Options for improving the load matching capability of distributed photovoltaics: Methodology and application to high-latitude data

Abstract At high latitudes, domestic electricity demand and insolation are negatively correlated on both an annual and a diurnal basis. With increasing integration of distributed photovoltaics (PV) in low-voltage distribution grids of residential areas, limits to the penetration level are set by voltage rise due to unmatched production and load. In this paper a methodology for determining the impacts of three options for increased load matching is presented and applied to high-latitude data. The studied options are PV array orientation, demand side management (DSM) and electricity storage. Detailed models for domestic electricity demand and PV output are used. An optimisation approach is applied to find an optimal distribution of PV systems on different array orientations and a best-case evaluation of DSM and a storage model are implemented. At high penetration levels, storage is the most efficient option for maximising the solar fraction, but at lower overproduction levels, the impact of DSM is equal or slightly better. An east–west orientation of PV arrays is suggested for high penetration levels, but the effect of the optimised orientation is small. Without an optimised storage operation, the overproduced power is more efficiently reduced by DSM than storage, although this is highly dependent on the applied DSM algorithm. Further research should be focused on the DSM potential and optimal operation of storage.

[1]  Linus Palmblad The Effects of the Investment Support Scheme on the Dynamics of the Swedish Photovoltaic Sector , 2006 .

[2]  Lennart Söder,et al.  Bättre kontakt via nätet : om anslutning av förnybar elproduktion , 2008 .

[3]  K. Ellegård,et al.  Complexity in daily life – a 3D-visualization showing activity patterns in their contexts , 2004 .

[4]  Kajsa Ellegård,et al.  Exploring Time Diaries Using Semi-Automated Activity Pattern Extraction , 2009 .

[5]  W. Beckman,et al.  SOLAR ENGINEERING OF THERMAL PROCESSES Second Edition , 2009 .

[6]  Juozas Abaravicius,et al.  Demand Side Activities for Electric Load Reduction , 2007 .

[7]  Mark W. Davis,et al.  Comparison of Photovoltaic Module Performance Measurements , 2006 .

[8]  J. Widén,et al.  Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation , 2009 .

[9]  D. L. King,et al.  Sandia National Laboratories , 2000 .

[10]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[11]  Christian Reise,et al.  Www.satel.light.com: The European database of daylight and solar radiation data based on meteosat images , 2000 .

[12]  Nick Jenkins,et al.  Embedded Generation (Power & Energy Ser. 31) , 2000 .

[13]  Jimmy Royer,et al.  Photovoltaics in cold climates , 1999 .

[14]  David Infield,et al.  Impact of widespread photovoltaics generation on distribution systems , 2007 .

[15]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[16]  Jukka Paatero,et al.  Effects of Large-Scale Photovoltaic Power Integration on Electricity Distribution Networks , 2007, Renewable Energy.

[17]  S. Conti,et al.  Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators , 2007 .

[18]  Nikos D. Hatziargyriou,et al.  Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .

[19]  Jukka Paatero,et al.  Impacts of energy storage in distribution grids with high penetration of photovoltaic power , 2007 .

[20]  Mats Rönnelid,et al.  Potential of solar electricity in the Swedish electricity grid , 2008 .

[21]  Jenny Palm,et al.  Adopting small-scale production of electricity , 2009 .