Autonomous Demand Smoothing for Efficiency Improvements on Military Forward Operating Bases

This paper presents autonomous control algorithms for electrical loads like HVAC (Heating, Ventilation, and Air Conditioning) systems. These algorithms permit a load to become “aware” of the operation of neighboring loads using nonintrusive measurements of the utility voltage. Analogous to the way a good driver is aware of neighboring cars while driving, loads can use this information to become “self-driving.” For HVAC systems, this permits maintaining occupant comfort while simultaneously reducing bulk peak electrical demand. Electric energy consumption for each load occurs on a schedule, interleaved with the operation of neighboring units. The algorithms are demonstrated here using simulation models developed from nonintrusive load monitoring (NILM) data collected from the microgrid of a US Army forward operating base (FOB). The general approach, however, can be applied in many other venues, e.g., to minimize peak load on distribution transformers in a section of the conventional utility.