Demand Response Management in Smart Homes Using Robust Optimization

Abstract

Abstract—Shortage of nonrenewable energy resources, the pollution arising from extraction and exploitation of these resources, and increasing demand for energy, along with the intermittent nature of renewable resources have resulted in more attention to demand response programs in smart grids. Demand response includes activities like load management, energy efficiency, and energy saving. The residential sector plays a significant role in achieving demand response goals. Home energy management systems shift or restrict a household demand; they consider power price and consumer’s wellbeing and improve consumption and production profile of a house. In this work, a price-based, real-time demand response management model is proposed as a home energy management system. The model helps conservative householders save energy and improve energy efficiency through managing electrical appliances. The proposed model can be embedded in an energy control unit or a smart meter and find the optimal operation of the house for next 5 minutes via robust optimization technique, considering uncertainties of power price and distributed generation. The objective is to minimize the electricity costs of a home and improve the role of distributed generation and energy storage system installed inside the home while satisfying user’s wellbeing and components constraints. The model is comprehensive, real-time, have high time resolution, and the results show that the model improves the costs of a conservative household.

Keywords:

• smart grid
• demand response
• home energy management system
• uncertainty
• robust optimization

Additional information

Notes on contributors

Mojtaba Mahmoudi

Salman Khodayifar received his B.Sc. and M.Sc. degrees in Mathematics from the University of Mohaghegh Ardabili and the University of Tehran, in 2006 and 2008, respectively. He got his Ph.D. degree in Applied Mathematics (Operation Research-Network Flows) from University of Tehran in 2013. Since then, he is an assistant professor at the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran. His special fields of interests include network flows optimization, combinatorial optimization.

Mohsen Afsharchi

Mojtaba Mahmoudi graduated from Zanjan University in applied mathematics in 2014. He got his master in Computer Science-Intelligent Systems from Information Technology and Computer Science faculty of Institute for Advanced Studies in Basic Sciences (IASBS) in 2018. His main research interests are application of mathematical optimization and machine learning for smart grids.

Salman Khodayifar

Mohsen Afsharchi received his M.Sc. degree in computer science from the Iran University of Science and Technology, in 1996, and his Ph.D. degree in artificial intelligence from the University of Calgary, Canada, in 2006. Since 2006, he has been a part of the Computer Engineering Department, University of Zanjan, Iran, where he leads the Multi-Agent Systems Lab (MASLab). He is currently an associate professor at the Computer Engineering Department, University of Zanjan. His research interests include multi-agent learning, probabilistic reasoning, distributed constraint optimization, and software testing.