Publication details

Local Load Optimization in Smart Grids with Bayesian Networks

Authors

CHREN Stanislav BÜHNOVÁ Barbora

Year of publication 2016
Type Article in Proceedings
Conference The 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1109/SMC.2016.7844862
Field Informatics
Keywords Smart Grids; Smart Meters; Bayesian Networks; Ripple Control; Load Management
Description One of the main goals of the power distribution utilities is to provide stable supply of the power load. The growing popularity of smart grids, i.e. power grids enhanced with modern ICT, opened new possibilities to make the grid more efficient, secure and reliable. However, by introducing new elements into the infrastructure, such as small-scale photovoltaic power plants, the management of power load is becoming more challenging. In the paper, we address the issue of prevalent load management methods, which often lack sufficient flexibility to match growing complexity of the grid. We have developed a local load management component as an alternative to the still widely used ripple control technology. Our component is capable of individual water heating control by utilising customized TOU tariff schedules. The component uses Bayesian network model to incorporate uncertainties caused by inconsistent or incomplete information collected from smart meters. Our solution was deployed by a major energy distribution company in the Czech Republic in a real smart grid infrastructure consisting of more than 300 consumption points. In the case study, we confirmed viability of our approach as well as pointed out potential challenges remaining to be solved.
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