NDLI: Smart grid distribution prediction and control using computational intelligence

Content Provider	IEEE Xplore Digital Library
Author	Chandler, S.A. Hughes, J.G.
Copyright Year	2013
Description	Author affiliation: Syst. Sci. PhD Program, Portland State Univ., Portland, OR, USA (Hughes, J.G.) \|\| Dept. of Smart Grid Technol., Portland Gen. Electr., Portland, OR, USA (Chandler, S.A.)
Abstract	Smart-grid systems (SGS) may comprise distributed generation, automated demand response, mega-watt scale batteries, and a variety of other utility energy resources and programs. Their physical characteristics and operating flexibility within the distribution grid introduce new challenges to solving the power economic dispatch (PED) problem. Good operation of an SGS requires efficient use of available assets over both the short and long-term. Ideally resources will be scheduled and dispatched to equal loads (demand) in an optimal way, i.e., at the lowest cost, taking into account the differing operating constraints of assets and the changing conditions of the system and its environment. An experimental intelligent controller has been developed as part of the Pacific Northwest Smart Grid Demonstration (PNWSGD) project to address SGS demand-dependent non-linear cost functions, microgrid reliability zone constraints, and dynamic availability states. The controller is embedded within an existing utility control system that provides real time, historical, and forecast data used to predict energy demand for the next 72 hours and to create a near-optimal dispatch schedule for the next 24 hours. Both demand forecasts and schedules are updated every 5 minutes. The modularity of the controller architecture allows for a variety of load forecast and dispatch optimization tools and methods to be used; the current version uses computational intelligence, specifically neural networks. Its generality and versatility provides guidance for development of intelligent controllers adaptable and scalable to a variety of SGS applications.
Starting Page	86
Ending Page	89
File Size	191887
Page Count	4
File Format	PDF
ISBN	9781467346306
DOI	10.1109/SusTech.2013.6617302
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Schedules Load forecasting Neural networks Microgrids Control systems Transactive control Real-time systems Smart grids Power economic dispatch Machine-learning Smart grid Computational intelligence
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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