TY - GEN
T1 - Optimal power demand management by aggregators using matching theory
AU - Ikegami, Hiroto
AU - Namerikawa, Toru
N1 - Funding Information:
*This work was supported by JST CREST Grant Number JPMJCR15K2, Japan.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.
AB - This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.
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U2 - 10.23919/ACC.2018.8431856
DO - 10.23919/ACC.2018.8431856
M3 - Conference contribution
AN - SCOPUS:85052593949
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 4584
EP - 4589
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -