Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning

By Yan Du, Fangxing Li. IEEE Transactions on Smart Grid, 2020

310 citations recorded by the source corpus.

In this paper, an intelligent multi-microgrid (MMG) energy management method is proposed based on deep neural network (DNN) and model-free reinforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator (DSO), the target is to decrease the demand-side peak-to-average ratio (PAR), and to maximize the profit from selling energy. To protect user privacy, DSO learns the MMG response by implementing a DNN without direct access to user’s information. Further, the DSO selects its retail pricing strategy via a Monte Carlo method from RL, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/067cc6bc8d666158a7fa23ba324e9a4763bf4fa6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Frede Blaabjerg
2. Zhe Chen
3. Xu Xu
4. Zhao Xu
5. Songjian Chai
6. Yan Xu
7. Youwei Jia
8. Chun Sing Lai

In Electrical and Electronic Engineering

1. Zhe Chen
2. Frede Blaabjerg
3. Xu Xu
4. Zhao Xu
5. Songjian Chai
6. Yan Xu
7. Youwei Jia
8. Chun Sing Lai

Citations in the corpus

• A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management (2020, methodology): “[21], the deep neural network and the model-free reinforcement learning is utilized to manage the energy in a multi-microgrid system.”
• Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review (2020, methodology): “The RL algorithms have also been applied to the optimiza‐ tion of microgrids in [50] - [55].”