A smart home demand-side management system considering solar photovoltaic generation

Lately, over the past decade both the growing environmental awareness and the subsidies provided for renewable energy sources triggered a remarkable residential investment in photovoltaic (PV) systems. Although this is a desirable change, the effects of these widespread distributed energy resources (DER) have to be considered, both financial and technical aspects. The intermittent electricity production of PV systems brings several issues on the grid operator's side. In order to be able to control and take the DER's production into the grid without risking power quality, demand side management (DSM) can make positive effects on this issue at residential level. Also the electricity provider's tariff structure has to be considered at the optimization of the monetary costs. In this paper an intelligent residential DSM system for a rooftop installed residential PV is introduced. The applied DSM technique aims to reduce costs of the customer and also power losses on the grid. In order to avoid reducing the consumer's convenience significantly, a scheduling algorithm is applied, using historical data of the consumer's habits and PV generation forecasts.

[1]  Giorgio Sartor,et al.  Optimal scheduling of smart home appliances using mixed-integer linear programming , 2012 .

[2]  Yu Zhang,et al.  Robust Energy Management for Microgrids With High-Penetration Renewables , 2012, IEEE Transactions on Sustainable Energy.

[3]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[4]  Luiz C. P. da Silva,et al.  Large-scale control of domestic refrigerators for demand peak reduction in distribution systems , 2013 .

[5]  Joachim Vanzetta,et al.  Current and imminent challenges for the transmission system operator in Germany , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[6]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[7]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[8]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  Akin Tascikaraoglu,et al.  A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey , 2014 .

[10]  Marco L. Della Vedova,et al.  Peak shaving through real-time scheduling of household appliances , 2014 .