Smart Grid: Economics and Reliability

Renewable generation, energy storage, and demand response are key components of the Smart Grid vision. Effective use of those resources in future grids will require appropriate control architecture. This research investigated control strategies for power grids with significant penetration of renewable generation, energy storage, and demand response resources. The goal was to understand how energy storage and demand response can provide ancillary services such as operational reserves and frequency regulation, thereby facilitating the use of volatile renewable generation in highly complex and constrained power networks. We developed computationally efficient control algorithms for operating power grids with renewable generators, flexible loads, and energy storage units. The proposed control algorithms were derived by combining reinforcement learning (RL) techniques with model predictive control (MPC). The application of the proposed algorithms to the dynamic economic dispatch problem was extensively studied; simulation studies indicated that combining MPC with RL can significantly reduce the computational complexity of the dynamic dispatch problem. In addition, the potential flexibility in the power consumption of heating, ventilation, and air conditioning (HVAC) loads of commercial buildings was investigated to enable extraction of ancillary services from these loads. Simple control strategies, such as manipulation of the fan speed, were studied in the context of regulation services. Simulation studies indicated that HVAC loads can be manipulated without discomfort to building occupants if the bandwidth of regulation is suitably constrained. Also, control of fan speeds of “suitable” HVAC loads alone can provide up to 6 GW of regulation reserves, which constitutes about 70% of the total requirements in the United States.