This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC) provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps), can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

With the rapid increase of renewable energy on the power system, an increase in reserve capabilities is required. As traditional reserve resources reach their maximum capacity for providing this service, alternative solutions must be found to supplement existing reserves. One such alternative is to utilize grid-scale energy storage to provide reserves. Though many technologies for grid-scale energy storage are cost prohibitive, one cost-effective solution is the use of existing residential water heaters as a distributed energy storage resource. This paper presents one possible control methodology, using Model Predictive Control, to demonstrate the feasibility of controlling such a resource. Simulation results show that Model Predictive Control can be effectively used to control multiple residential water heaters to provide reserve services for renewable energy resources.

No abstract