TY - CONF KW - Absorption cooling KW - Optimization KW - Mixed integer programming KW - Software-as-a-service KW - Thermal storage AU - Andrea A Mammoli AU - Michael Stadler AU - Nicholas DeForest AU - Hans Barsun AU - Richard Burnett AU - Chris Marnay AB -

The UNM Mechanical Engineering HVAC system incorporates cooling assisted by a 232 m2 solar thermal array providing heat to a 70 kW thermal absorption chiller. A 30 m3 heat storage tank solar decouples heat production and absorption cooling. Additionally, 350 m3 of chilled water storage shifts the cooling electrical load of this high desert location off-peak. While this system already provides substantial energy and cost savings compared to similar conventional buildings, there are still opportunities for improvement. Absorption cooling (augmented by an electrically powered central cooling loop) suffers from parasitic electric loads from a cooling tower pump, a cooling tower fan, and hot and chilled water circulation pumps. Moreover, depending on seasonal, weather, occupancy, and cost conditions, the cold storage tanks may only need partial charging to meet the next day's net building load, and losses need to be considered. Optimally operating this complex thermal-electrical system poses a challenging mathematical problem. A model of the system was built on LBNL’s Distributed Resources Customer Adoption Model (DER-CAM) platform. A direct interface between the building energy control system, and DER-CAM hosted on LBNL’s server was developed. This interface delivers daily scheduling based on weather forecasts, tariffs, etc., to the building controller. It is found that energy cost savings can be proportionally substantial (almost 30%) - although in this case the payback period for system implementation is long, due to the very low energy consumption of the building. Also, it is found that accurate weather forecasting is a key ingredient of the optimization, although local biases can be corrected for in the optimization.

BT - 3rd International Conference on Microgeneration and Related Technologies C2 - LBNL-6127E CY - Naples, Italy DA - 04/2013 N2 -

The UNM Mechanical Engineering HVAC system incorporates cooling assisted by a 232 m2 solar thermal array providing heat to a 70 kW thermal absorption chiller. A 30 m3 heat storage tank solar decouples heat production and absorption cooling. Additionally, 350 m3 of chilled water storage shifts the cooling electrical load of this high desert location off-peak. While this system already provides substantial energy and cost savings compared to similar conventional buildings, there are still opportunities for improvement. Absorption cooling (augmented by an electrically powered central cooling loop) suffers from parasitic electric loads from a cooling tower pump, a cooling tower fan, and hot and chilled water circulation pumps. Moreover, depending on seasonal, weather, occupancy, and cost conditions, the cold storage tanks may only need partial charging to meet the next day's net building load, and losses need to be considered. Optimally operating this complex thermal-electrical system poses a challenging mathematical problem. A model of the system was built on LBNL’s Distributed Resources Customer Adoption Model (DER-CAM) platform. A direct interface between the building energy control system, and DER-CAM hosted on LBNL’s server was developed. This interface delivers daily scheduling based on weather forecasts, tariffs, etc., to the building controller. It is found that energy cost savings can be proportionally substantial (almost 30%) - although in this case the payback period for system implementation is long, due to the very low energy consumption of the building. Also, it is found that accurate weather forecasting is a key ingredient of the optimization, although local biases can be corrected for in the optimization.

PP - Naples, Italy PY - 2013 T2 - 3rd International Conference on Microgeneration and Related Technologies T3 - 3rd International Conference on Microgeneration and Related Technologies TI - Software-as-a-Service Optimised Scheduling of a Solar-Assisted HVAC System with Thermal Storage ER -