Multi-Market Bidding Behavior Analysis of Energy Storage System Based on Inverse Reinforcement Learning
Author:
Affiliation:
1. Electrical Engineering, Tsinghua University, Beijing, China
2. State Key Laboratory of Power Systems, Electrical Engineering Department, Tsinghua University, Beijing, China
Funder
National Natural Science Foundation of China
Shuimu Tsinghua Scholar Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
http://xplorestaging.ieee.org/ielx7/59/9927170/09712169.pdf?arnumber=9712169
Reference24 articles.
1. Multi-Period and Multi-Spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach
2. Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm
3. Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning
4. Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model
5. A Strategic Day-ahead bidding strategy and operation for battery energy storage system by reinforcement learning
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