MULTIAGENT-BASED MICROGRID WITH ELECTRIC VEHICLE ALLOCATION PLANNING

The power grid paradigm is expected to be flexible, reliable, and capable of maintaining the stability of power flow from sources to loads within the grid as well as undergo unexpected circumstances during a disaster. It is expected to be able to deal with the problems which may occur in the grid du...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: , ANITA PURBA NILAM HAPSARI, , Eka Firmansyah, S.T., M.Eng., Ph.D.
التنسيق: Theses and Dissertations NonPeerReviewed
منشور في: [Yogyakarta] : Universitas Gadjah Mada 2014
الموضوعات:
ETD
الوصول للمادة أونلاين:https://repository.ugm.ac.id/127930/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=68246
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الوصف
الملخص:The power grid paradigm is expected to be flexible, reliable, and capable of maintaining the stability of power flow from sources to loads within the grid as well as undergo unexpected circumstances during a disaster. It is expected to be able to deal with the problems which may occur in the grid during a disaster, such as disconnected power links, power shortages, etc. The development of the power grid paradigms involves the development of Smart Grid and microgrid. In order to support the operation of Smart Grid and microgrid to deal with problems which may occur during a disaster, electric vehicle has been considered as the ancillary services. This research focuses on microgrid which are low voltage electrical networks that interconnect small, modular generation sources, and proposes an allocation planning method for electric vehicle (EV) in a multiagent-based microgrid. The proposed method allocates EV to provide the demanding loads with power based on demand of loads, stored power in each EV and distance from EVâ��s location to loads. The simulation is performed using software agent named DASH and IDE named IDEA framework. The agents are planted on several PCs to prove the effectiveness of distributed control on microgrid as well as to demonstrate the communication during the allocation method process. From the simulation results we proved our method is effectively and efficiently able to allocate EVs to several demanding loads based on demands, EVâ��s charged power, and distance between EVâ��s location and loads.