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A modified approach for residential load scheduling using smart meters

Bahrami, Sh ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/ISGTEurope.2012.6465717
  3. Publisher: 2012
  4. Abstract:
  5. Implementation of various incentive-based demand response strategies has great potential to decrease peak load growth and customer electricity bill cost. Using advanced metering and automatic demand management makes it possible to optimize energy consumption, to reduce grid loss, and to release generation capacities for the sake of providing sustainable electricity supply. Executing an incentive-based program is a simple way for customers to monitor and manage their energy consumption, and therefore, to reduce their electricity bill. With these objectives, this paper examines the previously suggested load scheduling programs and proposes a new practical one for residential energy management. The method is aimed at optimizing customers' bill cost and satisfaction by taking into consideration the generation capacity limitation and dynamic electricity price in different time slots of a day. Moreover, the proposed optimization algorithm is compared with Particle Swarm Optimization (PSO) algorithm to illustrate high efficiency of the proposed algorithm as a practical industrial tool for peak load shaving
  6. Keywords:
  7. Energy consumption ; Load management ; Power market ; Smart Grid ; Advanced metering ; Electricity bill ; Electricity prices ; Electricity supply ; Generation capacity ; Grid loss ; Incentive-based demand response ; Incentive-based programs ; Industrial tools ; Load scheduling ; Optimal scheduling ; Optimization algorithms ; Particle swarm optimization algorithm ; Peak load ; Power markets ; Residential energy ; Residential loads ; Smart grid ; Smart homes ; Time slots ; Algorithms ; Automation ; Costs ; Customer satisfaction ; Electric load management ; Electricity ; Energy utilization ; Housing ; Intelligent buildings ; Particle swarm optimization (PSO) ; Sales ; Scheduling ; Smart power grids
  8. Source: IEEE PES Innovative Smart Grid Technologies Conference Europe ; 2012 ; 9781467325974 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6465717