Appliance Scheduling for Energy Management with User Preferences

Demand Side Management (DSM) technique encourages the consumers to adjust their energy usage pattern to get optimized results for achieving the goal of minimizing the electricity consumption cost. This mechanism provides benefits to both side customer and utility in terms of cost reduction and grid stability. To regulate the increasing energy demand extensive research is being carried out for possible implementation of different DSM techniques. DSM technique ensures the user participation for achieving the energy optimization goal. User preferences and comfort is much desirable while achieving the goal of reducing Peak to average ratio (PAR), energy cost reduction and grid stability. In this paper we are proposing an Energy Management System (EMS) by considering different user environments along with the users preferences for shifting the appliances operational time. We have selected home and office environments for implementing our EMS. We are using GA, a heuristic technique, to solve the energy management problem.

[1]  Chi Zhou,et al.  Real-Time Opportunistic Scheduling for Residential Demand Response , 2013, IEEE Transactions on Smart Grid.

[2]  Georgios B. Giannakis,et al.  Residential Load Control: Distributed Scheduling and Convergence With Lost AMI Messages , 2012, IEEE Transactions on Smart Grid.

[3]  Vincent K. N. Lau,et al.  Optimal Energy Scheduling for Residential Smart Grid With Centralized Renewable Energy Source , 2014, IEEE Systems Journal.

[4]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[5]  Hussein T. Mouftah,et al.  Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[6]  Abdelkader Bousselham,et al.  Residential demand response scheduling with consideration of consumer preferences , 2016 .

[7]  S. H. Lee,et al.  A Practical Approach to Appliance Load Control Analysis: A Water Heater Case Study , 1983, IEEE Power Engineering Review.

[8]  Yuan-Yih Hsu,et al.  Dispatch of direct load control using dynamic programming , 1991 .

[9]  Dipti Srinivasan,et al.  Improved multi-objective evolutionary algorithm for day-ahead thermal generation scheduling , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[10]  Zhong Fan,et al.  An integer linear programming based optimization for home demand-side management in smart grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[11]  Zheng Wen,et al.  Optimal Demand Response Using Device-Based Reinforcement Learning , 2014, IEEE Transactions on Smart Grid.

[12]  Sachin S. Sapatnekar,et al.  Residential task scheduling under dynamic pricing using the multiple knapsack method , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[13]  Guo Chen,et al.  A Genetic Evolutionary Task Scheduling Method for Energy Efficiency in Smart Homes , 2012 .