Demand management for home energy networks using cost-optimal appliance scheduling

Rakocevic, V., Jahromizadeh, S., Gruber, J. K. & Prodanovic, M. (2014). Demand management for home energy networks using cost-optimal appliance scheduling. Paper presented at the 3rd International Conference on Smart Grids and Green IT Systems, 03-04 Apr 2014, Barcelona, Spain.

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Abstract

This paper uses problem decomposition to show that optimal dynamic home energy prices can be used to reduce the cost of supplying energy, while at the same time reducing the cost of energy for the home users. The paper makes no specific recommendations on the nature of energy pricing, but shows that energy prices can normally be found that not only result in optimal energy consumption schedules for the energy provider's problem and are economically viable for the energy provider, but also reduce total users energy costs. Following this, the paper presents a heuristic real-time algorithm for demand management using home appliance scheduling. The presented algorithm ensures users' privacy by requiring users to only communicate their aggregate energy consumption schedules to the energy provider at each iteration of the algorithm. The performance of the algorithm is evaluated using a comprehensive probabilistic user demand model which is based on real user data from energy provider E.ON. The simulation results show potential reduction of up to 17% of the mean peak-to-average power estimate, reducing the user daily energy cost for up to 14%.

Item Type: Conference or Workshop Item (Paper)
Additional Information: These papers were published in the 3rd International Conference on Smart Grids and Green IT Systems (SMARTGREENS 2014), Edited by Markus Helfert; Karl-Heinz Krempels and Brian Donnellan, ISBN: 978-989-758-025-3 and published by SCITEPRESS.
Uncontrolled Keywords: Smart Homes, Optimization for Efficient Energy Consumption, Energy Profiling and Measurement, Energy Demand Management, Economic Models of Energy Efficiency
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: School of Engineering & Mathematical Sciences > Engineering
URI: http://openaccess.city.ac.uk/id/eprint/6285

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