@RISK Helps Integrate Renewable Energy Sources
Power plants can estimate short-term demand for electricity with a fair degree of confidence when dealing with traditional, controllable energy sources, such as water and natural gas. But this is not the case when dealing with solar- and wind-generated power, which can vary significantly on a day-to-day basis. As solar and wind outputs are uncertain, they are uncontrollable. This means they require virtually 100% backup with fossil, nuclear, and hydro sources of power to prevent blackouts. Think of the repercussions of a solar eclipse and calm winds on renewable energy output, which occurred in Europe in 2015.
Solar and wind can be transformed to become more controllable sources of power if there is enough storage of electricity to compensate for when there is too much cloud cover, or wind speeds drop significantly or are too high for wind turbine operation. "Electricity storage can be compared to an inventory of products – products are stored when demand is low, then become available when demand goes back up," explains Roy Nersesian, a professor at the Leon Hess Business School at Monmouth University and author of the book, Energy Risk Modeling.
Unfortunately, conventional electricity storage batteries cannot handle a utility-sized storage system of both conventional and renewable power supplies. However, pumped storage plants – or gravity batteries – are able to store and supply electricity to cover the mismatch between electricity supply and demand.
Using @RISK, Nersesian developed a detailed model that takes into account the size and location of farms, fluctuations in electricity supply and demand, and electricity storage requirements. You can get an in-depth look, including example models, in the free on-demand minicourse "Integrating Renewables with Electricity Storage."
Nersesian's research demonstrates that uncontrollable, renewable energy sources can be successfully integrated into large power systems without impacting system stability, if appropriate electricity storage via pumped storage plants is available. With Palisade’s @RISK, utility operators can determine if – and how – they can leverage pumped storage plants, and transform wind and solar into reliable sources of power.
» Read more about the study here
» Get the "Integrating Renewables" minicourse on demand
- 1-hour webinar delivered by Professor Roy Nersesian
- Example models
- Roy's 50-page whitepaper "Integrating Renewables with Electricity Storage"
- Presentation slidedeck
There are three easy ways to get the DecisionTools Suite for your students:
Course Licenses available from Palisade.
These are economical, annually renewing bulk licenses for both
network lab and laptop installation.
» Learn more
Student Versions from Palisade.
These are 12-month versions available for purchase individually by students through either the Palisade web site or the school store.
» Learn more
Textbook Editions available bundled with textbooks.
These are time- and model-limited licenses.
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Time Series for Forecasting
Excerpted from Learning Statistics with StatTools by Dr. Chris Albright, published by Palisade
As mentioned in our March Academic Newsletter, working with time series data presents a unique challenge. The goals are typically to understand the patterns in historical data and to forecast these patterns into the future. From a practical standpoint, there are two complications: randomness (also called "noise") in the historical data, and the uncertainty of whether history will repeat itself in the future. Last month we covered how to adjust for the noise in the historical data. We also took a look at the uncertainty of whether history will repeat itself. Now we are going to explore the forecasting aspect of a time series analysis using StatTools.
» Learn more and download the example files for Forecasting
with Time Series
» Example files from last month's Time Series Example
» More about StatTools
Q: Does @RISK have a multinomial distribution?
A: The multinomial distribution is a generalized form of the binomial distribution. In a binomial, you have a fixed sample size or number of trials, n. Every member of the population falls into one of two categories, usually called "success" and "failure". The probability of success on any trial is p, and the probability of failure on any trial is 1–p. The RiskBinomial distribution takes the parameters n and p, and at each iteration it returns a number of successes. The number of failures in that iteration is implicitly n minus the number of successes.
In a multinomial, you have three or more categories, and a probability is associated with each category. The total of the probabilities is 1, since each member of the population must be a member of some category. As with the binomial, you have a fixed sample size, n. At each iteration you want the count of each category, and the total of those counts must be n.
@RISK doesn't have a multinomial distribution natively, but you can construct one using binomial distributions and some simple logic. This workbook shows you how to do it.
» Download example
Evaluating economic costs and benefits of climate resilient livelihood strategies
Liu, S., Connor, J., Butler, J. R. A., Jaya, I. K. D., & Nikmatullah, A. (2015). Climate Risk Management.
A major challenge for international development is to assist the poorest regions to achieve development targets while taking climate change into account. Such ‘climate resilient development’ (CRD) must identify and implement adaptation strategies for improving livelihoods while also being cost-effective. While the idea that climate resilience and development goals should be compatible is often discussed, empirical evaluations of the economic impacts of actual CRD investments are practically non-existent. This paper outlines a framework to evaluate economic returns to CRD and applies it in two adaptation strategies trialed in Nusa Tenggara Barat Province, eastern Indonesia. The authors used @RISK to conduct a full risk analysis.
Read the paper here
The Academic Global Leader in
Risk and Decision
Palisade is the world's leading provider of risk and decision analysis software and solutions.
Founded in 1984, its products @RISK and the DecisionTools Suite are used by over 95% of the Fortune 100, in nearly every industry around the world.
Palisade is headquartered in New York State, and has offices in London, Sydney, Rio de Janeiro, and Tokyo.
What is The DecisionTools Suite?
The DecisionTools Suite is the world’s only integrated set of risk and decision analysis programs.
The Suite includes @RISK for Monte Carlo simulation, BigPicture for mind mapping and data exploration, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis.
In addition, DecisionTools Suite comes with StatTools for statistical
analysis and forecasting, NeuralTools for predictive neural networks, and
Evolver and RISKOptimizer for optimization.
All programs work together seamlessly, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.
The Suite is offered in three affordable and flexible licensing options
for those in academia: Student Versions, Course Licenses and Full Academic Versions.
» Schools using Palisade software
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