Real time machine coordination for instantaneous load smoothing and photovoltaic intermittency mitigation

With industrial equipment causing large and variable power loads, the industrial sector has a significant impact on the power quality in the United States. Also impacting power quality is the large growth of distributed intermittent renewable energy resources such as photovoltaics and windmills. Coupling factories' high demand variability with intermittent energy resources' high generation variability creates large unpredictable swings in net demand for the utility. While we cannot control the weather driven intermittency of renewable resources, in this paper we show it may be possible to control the power demands of industrial processes in order to reduce the variability in net demand observed by the utility. This research looks at a new alternative way to alleviate some of the burden imposed by renewable energy sources on the grid by coordinating a fleet of industrial machines. By smoothing a machine fleet's power demands over time we demonstrate a significant reduction in the variability of net power draws on the power grid of photovoltaic equipped factories. We demonstrate the impact of net demand variability reduction in both a stochastic and deterministic production setting using 10 months of 1 Hz solar irradiance data. When floor shop variability is high as in the stochastic system, net demand variability can be reduced by 7% while maintaining throughput with minimal impact to total energy consumed and part time in system. When shop floor variability can be highly controlled, such as in the deterministic system presented, the net demand variability can be reduced by 35%. By reducing the net demand variability of renewable energy equipped factories, the application of instantaneous load smoothing strategies provides a new alternative solution to mitigating the impact of renewable energy source intermittency on the grid, and could provide improved power quality to facilities with integrated renewables.