Powernet in Farms: A could-based approach to manage electrical loads in livestock farming

G. Cezar, T. Navidi, L. Buechler, N. Milivojevic, A. El-Gamal, R. Rajagopal
Stanford University,
United States

Keywords: power systems, cloud computing, optimization, DER, battery storage


Behind the meter (BTM) resources have received a lot of attention in the past years as one of the key ways in order to increase penetration of renewable generation in the power grid and allowing the US - and other countries around the globe - to meet state and federal government renewable energy generation and emissions goals. Additionally, with more devices adopting the Internet of Things concept, the ability of these resources to communicate over the cloud create new possibilities to manage BTM resources to reduce customers’ electricity consumption, support utility operations and ultimately reduce emissions. In this new scenario, the authors developed Powernet. Powernet is an end-to-end, open-source system that enables real-time coordination of utilities’ centralized, large assets with millions of distributed resources. It integrates embedded sensing and computing, power electronics, data analytics and networking with cloud computing. Powernet is scalable and can be utilized in different applications such as residential, commercial, agriculture and many others. In this paper we present one application of Powernet in a dairy farm in California. During Summer season – May through October – farm’s electricity consumption usually doubles due to an increase demand in motor-based loads (mainly fans). These fans help cows in keeping core body temperature within a comfortable range to prevent diseases and heat stress which affect milk production and ultimately can cause death of animals. To manage this increase in demand for electricity a system including battery energy storage systems, solar, motor control and sensing was designed within the Powernet framework and deployed in the farm. Based on the current rate structure this facility is subject to and facility operation constraints, optimization algorithms were designed and tested to minimize electricity consumption based on resource availability, energy arbitrage, and demand. Powernet platform was able to reduce the monthly electricity consumption in dairy barns, and thus overall electricity costs to farm owner, ranging from 30% to 50% depending on the month.