دانلود مقاله ISI انگلیسی شماره 139806
ترجمه فارسی عنوان مقاله

یک الگوریتم تجاری غیر متمرکز برای یک بازار برق با عدم قطعیت تولید

عنوان انگلیسی
A decentralized trading algorithm for an electricity market with generation uncertainty
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
139806 2018 13 صفحه PDF
منبع

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

Journal : Applied Energy, Volume 218, 15 May 2018, Pages 520-532

پیش نمایش مقاله
پیش نمایش مقاله  یک الگوریتم تجاری غیر متمرکز برای یک بازار برق با عدم قطعیت تولید

چکیده انگلیسی

The uncertainties in renewable power generators and the proliferation of price-responsive load aggregators make it a challenge for independent system operators (ISOs) to manage the energy trading in the power markets. Hence, a centralized framework for the energy trading market may not be remained practical for the ISOs mainly due to violating the privacy of different entities, i.e., load aggregators and generators. It can also suffer from the high computational burden in a market with a large number of entities. Instead, in this paper, we focus on proposing a decentralized energy trading framework enabling the ISO to incentivize the entities toward an operating point that jointly optimize the cost of load aggregators and profit of the generators, as well as the risk of shortage in the renewable generation. To address the uncertainties in the renewable resources, we apply a risk measure called the conditional value-at-risk (CVaR) with the goal of limiting the likelihood of high renewable generation shortage with a certain confidence level. Then by considering the risk attitude of the ISO and the generators, we develop a decentralized energy trading algorithm with some control signals that properly coordinate the entities toward the market operating point of the ISO’s centralized approach. Simulation results on the IEEE 30-bus test system show that the proposed decentralized algorithm converges to the solution of the ISO’s centralized problem in a timely fashion. Furthermore, the load aggregators can help their consumers reduce their electricity cost by 18% on average through managing their loads using locally available information. Meanwhile, the generators can benefit from 17.1% increase in their total profit through decreasing their generation cost.