Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies
CoRR(2024)
摘要
A continuous rise in the penetration of renewable energy sources, along with
the use of the single imbalance pricing, provides a new opportunity for balance
responsible parties to reduce their cost through energy arbitrage in the
imbalance settlement mechanism. Model-free reinforcement learning (RL) methods
are an appropriate choice for solving the energy arbitrage problem due to their
outstanding performance in solving complex stochastic sequential problems.
However, RL is rarely deployed in real-world applications since its learned
policy does not necessarily guarantee safety during the execution phase. In
this paper, we propose a new RL-based control framework for batteries to obtain
a safe energy arbitrage strategy in the imbalance settlement mechanism. In our
proposed control framework, the agent initially aims to optimize the arbitrage
revenue. Subsequently, in the post-processing step, we correct (constrain) the
learned policy following a knowledge distillation process based on properties
that follow human intuition. Our post-processing step is a generic method and
is not restricted to the energy arbitrage domain. We use the Belgian imbalance
price of 2023 to evaluate the performance of our proposed framework.
Furthermore, we deploy our proposed control framework on a real battery to show
its capability in the real world.
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