Day-Ahead Scheduling of Distributed Energy Resources in MMG Based Reconfigurable Distribution Network

سال انتشار: 1399
محل انتشار: هفتمین کنگره ملی تازه یافته های مهندسی برق ایران
کد COI اختصاصی: COMCONF07_005
زبان مقاله: انگلیسی
تعداد مشاهده: 719

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نویسندگان

Masoumeh karimi

Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,Iran

Farhad Rahbaran

Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,Iran

Mohammad Reza Jannati Oskuee

Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,Iran

Sajad Najafi Ravadanegh

Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,Iran

چکیده

The number of challenges, including: depletion of fossil fuel resources, stricter regulations on environmental emissions, lower efficiency, a need to enhance the security and the quality of power supply, emerging technologies of electrical vehicles (EVs) and ever increasing expansion of renewable energy resources (RERs) are the vital drivers to the smart grids (SGs). Micro Grid (MG) as the building blocks of SG structure is envisaged with ever increasing Distributed Energy Resources (DER) e.g. distributed generation (DG) especially RERs, demand response (DR) and EVs with grid-able capability (V2G). By increasing the DERs penetration, distribution network operator (DNO) will need precise scheduling techniques in order to guarantee demand satisfaction in possible lowest costs and accepted reliability level. This paper solves the day-ahead scheduling of Multi-MG (MMG) based reconfigurable distribution networks with high penetration of DERs problem. The genetic algorithm is chosen to minimize the proposed eco -reliability cost function. Moreover, to quantify the influence of different load models, a 33-node distribution network is adopted with a load class mix of residential, industrial and commercial loads. Eventually, the obtained results are reported which demonstrated the efficiency of the proposed method.

کلیدواژه ها

Multi-Micro Grids; Electrical Vehicle; Demand Response;Renewable Energy Resource; Genetic Algorithm

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