Abstract
During the last few decades, the concept of demand response (DR) in the energy sector has gained substantial momentum. Research has led to a range of DR solutions. These solutions mostly differ in their applications, the hosting power system, the energy market etc. Moreover, as per the EU directive, DR aggregators should be allowed to trade DR alongside supply in both day-ahead and real time electricity markets. Meanwhile, independent aggregators do not consider physical limitations of a network, thus setting up new a challenges for network operation. In this paper, an active learning technique for real-time congestion management is proposed to tackle this challenge. This enables distributed system operator (DSO) to incenticize independent aggregators efficiently in order to use DR for overloading mitigation. Lastly, a case study is simulated which verifies the performance of a new approach for congestion management.
Original language | English |
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Title of host publication | 2017 IEEE Manchester PowerTech, 18-22 June 2017, Manchester, United Kingdom |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1-6 |
ISBN (Electronic) | 978-1-5090-4237-1 |
ISBN (Print) | 978-1-5090-4238-8 |
DOIs | |
Publication status | Published - 18 Jun 2017 |
Event | 12th IEEE PES PowerTech Conference - University of Manchester, Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017 Conference number: 12 http://ieee-powertech.org/ |
Conference
Conference | 12th IEEE PES PowerTech Conference |
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Abbreviated title | PowerTech 2017 |
Country | United Kingdom |
City | Manchester |
Period | 18/06/17 → 22/06/17 |
Other | Towards and Beyond Sustainable Energy Systems |
Internet address |
Keywords
- active distribution network
- congestion management
- demand response
- market-based control mechanism
- reinforcement learning