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Research Papers

Demand Management of Distributed Energy Loads Based on Genetic Algorithm Optimization

[+] Author and Article Information
Jiaming Li

CSIRO ICT Centre,
Corner of Vimiera & Pembroke Roads,
Marsfield NSW, Australia
e-mail: jiaming.li@csiro.au

Glenn Platt, Geoff James

CSIRO Energy Technology,
Steel River Estate,
10 Murray Dwyer Circuit,
Mayfield West NSW 2304, Australia

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 5, 2012; final manuscript received October 16, 2013; published online December 16, 2013. Assoc. Editor: Eugenio Schuster.

J. Dyn. Sys., Meas., Control 136(2), 021014 (Dec 16, 2013) (7 pages) Paper No: DS-12-1075; doi: 10.1115/1.4025751 History: Received March 05, 2012; Revised October 16, 2013

Management of a very large number of distributed energy resources, energy loads, and generators, is a hot research topic. Such energy demand management techniques enable appliances to control and defer their electricity consumption when price soars and can be used to cope with the unpredictability of the energy market or provide response when supply is strained by demand. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents (RAs) coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between RAs and a broker agent (BA). This gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm (GA) and a fast coordination convergence has been achieved.

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Figures

Grahic Jump Location
Fig. 1

Stigspace based coordination system

Grahic Jump Location
Fig. 2

Set-points variable coordination: (a) initial plan and (b) final plan (final coordination result)

Grahic Jump Location
Fig. 3

Coordination convergence, measured by the reduction of total power demand that exceeds supply cap during coordination process: (a) 100 RA coordination, (b) 1000 RA coordination, and (c) 10,000 RA coordination

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