TY - GEN
T1 - Optimizing energy storage participation in emerging power markets
AU - Chen, Hao
AU - Liu, Zhenhua
AU - Coskun, Ayse K.
AU - Wierman, Adam
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/26
Y1 - 2016/1/26
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84962800141
U2 - 10.1109/IGCC.2015.7393718
DO - 10.1109/IGCC.2015.7393718
M3 - Conference contribution
AN - SCOPUS:84962800141
T3 - 2015 6th International Green and Sustainable Computing Conference
BT - 2015 6th International Green and Sustainable Computing Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Green and Sustainable Computing Conference, IGSC 2015
Y2 - 14 December 2015 through 16 December 2015
ER -