Software-as-a-Service Optimised Scheduling of a Solar-Assisted HVAC System with Thermal Storage

TitleSoftware-as-a-Service Optimised Scheduling of a Solar-Assisted HVAC System with Thermal Storage
Publication TypeConference Proceedings
Year of Publication2013
AuthorsAndrea A Mammoli, Michael Stadler, Nicholas DeForest, Hans Barsun, Richard Burnett, Chris Marnay
Conference Name3rd International Conference on Microgeneration and Related Technologies
Date Published04/2013
Conference LocationNaples, Italy
Keywordsabsorption cooling, mixed integer programming, optimisation, software-as-a-service, thermal storage

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.

LBNL Report Number


Refereed DesignationRefereed