AI-Empowered Decarbonization for Modern Power Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 1419

Special Issue Editors


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Guest Editor
Department of Electrical and Electronics Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: solar forecasting; photovoltaics; machine learning; smart grids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: transportation electrification; smart grid
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: electricity markets and optimal operation of integrated energy system

Special Issue Information

Dear Colleagues,

Achieving carbon neutrality has become a global goal to limit continuously rising global temperatures. Among various industries, the power sector plays a crucial role as it serves both as an engine for economic development and as a major source of carbon emissions. Thus far, countries have implemented significant measures to promote the low-carbon transformation of their power systems, such as increasing renewable energy penetration into the grid, promoting electric vehicle and energy technologies, implementing carbon emission trading markets, etc. Moving forward, however, modern power system decarbonization may face pressure from complex energy and social systems. This point considered, artificial intelligence technologies have the potential to enable a more efficient, reliable, and economical transition toward the implementation of low-carbon smart grids.

This Special Issue will collect emerging research achievements within the field of artificial intelligence’s application for power system decarbonization. Prospective authors are invited to submit original contributions or survey papers for peer review for publication in Electronics. Topics of interest for the Special Issue include, but are not limited to, the following:

  • Low-carbon power system optimization and operation;
  • Vehicle-to-grid technologies;
  • Renewable energy integration;
  • Virtual power plant technologies;
  • Intelligent power system control methods;
  • Data-driven power system modelling and monitoring theories;
  • Integrated energy systems;
  • Electricity and carbon markets;
  • Energy internet.

Dr. Xiaoyang Chen
Dr. Chaoxian Wu
Dr. Zhong Zhang
Guest Editors

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Keywords

  • power system decarbonization
  • smart grid
  • artificial intelligence
  • renewable energy
  • electric vehicles
  • energy storage

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Published Papers (1 paper)

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Research

20 pages, 3855 KiB  
Article
Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests
by Yajun Zhang, Xingang Yang, Lurui Fang, Yanxi Lyu, Xuejun Xiong and Yufan Zhang
Electronics 2024, 13(22), 4357; https://doi.org/10.3390/electronics13224357 - 6 Nov 2024
Viewed by 917
Abstract
The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between [...] Read more.
The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies. Full article
(This article belongs to the Special Issue AI-Empowered Decarbonization for Modern Power Grids)
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