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- W4378841444 abstract "The joint strategy optimization problem of a load serving entity (LSE) in both wholesale electricity market (WEM) and retail electricity market (REM) is converted into an aggregated load prediction problem and a sequential decision optimization problem, under the condition of price-taker. By formulating the original retail price optimizing problem as a Markov decision process (MDP), a novel deep deterministic policy gradient (DDPG) algorithm combining with a transformer based representation network (DDPG-TSFR) is proposed to solve this MDP. A new network structure is designed for the proposed DDPG-TSFR by using multiple loss functions, and a method based on gradient normalization (GradNorm) is adopted to realize adaptive loss weighting factors. We conduct several numerical experiments to compare the training and computational behaviors of the proposed DDPG-TSFR with different DRL based approaches. Numerical results validate the effectiveness and superiority of the proposed approach." @default.
- W4378841444 created "2023-06-01" @default.
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- W4378841444 date "2023-04-01" @default.
- W4378841444 modified "2023-10-17" @default.
- W4378841444 title "Price-taker Bidding and Pricing Strategy Using Deep Deterministic Policy Gradient Algorithm with Transformer Neural Networks" @default.
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- W4378841444 doi "https://doi.org/10.1109/acpee56931.2023.10135772" @default.
- W4378841444 hasPublicationYear "2023" @default.
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