Stephen Treado

A review of multi-agent systems concepts and research related to building HVAC control

This article is a primer for how multi-agent system technology may influence building HVAC control technologies. The multi-agent system paradigm brings some inherent disruption to current HVAC control technologies, requiring increased embedded intelligence and more advanced communication protocols. Along with HVAC-specific multi-agent system research, two other fields that have active multi-agent system research are incorporated into the review. They are the smart grid and ambient intelligence domains, which merit inclusion because of their distinct overlap with HVAC issues. The review culminates in posing a few perspectives and directions of how multi-agent system could alter building HVAC control implementations.

Real-Time Optimization of Shared Resource Renewable Energy Networks

Shared resource renewable energy networks allow for the burden of high capital cost to be managed by sharing the cost and benefits of renewable energy use. In order to maximize the benefit gained from shared renewable energy, we propose a methodology to optimize the use of renewables via scheduling of energy use. By offering reduced energy rates, residents will be encouraged to run heavy energy consumers such as clothes dryers at times which improve the load generation and energy demand matching as deemed by a designed and optimized decision engine.

Parametric Study of Community Load Aggregation on Thermal StorageEfficiency

Renewable energy technologies, most notably wind, solar hot water, and solar photovoltaic are not always available to the residential sector due to financial and feasibility challenges. In this paper we investigate the potential benefit of aggregating residential loads to more closely match the renewable energy generation profiles and to have a smoother energy demand curve which can be more efficiently supplied by an energy storage system. Four individual residential load profiles are matched against an optimized combination of wind, solar hot water, and solar photovoltaic generation. A simulation is then run to assess the percentage of the demand which must be supplied via auxiliary energy sources (i.e. the grid) with and without a thermal energy storage system. Finally, these four load profiles are randomly combined to create a 50 user community load profile. This aggregated profile is also matched against the renewable energy generation and the results are compared to individual load profile performance metrics for January, April, and July data. In the April and July cases, the community load profile reduced the demand supplied by auxiliary energy by as much as 5% on average in a simple system without storage (An improvement over the average of the individual loads of about 11%). With storage, a community system reduces demand supplied by auxiliary energy by about 0.8%, which is an improvement over the average individual loads of also about 11%. It is concluded that community shared renewable energy systems can be beneficial not only in terms of economics and feasibility, but also in terms of thermodynamics, which is often overlooked.

Energy efficient operating strategies for building combined heat and power systems

A method was developed and demonstrated for evaluating and visualizing the optimal operating strategies for building combined heat and power systems. The method uses models of the component load-dependent operating characteristics matched to specific electrical and thermal load combinations for the building in a framework that substantially reduces the size of the search space and associated computational burden. The overall system primary energy input is determined for the range of possible operating conditions and is represented in the form of a performance map, allowing optimal component operating conditions and allocation of thermal energy to be determined by the operator or supervisory control system. The method was demonstrated for eight scenarios that were selected to cover a range of load combinations, and the optimal operating conditions were determined along with several metrics of energy use for the combined heat and power system compared to a conventional system and the overall system energy utilization factor.