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Demand response program based load management for an islanded smart microgrid

Publication Type : Journal Article

Publisher : Advances in Intelligent Systems and Computing

Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 517, p.255-267 (2017)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027124852&doi=10.1007%2f978-981-10-3174-8_23&partnerID=40&md5=dff5b7d537a3297e49c1701362586fef

ISBN : 9789811031731

Keywords : Artificial intelligence, Demand Response, Demand response programs, Electric load management, Electric power plant loads, Electrical energy, Electricity use, Energy management, Energy management systems, Energy utilization, Fuzzy logic, Informed decision, Management scheme, Renewable energies, Smart Micro Grids

Campus : Coimbatore

School : School of Engineering

Department : Electrical and Electronics

Year : 2017

Abstract : There has been an ever-growing demand for electrical energy causing supply–demand imbalance in the power system. To overcome this imbalance, a reliable, efficient, less centralised, and more interactive energy management system (EMS) has to be realized. One of the methods to bridge the supply–demand gap using EMS is through Load Management. The evolution of EMS allows loads to respond to the demand (Demand Response) and assist customers to make informed decisions about their energy consumption, adjusting both the timing and quantity of their electricity use. This paper deals with the development of Fuzzy logic-based load management scheme using Demand Response Programs in smart microgrids. The developed system is tested on a smart micro-grid simulator (SMGS) operated in islanded mode installed in the Renewable Energy Laboratory in Amrita Vishwa Vidyapeetham, Coimbatore. © Springer Nature Singapore Pte Ltd. 2017.

Cite this Research Publication : K. Gokuleshvar, Anand, S., S. Babu, V., and D. Vadana, P., “Demand response program based load management for an islanded smart microgrid”, Advances in Intelligent Systems and Computing, vol. 517, pp. 255-267, 2017.

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