Solar buildings in Austria: Methodology to assess the potential for optimal PV deployment

A high rate of integration of distributed photovoltaic systems (PV) may cause problems in the distribution grid. We propose a methodology to spatially and temporally analyze the potential reverse load in the distribution grid of Austria. The goal lies in determining the maximum generation of PV with no investments into grid enforcements. First, we use measured load profiles for households and simulate stochastic load profiles for commercial consumers. We combine the generated load profiles with data on PV output to determine the net demand load profiles at 1 km2 grid. Thirdly, we study the effects of a large scale implementation of rooftop PV on the energy system using the JRC EU TIMES model. We find that (excluding night hours) on average in 9% of the hours supply exceeds demand, differing between 0% and 60% depending on the grid cell. When only including household load profiles, surplus production increases to 23%. This suggests to giving priority to decentralized PV development in areas with a higher share of commercial consumers. Lastly, we show that a large scale deployment of distributed PV leads to reduced imported electricity.

[1]  Yasumasa Fujii,et al.  Analysis of possible introduction of PV systems considering output power fluctuations and battery technology, employing an optimal power generation mix model , 2013 .

[2]  Thomas Huld,et al.  PV-GIS: a web-based solar radiation database for the calculation of PV potential in Europe , 2005 .

[3]  Jaroslav Hofierka,et al.  A New GIS‐based Solar Radiation Model and Its Application to Photovoltaic Assessments , 2004, Trans. GIS.

[4]  Fjo De Ridder,et al.  Electricity storage for grid-connected household dwellings with PV panels , 2010 .

[5]  Zvonimir Klaić,et al.  Expansion of the residential photovoltaic systems and its harmonic impact on the distribution grid , 2012 .

[6]  A. Schroeder Modeling storage and demand management in power distribution grids , 2011 .

[7]  Sofia Simoes,et al.  The impact of location on competitiveness of wind and PV electricity generation - Case study for Austria , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[8]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[9]  Jukka Paatero,et al.  Impacts of distributed photovoltaics on network voltages: Stochastic simulations of three Swedish low-voltage distribution grids , 2010 .

[10]  E. Dunlop,et al.  A power-rating model for crystalline silicon PV modules , 2011 .

[11]  R. Hollmann,et al.  The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance - a LUT based eigenvector hybrid approach. , 2009 .

[12]  S. Abu-Sharkha,et al.  Can microgrids make a major contribution to UK energy supply ? , 2005 .

[13]  Richard A. Buswell,et al.  A simulation and optimisation study: Towards a decentralised microgrid, using real world fluctuation data , 2012 .