NDLI: Optimizing energy storage participation in emerging power markets

Content Provider	IEEE Xplore Digital Library
Author	Hao Chen Zhenhua Liu Coskun, A.K. Wierman, A.
Copyright Year	2015
Description	Author affiliation: Electr. & Comput. Eng., Boston Univ., Boston, MA, USA (Hao Chen; Coskun, A.K.) \|\| Appl. Math. & Stat., Stony Brook Univ., Stony Brook, NY, USA (Zhenhua Liu) \|\| Comput. & Math. Sci., California Inst. of Technol., Pasadena, CA, USA (Wierman, A.)
Abstract	The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.
Starting Page	1
Ending Page	6
File Size	331203
Page Count	6
File Format	PDF
e-ISBN	9781509001729
DOI	10.1109/IGCC.2015.7393718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electric potential Load management Discharges (electric) Real-time systems Batteries Optimization
Content Type	Text
Resource Type	Article

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