Behind the Meter - Distributed Load Control Strategies: September 2021 issue of IEEE Electrification Magazine (Video)
Lingling Fan
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PES
IEEE Members: $11.00
Non-members: $15.00Length: 01:18:27
Traditionally, behind-the-meter loads are not for active control. With more and more behind-the-meter electric vehicles, distributed energy resources such as solar, wind, and batteries, load control may create economic and reliability benefits. This topic of load control is addressed by the September 2021 issue “behind the meter – distributed load control strategies, ” edited by Prof. Mohammad Shahildepour. In this webinar, three papers from this issue will be presented.
1. Behind the meter strategies Energy Management System with a Swedish case study. Speakers: Lina Bertling Tjernberg and Hamza Shafique
2. Energy Storage: Improving System Reliability, Deferring Network Upgrading, Taking Advantage of Markets and Beyond. Speakers: Xuan Wu, Antonio Conejo
3. Mining Smart Meter Data to Enhance Distribution Grid Observability for Behind-the-Meter Load Control. Speakers: Yuxuan Yuan and Zhaoyu Wang
The authors will examine behind the meter load control from various point of views, including real-world EMS design for peak shaving and frequency regulation using loads, how to plan battery energy storage for reliability improvement, and load control via smart meter data mining.
1. Behind the meter strategies Energy Management System with a Swedish case study. Speakers: Lina Bertling Tjernberg and Hamza Shafique
2. Energy Storage: Improving System Reliability, Deferring Network Upgrading, Taking Advantage of Markets and Beyond. Speakers: Xuan Wu, Antonio Conejo
3. Mining Smart Meter Data to Enhance Distribution Grid Observability for Behind-the-Meter Load Control. Speakers: Yuxuan Yuan and Zhaoyu Wang
The authors will examine behind the meter load control from various point of views, including real-world EMS design for peak shaving and frequency regulation using loads, how to plan battery energy storage for reliability improvement, and load control via smart meter data mining.