دانلود مقاله ISI انگلیسی شماره 101787
ترجمه فارسی عنوان مقاله

یک چارچوب چند منظوره ای برای بازار برای کنترل قدرت در شبکه هوشمند

عنوان انگلیسی
A multi-objective market-driven framework for power matching in the smart grid
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
101787 2018 17 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Engineering Applications of Artificial Intelligence, Volume 70, April 2018, Pages 199-215

ترجمه کلمات کلیدی
شبکه هوشمند، بازار برق تقاضا و عرضه، مطابقت قدرت، بهینه سازی چند هدفه،
کلمات کلیدی انگلیسی
Smart grid; Electricity market; Demand and supply; Power matching; Multi-objective optimization;
پیش نمایش مقاله
پیش نمایش مقاله  یک چارچوب چند منظوره ای برای بازار برای کنترل قدرت در شبکه هوشمند

چکیده انگلیسی

Smart grids, to facilitate the electricity production, distribution, and consumption, employ information and communication technologies simultaneously. Electricity markets, through stabilizing the electricity prices, attempt to alleviate the challenges of power exchange. On one hand, buyers, by considering their full demand satisfaction, endeavor to purchase the electricity cost-effectively. On the other hand, sellers, by taking their limited electricity generation capacity into account, are interested in increasing their financial benefits. To address this challenge, this paper introduces a highly-functional semi-decentralized power matching framework based on multi-objective optimization techniques executing in a day-ahead electricity market. A two-stage price updating mechanism to continuously balance the electricity prices is also provided. At each time interval, buyers and sellers submit their individual electricity price offers to the market operator. The market operator tunes them and then, announces the electricity market price. A robust multi-objective power matching algorithm is developed to make the matching contracts considering buyers’ and sellers’ objectives along with grid stability constraints imposed by distribution system operators. It also considers the minimization of electricity distribution loss in the matching procedure. Simulation results indicate that the framework successfully reaches a reasonable balance of aforementioned conflicting objectives while conducing negotiating electricity price offers to an equilibrium. It is shown that the proposed algorithm behaves better compared to well-known multi-objective evolutionary algorithms in terms of both optimizing the social welfare and computational complexity (scalability). Finally, effects of the two-stage price updating mechanism on the stability of the proposed evolutionary algorithm is discussed. Performance comparisons show that the proposed framework outperforms the similar approaches available in the literature.