Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market

--With the large number of prosumers deploying distributed energy resources (DERs), integrating these prosumers into a transactive energy market (TEM) is a trend for the future smart grid. A community-based double auction market is considered a promising TEM that can encourage prosumers to participate and maximize social welfare. However, the traditional TEM is challenging to model explicitly due to the random bidding behavior of prosumers and uncertainties caused by the energy operation of DERs. Furthermore, although reinforcement learning algorithms provide a model-free solution to optimize prosumers' bidding strategies, their use in TEM is still challenging due to their scalability, stability, and privacy protection limitations. T o address the above challenges, in this study, we design a double auction-based TEM with multiple DERs-equipped prosumers to transparently and efficiently manage energy transactions. We also propose a deep reinforcement learning (DRL) model with distributed learning and execution to ensure the scalability and privacy of the market environment. Simulation results show that (1) the designed TEM and DRL model are robust; (2) the proposed DRL model effectively balances the energy payment and comfort satisfaction for prosumers and outperforms the state-of-the-art methods in optimizing the bidding strategies. ITH the extensive deployment of energy storage systems, solar photovoltaics (PVs), smart home appliances, and information technology, passive consumers in the traditional electricity market are gradually converted to active prosumers (producers + consumers) with distributed energy resources (DERs), who can monitor and control energy generation, consumption, storage, and transaction to achieve specific goals, such as balancing energy costs and user comfort levels [1]-[3]. However, the bi-directional energy and information flow, as well as the variability of distributed renewable energy, raises great challenges in the operation of power systems in a flexible and economically efficient way [4]. Liu are with the Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA, USA (e-mail: jun3525114@gmail.com, Li, M. Ghafouri, and J. Y an are with Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada (e-mail: {yuanliang.li, L. Hou is with Beijing University of Posts and Telecommunications, Beijing, China (e-mail: luyang.hou@bupt.edu.cn) Zhang is with the College of Information Engineering, Shenzhen University, Shenzhen, China (e-mail: zhangp@szu.edu.cn)