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DOI | 10.1016/j.enpol.2021.112522 |
Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design | |
Poplavskaya K.; Lago J.; Strömer S.; de Vries L. | |
发表日期 | 2021 |
ISSN | 0301-4215 |
卷号 | 158 |
英文摘要 | Electricity balancing is one of the main demanders of short-term flexibility. To improve its integration, the recent regulation of the European Union introduces a common standalone balancing energy market. It allows actors that have not participated or not been awarded in the preceding balancing capacity market to participate as voluntary bidders or ‘second-chance’ bidders. We investigate the effect of these changes on balancing market efficiency and on strategic behavior in particular, using a combination of agent-based modelling and reinforcement learning. This paper is the first to model agents' interdependent bidding strategies in the balancing capacity and energy markets with the help of two collaborative reinforcement learning algorithms. Results reveal considerable efficiency gains in the balancing energy market from the introduction of voluntary bids even in highly concentrated markets while offering a new value stream to providers of short-term flexibility. ‘Second-chance’ bidders further drive competition, reducing balancing energy costs. However, we warn that this design change is likely to shift some of the activation costs to the balancing capacity market where agents are prompted to bid more strategically in the view of lower profits from balancing energy. As it is unlikely that the balancing capacity market can be removed altogether, we recommend integrating European balancing capacity markets on par with balancing energy markets and easing prequalification requirements to ensure sufficient competition. © 2021 The Author(s) |
英文关键词 | Balancing market; Bidding strategy; Machine learning; Market design; Market efficiency; Regulation |
语种 | 英语 |
scopus关键词 | Autonomous agents; Balancing; Computational methods; Energy efficiency; Learning algorithms; Reinforcement learning; Balancing energy; Balancing market; Bidding strategy; Capacity markets; Energy markets; Machine-learning; Market design; Market efficiency; Regulation; Short term; Power markets; electricity supply; energy balance; energy market; European Union; machine learning; regulatory framework |
来源期刊 | Energy Policy
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/256574 |
作者单位 | AIT Austrian Institute of Technology, Center for Energy, Integrated Energy Systems, 1210Vienna, Austria; Delft University of Technology, Faculty of Technology, Policy, and Management, GA Delft2600, Netherlands; Algorithms, Modeling, and Optimization, Energyville-VITO, Genk, 3600, Belgium; TU Vienna, Institute of Statistics and Mathematical Methods in Economics, Wiedner Hauptstrasse 8-10Vienna 1040, Austria |
推荐引用方式 GB/T 7714 | Poplavskaya K.,Lago J.,Strömer S.,et al. Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design[J],2021,158. |
APA | Poplavskaya K.,Lago J.,Strömer S.,&de Vries L..(2021).Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design.Energy Policy,158. |
MLA | Poplavskaya K.,et al."Making the most of short-term flexibility in the balancing market: Opportunities and challenges of voluntary bids in the new balancing market design".Energy Policy 158(2021). |
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