A Methodology for Accounting the CO 2 Emissions of Electricity Generation in Finland The contribution of home automation to decarbonisation in the residential sector

To achieve the decarbonisation of the energy sector in Europe, the CO 2 emission profile of energy consumption must be fully understood. A new methodology for accounting for CO 2 emissions is required for representing the dynamics of emissions. In this article, a dynamic integration of CO 2 emissions due to the electricity production and trade was developed. Electricity consumption and related CO 2 emissions are studied for a typical Finnish household. A model detached house is used to simulate the effect of home automation on CO 2 emissions. Hourly electricity production data are used with an hourly electricity consumption profile generated using fuzzy logic. CO 2 emissions were obtained from recorded data as well as estimated based on monthly, weekly, and daily generated electricity data. The CO 2 emissions due to the use of electric appliances are around 543 kgCO 2 /y per house when considering only the generated electricity, and 335 kgCO 2 /y when balancing the emissions with exported and imported electricity. The results of the simulation indicate that home automation can reduce CO 2 emissions by 13%. Part of emission reduction was achieved through peak shifting, by moving energy consumption load from daytime to night time. The paper highlights the role of home automation in reducing CO 2 emissions of the residential sector in the context of smart grid development. Keywords-CO 2 emissions calculation, home automation, load

[1]  H. Hens,et al.  Impact of energy efficiency measures on the CO2 emissions in the residential sector, a large scale analysis , 2001 .

[2]  Melody Stokes.  Removing barriers to embedded generation : a fine-grained load model to support low voltage network performance analysis , 2005 .

[3]  Stéphane Ploix,et al.  Home Energy Management Problem: towards an Optimal and Robust Solution , 2010 .

[4]  Jianhua Ma,et al.  Agents based approach for smart eco-home environments , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[5]  Rahul V. Ralegaonkar,et al.  Improving environmental performance of building through increased energy efficiency: A review , 2011 .

[6]  Christian Thiel,et al.  Evaluation of Several Measures to Improve the Energy Efficiency and CO2 Emission in the European Single-family houses , 2012 .

[7]  Liyanage C. De Silva,et al.  State of the art of smart homes , 2012, Eng. Appl. Artif. Intell..

[8]  Stéphane Ploix,et al.  Prediction of appliances energy use in smart homes , 2012 .

[9]  A. Grandjean,et al.  A review and an analysis of the residential electric load curve models , 2012 .

[10]  Sarah C. Darby,et al.  Potential carbon impacts of smart grid development in six European countries , 2013 .

[11]  S. Powers,et al.  Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City , 2013 .

[12]  Stéphane Ploix,et al.  Managing Energy Smart Homes according to Energy Prices: Analysis of a Building Energy Management System , 2014 .

[13]  Sanna Syri,et al.  Potential of new lighting technologies in reducing household lighting energy use and CO2 emissions in Finland , 2014 .

[14]  K. Leiviskä,et al.  Home automation for a sustainable living : modelling a detached house in Northern Finland , 2014 .

[15]  E. Pongrácz,et al.  Smart Houses for Energy Efficiency and Carbon Dioxide Emission Reduction , 2014 .

[16]  Gaetano Zizzo,et al.  Impact of building automation control systems and technical building management systems on the energy performance class of residential buildings: An Italian case study , 2014 .

[17]  L. Nordström,et al.  Including dynamic CO2 intensity with demand response , 2014 .

[18]  Phani Chavali,et al.  A Distributed Algorithm of Appliance Scheduling for Home Energy Management System , 2014, IEEE Transactions on Smart Grid.