AI agents assessing flexibility: the value of demand side management in times of high energy prices

Alexander Dreher, Lisa Marie Martmann,Malte Lehna, Cyriana Roelofs, Jonathan Bergstraser,Christoph Scholz, Wolfgang Slaby,Heike Wetzel

2022 18th International Conference on the European Energy Market (EEM)(2022)

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摘要
High energy and electricity prices, coupled with high price volatility, increase the value of demand response and demand side management. Energy management systems that are predictive and exchange-price oriented can help to leverage flexibility while lowering energy costs. In this paper, the value of flexibility is assessed in two scenarios of low and high energy prices. To that end, a self-learning home energy management system is introduced that takes into account the new volatility of high stock market electricity prices. The proposed approach is compared to a baseline system, which is a typical household self-consumption optimization. The findings indicate a significant economic potential of an exchange-price oriented usage of residential flexibility options in contrast to self-consumption optimization. Furthermore, the value of flexibility for the exemplary residential system increased more than fivefold between 2021 and 2022, while prices increased about fourfold.
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关键词
Energy Management,Reinforcement Learning,Power System Economics,Demand Side Management
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