The energy for conditioning of buildings accounts for approximately 40% of the energy consumption in the US (Baldwin et al., 2015). At the same time, to incentivize consumers to maintain an as mush as possible constant load, utility companies employ complex pricing structures with, for example, time-varying and dynamically changing pricing and penalties for the highest energy consumption during a billing period. Operating in such complex and dynamic environment is challenging. Accordingly, in a collaboration with Johnson Controls, the group develops optimization methods for the online scheduling of large-scale heating, ventilation, and air-conditioning (HVAC) systems (Rawlings et al., 2018). Specifically, we develop models accounting for all critical-to-cost decisions, such as equipment on/off status, equipment load, and storage tank usage (Risbeck et al., 2017); and solution methods (e.g., reformulations, decomposition approaches, and approximation strategies) to improve tractability for real time optimization of realistically sized systems (Risbeck et al., 2019).
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Rawlings JB, Patel NR, Risbeck MJ, Maravelias CT, Wenzel MJ, Turney RD. Economic MPC and real-time decision making with application to large-scale HVAC energy systems. Computers and Chemical Engineering, 114, 89–98, 2018.
Risbeck MJ, Maravelias CT, Rawlings JB, Turney RD. A mixed-integer linear programming model for real-time cost optimization of building heating, ventilation, and air conditioning equipment. Energy and Buildings, 142, 220–235, 2017.
Risbeck MJ, Maravelias CT, Rawlings JB, Turney RD. Mixed-integer optimization methods for online scheduling in large-scale HVAC systems. Optimization Letters, 14, 889-924, 2020.