TY - GEN
T1 - Strategic Battery Storage Management of Aggregators in Energy Demand Networks
AU - Okajima, Yusuke
AU - Hirata, Kenji
AU - Gupta, Vijay
AU - Uchida, Kenko
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - This paper considers optimization problems of energy demand networks including aggregators and investigates strategic behavior of the aggregators. The participants of the network are a utility company, who plays a role of energy supply source, aggregators and a large number of consumers. We suppose that the network will be optimized by price response based or, in other words, market based optimization processes. We also suppose that the aggregator has a strategic parameter in its cost function and, by choosing the parameter strategically, the aggregator will try to pursue its own benefit. This general problem formulation will apply to a specific problem setting, where the aggregator possess battery storage with different specifications: The one is high-performance and expensive and the other is low-performance and cheap. The aggregator will choose total capacity of storage to be installed and a ratio of high-performance storage to low-performance storage as the strategic parameters and try to increase its own benefit. By using numerical examples, we show that the strategic decision making by the aggregator could provide useful insights in qualitative analysis of energy demand networks.
AB - This paper considers optimization problems of energy demand networks including aggregators and investigates strategic behavior of the aggregators. The participants of the network are a utility company, who plays a role of energy supply source, aggregators and a large number of consumers. We suppose that the network will be optimized by price response based or, in other words, market based optimization processes. We also suppose that the aggregator has a strategic parameter in its cost function and, by choosing the parameter strategically, the aggregator will try to pursue its own benefit. This general problem formulation will apply to a specific problem setting, where the aggregator possess battery storage with different specifications: The one is high-performance and expensive and the other is low-performance and cheap. The aggregator will choose total capacity of storage to be installed and a ratio of high-performance storage to low-performance storage as the strategic parameters and try to increase its own benefit. By using numerical examples, we show that the strategic decision making by the aggregator could provide useful insights in qualitative analysis of energy demand networks.
UR - http://www.scopus.com/inward/record.url?scp=85056813178&partnerID=8YFLogxK
U2 - 10.1109/CCTA.2018.8511539
DO - 10.1109/CCTA.2018.8511539
M3 - 会議への寄与
AN - SCOPUS:85056813178
T3 - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
SP - 444
EP - 449
BT - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Conference on Control Technology and Applications, CCTA 2018
Y2 - 21 August 2018 through 24 August 2018
ER -