based on Monte Carlo method to determine the optimal scheduling for MG resources effectively. The main objective is minimization of the daily operating cost with the maximization of MG operator (MGO) benefit. The operating cost includes the conventional diesel generator operating cost and the cost of power transactions with the grid. The performance of the proposed algorithm is assessed using different standard benchmark test functions. A ranking order for the test function based on the average value and Tied rank technique, Wilcoxon's rank test based on median value, and Anova Kruskal–Wallis test showed that QARO achieved best results on the most functions and outperforms all other compared technique. The obtained results of the proposed QARO are compared with those obtained by employing well-known and newly-developed algorithms. Moreover, the proposed QARO is used to solve two case studies of day-ahead EM problem in MG, then the obtained results are also compared with other well-known optimization techniques, the results demonstrate the effectiveness of QARO in reducing the operating cost and maximization the MGO benefit.

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“…The Tied rank (TR) technique is utilized to rank these functions, where each technique is assigned, a rank based on its average value, with the algorithm having the smallest average value receiving rank 1, and so on. The algorithm with the lowest TR value is considered the most effective when compared to the other techniques 28 .…”

“…It is important to note that the No Free Lunch (NFL) theorem asserts that no single technique can perform optimally on all problems 28 – 30 . This motivates academics to suggest new techniques or enhance present ones to address specific optimization challenges.…”

An improved Artificial Rabbit Optimization for structural damage identification

“…For instance, (Alsaiari et al, 2023) merged ARO with a multi-layer perceptrons (MLP) model to predict the water efficiency of different configurations of solar stills (SSs). Another study (Alamir et al, 2023) introduced a modified ARO approach, infused with principles from quantum mechanics, showcasing its efficacy in solving energy management (EM) problems by optimizing benefits and reducing time consumption. Furthermore, (Samal et al, 2023) effectively employed ARO to address economic load dispatch challenges.…”