TY - JOUR
T1 - A Competitive Scheduling Algorithm for Online Demand Response in Islanded Microgrids
AU - Karapetyan, Areg
AU - Khonji, Majid
AU - Chau, Sid Chi Kin
AU - Elbassioni, Khaled
AU - Zeineldin, Hatem
AU - El-Fouly, Tarek H.M.
AU - Al-Durra, Ahmed
N1 - Funding Information:
Manuscript received June 24, 2020; revised November 1, 2020; accepted December 5, 2020. Date of publication December 21, 2020; date of current version June 18, 2021. This work was supported by the Khalifa University of Science and Technology under Award CIRA-2019-049. Paper no. TPWRS-01 043-2020. (Corresponding author: Areg Karapetyan.) Areg Karapetyan, Tarek H. M. EL-Fouly, and Ahmed Al-Durra are with the Advanced Power & Energy Center (APEC), Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - A routine task faced by Microgrid (MG) operators is to optimally allocate incoming power demand requests while accounting for the underlying power distribution network and the associated constraints. Typically, this has been formulated as an offline optimization problem for day-ahead scheduling, assuming perfect forecasting of the demands. In practice, however, these loads are often requested in an ad-hoc manner and the control decisions are to be computed without any foresight into future inputs. With this in view, the present work contributes to the modeling and algorithmic foundations of real-time load scheduling problem in a demand response (DR) program. We model the problem within an AC Optimal Power Flow (OPF) framework and design an efficient online algorithm that outputs scheduling decisions provided with information on past and present inputs solely. Furthermore, a rigorous theoretical bound on the competitive ratio of the algorithm is derived. Practicality of the proposed approach is corroborated through numerical simulations on two benchmark MG systems against a representative greedy algorithm.
AB - A routine task faced by Microgrid (MG) operators is to optimally allocate incoming power demand requests while accounting for the underlying power distribution network and the associated constraints. Typically, this has been formulated as an offline optimization problem for day-ahead scheduling, assuming perfect forecasting of the demands. In practice, however, these loads are often requested in an ad-hoc manner and the control decisions are to be computed without any foresight into future inputs. With this in view, the present work contributes to the modeling and algorithmic foundations of real-time load scheduling problem in a demand response (DR) program. We model the problem within an AC Optimal Power Flow (OPF) framework and design an efficient online algorithm that outputs scheduling decisions provided with information on past and present inputs solely. Furthermore, a rigorous theoretical bound on the competitive ratio of the algorithm is derived. Practicality of the proposed approach is corroborated through numerical simulations on two benchmark MG systems against a representative greedy algorithm.
KW - Combinatorial optimization
KW - Competitive online algorithm
KW - Discrete demand requests
KW - Microgrid
KW - Online demand response
KW - Optimal power flow
KW - Real-time load scheduling
UR - http://www.scopus.com/inward/record.url?scp=85098794845&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2020.3046144
DO - 10.1109/TPWRS.2020.3046144
M3 - Article
AN - SCOPUS:85098794845
SN - 0885-8950
VL - 36
SP - 3430
EP - 3440
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 4
M1 - 9301221
ER -