NDLI: A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

Content Provider	IEEE Xplore Digital Library
Author	Ghofrani, M. Arabali, A. Etezadi-Amoli, M. Fadali, M.S.
Copyright Year	2010
Abstract	This paper deals with optimal placement of the energy storage units within a deregulated power system to minimize its hourly social cost. Wind generation and load are modeled probabilistically using actual data and a curve fitting approach. Based on a model of the electricity market, we minimize the hourly social cost using probabilistic optimal power flow (POPF) then use a genetic algorithm to maximize wind power utilization over a scheduling period. A business model is developed to evaluate the economics of the storage system based on the energy time-shift opportunity from wind generation. The proposed method is used to carry out simulation studies for the IEEE 24-bus system. Transmission line constraints are addressed as a bottleneck for efficient wind power integration with higher penetration levels. Distributed storage is then proposed as a solution to effectively utilize the transmission capacity and integrate the wind power more efficiently. The potential impact of distributed storage on wind utilization is also evaluated through several case studies.
Starting Page	434
Ending Page	442
Page Count	9
File Size	1107249
File Format	PDF
ISSN	19493029
Volume Number	4
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy storage Wind power generation Generators Load modeling Probabilistic logic Power systems Wind speed two-point estimate method Compressed air energy storage (CAES) distributed storage electricity market energy arbitrage genetic algorithm optimal placement optimal power flow
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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