# A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization

arXiv: https://arxiv.org/abs/2511.18093 · HTML: https://mlsanity.com/paper/2511.18093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning](https://mlsanity.com/paper/2111.10898/) (2021)
- [A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2403.01013/) (2024)
- [Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience](https://mlsanity.com/paper/2203.03030/) (2022)

## Abstract (arXiv)

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.