Data-driven decision-making strategies for electricity retailers: Deep reinforcement learning approach
Author:
Publisher
Power System Technology Press
Subject
Electrical and Electronic Engineering,General Energy,Electronic, Optical and Magnetic Materials
Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects;Journal of Revenue and Pricing Management;2024-07-23
2. Utilizing Deep Reinforcement Learning for High-Voltage Distribution Network Expansion Planning;2024 IEEE 2nd International Conference on Power Science and Technology (ICPST);2024-05-09
3. Intelligent strategic bidding in competitive electricity markets using multi-agent simulation and deep reinforcement learning;Applied Soft Computing;2024-02
4. Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization;Journal of Modern Power Systems and Clean Energy;2024
5. Operation and evaluation of digitalized retail electricity markets under low-carbon transition: recent advances and challenges;Frontiers in Energy Research;2023-12-29
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