Portfolio Analysis of Solar Photovoltaics: Quantifying the Contribution of Locational Marginal Pricing and Solar Irradiation Power on Overall Revenue and Investment Risk

Open Access
Author:
Kumpf, Katrina Heidi
Graduate Program:
Energy and Mineral Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 31, 2014
Committee Members:
  • Seth Adam Blumsack, Thesis Advisor
  • Jeffrey Brownson, Thesis Advisor
Keywords:
  • solar photovoltaics
  • solar irradiation
  • beta values
  • locational marginal pricing
  • investment
  • portfolio risk
  • portfolio analysis
Abstract:
The goal of this study was to perform a portfolio analysis on a geo-spatially dispersed portfolio of utility-scale solar photovoltaic (PV) arrays from the viewpoint potential investors. It sought to examine how maximum solar irradiation power and locational marginal pricing (LMP) of electricity each affect overall revenue output, as well as how they each impact the risk associated with a portfolio of solar sites. Ten different sites were used along the east coast of the U.S. to give a range of both solar availability and LMP zones within the PJM interconnection operating grid. TRNSYS software was used to simulate an array of solar photovoltaics based on the specifications of a current-market PV panel, in order to generate maximum power from the solar irradiation data. Beta values were calculated to determine how the two factors affected the overall risk, as well as how individual sites contributed to the portfolio. A standardized regression model was run on the average maximum power and average LMP in order to evaluate which has a greater impact on revenue variance. The results showed relatively low beta values of around 0.05 for maximum power and 0.09 for LMP (annual timescale), indicating a low level of risk for each site relative to the entire portfolio. A breakdown of the beta values showed that maximum power exhibited low variance and covariance, while LMP had high variance and covariance across sites. This indicated that the more volatile LMP values were the bigger driving force behind revenue variance. The standardized regression analysis further verified the conclusion that the average values of LMP have a greater impact on the variance overall revenue than the average values of maximum solar power. Further study showed that the beta values varied by location, depending on the individual zonal electricity pricing and the climate region of the site. It was determined that when investing in solar PV utility arrays, a core base of low beta sites from the same climate region and stable electricity pricing zones yielded a low risk portfolio. The portfolio could then be expanded upon by bridging over to sites in different climate regions and zones with more volatile LMPs.