- Industry: Utilities
- Product(s): @RISK
- Application: Energy supply and Electricity Demand
Palisade’s risk analysis software was used by Roy Nersesian to create a planning methodology to assist utilities that are making major investments in renewable power sources. This case study links to a free minicourse including webinar, example models, whitepaper, and slidedeck.
With Palisade’s @RISK, utility operators can now more easily determine if – and how – they can leverage pumped storage plants, and transform wind and solar into reliable sources of power.Professor Roy Nersesian, Leon Hess Business School, Monmouth University
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. However, this is not the case when dealing with solar- and wind-generated power, which can vary significantly on a day-to-day basis. Maintaining system stability where solar and wind play a significant role in generating electricity is a growing challenge facing utility operators. Palisade’s risk analysis software was used to create a planning methodology to assist utilities that are making major investments in these power sources, which is detailed in the minicourse Integrating Renewables with Electricity Storage, presented by Roy Nersesian, a professor at the Leon Hess Business School at Monmouth University and author of the book, Energy Risk Modeling.
Solar power is inherently unreliable, fluctuating with time of day and degree of cloudiness, and wind power is a victim of weather patterns. 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,” explained Roy Nersesian, a professor at the Leon Hess Business School at Monmouth University. In a similar way, if unpredictable solar and wind output can be directed to and from electricity storage of sufficient capacity, then these energy sources can be transformed to become a more controllable and reliable power supply.
Unfortunately, conventional electricity storage batteries cannot handle a utility-sized system of both conventional and renewable power supplies. However, pumped storage plants – or gravity batteries – can store and supply electricity to cover the mismatch between electricity supply and demand.
A pumped storage plant consists of two water reservoirs at different heights, fitted with reversible pump-turbines, which shift water between these reservoirs. Electricity is generated by the gravity flow of water, moving from the upper reservoir to the lower reservoir. At times of high electrical demand, water is released into the lower reservoir through a turbine, which generates electricity. At times of low electrical demand, surplus electricity is then used to pump water back to the higher reservoir.
With Palisade’s @RISK, utility operators can now more easily determine if – and how – they can leverage pumped storage plants, and transform wind and solar into reliable sources of power.
Building the Methodology
To create the methodology, Nersesian used Palisade’s @RISK simulation software and its Fit Distribution feature to determine the output of a system of solar and wind farms. He modelled different sizes of farms, which were located in different sites, to obtain a probability distribution of the power supply. For the purposes of his research, he started with three 1-megawatt solar sites and three 1-megawatt wind sites, then expanded to farms with larger outputs to model a utility-sized enterprise. The wind sites were statistically linked with the Copula function in @RISK 7 to correlate variables, while the solar sites were statistically linked using conditional probabilities. However, a single Fit Distribution probability didn’t adequately model the various outputs. “So, I got creative and segmented the data so that each provided a ‘best fitting’ curve, then added them together to create a single, ‘best fitting’ curve,” said Nersesian.
Once the variables for wind and solar power output were understood, Nersesian needed to model the fluctuations of electricity supply and demand for fossil fuels and nuclear plants, using data inputs including both base and peak power loads, as well as seasonal adjustments. He also used RISKOptimizer to understand the desired capacity of natural gas plants. However, this still wasn’t enough to create a reliable planning methodology.
For the final step, Nersesian needed to model electricity storage requirements to determine sufficient storage for utilities to transform solar and wind to a reliable source of energy. This required data relating to the design of a pumped storage plant, including the height of the upper reservoir above the turbines, as well as the depth and surface area of the reservoirs.
The final results of this research demonstrated that uncontrollable, renewable energy sources such as solar and wind can be successfully integrated into large power systems without impacting system stability, if appropriate electricity storage via pumped storage plants is available.
“Maintaining system stability where solar and wind play a significant role in generating electricity is a growing challenge facing utility operators,” said Nersesian. “Hopefully this will spur interest in the described methodology for planning purposes when utilities think about making major investments in renewable power sources.”
“Integrating Renewables with Electricity Storage, with @RISK”
A free on-demand minicourse
1-hour webinar delivered by Roy Nersesian Energy example models Roy’s 50-page whitepaper “Integrating Renewables with Electricity Storage” Presentation slidedeck
» Get "Integrating Renewables" on-demand now - http://go.palisade.com/WC2016-04NersesianReNew.html
“Energy Risk Modeling”
a book by Roy Nersesian
Growing out of Roy Nersesian’s energy courses he delivers at the School of International and Public Affairs at Columbia University, Energy Risk Modeling is a thorough reference for those looking for simulation, decision trees, and optimization techniques for energy applications. The book is loaded with real-life examples that demonstrate how @RISK, PrecisionTree, and Evolver can be used to make better financial decisions within the oil and gas, electricity, and renewable energy industries. Roy Nersesian’s easy-to-read, step-by-step approach makes his techniques accessible to anyone who uses Microsoft Excel. All examples covered in the book are provided in Excel spreadsheets.
» More about "Energy Risk Modeling" - http://www.palisade.com/books/energy.asp