Imperial College London
Alternate

Professor Goran Strbac

Faculty of Engineering, Department of Electrical and Electronic Engineering

Chair in Electrical Energy Systems
 
 
 
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Contact

 

+44 (0)20 7594 6169g.strbac

 
 
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Assistant

 

Miss Guler Eroglu +44 (0)20 7594 6170

 
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Location

 

1101Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ye:2020:10.1109/tsg.2020.2976771,
author = {Ye, Y and Qiu, D and Wu, X and Strbac, G and Ward, J},
doi = {10.1109/tsg.2020.2976771},
journal = {IEEE Transactions on Smart Grid},
pages = {1--1},
title = {Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning},
url = {http://dx.doi.org/10.1109/tsg.2020.2976771},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.
AU  - Ye,Y
AU  - Qiu,D
AU  - Wu,X
AU  - Strbac,G
AU  - Ward,J
DO  - 10.1109/tsg.2020.2976771
EP  - 1
PY  - 2020///
SN  - 1949-3053
SP  - 1
TI  - Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning
T2  - IEEE Transactions on Smart Grid
UR  - http://dx.doi.org/10.1109/tsg.2020.2976771
ER  -
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