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The Application of a Microgrid Considering Wind Generation

[+] Author Affiliations
Javad Katooli

University of Kashan, Kashan, Iran

Mohammad Ameri, Mohammad Hadi Katooli

Shahid Beheshti University, Tehran, Iran

Paper No. ES2015-49787, pp. V002T13A010; 7 pages
  • ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum
  • Volume 2: Photovoltaics; Renewable-Non-Renewable Hybrid Power System; Smart Grid, Micro-Grid Concepts; Energy Storage; Solar Chemistry; Solar Heating and Cooling; Sustainable Cities and Communities, Transportation; Symposium on Integrated/Sustainable Building Equipment and Systems; Thermofluid Analysis of Energy Systems Including Exergy and Thermoeconomics; Wind Energy Systems and Technologies
  • San Diego, California, USA, June 28–July 2, 2015
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5685-7
  • Copyright © 2015 by ASME


Conventional power networks have experienced a gradual evolution from a centralized nature to distributed and localized structures. The upgrading of power system toward a smart grid is being developed to improve reliability and facilitate the integration of different types of renewable energies and improve load management. Due to different uncertainties linked to electricity supply in renewable MGs, probabilistic energy management techniques are going to be necessary to analyze the system. In this study, the short-term operation planning of a typical microgrid (MG) with diverse units for achieving the maximum profit, considering technical and economical constraints, for the next 24 hours, using gravitational search algorithm (GSA) with SPSS software is presented and the effect of wind generation in the planning is investigated. The MG consists of a diverse variety of power system components such as wind turbine, microturbine, photovoltaic, fuel cell, Hydrogen storage tank, reformer, a boiler, and electrical and thermal loads. Moreover, MG is connected to an electrical grid for exchange of power. The MG is managed and controlled through a central controller. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. Total obtained profit from the MG, considering with US electricity and natural gas prices is $5.312902×103.

Copyright © 2015 by ASME
Topics: Microgrids , Wind



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