PNNL

Abstract

The need to mitigate the impacts of climate change is driving efforts to make electric generation in the United States cleaner through installation of rooftop solar photovoltaic (PV) and utility-scale wind generation systems. Although these renewable generation technologies are cleaner, their generation capabilities remain variable in nature. These technologies form a significant (>20%) fraction of grid capacity; therefore, utilities will be forced to maintain a significant standby capacity to mitigate the imbalance between supply and demand. Because more than 75% of electricity consumption occurs in buildings, building loads can be used to mitigate some of the imbalance. This paper describes the development and validation of an intelligent load control (ILC) algorithm that can be used to manage loads in a building or group of buildings using both quantitative and qualitative criteria. ILC uses an analytic hierarchy process to prioritize the loads for curtailment. The ILC process was developed and tested in a simulation environment to control a group of rooftop units (RTUs) to manage a building’s peak demand while still keeping zone temperatures within acceptable deviations. The ILC process can be implemented at a low cost on a supervisory controller without the need for additional sensing. By anticipating future demand, the process can be extended to add advanced control features such as precooling and preheating to alleviate comfort when operation of the RTUs is curtailed to manage the peak demand. Although the ILC process described in this paper was highly tailored to work with RTUs, it can be generalized and applied to any controllable loads in a building such as those of variable air volume boxes and lighting. Furthermore, the ILC process can be extended to manage building loads based on an energy budget instead of peak consumption.