Bidding Model of Generation Company Considering Volatility of Futures Market

Yu, Guangrong; Zhang, Hongming; Yang, Pengpeng; Chen, Zhongyao

doi:10.1109/appeec48164.2020.9220363

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…[26], [27], [28] Artificial Neural Networks [11], [29], [30], [31], [32], [33], [34] [35], [12], [36], [37], [38], [39], [40] Reinforcement Learning [41], [42], [43], [44], [45] [46], [47], [48], [49], [50], [51], [52], [10], [53] Nature-Inspired Intelligence [54] [55]…”

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

“…Several algorithmic approaches focus on automating the processes that implement bidding strategies on electricity markets from the perspective of electricity suppliers, i.e., GENCOs. [31] propose a Q-learning approach to maximize the profit from selling electricity on the day-ahead market and on a futures market. Similarly, [36] present a RL approach for wind electricity generators on a day-ahead and a subsequent balancing market.…”

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

See 1 more Smart Citation

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

Hanny¹,

Körner²,

Leinauer³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems.

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Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

Hanny¹,

Körner²,

Leinauer³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

Bidding Model of Generation Company Considering Volatility of Futures Market

Cited by 1 publication

References 12 publications

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

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