Ocean Wave Energy Farms
Wave farm operations and grid interactions require accurate power-output forecasts that account for site-specific future wave conditions, hydrodynamic interactions among WECs, and WEC geometry. We are developing models and methods for mproving both short- and long-term forecasts of ocean conditions; and using these predictions, array geometry, and power-take off dynamics to provide reliable calculations of wave farm output power.
The locations of WECs within a wave farm can have a significant impact on the total power of the farm due to the hydrodynamic interactions among the WECs. The total power is a nonconvex function of the WEC locations, and it has been observed that a wave farm optimized for a particular wave environment tends to perform quite poorly when the environment changes just a little. We use optimization models and algorithms to deliver more robust solutions to wave farm layout.
We are studying the types and locations of sensors that should be placed near wave farms to collect data on wave characteristics and forecast the actual waveforms at the WECs, thus improving the control of the WECs. To deal with noisy sensor measurements, we have developed optimization models and algorithms for maximum-likelihood estimation of wave realizations as a function of sensor system design, and we have looked to optimize the design itself with respect to the types and locations of sensors. In addition, we are developing rapid fault detection tests that use wave sensor data to assess system state and conduct hypothesis tests to identify system faults.
We are developing algorithms for simultaneous control of multiple WECs within a wave farm. Our models account for and exploit both the spatial-temporal variations in the ocean environment and the hydrodynamic coupling between the WECs. Additionally, we are developing predictive control methods using short-term forecasts enabled by wave sensors in and around the farm. Specific control strategies of interest include modifying damping coefficients of WECs and using storage to stabilize the farm’s power output to enhance grid stability and reduce CO2 emissions due to ramping of conventional generators.
We are studying reliable integration of wave farms into deregulated electricity markets and quantifying reductions in CO2 emissions enabled by wave power due to reduction of both fossil-fuel-based generation and compensatory ramping of conventional generators required by more volatile forms of renewable energy. We explore market opportunities enabled by the predictability of waves and study optimal market interactions for wave power producers.
We are designing a cyber control system to coordinate the various components (WEC hydrodynamics, multi-WEC control, storage control, forecasting techniques, etc.) of the wave farm. Our goal is to develop a multi-agent system model that combines symbolic and numeric reasoning with learning capabilities. We will combine these components with ontological information that models entities and interactions of WEC operations.