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

Suyang Zhou, Zijian Hu, Zhi Zhong, Di He and Meng Jiang
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Suyang Zhou: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Zijian Hu: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Zhi Zhong: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Di He: School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
Meng Jiang: Department of Computer Science and Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

Sustainability, 2019, vol. 11, issue 23, 1-15

Abstract: 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.

Keywords: data-driven method; integrated energy system (IES); generative adversarial network (GAN) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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