Characterizing the Effect of an Off-Peak Ground Pre-1 Cool Control Strategy 2 on Hybrid Ground Source Heat Pump Systems

Geo-exchange systems are a sustainable alternative to conventional space conditioning systems due to their high operating efficiency, resulting in reduced energy consumption and greenhouse gas emissions. However, geo-exchange’s ability to penetrate the market has been throttled by large capital investments, resulting in undesirable payback periods. Optimized hybrid ground source heat pump systems (HGSHP) systems have been introduced as a remedy to overcome the current economic hurtles associated to the installation of geo-exchange systems. In both the literature, as well as in practice, there still remains potential for increased economic feasibility of this technology through integration of intelligent operational strategies. This paper presents a novel control methodology referred to as an off-peak ground pre-cool strategy, employing a time-of-use conscious operating logic which artificially pre-condition the system’s bore-field. Reducing peak power consumption is achieved by creating improved thermal characteristics during mid-peak/peak time-of-use operating brackets. A comprehensive numerical model was developed to characterize the operation of HGSHP systems for three real case studies. The mode implemented a base case set-point control scheme, used as a reference to assess the operational benefit of the proposed off-peak ground pre-cool control strategy. The preliminary analyses indicated operational cost savings of up to 16.4%, under specific pre-cool scheduling. The strategy indicated reductions in both carbon emission and peak power consumption of up t 19 15.0% and 58.5%, respectively. In all cases increasing cooling supplied by the hybrid geo 20 exchange system was indicated, with a maximum observed capacity increase of 43.7%.

Characterizing the Effect of an Off-Peak Ground Pre-1 Cool Control Strategy 2 on Hybrid Ground Source Heat Pump Systems

Geo-exchange systems are a sustainable alternative to conventional space conditioning systems due to their high operating efficiency, resulting in reduced energy consumption and greenhouse gas emissions. However, geo-exchange’s ability to penetrate the market has been throttled by large capital investments, resulting in undesirable payback periods. Optimized hybrid ground source heat pump systems (HGSHP) systems have been introduced as a remedy to overcome the current economic hurtles associated to the installation of geo-exchange systems. In both the literature, as well as in practice, there still remains potential for increased economic feasibility of this technology through integration of intelligent operational strategies. This paper presents a novel control methodology referred to as an off-peak ground pre-cool strategy, employing a time-of-use conscious operating logic which artificially pre-condition the system’s bore-field. Reducing peak power consumption is achieved by creating improved thermal characteristics during mid-peak/peak time-of-use operating brackets. A comprehensive numerical model was developed to characterize the operation of HGSHP systems for three real case studies. The mode implemented a base case set-point control scheme, used as a reference to assess the operational benefit of the proposed off-peak ground pre-cool control strategy. The preliminary analyses indicated operational cost savings of up to 16.4%, under specific pre-cool scheduling. The strategy indicated reductions in both carbon emission and peak power consumption of up t 19 15.0% and 58.5%, respectively. In all cases increasing cooling supplied by the hybrid geo 20 exchange system was indicated, with a maximum observed capacity increase of 43.7%.

PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

Application of Predictive Control in Scheduling of Domestic Appliances

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, the HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature, etc.). Simulation results exhibit the benefits of the proposed HEMS by showing the reduction of up to 70% in electricity cost and up to 57% in peak power consumption.

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

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.

Automated Scheduling of Household Appliances Using Predictive Mixed Integer Programming

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in a dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature etc). Simulation results exhibit the benefits of proposed HEMS by showing the reduction of up to 47% in electricity cost and up to 48% in peak power consumption.