Local Load Optimization in Smart Grids with Bayesian Networks

CHREN, Stanislav and Barbora BÜHNOVÁ. Local Load Optimization in Smart Grids with Bayesian Networks. Online. In The 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). Neuveden: IEEE, 2016, p. 4021-4027. ISBN 978-1-5090-1897-0. Available from: https://dx.doi.org/10.1109/SMC.2016.7844862.

Basic information

• Original name: Local Load Optimization in Smart Grids with Bayesian Networks
• Authors: CHREN, Stanislav (703 Slovakia, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution).
• Edition: Neuveden, The 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), p. 4021-4027, 7 pp. 2016.
• Publisher: IEEE

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/16:00090403
• Organization unit: Faculty of Informatics
• ISBN: 978-1-5090-1897-0
• Doi: http://dx.doi.org/10.1109/SMC.2016.7844862
• UT WoS: 000402634703144
• Keywords in English: Smart Grids; Smart Meters; Bayesian Networks; Ripple Control; Load Management
• Tags: firank_A
• Tags: International impact, Reviewed

Abstract

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.

Links

• MUNI/A/0765/2013, interní kód MU: Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)

Investor: Masaryk University, Category A

• MUNI/A/0997/2016, interní kód MU: Name: Aplikovaný výzkum na FI: vyhledávacích systémy, bezpečnost, vizualizace dat a virtuální realita.

Investor: Masaryk University, Applied research at FI: search systems, security, data visualization and virtual reality, Category A