TY - GEN
T1 - A Data-Driven Based Strategy to Evaluate Participation of Diverse Social Classes in Smart Electric Grids
AU - He, Mingyue
AU - Khorsand, Mojdeh
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - The grand transition of electric grids from conventional fossil fuel resources to intermittent bulk renewable resources and distributed energy resources (DERs) has initiated a paradigm shift in power system operation. Distributed energy resources (i.e. rooftop solar photovoltaic, battery storage, electric vehicles, and demand response), communication infrastructures, and smart measurement devices provide the opportunity for electric utility customers to play an active role in power system operation and even benefit financially from this opportunity. However, new operational challenges have been introduced due to the intrinsic characteristics of DERs such as intermittency of renewable resources, distributed nature of these resources, variety of DERs technologies and human-in-the-loop effect. This paper mainly focuses on demand response (DR), which is a major type of DERs and is highly influenced by human-in-the-loop effect. A data-driven based analysis is implemented to analyze and reveal the human-in-the-loop effect. The results confirm the critical impact of demographic characteristics of customers on their interaction with smart grid and their quality of service (QoS). The proposed framework is also applicable to other types of DERs.
AB - The grand transition of electric grids from conventional fossil fuel resources to intermittent bulk renewable resources and distributed energy resources (DERs) has initiated a paradigm shift in power system operation. Distributed energy resources (i.e. rooftop solar photovoltaic, battery storage, electric vehicles, and demand response), communication infrastructures, and smart measurement devices provide the opportunity for electric utility customers to play an active role in power system operation and even benefit financially from this opportunity. However, new operational challenges have been introduced due to the intrinsic characteristics of DERs such as intermittency of renewable resources, distributed nature of these resources, variety of DERs technologies and human-in-the-loop effect. This paper mainly focuses on demand response (DR), which is a major type of DERs and is highly influenced by human-in-the-loop effect. A data-driven based analysis is implemented to analyze and reveal the human-in-the-loop effect. The results confirm the critical impact of demographic characteristics of customers on their interaction with smart grid and their quality of service (QoS). The proposed framework is also applicable to other types of DERs.
KW - Distributed energy resources
KW - demand response
KW - machine learning
KW - participation in grid services
KW - prosumers
KW - quality of service estimation
KW - social value of energy
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U2 - 10.1109/NAPS46351.2019.9000241
DO - 10.1109/NAPS46351.2019.9000241
M3 - Conference contribution
T3 - 51st North American Power Symposium, NAPS 2019
BT - 51st North American Power Symposium, NAPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st North American Power Symposium, NAPS 2019
Y2 - 13 October 2019 through 15 October 2019
ER -