# A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2403.01013 · HTML: https://mlsanity.com/paper/2403.01013/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## 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, 8 of 8)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time](https://mlsanity.com/paper/1912.10697/) (2019)
- [Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning](https://mlsanity.com/paper/2111.10898/) (2021)
- [Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems](https://mlsanity.com/paper/1702.04699/) (2017)
- [Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision](https://mlsanity.com/paper/2206.11398/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction](https://mlsanity.com/paper/2407.08010/) (2024)
- [Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting](https://mlsanity.com/paper/2511.09789/) (2025)
- [A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization](https://mlsanity.com/paper/2511.18093/) (2025)

## Abstract (arXiv)

The global energy landscape is undergoing a transformation towards decarbonization, sustainability, and cost-efficiency. In this transition, microgrid systems integrated with renewable energy sources (RES) and energy storage systems (ESS) have emerged as a crucial component. However, optimizing the operational control of such an integrated energy system lacks a holistic view of multiple environmental, infrastructural and economic considerations, not to mention the need to factor in the uncertainties from both the supply and demand. This paper presents a holistic datadriven power optimization approach based on deep reinforcement learning (DRL) for microgrid control considering the multiple needs of decarbonization, sustainability and cost-efficiency. First, two data-driven control schemes, namely the prediction-based (PB) and prediction-free (PF) schemes, are devised to formulate the control problem within a Markov decision process (MDP). Second, a multivariate objective (reward) function is designed to account for the market profits, carbon emissions, peak load, and battery degradation of the microgrid system. Third, we develop a Double Dueling Deep Q Network (D3QN) architecture to optimize the power flows for real-time energy management and determine charging/discharging strategies of ESS. Finally, extensive simulations are conducted to demonstrate the effectiveness and superiority of the proposed approach through a comparative analysis. The results and analysis also suggest the respective circumstances for using the two control schemes in practical implementations with uncertainties.