Mixed-integer model predictive control of variable-speed heat pumps

Lee, Zachary E.; Gupta, Kartikay; Kircher, Kevin J.; Zhang, K. Max

doi:10.1016/j.enbuild.2019.05.060

Cited by 35 publications

(16 citation statements)

References 17 publications

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“…However, the uncertainty of disturbance predictions and the fidelity of the model can significantly degrade the performance and must be carefully considered. Common prediction methods for disturbances for heat pump control include numerical weather predictions, occupancy schedules [77,78], autoregressive regression, and neural networks [79,80]. The effect of model fidelity on MPC performance was explored in Ref.…”

Section: Market Participationmentioning

confidence: 99%

Providing Grid Services With Heat Pumps: A Review

Lee

Sun

et al. 2020

ASME Journal of Engineering for Sustainable Buildings and Cities

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The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system.

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Section: Market Participationmentioning

confidence: 99%

Providing Grid Services With Heat Pumps: A Review

Lee

Sun

et al. 2020

ASME Journal of Engineering for Sustainable Buildings and Cities

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“…There are also attempts to optimize the operation of the heat pump compressor from the programming side as well. Recently, Zachary Lee et al (2019) demonstrated a variable speed heat pump control optimization model in their work. This model uses a machine learning method to predict the magnitude of the heat load and the outside temperature, as well as mixed-integrated programming to optimize compressor control.…”

Section: Introductionmentioning

confidence: 99%

“…This model uses a machine learning method to predict the magnitude of the heat load and the outside temperature, as well as mixed-integrated programming to optimize compressor control. This made it possible to offer optimal heat pump operation schedules for the selected area and reduce electricity costs by 9% (Lee et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Energy Conversion Modes Depending on the Outdoor Temperature for an Air Handling Unit With a Heat Pump

Frik

Martinaitis

Bielskus

2020

International Conference “Environmental Engineering”

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Modern air handling units (AHU) are increasingly finding solutions in which the main energy transformers are an air heat pump (HP) and a heat recovery exchanger (HRE). The energy conversion modes of such devices are constantly changing in accordance with the constant change on the state of the outdoor air (temperature, humidity). Flexibility, being able to respond to ever-changing ambient air parameters, is an important feature of energy transformation component mode control. The overall seasonal efficiency of the air handling unit depends on this. In this work, a thermodynamic analysis of the characteristic energy transformations of the air handling unit is performed, linking the outdoor and ventilated indoor air and HP refrigerant states, flow rates and component loads. Such parametric analysis with respect to the changing outdoor air temperature allowed to clearly reveal, through various indicators, the influence of the individual components on the operating efficiency of the air handling unit. Combinations of parameters have been obtained that enable the selection of the optimal control concept for the energy conversion mode of the components in the air handling unit (component loads, fluids state parameters and flow rates) over a wide range of outdoor air temperatures.

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“…For solving such problems, the sliding-window technique is deployed in tandem with the hierarchical optimization framework [25]. Depending on the factors such as electricity pricing, energy demand, and size of the thermal storage, selection of the appropriate window size (number of periods), despite reducing the size of the concomitant optimization problem, still, deliver close-to-optimal results.…”

Section: Motivation and Problem Statementsmentioning

confidence: 99%

“…This is the likely reason which inhibits the real-time deployment of algorithms for the optimization of operations. Where real-time control is concerned, model predictive control (MPC) techniques are often deployed for performance optimization [25].…”

Section: How To Increase Real-time Applicability Of the Optimization Framework?mentioning

confidence: 99%

Holistic optimization framework for the operation of urban energy systems

Chiam¹

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Mixed-integer model predictive control of variable-speed heat pumps

Cited by 35 publications

References 17 publications

Providing Grid Services With Heat Pumps: A Review

Providing Grid Services With Heat Pumps: A Review

Energy Conversion Modes Depending on the Outdoor Temperature for an Air Handling Unit With a Heat Pump

Holistic optimization framework for the operation of urban energy systems

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