Editorial: AI, data analytics, and mechanism design for DER integration toward net zero

Wang, Hao and Ye, Yujian and Chen, Yue and Chen, Yize and Yang, Qing and Cui, Qiushi and You, Pengcheng (2022) Editorial: AI, data analytics, and mechanism design for DER integration toward net zero. Frontiers in Energy Research, 10. ISSN 2296-598X

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Abstract

The energy system is undergoing a fundamental energy transition by integrating low-carbon distributed energy resources (DERs) in distribution networks to accelerate net zero. The increased DER uptake poses significant challenges in operating energy systems to achieve net zero with high reliability and low cost. In particular, the inherent variability of renewable generation, such as solar photovoltaic systems, brings significant uncertainties to the energy system, causing reliability concerns. DERs also cause power quality issues, such as voltage fluctuations. But the distribution system was not designed to support large bidirectional power flow and host a high uptake of DERs. The existing economic mechanism, which worked in the past, cannot provide effective economic signals to manage DERs in the grid. How to effectively integrate DERs and harness their flexibility has become one of the most challenging problems in the energy sector.

AI, optimization, and mechanism design have become promising tools to address the aforementioned challenges in the DER integration toward net zero. For example, AI and advanced optimization can improve the efficiency and effectiveness of system operations with a high uptake of DERs by scheduling energy storage and dispatching DERs in real-time. Mechanism design can contribute to market-based or price-based solutions to manage DERs for higher reliability and lower costs.

Item Type: Article
Subjects: Archive Paper Guardians > Energy
Depositing User: Unnamed user with email support@archive.paperguardians.com
Date Deposited: 11 May 2023 09:04
Last Modified: 13 Mar 2024 04:26
URI: http://archives.articleproms.com/id/eprint/895

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