Online Interactive Bidding Strategy for Demand Response Based on DDPG Machine Learning Method

  • 李 冠承

Student thesis: Doctoral Thesis


Demand response (DR) as one of the energy resources in future’s grid provides the services of peak shaving enhancing the efficiency of renewable energy utilization with short response period and low cost Various categories of DR are established e g automated DR incentive DR emergency DR and demand bidding However the researches about demand bidding aggregator are just on the beginning stage For this issue the bidding price and bidding curtailment quantity are two bidding decisions required to be determined while considering the potential provided by participants Therefore this thesis emphases how to aggregate the bids from participated customers and then determine the bidding decisions by machine learning method while ensuring customers’ stable curtailment quantity to maximize the profit Deep deterministic policy gradient (DDPG) method is employed to optimize the two bidding decisions through learning historical bidding experiences with the corresponding reserve rate and customers’ plans The online learning further utilizes the daily newest bidding experience attained to ensure the trend tracing and self- adaptation Two environment simulators are adopted for testifying the robustness of the model The results prove that when facing diverse situations the proposed model is able to earn the optimal profit via off/on-line learning the bidding laws and making the proper bid
Date of Award2020
Original languageEnglish
SupervisorHong-Tzer Yang (Supervisor)

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