A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies. This paper presents a new error temporal difference (ETD) algorithm for DRL to address the uncertainty in predictions,aiming to improve the performance of microgrid operations. First,a microgrid system integrated with renewable energy sources (RES) and energy storage systems (ESS), along with its Markov decision process (MDP), is modelled. Second, a predictive control approach based on a deep Q network (DQN) is presented, in which a weighted average algorithm and a new ETD algorithm are designed to quantify and address the prediction uncertainty, respectively. Finally, simulations on a realworld US dataset suggest that the developed ETD effectively improves the performance of DRL in optimizing microgrid operations.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningSimilar Papers 제목 키워드 기반
Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting
In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement le…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Deep Reinforcement Learning and The Tale of Two Temporal Difference Errors
The temporal difference (TD) error was first formalized in Sutton (1988), where it was first characterized as the difference between temporally successive predictions, and later, in that same work, formulated as the diff…
Reinforcement LearningLifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
The lifelong control problem of an off-grid microgrid is composed of two tasks, namely estimation of the condition of the microgrid devices and operational planning accounting for the uncertainties by forecasting the fut…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Temporal Difference Weighted Ensemble For Reinforcement Learning
Combining multiple function approximators in machine learning models typically leads to better performance and robustness compared with a single function. In reinforcement learning, ensemble algorithms such as an averagi…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can…
Q-LearningReinforcement Learning