TY - JOUR
T1 - A Hierarchical Approach to Multienergy Demand Response
T2 - From Electricity to Multienergy Applications
AU - Hassan, Ali
AU - Acharya, Samrat
AU - Chertkov, Michael
AU - Deka, Deepjyoti
AU - Dvorkin, Yury
N1 - Funding Information:
Manuscript received November 28, 2019; revised February 6, 2020; accepted March 16, 2020. Date of publication April 22, 2020; date of current version August 20, 2020. This work at NYU was supported in part by the National Science Foundation (NSF) under Award EECS-1847285 and in part by the U.S. Department of Energy under Award DE-AC52-07NA27344. (Corresponding author: Yury Dvorkin.) Ali Hassan and Samrat Acharya are with the Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, New York, NY 11201 USA. Michael Chertkov is with the Department of Mathematics, The University of Arizona, Tucson, AZ 85721 USA. Deepjyoti Deka is with Theoretical Division (T-5), Los Alamos National Laboratory, Los Alamos, NM 87544 USA. Yury Dvorkin is with the Center for Urban Science and Progress, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, New York, NY 11201 USA (e-mail: dvorkin@nyu.edu).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i.e., cannot be adjusted in return for an adequate incentive. To allow for a less centralized operating paradigm, consumer-end perspective and abilities should be integrated in current dispatch practices and accounted for in switching between different energy sources not only at the system but also at the individual consumer level. Since consumers are confined within different built environments, this article looks into an opportunity to control energy consumption of an aggregation of many residential, commercial, and industrial consumers, into an ensemble. This ensemble control becomes a modern demand response (DR) contributor to the set of modeling tools for multienergy infrastructure systems.
AB - Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i.e., cannot be adjusted in return for an adequate incentive. To allow for a less centralized operating paradigm, consumer-end perspective and abilities should be integrated in current dispatch practices and accounted for in switching between different energy sources not only at the system but also at the individual consumer level. Since consumers are confined within different built environments, this article looks into an opportunity to control energy consumption of an aggregation of many residential, commercial, and industrial consumers, into an ensemble. This ensemble control becomes a modern demand response (DR) contributor to the set of modeling tools for multienergy infrastructure systems.
KW - Demand response (DR)
KW - Markov decision process (MDP)
KW - multi-energy systems
KW - reinforcement learning
KW - robust optimization
KW - smart buidlings
KW - stochastic optimization
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U2 - 10.1109/JPROC.2020.2983388
DO - 10.1109/JPROC.2020.2983388
M3 - Article
AN - SCOPUS:85083776798
SN - 0018-9219
VL - 108
SP - 1457
EP - 1474
JO - Proceedings of the Institute of Radio Engineers
JF - Proceedings of the Institute of Radio Engineers
IS - 9
M1 - 9076178
ER -