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Optimal energy management of microgrids-integrated nonconvex distributed generating units with load dynamics

INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18919-18934页

作者： Raghav, Lolla Phani Rangu, Seshu Kumar Dhenuvakonda, Koteswara Raju Singh, Arvind R. Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Qianfoshan Campus Jinan 250100 Shandong Peoples R China

Microgrid (MG) energy management is a complex task for MG operators to integrate and utilize consumer-based power sources. The MG energy management systems' problem will become tedious by considering distributed generation (DG) units' nonconvex characteristics. Therefore, a novel attempt is made to solve the day-ahead dispatch problem of grid-connected MG with the nonconvex cost function of DG units, including weekend and weekday load dynamics. At first, the utility-induced flexible load shaping strategy is implemented to enhance the DG units' operation cost and reduce the peak loads. Then, demand-side management (DSM) programs are plausibly the essential form of energy management to regulate the consumers' energy usage without violating grid price policies. Next, the DSM program is implemented to study the impact of DSM participation levels with convex and nonconvex cost functions. Further, the day-ahead scheduling time duration with a resolution of 15 minutes is considered to examine the impact of a typical weekend and weekday load dynamics on DG units' nonconvex cost function. Finally, the quantum teaching-learning-based optimization algorithm (QTLA) is devised to handle the nonconvex cost function of DG units and optimize MG's total operating costs for the first time. The proposed QTLA algorithm is compared with other metaheuristic optimization techniques such as differential evolution (DE), real-coded genetic algorithm (RCGA), and teaching-learning-based optimization (TLBO). The results show that the proposed strategy reduces the MG operating cost by 3.14% compared to the case study, where no DSM participation is considered. Finally, the QTLA algorithm outperforms in terms of efficacy, convergence characteristics, and computational time.

关键词： demand-side management dynamic loading microgrid energy management nonconvex cost function quantum teaching-learning-based optimization

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