article · Soft Computing
Microgrid energy management addresses technical and economic requirements while improving load curves by incorporating demand response programmes. Because of numerous constraints, scheduling microgrid resources is treated as a complex, non-linear optimisation challenge. A modified optimisation method named the quantum artificial rabbits optimiser, or QARO, integrates quantum mechanics principles using the Monte Carlo method to determine optimal day-ahead operational schedules. The formulation focuses on reducing daily operating costs, which comprise diesel generator expenses and grid power transactions, alongside maximising financial benefits for the microgrid operator. Evaluated across standard benchmark test functions using statistical assessments, the algorithm outranked several established and recent techniques. Further testing on two microgrid case studies confirmed the method effectively decreases daily operational costs while increasing operator returns compared to alternative algorithms.
Operating microgrids efficiently is critical for balancing local power generation, storage, and grid interactions while maintaining low running costs. Using advanced computational methods to schedule energy resources and respond to shifting consumer demand allows grid controllers to lower diesel fuel consumption and market transaction expenses, leading to more economical and reliable local energy systems.
The algorithm could be incorporated into energy management software used by microgrid operators and utility planners to automate optimal day-ahead dispatch and demand response scheduling. Tested on benchmark functions and simulated microgrid case studies, the tool represents early-stage applied algorithmic research that would require integration and validation within real-world industrial control platforms before commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s00500-023-08814-5
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
