A simulator for self-adaptive energy demand management

Ying Guo*, Rongxin Li, Geoff Poulton, Astrid Zeman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

15 Citations (Scopus)


A Demand-Side Program Simulation Tool is designed to predict the response from different deployment strategies of distributed domestic energy management. To date, there are several case studies of demand management and control projects from around the world. To achieve results with sufficient generality, case studies need to be conducted over long periods, with a reasonable number of diverse households. Such case studies require large capital to set up hardware and software. To bypass these financial and temporal investments, we have designed a simulator for energy suppliers to use in order to learn the likely performance of large-scale deployments. Of main interest is the prediction of not only the level and firmness of demand response in critical peak pricing trials, but also the household's comfortable level and satisfaction level. As an example of the power of the simulator we have used it to develop and test a new self-adaptive methodology to intelligently control the energy demand. The methodology is adaptive to global factors, such as the market energy price, as well as local conditions, such as the satisfaction level of households. This paper outlines self-adaptive methodologies used within the simulator. Experimental results show energy consumption under different control strategies and the improvement of system behaviour through adaptive design. With the self-adaptive demand management strategy, the total energy consumed by one million households' controllable loads has reduced dramatically while the satisfaction level of households is well maintained. This is one of the very first simulators that take into account both technical and human behaviour aspects.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationSecond IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2008, 20-24 October 2008, Venice, Italy
EditorsSven Brueckner, Paul Robertson, Umesh Bellur
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Print)9780769534046
Publication statusPublished - 2008
Event2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2008 - Venice, Italy
Duration: 20 Oct 200824 Oct 2008


Other2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2008

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