Tioga Energy uses @RISK from Palisade to predict financial savings on solar energy agreements in California

Background
Founded in 2006 and based in Silicon Valley, Tioga Energy, is a leading supplier of renewable energy services to commercial, government, and non-profit institutions.  It enables access to affordable 'clean' energy in Ontario, Canada, and various US states, including California, which consumes more electricity than all but twelve of the largest countries in the world. 

Tioga provides project financing through its SurePath^SM Solar (PPAs), and maintains and operates solar systems on behalf of its customers.  Its offering delivers predictably priced power and enables organisations to both 'green' their operations and reduce energy costs.  In order to illustrate the benefits of solar, Tioga needs to estimate future electricity prices and make comparisons by showing the savings from a new solar system.

'Hedging Against Utility Rate Fluctuations with a Solar PPA'
With a solar PPA, customers pay only for the electricity generated by a solar system – the capital equipment required for that system, along with its design, implementation and maintenance are financed and managed by the solar power provider. However, PPAs require customers to commit to a fixed energy price over a long period of time, such as 20 years.  To illustrate the financial benefits of being 'locked-in' to such an agreement, Tioga Energy prepared a report, 'Hedging Against Utility Rate Fluctuations with a Solar PPA' (available for download), that compared probable future electric utility rates in California with the cost of electricity from a solar PPA.

Using history to predict the future with @RISK
The many factors likely to affect utility electricity rates in the future include natural gas pricing and supply constraints, carbon regulation and higher power plant development costs.  These are all complex and interdependent – so much so that it is not possible to accurately predict future trends using them.

Instead, to forecast possible price increases, Tioga Energy inputs historical electricity rate data into a model developed using Palisade's risk analysis software, @RISK. (The statistical method of using historical data to predict future trends is popular with the financial and insurance industries, as well as physical sciences who use it to study complex systems with a large number of interrelated variables).

Looking at historical data, it is possible to see how economic and regulatory factors have resulted in rate changes in the past. For example, 'deregulation' of electricity markets in California led to the 2000-2001 energy crisis in the state and significant increases in electricity prices. In the late 1970s and early 1980s, inflation combined with safety concerns about nuclear power plants led to a period of dramatic rate increases.

Although disruptive events in the future are likely to be different, it is likely that what does take place will create similar, if not greater, energy price volatility.

@RISK predicts potential savings
Using California's electricity rate data for the past 35 years, the @RISK model generates a probability distribution for electricity rate rises over the 20-year PPA period. This shows that there is a 25 percent likelihood that price increases will be less than 4.8 percent, and a 25 percent chance that rate rises would be more than 8.7 percent.

Comparing a solar PPA’s cost against potential future electric utility rates also shows the magnitude of savings realised if those utility rate increases were to occur. For example, the @RISK simulation model that determines the range of potential utility rate rises might establish that there is a 65 percent chance that utility rates will rise at given rate per year.  This indicates that the savings made by a customer with a fixed solar PPA will also equate to 65 percent of all savings scenarios.

The @RISK model helps Tioga Energy to evaluate how likely it is that a customer will save money for a variety of PPA scenarios (i.e. the rate at which electricity would initially be charged and the amount by which it would then increase each year). It also calculates the magnitude of savings for the different combinations of first year costs and subsequent rises.

@RISK enables informed decision-making
The @RISK model enables Tioga Energy to move away from the way in which the financial and economic benefits of a solar PPA have traditionally been viewed. Customers typically decide on whether or not to enter an agreement based on an economic saving model that uses an assumed and fixed value for the annual growth rate of the electricity cost from the utility. However, this does not take into account that, if electricity prices rise less than the predicted value used in the model, savings are over-estimated, but if they grow more than initially forecast, the savings from a PPA will be more.

In contrast the @RISK model allows Tioga Energy to provide potential customers considering a PPA with a wide range of scenarios (initial electricity rate vs. price increase), and the different probabilities associated with each. This information includes the likelihood of making a range of savings.  For example, in a scenario where there is a 50 percent chance of saving 12 percent, the chance of saving around 30 percent is 20 percent.  Consumers are therefore able to better understand the pricing and make an informed decision about whether to sign up for a PPA.

@RISK offers competitive advantage
“Using historical data and @RISK's modelling capacity, we can offer consumers a robust view of the potential benefits of a solar PPA. This enables them to hedge against rising electricity rates, as well as feel confident that they are playing a part in tackling global warming,” explains Kristian Hanelt, VP Project Finance for Tioga Energy.

Hanelt confirms: “@RISK is a flexible and technically adept tool that, in addition to enabling in-depth analysis, makes it easy for us to present relatively complex ideas in an easy-to-understand graphical format. As a result, it plays a key role in helping Tioga Energy to differentiate itself from its competitors.”

Additional information:

Key software features useful to Tioga Energy

• Monte Carlo simulation
• Distribution fit
• Graphic tools
• The ability to run different simulations with different inputs, at the same time

Specific techniques used for quantitative analysis
Besides the simulations themselves, the distribution fit is the main technique used, although Tioga Energy relies on @RISK to advise on the best distribution

Illustrations
http://www.tiogaenergy.com/tioga-energy-reports.php

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