Customers & Industries: Rudd Asset Management

@RISK Helps Turn Waste Into Energy

  • Industry: Utilities
  • Product(s): @RISK
  • Application: Waste disposal and renewable energy

Summary

A large horse racing and competition complex wanted to develop “green” policies that made economic sense. Rudd Asset Management used the DecisionTools Suite to determine that converting stall bedding and manure waste into renewable energy was a feasible, cost-effective solution—helping the complex turn a waste-disposal problem into a profit.

Palisade DecisionTools software is a great tool for our process. We help the client make better decisions, quicker using the power of Monte Carlo simulation and useful statistics.
Mark Rudd, President, Rudd Asset Management

The energy market has shifted, ushering in an era where so-called wastes are being re-examined, and the desire for energy security and clean/green energy sources is rising. Rudd Asset Management (RAM), an energy consulting company specializing in cost-effective solutions, recently helped a large horse racing and competition complex that wanted to develop “green” policies that made economic sense. By using Palisade DecisionTools Suite, RAM determined that converting stall bedding and manure waste into renewable energy was a feasible, cost-effective solution—helping the complex turn a waste-disposal problem into a profit.

Background

RAM often works with companies looking to add renewable power sources. More and more industries are interested in reliable and renewable energy sources, creating a vastly different energy market than the one we had just a few years ago. The uncertainty of all energy production-related variables can make investment highly challenging. As-developing countries” grow their economies, the necessity for clean, safe, affordable power becomes critical. While renewable energies can open up promising opportunities--what once was a disposal problem can sometimes become an energy advantage—their costs and aggregate risks remain unknown.

RAM worked on a project that posed these important questions. The client was a large horse racing and competition complex that wanted a “green” image. While the environmentally-friendly image was important to them, it also had to make economic sense.

Further constraining their budget was ever-rising costs for disposal of stall bedding and manure and steadily increasing energy bills.

The opportunity to avoid the costs to transport and dispose of the waste became an opportunity for free fuel or even “negative cost fuel” for the project. The opportunity to create a win-win scenario and accomplish this conversion of waste to energy in a “green” way made it even more enticing. But, did it make economic sense? With only a few constants and almost everything a variable, RAM decided to apply risk management and Monte Carlo simulation.

Considering All Uncertainties

To begin with, RAM did a review of gasification of waste materials from a number of nearby tracks. The ultimate goal was simple: get a win-win by changing a negative to a positive. Convert an increasing waste expense to a sustainable source of “green” renewable energy.

Unsurprisingly, the uncertainties were numerous. Variables that had to be considered included: cost to gasify the “fuel”; the cost to aggregate the multiple fuel supplies; the cost to generate the power; the value of combined heat and power thermal energy; the quantity of fuel available each year, the energy used by the facilities each year; and the alternative costs to purchase the power from the local utility. RAM opted to use the Palisade DecisionTools Suite for the analysis. Energy use of the facility for the last 5 years was reviewed to get an energy use range. Then, an estimate of the future energy consumption was made to get an electric power and thermal energy use range to be modeled into @RISK. Similar estimates for the variance for amount of waste (tons); energy content of the fuels; the cost to manage the fuel; and the wholesale value for sale of excess energy was also modeled. All these variables had to be considered in addition to the capital costs for the new power plant, operations and maintenance costs, and funding costs of the project.

Focusing On Value Drivers

Typically, unless there is enough history to know variables and their variance, RAM starts with a triangular distribution. Assumptions are reviewed with the client or other available experts to ensure the client approves of the goals. This initial process is iterative, and assumptions are refined later. Once RAM collaborates with the clients, they improve the assumptions by looking at what variables drive value.

In this case, initial results for project Net Present Value showed that the outcome was most affected by the price of wholesale power. The affect to NPV from the expected variance in engine gross generation and the paid disposal fee was small. Knowing that was the key value driver, we were able to focus on our assumptions for the price of wholesale power. The use of @RISK’s tornado diagram is an effective aid in focusing on the value drivers.

Another useful tool RAM used to guide the project is the probability distribution chart. Examining the predicted NPV, we saw that this project shows an expected NPV of nearly $ 1.2 million dollars within 5 years, with a 94% probability of a positive NPV. This amounted to an 18% IRR and debt coverage ratio of over 3.6, an excellent result considering the total investment was under $ 9.2 million.

Refining a Model

Once a project has been screened to see if it makes financial sense, RAM then switches its focus towards improving their model assumptions. During this stage, RAM, consults the other stakeholders of the project and looks at the results.

In this case, because the simulation determined that the wholesale power price drove the value, RAM next needed to confirm that the price was truly accurate. By focusing on variables with the most impact, RAM was able to do a review in a lot less time, while getting better accuracy.

The general format of the spreadsheet that RAM used for this project is shown below. The simulation results show the project is feasible:

  • Internal Rate of Return> 15 %
  • Debt Coverage Ratio> 2
  • Net Present Value positive within 5 years
  • Strong and growing after tax cash flows

Thus, with RAM’s methodology of taking a project, starting with simple proactive assumptions, getting buy-in from all stakeholders, looking at the results, refining the assumptions, and iterating where necessary, a model is developed that allows accurate decision-making. According to Mark Rudd, RAM President, “Palisade DecisionTools software is a great tool for our process. We help the client make better decisions, quicker using the power of Monte Carlo simulation and useful statistics.”

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