Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators

Abstract

The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?

“…This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it.Energies 2020, 13, 1480 2 of 27 explored from various angles including predictive controls for refrigeration [3], green performance evaluation system for construction site layout [4], performance comparisons of energy systems [5], indoor air quality assessments [6] and others.The modern ways of living drive substantial energy use inside the buildings. United Nations Energy Program (UN-Energy) reports that buildings consume 30-40% of the total global energy [7,8].…”

“…Energies 2020, 13, 1480 2 of 27 explored from various angles including predictive controls for refrigeration [3], green performance evaluation system for construction site layout [4], performance comparisons of energy systems [5], indoor air quality assessments [6] and others.…”

Towards Sustainable Inventory Management: A Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains

“…This strategy and the energy consumption modelling is also novel. It is modelled uniquely for each type of storage location, heavily influenced by the works conducted by Sabegh and Bingham [34], Zavvar Sabegh and Bingham [35], Wolisz et al [36], Lewczuk et al [37]. Finally, the shortage risk is modelled through a novel, dimensionless approach, comprised of three components-a risk of immediate shortage, shortage considering one standard deviation from the suppliers' lead time, and a general measure of capacity to fulfil material requirements.…”

“…Proof of Theorem 1. According to Sabegh and Bingham [34] and Zavvar Sabegh and Bingham [35], a refrigerator filled with products has a higher specific heat capacity than a empty one. The authors present experimental data showing that an empty refrigerator increases its energy consumption with the increase in the hysteresis band, while one at 40% useful capacity reduces its energy consumption with the increase in the hysteresis band.…”

“…The energy consumption dynamics with the degree of fullness and allowed hysteresis band were assumed to be identical for industrial freezers as the ones proposed in [34,35] and presented in Theorem 1.…”