An assessment framework to quantify the interaction between the built environment and the electricity grid

Electricity consumption in buildings is highly variable on time scales of seasons, hours, minutes, and even seconds. Yet, energy performance in building sustainability standards and rating systems is typically assessed in terms of total annual energy use, cost, and/or GHG emissions. Given that in North America buildings account for between 45 and 75% (depending on the region) of total electricity consumed, it is relevant to define an assessment framework to quantify the impact of variability in building electricity demand on the electricity system. This study proposes “Grid Compensation Scores” (GCS) that assess the contribution of a building electricity demand profile to increasing or decreasing the variability in the system electricity demand profile.

[1]  Martin Braun,et al.  Is the distribution grid ready to accept large‐scale photovoltaic deployment? State of the art, progress, and future prospects , 2012 .

[2]  Wolf-Peter Schill.  Residual load, renewable surplus generation and storage requirements in Germany , 2014 .

[3]  H. Gonçalves,et al.  Design issues for net zero energy buildings , 2012 .

[4]  Dirk Saelens,et al.  Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation , 2012 .

[5]  Martin Braun,et al.  Is the distribution grid ready to accept large‐scale photovoltaic deployment? State of the art, progress, and future prospects , 2012 .

[6]  Anna Stoppato,et al.  Life cycle assessment of photovoltaic electricity generation , 2008 .

[7]  E. Wilson,et al.  2014 Building America House Simulation Protocols , 2014 .

[8]  Grant D. Jacobsen.  Estimating end-use emissions factors for policy analysis: the case of space cooling and heating. , 2014, Environmental science & technology.

[9]  Joule A. Bergerson,et al.  Incorporation of electricity GHG emissions intensity variability into building environmental assessment , 2015 .

[10]  Asgeir Tomasgard,et al.  A stochastic model for scheduling energy flexibility in buildings , 2015 .

[11]  Sebastian Herkel,et al.  Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options , 2017 .

[12]  Josef Ayoub.  Towards net-zero energy building : Lessons learned from the Joint IEA SHC Task 40 EBC Annex 52 , 2017 .

[13]  J. Driesen,et al.  Grid impact indicators for active building simulation , 2011, ISGT 2011.

[14]  Per J. Agrell,et al.  Smart-grid Investments, Regulation and Organization , 2013 .

[15]  Per Heiselberg,et al.  Zero energy buildings and mismatch compensation factors , 2011 .

[16]  Karsten Voss,et al.  Net zero energy buildings: A consistent definition framework , 2012 .

[17]  Anna Joanna Marszal,et al.  IEA EBC Annex 67 Energy Flexible Buildings , 2017 .

[18]  P. Lund,et al.  Improved flexibility with large-scale variable renewable power in cities through optimal demand side management and power-to-heat conversion , 2016 .

[19]  M. Nicolosi.  Wind power integration and power system flexibility–An empirical analysis of extreme events in Germany under the new negative price regime , 2010 .

[20]  P. Torcellini,et al.  Understanding Zero-Energy Buildings , 2006 .

[21]  Sally M. Benson,et al.  The energetic implications of curtailing versus storing solar- and wind-generated electricity , 2013 .

[22]  Sebastian Stinner,et al.  Quantifying the operational flexibility of building energy systems with thermal energy storages , 2016 .

[23]  Jaume Salom,et al.  Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data , 2014 .

[24]  Ann Heylighen,et al.  Enriching our understanding of architecture through disability experience , 2013 .

[25]  Eike Musall,et al.  Design strategies for non-residential zero-energy buildings: lessons learned from Task40/Annex 52: towards net zero-energy solar buildings , 2013 .