Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties
In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader...
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sg-ntu-dr.10356-1602842022-07-18T08:55:42Z Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties Xu, Xu Xu, Yan Wang, Ming-Hao Li, Jiayong Xu, Zhao Chai, Songjian He, Yufei School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Energy Sharing Energy Storage In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information. Agency for Science, Technology and Research (A*STAR) This work is partially supported by the National Natural Science Foundation of China under Grant 71971183. The work of Jiayong Li is supported by the National Natural Science Foundation of China under Grant 51907056. Yan Xu’s works is partially supported by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund - Industry Collaboration Projects Grant. 2022-07-18T08:55:42Z 2022-07-18T08:55:42Z 2020 Journal Article Xu, X., Xu, Y., Wang, M., Li, J., Xu, Z., Chai, S. & He, Y. (2020). Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties. IEEE Transactions On Industrial Informatics, 17(7), 4480-4491. https://dx.doi.org/10.1109/TII.2020.3016336 1551-3203 https://hdl.handle.net/10356/160284 10.1109/TII.2020.3016336 2-s2.0-85097380049 7 17 4480 4491 en IEEE Transactions on Industrial Informatics © 2020 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Energy Sharing Energy Storage Xu, Xu Xu, Yan Wang, Ming-Hao Li, Jiayong Xu, Zhao Chai, Songjian He, Yufei Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
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In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Xu, Xu Xu, Yan Wang, Ming-Hao Li, Jiayong Xu, Zhao Chai, Songjian He, Yufei |
format |
Article |
author |
Xu, Xu Xu, Yan Wang, Ming-Hao Li, Jiayong Xu, Zhao Chai, Songjian He, Yufei |
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Xu, Xu |
title |
Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
title_short |
Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
title_full |
Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
title_fullStr |
Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
title_full_unstemmed |
Data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
title_sort |
data-driven game-based pricing for sharing rooftop photovoltaic generation and energy storage in the residential building cluster under uncertainties |
publishDate |
2022 |
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https://hdl.handle.net/10356/160284 |
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1738844836613586944 |