TY - JOUR
T1 - Life-Aware Operation of Battery Energy Storage in Frequency Regulation
AU - Ma, Qianli
AU - Wei, Wei
AU - Wu, Lei
AU - Mei, Shengwei
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - The rapid growth of renewable generation in power systems imposes unprecedented challenges on maintaining power balance in real time. With the continuous decrease of thermal generation capacity, battery energy storage is expected to take part in frequency regulation service. However, accurately following the automatic generation control (AGC) signal leads to more frequent switching between charging and discharging states, which may shorten battery life. Because battery life is a consequence of long-term operation depending on the depth of discharge, it is difficult to model battery health in frequency regulation problems. This paper establishes an online operation policy in response to the real-time AGC signal considering battery health. Based on the empirical relation between cycling number and depth of discharge, a cost function is suggested to approximate the impact of charging-discharging action on battery life in the long run. Then, Lyapunov drift-plus-penalty technique is employed to derive an explicit online policy compromising the revenue of frequency regulation and battery life without requiring future AGC data. The offline operation problem after knowing the entire sequence of AGC signal is cast as a difference-of-convex program and solved by a sequential convexification algorithm. The gap between the offline and online optimum is shown to be a constant, which quantifies the value of knowing future information. Case studies based on AGC data from the PJM market validate the proposed method.
AB - The rapid growth of renewable generation in power systems imposes unprecedented challenges on maintaining power balance in real time. With the continuous decrease of thermal generation capacity, battery energy storage is expected to take part in frequency regulation service. However, accurately following the automatic generation control (AGC) signal leads to more frequent switching between charging and discharging states, which may shorten battery life. Because battery life is a consequence of long-term operation depending on the depth of discharge, it is difficult to model battery health in frequency regulation problems. This paper establishes an online operation policy in response to the real-time AGC signal considering battery health. Based on the empirical relation between cycling number and depth of discharge, a cost function is suggested to approximate the impact of charging-discharging action on battery life in the long run. Then, Lyapunov drift-plus-penalty technique is employed to derive an explicit online policy compromising the revenue of frequency regulation and battery life without requiring future AGC data. The offline operation problem after knowing the entire sequence of AGC signal is cast as a difference-of-convex program and solved by a sequential convexification algorithm. The gap between the offline and online optimum is shown to be a constant, which quantifies the value of knowing future information. Case studies based on AGC data from the PJM market validate the proposed method.
KW - Battery energy storage
KW - Lyapunov optimization
KW - battery life
KW - frequency regulation
KW - online policy
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U2 - 10.1109/TSTE.2023.3245197
DO - 10.1109/TSTE.2023.3245197
M3 - Article
AN - SCOPUS:85149377327
SN - 1949-3029
VL - 14
SP - 1725
EP - 1736
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 3
ER -