Timing residential photovoltaic investments in the presence of demand uncertainties

Abstract As investment in residential photovoltaic systems is increasing at a rapid pace, it is important to investigate whether delaying or otherwise timing these investments can maximize long term investment gains. Conventional financial analysis methods for evaluating investment decisions in solar-electric system are all based on a one-time installation of the PV systems and cannot be applied to analyze the benefit of delayed and staged investment. Such benefits could be declining costs of PV systems thus tempting investors to hold off and wait for a better moment to invest. This paper proposes a decision making framework using the real option method to analyze the optimum time to invest in a residential PV system in different scenarios. A reference residential house is used to test the effect of different investment strategies. The results show the type of staged investment of installing residential PV system that maximizes the long-term payoff. This reveals when the option to delay investment is preferred. The supporting source code and data are available for download at https://github.com/reisiga2/SolarPanelInvestment .

[1]  Shuangzhe Liu,et al.  Global Sensitivity Analysis: The Primer by Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola , 2008 .

[2]  Ian Ellingham,et al.  New Generation Whole-Life Costing: Property and Construction Decision-Making Under Uncertainty , 2006 .

[3]  Alan Goodrich,et al.  Photovoltaic System Pricing Trends: Historical, Recent, and Near-Term Projections 2013 Edition , 2013 .

[4]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[5]  R. Madlener,et al.  Evaluation of Economically Optimal Retrofit Investment Options for Energy Savings in Buildings , 2011 .

[6]  Yuming Sun,et al.  Closing the building energy performance gap by improving our predictions , 2014 .

[7]  Baabak Ashuri,et al.  Investment Valuation Model for Renewable Energy Systems in Buildings , 2015 .

[8]  J. Michalsky,et al.  Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .

[9]  Jerry Yudelson,et al.  Greening Existing Buildings , 2009 .

[10]  Godfried Augenbroe,et al.  RIGHT-SIZING A RESIDENTIAL PHOTOVOLTAIC SYSTEM UNDER THE INFLUENCE OF DEMAND RESPONSE PROGRAMS AND IN THE PRESENCE OF SYSTEM UNCERTAINTIES , 2014 .

[11]  M. Ilić,et al.  Stochastic modeling of electric power prices in a multi-market environment , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[12]  J. Mutale,et al.  Assessment of domestic photovoltaic systems based on real options theory , 2013 .

[13]  Qinpeng Wang,et al.  The Role of Construction Detailing and Workmanship in Achieving Energy Efficient Buildings , 2014 .

[14]  Hyun Woo Lee,et al.  Real Options Valuation of Phased Investments in Commercial Energy Retrofits under Building Performance Risks , 2014 .

[15]  Johan Albrecht,et al.  The future role of photovoltaics: A learning curve versus portfolio perspective , 2007 .

[16]  Eduardo S. Schwartz,et al.  Investment Under Uncertainty. , 1994 .

[17]  Godfried Augenbroe,et al.  Analysis of uncertainty in building design evaluations and its implications , 2002 .

[18]  Huafen Hu,et al.  Risk-conscious design of off-grid solar energy houses , 2009 .

[19]  Carol C. Menassa,et al.  Evaluating sustainable retrofits in existing buildings under uncertainty , 2011 .

[20]  Baabak Ashuri,et al.  A Real Options Approach to Evaluating Investment in Solar Ready Buildings , 2011 .

[21]  Makarand Hastak,et al.  Valuing the Warranty Ceiling Clause on New Mexico Highway 44 Using a Binomial Lattice Model , 2008 .

[22]  Christiaan J. J. Paredis,et al.  TOWARDS BETTER PREDICTION OF BUILDING PERFORMANCE: A WORKBENCH TO ANALYZE UNCERTAINTY IN BUILDING SIMULATION , 2013 .

[23]  Gregory F. Nemet,et al.  Beyond the learning curve: factors influencing cost reductions in photovoltaics , 2006 .

[24]  Sarah M. Ryan,et al.  On The Validity of The Geometric Brownian Motion Assumption , 2005 .

[25]  Heng Su,et al.  Uncertainty Quantification Of Solar Diffuse Irradiation On Inclined Surfaces For Building Energy Simulation , 2013, Building Simulation Conference Proceedings.

[26]  Godfried Augenbroe,et al.  Implications Of The Uncoupling Of Building And Hvac Simulation In The Presence Of Parameter Uncertainties , 2013, Building Simulation Conference Proceedings.

[27]  Javad Khazaii,et al.  Effects of sub-optimal component performance on overall cooling system energy consumption and efficiency , 2012 .

[28]  Christiaan J. J. Paredis,et al.  A FRAMEWORK FOR GENERATING STOCHASTIC METEOROLOGICAL YEARS FOR RISK-CONSCIOUS DESIGN OF BUILDINGS , 2012 .

[29]  Godfried Augenbroe,et al.  Uncertainty quantification of microclimate variables in building energy models , 2014 .

[30]  Adam Borison,et al.  Real Options Analysis: Where Are the Emperor's Clothes? , 2005 .

[31]  Byungil Kim,et al.  Determining the Value of Governmental Subsidies for the Installation of Clean Energy Systems Using Real Options , 2012 .

[32]  Emiel van der Maaten.  Uncertainty, Real Option Valuation, and Policies toward a Sustainable Built Environment , 2011 .

[33]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .