TY - GEN
T1 - Hierarchical Residential Aggregation Method Incorporating Energy Demand Forecast
AU - Yoshida, Akira
AU - Saito, Toranosuke
AU - Kashikawa, Takahiro
AU - Kimura, Koichi
AU - Amano, Yoshiharu
N1 - Funding Information:
The part of this work is supported by JSPS KAKENHI Grant Number JP18K14170 and Fujitsu Digital Annealer project.
Publisher Copyright:
© ECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
PY - 2021
Y1 - 2021
N2 - The smart energy grid is expected to have a mechanism that demand-side and supply-side get managed onto the whole energy grid to balance. The research question is which energy management architecture is suitable to operate a large-scale energy system within a specific limited time step for computing and communicating. The previous work has proposed a hierarchical management method, which handles local status in the demand-side Energy Management System layer and global status in the aggregation layer. The hierarchical method appeals to scalable, robust, and privacy protection merits compared to a centralized method. The performance of hierarchical management, including demand forecast, is still an open question. This article shows on incorporating energy demand forecast into the hierarchical management method. The paper also evaluates the energy storage capacity needed to balance under the effect on forecast error of energy demand.
AB - The smart energy grid is expected to have a mechanism that demand-side and supply-side get managed onto the whole energy grid to balance. The research question is which energy management architecture is suitable to operate a large-scale energy system within a specific limited time step for computing and communicating. The previous work has proposed a hierarchical management method, which handles local status in the demand-side Energy Management System layer and global status in the aggregation layer. The hierarchical method appeals to scalable, robust, and privacy protection merits compared to a centralized method. The performance of hierarchical management, including demand forecast, is still an open question. This article shows on incorporating energy demand forecast into the hierarchical management method. The paper also evaluates the energy storage capacity needed to balance under the effect on forecast error of energy demand.
KW - Aggregation
KW - Demand Forecast
KW - Demand Response
KW - Digital Annealer
KW - Energy Management
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M3 - Conference contribution
AN - SCOPUS:85134384951
T3 - ECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
SP - 1511
EP - 1521
BT - ECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
PB - ECOS 2021 Program Organizer
T2 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2021
Y2 - 28 June 2021 through 2 July 2021
ER -