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Open AccessArticle

An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network

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School of Electrical Engineering, Southeast University, Nanjing 210096, China
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School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
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School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
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Department of Computer Science and Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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Author to whom correspondence should be addressed.
Sustainability 2019, 11(23), 6699; https://doi.org/10.3390/su11236699
Received: 2 November 2019 / Revised: 14 November 2019 / Accepted: 18 November 2019 / Published: 27 November 2019
(This article belongs to the Section Energy Sustainability)
The convergence of energy security and environmental protection has given birth to the development of integrated energy systems (IES). However, the different physical characteristics and complex coupling of different energy sources have deeply troubled researchers. With the rapid development of AI and big data, some attempts to apply data-driven methods to IES have been made. Data-driven technologies aim to abandon complex IES modeling, instead mining the mapping relationships between different parameters based on massive volumes of operating data. However, integrated energy system construction is still in the initial stage of development and operational data are difficult to obtain, or the operational scenarios contained in the data are not enough to support data-driven technologies. In this paper, we first propose an IES operating scenario generator, based on a Generative Adversarial Network (GAN), to produce high quality IES operational data, including energy price, load, and generator output. We estimate the quality of the generated data, in both visual and quantitative aspects. Secondly, we propose a control strategy based on the Q-learning algorithm for a renewable energy and storage system with high uncertainty. The agent can accurately map between the control strategy and the operating states. Furthermore, we use the original data set and the expanded data set to train an agent; the latter works better, confirming that the generated data complements the original data set and enriches the running scenarios. View Full-Text
Keywords: data-driven method; integrated energy system (IES); generative adversarial network (GAN) data-driven method; integrated energy system (IES); generative adversarial network (GAN)
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Zhou, S.; Hu, Z.; Zhong, Z.; He, D.; Jiang, M. An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network. Sustainability 2019, 11, 6699.

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