TY - GEN
T1 - Real-Time Peak Control algorithm using Stochastic Optimization
AU - Acquah, Moses Amoasi
AU - Han, Sekyung
AU - Kim, Hongjoon
AU - Park, Soonwoo
AU - Han, Heeje
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Battery energy storage systems (BESS) has several uses in electrical system such as peak demand control and energy arbitrage. The benefits of controlling peak can be viewed in both short term and long term. In a long term, it results in a lower electric cost in subsequent years, also it enhances stability and contributes to cost saving in a short term. In this study, we propose a novel Real Time Peak Demand Control, which incorporates a new time series dimensionality reduction technique dubbed TOU base Piecewise Approximation (TPA) for Dynamic Stochastic Optimization. Most peak demand control algorithms employ deterministic approach to controlling peak demand. These methods are not robust and are susceptible to errors. For analysis, we used past load profile obtained from a real site in South Korea. Simulations and the results obtained show that the proposed method achieves a better prediction and as such better peak demand control.
AB - Battery energy storage systems (BESS) has several uses in electrical system such as peak demand control and energy arbitrage. The benefits of controlling peak can be viewed in both short term and long term. In a long term, it results in a lower electric cost in subsequent years, also it enhances stability and contributes to cost saving in a short term. In this study, we propose a novel Real Time Peak Demand Control, which incorporates a new time series dimensionality reduction technique dubbed TOU base Piecewise Approximation (TPA) for Dynamic Stochastic Optimization. Most peak demand control algorithms employ deterministic approach to controlling peak demand. These methods are not robust and are susceptible to errors. For analysis, we used past load profile obtained from a real site in South Korea. Simulations and the results obtained show that the proposed method achieves a better prediction and as such better peak demand control.
KW - battery energy storage system (BESS)
KW - peak demand control
KW - short term load forecasting
KW - stochastic optimization
KW - time series dimension reduction
UR - http://www.scopus.com/inward/record.url?scp=85016790974&partnerID=8YFLogxK
U2 - 10.1109/CCWC.2017.7868470
DO - 10.1109/CCWC.2017.7868470
M3 - Conference contribution
AN - SCOPUS:85016790974
T3 - 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017
BT - 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017
A2 - Saha, Himadri Nath
A2 - Chakrabarti, Satyajit
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2017
Y2 - 9 January 2017 through 11 January 2017
ER -