» @RISK Helps to Analyze
Energy Market Risks in
» Bastian Solutions Illustrates
use of @RISK to Understand
@RISK Modeling Tips
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Decision-Making and Quantitative Risk Analysis using @RISK
Decision-Making and Quantitative Risk Analysis using the DecisionTools Suite
Project Risk Assessment Using @RISK for Project
@RISK Helps to Analyze Energy
According to Dr. Ionut Purica, executive director at the Advisory Center for Energy and Environment in Romania, who helped to advise and implement the changes at Transelectrica, “@RISK’s parameter control and Monte Carlo simulation capability makes it very straightforward to manage and analyze data, as well as predict future trends. At the same time, the results are easy to understand which ensures their credibility. This is reinforced by @RISK’s flexibility – far from being a ‘black box’ that produces un-explained results, it allows users to build and alter the model for their
According to Britt Calloway, Research and Development Engineer for Bastian Solutions, they can be. Bastian is an independent system integrator whose aim is to increase productivity for its clients through automation, information systems, and sound operating procedures. In Britt’s case, he recently used a toy robot to model target throughput of an actual robot in a palletizing operation, and his concerns over uncertainty in this test lead him to @RISK.
Britt discovered that errors in picking up pieces of puzzle parts with his robot arm could drastically affect the cycle time he was measuring. For example, he would bungle the grab for a part, and it would slide away from the robot. He initially thought he would reject the mistakes in the test, but then realized those types of anomalies happen all of the time in the real world, and they would need to be represented as uncertainty in a model using Monte Carlo simulation.
Britt elaborated, “In a robotic palletizing system, there can be box flaps missing, vacuum cups that my need to be replaced, human error, maintenance downtime, and other factors that affect throughput . . . Monte Carlo methods are a way to use engineering insight and more qualitative assessments of your inputs to define a quantitative output.”
You can see a complete description of Britt’s experiments using @RISK at his blog.
Renewable energy in both Europe and the US is based largely on biomass. More than 65% of the total renewable energy that Europe generates is from biomass and the majority of that is from the combustion of wood. In the US about 50% of the total renewable energy generated is from biomass. Hydroelectric power makes up 19% in Europe and 35% in the US. Wind is 8% in Europe and 9% in the US, and solar is 1.6% in Europe and 1% in the US.
The use of this renewable, sustainable, and carbon neutral resource is growing in the northeastern US as modern wood pellet boilers replace heating oil boilers. But the real growth in the market is for export to Europe. The US is becoming a major exporter of refined utility grade wood pellet fuel for co-firing with coal in European utility generators so they can lower carbon emissions.
As a result, there is a need for price stability for the fuel and therefore price stability of the input raw materials. The traditional market for biomass grade wood as an input in the pulp and paper business is prone to large price fluctuations. The researchers have identified a number of supply side and demand side effects as well as some macroeconomic effects that predict biomass wood prices. Their econometric model yields an R-square of 0.926. Based on this research, a new derivative financial product was developed that allows a wood supplier to hedge these price effects and essentially supply wood into the energy markets at more or less constant real prices, even with long-term contracts.
This case study shows how the authors have used the DecisionTools Suite to assist in the development of the hedging product and how they have used @RISK to quantify the risk to the hedged supplier from their long-term supply agreements.
@RISK can be used to evaluate various risk mitigation strategies. One such hedging strategy is the use of swap to guard against price fluctuations in the oil market.
Suppose that an oil project consists of a group of oil wells that are expected to produce a minimum of 100,000 bpd. Over time, the output is expected to grow as more of the project infrastructure is completed and improved.
The cash flows change with fluctuating oil prices. In this example, we can examine the effects on working capital of the changes in oil prices both with and without the purchase of swaps. Risk and reward can be evaluated by running @RISK simulations to analyze the cost-benefit relationship.
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