Abstract
The popularity of rooftop solar and home-scale batteries brings a fantastic opportunity to homeowners for using energy more flexibly and achieving benefits from trading electricity. This paper narrates a research work of how home energy management systems (HEMSs) maximize profits from buying electricity at a fixed price from utility and selling the surplus energy to an aggregator. Appropriate optimization problems are formulated with technical constraints of household devices and from connection requirements including a volt-watt function installed in the inverter of the rooftop solar-battery system. HEMS responses are optimized in two stages. The first stage is a stochastic program for planning an optimal energy commitment to an aggregator considering forecast errors and the predicted power curtailment. On the day of operation, each HEMS tries to follow the committed energy plan when determining optimal responses in the real-Time condition. Effectiveness of the proposed method is well illustrated through a simulation of a Japanese low-voltage system. Results show that forecast errors and power limitation due to volt-watt functions should be addressed to improve home benefits.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538645055 |
| DOIs | |
| Publication status | Published - 2018 Dec 10 |
| Event | 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018 - Sarajevo, Bosnia and Herzegovina Duration: 2018 Oct 21 → 2018 Oct 25 |
Other
| Other | 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018 |
|---|---|
| Country | Bosnia and Herzegovina |
| City | Sarajevo |
| Period | 18/10/21 → 18/10/25 |
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Keywords
- Active power curtailment
- aggregator
- battery
- home energy management system
- Monte Carlo simulation
- rooftop solar
- stochastic programming
- volt-watt function
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Safety, Risk, Reliability and Quality
- Control and Optimization
Cite this
Optimal Responses of Home Energy Management Systems to Volt-watt Functions and Forecast Errors. / Dao, Van Tu; Ishii, Hideo; Hayashi, Yasuhiro; Takasawa, Y.; Murakami, K.; Takenobu, Y.; Kikusato, H.; Kaneko, A.; Yoshizawa, Shinya.
Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8571717.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Optimal Responses of Home Energy Management Systems to Volt-watt Functions and Forecast Errors
AU - Dao, Van Tu
AU - Ishii, Hideo
AU - Hayashi, Yasuhiro
AU - Takasawa, Y.
AU - Murakami, K.
AU - Takenobu, Y.
AU - Kikusato, H.
AU - Kaneko, A.
AU - Yoshizawa, Shinya
PY - 2018/12/10
Y1 - 2018/12/10
N2 - The popularity of rooftop solar and home-scale batteries brings a fantastic opportunity to homeowners for using energy more flexibly and achieving benefits from trading electricity. This paper narrates a research work of how home energy management systems (HEMSs) maximize profits from buying electricity at a fixed price from utility and selling the surplus energy to an aggregator. Appropriate optimization problems are formulated with technical constraints of household devices and from connection requirements including a volt-watt function installed in the inverter of the rooftop solar-battery system. HEMS responses are optimized in two stages. The first stage is a stochastic program for planning an optimal energy commitment to an aggregator considering forecast errors and the predicted power curtailment. On the day of operation, each HEMS tries to follow the committed energy plan when determining optimal responses in the real-Time condition. Effectiveness of the proposed method is well illustrated through a simulation of a Japanese low-voltage system. Results show that forecast errors and power limitation due to volt-watt functions should be addressed to improve home benefits.
AB - The popularity of rooftop solar and home-scale batteries brings a fantastic opportunity to homeowners for using energy more flexibly and achieving benefits from trading electricity. This paper narrates a research work of how home energy management systems (HEMSs) maximize profits from buying electricity at a fixed price from utility and selling the surplus energy to an aggregator. Appropriate optimization problems are formulated with technical constraints of household devices and from connection requirements including a volt-watt function installed in the inverter of the rooftop solar-battery system. HEMS responses are optimized in two stages. The first stage is a stochastic program for planning an optimal energy commitment to an aggregator considering forecast errors and the predicted power curtailment. On the day of operation, each HEMS tries to follow the committed energy plan when determining optimal responses in the real-Time condition. Effectiveness of the proposed method is well illustrated through a simulation of a Japanese low-voltage system. Results show that forecast errors and power limitation due to volt-watt functions should be addressed to improve home benefits.
KW - Active power curtailment
KW - aggregator
KW - battery
KW - home energy management system
KW - Monte Carlo simulation
KW - rooftop solar
KW - stochastic programming
KW - volt-watt function
UR - http://www.scopus.com/inward/record.url?scp=85060235230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060235230&partnerID=8YFLogxK
U2 - 10.1109/ISGTEurope.2018.8571717
DO - 10.1109/ISGTEurope.2018.8571717
M3 - Conference contribution
AN - SCOPUS:85060235230
BT - Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -