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Journal Article Robust uncertainty-aware control of energy storage systems using biased renewable energy forecast
Cited 2 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Jangkyum Kim, Yoon-Sik Yoo, Hyo Sik Yang, Ho Seon Choi
Issue Date
2024-08
Citation
Applied Energy, v.367, pp.1-10
ISSN
0306-2619
Publisher
Elsevier Ltd.
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.apenergy.2024.123309
Abstract
This study presents a novel building energy management system scheme addressing the challenges of biased photovoltaic voltage prediction and optimal energy management methods applicable to existing infrastructure. A biased PV prediction model is proposed to obtain predictions aligned with the intended uncertainty tendencies. In addition, an energy management scheme is introduced to increase energy efficiency by reducing the overall cost without requiring additional controllable power sources. Furthermore, a control scheme for optimizing the operational cost while considering the occupants’ thermal discomfort is proposed by analyzing the occupancy rate in the building and implementing appropriate temperature settings and elevator operations. This approach has the advantage of minimizing overall electricity cost while considering the users’ dissatisfaction, without the installation of additional controllable power resources. Here, the feasibility of the proposed approach is demonstrated through a reduction in the daily electricity cost and peak power by 8.56% and 11.75%, respectively, compared with scenarios without a proper building energy management system. These results demonstrated the effectiveness of the proposed building energy management system for achieving energy efficiency and cost savings in practical building environments. The proposed scheme can be used in buildings with diverse users to reduce energy consumption and overall building cost.
KSP Keywords
Control scheme, Cost savings, Electricity cost, Energy Management Scheme, Energy Storage Systems(ESS), Energy efficiency, Energy forecast, Energy storage(ES), Management methods, Occupancy rate, Optimal energy management