Ram Rajagopal, Civil and Environmental Engineering
This project is developing several approaches to enable cost-effective high penetration of wind energy by reducing the planning, safety, and operating costs that derive from wind’s uncertainty. Intermittent renewable resources dramatically raise uncertainty for utilities, leading to increased costs of reserve power as well as greater emissions. This project uses three approaches to reduce these consequences: novel control approaches for power dispatch by the system operator, improved forecast algorithms for wind-power generation, and improved sensing for wind-energy generators.
The risk-limiting dispatch algorithm researchers developed reduces the costs of integrating wind power up to 60% by using forecast information appropriately. The algorithm is able to address the full market model and includes the possibility of network congestion. Also, it does not require expensive and complex Monte Carlo simulations, and can be directly implemented in existing system-operator software.
The pattern-based forecasting method the researchers developed is able to predict wind generation with reasonable accuracy for the next day without complex computing tools. The researchers have also developed the first wind-ramp detection and characterization methodology to improve dispatch against large swings in wind production. Finally, the researchers are developing an airborne wind sensor that is less expensive than those currently used because it does not require a tower. The concept is that these sensors can be deployed in greater quantities and at greater distances from wind-power farms in order to more accurately forecast production. The researchers have built a prototype transducer system that converts wind energy to an electronic signal transmitted in our laboratory.
By integrating sensors, statistical modeling, and control mechanisms with the use of a modern data-mining techniques, the researchers will significantly improve the forecasting of wind generation. The researchers have presented their work at the General Power Engineering Society Meeting and the IEEE Probabilistic Models in Power Systems Conference .
"Wind aggregation via risky power markets" IEEE Transactions on Power Systems 30 (2015): 1571-1581.
"Network risk-limiting dispatch: Optimal control and price of uncertainty" IEEE Transactions on Automatic Control 59 (2014): 2442-2456.
"Detection and statistics of wind-power ramps" IEEE Transactions on Power Systems 28 (2013): 3610-3620.
“Risk-limiting dispatch with ramping constraints” IEEE International Conference onSmart Grid Communications (SmartGridComm) (2013): 791-796.
"Risky power forward contracts for wind aggregation" 51st Annual Allerton Conference on Communication, Control, and Computing (2013): 54-61.
"Financial transmission rights perform well in power markets with high penetration of wind energy" IEEE Power and Energy Society General Meeting (2013): 1-6.
"Dynamic programming solution to distributed storage operation and design" IEEE Power and Energy Society General Meeting (2013): 1-5.
"Storage in risk-limiting dispatch: Control and approximation" American Control Conference (2013): 4202-4208.
"Risk-limiting dispatch for integrating renewable power" International Journal of Electrical Power and Energy Systems 44 (2013): 615-628.
“Risk-limiting dispatch of wind power” IEEE American Control Conference(2012): 4417–4422.
"Bringing wind energy to market" IEEE Transactions on Power Systems (2012): 1225-1235.
"Risk-limiting dispatch in congested networks" 50th Annual Allerton Conference on Communication, Control, and Computing (2012): 1900-1907.
"Risk-limiting dispatch" IEEE Power and Energy Society General Meeting(2012): 1-5.
"Wind-power ramps: Detection and statistics" IEEE Power and Energy Society General Meeting(2012): 1-8.
"The role of co-located storage for wind-power producers in conventional electricity markets" American Control Conference (2011): 3886-3891.
Awarded 2010 as part of the TomKat's Smart Grid: Sustainable Grid Efforts.