Title: CGAN-filter for model-free photovoltaic scenario generation with conditional maximum mean discrepancy indicator

Authors: Jie Li; Min Gao; Yi Xiang; Xiaochao Fan

Addresses: Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China ' Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China ' Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China ' China Resources Microelectronics (Chongqing) Co., Ltd, Chongqing, 401331, China

Abstract: It is difficult to produce credible photovoltaic (PV) scenarios depending on required accuracy, climate, cloud, and other environmental conditions. To resolve it, this paper presents a model-free approach to generate efficient PV scenarios through learning the inherent distribution of historical data based on conditional generative adversarial network (CGAN). Unlike probabilistic models with specific system models, the proposed CGAN-Filter obtains solar energy production patterns by discovering distribution characters and uncertainties in temporal dimensions. In order to offset severe disturbances in PV scenarios caused by random clouds and climates, a filter layer is designed between the two neural networks in CGAN to reduce amplified environmental randomness. Additionally, to observe learning processes of the proposed approach, a conditional maximum mean discrepancy (C-MMD) is theoretically and practically demonstrated to be able to monitor the performance of PV scenario generation. Experimental results on real-world PV scenario datasets have validated the effectiveness of our proposed approach.

Keywords: CGAN; conditional generative adversarial network; PV; photovoltaic; C-MMD; conditional maximum mean discrepancy; scenario generation.

DOI: 10.1504/IJSCIP.2020.114283

International Journal of System Control and Information Processing, 2020 Vol.3 No.2, pp.129 - 149

Received: 03 Sep 2020
Accepted: 08 Dec 2020

Published online: 28 Mar 2021 *

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