31 تیر 1403
مهدي احمدي جيردهي

مهدی احمدی جیردهی

مرتبه علمی: دانشیار
نشانی: ایران- کرمانشاه- بزرگراه امام خمینی- دانشگاه صنعتی کرمانشاه - دانشکده مهندسی برق - مهندسی برق (گرایش های قدرت و کنترل)
تحصیلات: دکترای تخصصی / مهندسی برق- قدرت
تلفن: 0838305001
دانشکده: دانشکده مهندسی برق

مشخصات پژوهش

عنوان
Smart grid optimization considering decentralized power distribution and demand side management
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Demand side management, Decentralized control, Smart grid optimization, Renewable resource.
پژوهشگران فاطمه افسری (نفر اول)، مهدی احمدی جیردهی (نفر دوم)

چکیده

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighboring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%.