Nghiem, Truong X

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Control Theory
Controls and Control Theory
Dynamics and Dynamical Systems
Energy Systems
Power and Energy
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2011 - 201516
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Now showing 1 - 10 of 16
  • Publication
    Green Scheduling for Radiant Systems in Buildings
    (2012-10-01) Nghiem, Truong X; Behl, Madhur; Pappas, George J.; Mangharam, Rahul
    In this report we look at the problem of peak power reduction for buildings with electric radiant floor heating systems. Uncoordinated operation of a multi-zone radiant floor heating system can result in temporally correlated electricity demand surges or peaks in the building’s electricity consumption. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity can result in high electricity costs and expensive system operation. We have previously presented green scheduling as an approach for reducing the aggregate peak power consumption in buildings while ensuring that indoor thermal comfort is always maintained. This report extends the theoretical results for general affine dynamical systems and applies them to electric radiant floor heating systems. The potential of the proposed method in reducing the peak power demand is demonstrated for a small-scale system through simulation in EnergyPlus and for a large-scale system through simulation in Matlab.
  • Publication
    Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants
    (2012-10-01) Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    In large building systems, such as a university campus, the air-conditioning systems are commonly served by chiller plants, which contribute a large fraction of the total electricity consumption of the campuses. The power consumption of a chiller is highly affected by its Coefficient of Performance (COP), which is optimal when the chiller is operated at or near full load. For a chiller plant, its overall COP can be optimized by utilizing a Thermal Energy Storage (TES) and switching its operation between COP-optimal charging and discharging modes. However, uncoordinated mode switchings of chiller plants may cause temporally-correlated high electricity demand when multiple plants are charging their TES concurrently. In this technical report, a Green Scheduling approach, proposed in our previous work, is used to schedule the chiller plants to reduce their peak aggregate power demand while ensuring safe operation of the TES. We present a scheduling algorithm based on backward reach set computation of the TES dynamics. The proposed algorithm is demonstrated in a numerical simulation in Matlab to be effective for reducing the peak power demand and the overall electricity cost.
  • Publication
    Peak Power Reduction in Hybrid Energy Systems with Limited Load Forecasts
    (2014-03-18) Pant, Yash Vardhan; Nghiem, Truong X; Mangharam, Rahul
    Hybrid energy systems, which consist of a load powered by a source and a form of energy storage, find applications in many systems, e.g., the electric grid and electric vehicles. A key problem for hybrid energy systems is the reduction of peak power consumption to ensure cost-efficient operation as peak power draws require additional resources and adversely affect the system reliability and lifetime. Furthermore, in some cases such as electric vehicles, the load dynamics are fast, not perfectly known in advance and the on-board computation power is often limited, making the implementation of traditional optimal control difficult. We aim to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts. We propose a scheme with two control levels and provide a sufficient condition for control of the different energy storage/generation components to meet the instantaneous load while satisfying a peak power threshold. The scheme provides performance comparable to Model Predictive Control, while requiring less computation power and only coarse-grained load predictions. For a case study, we implement the scheme for a battery-supercapacitor-powered electric vehicle with real world drive cycles to demonstrate the low execution time and effective reduction of the battery power (hence temperature), which is crucial to the lifetime of the battery.
  • Publication
    Scalable Scheduling of Building Control Systems for Peak Demand Reduction
    (2012-06-15) Nghiem, Truong; Behl, Madhur; Mangharam, Rahul; Pappas, George
    In large energy systems, peak demand might cause severe issues such as service disruption and high cost of energy production and distribution. Under the widely adopted peak-demand pricing policy, electricity customers are charged a very high price for their maximum demand to discourage their energy usage in peak load conditions. In buildings, peak demand is often the result of temporally correlated energy demand surges caused by uncoordinated operation of sub-systems such as heating, ventilating, air conditioning and refrigeration (HVAC&R) systems and lighting systems. We have previously presented green scheduling as an approach to schedule the building control systems within a constrained peak demand envelope while ensuring that custom climate conditions are facilitated. This paper provides a sufficient schedulability condition for the peak constraint to be realizable for a large and practical class of system dynamics that can capture certain nonlinear dynamics, inter-dependencies, and constrained disturbances. We also present a method for synthesizing periodic schedules for the system. The proposed method is demonstrated in a simulation example to be scalable and effective for a large-scale system.
  • Publication
    MLE+: A Tool for Integrated Design and Deployment of Energy Efficient Building Controls
    (2012-10-01) Bernal, Willy; Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    We present MLE+, a tool for energy-efficient building automation design, co-simulation and analysis. The tool leverages the high-fidelity building simulation capabilities of EnergyPlus and the scientific computation and design capabilities of Matlab for controller design. MLE+ facilitates integrated building simulation and controller formulation with integrated support for system identification, control design, optimization, simulation analysis and communication between software applications and building equipment. It provides streamlined workflows, a graphical front-end, and debugging support to help control engineers eliminate design and programming errors and take informed decisions early in the design stage, leading to fewer iterations in the building automation development cycle. We show through an example and two case studies how MLE+ can be used for designing energy-efficient control algorithms for both simulated buildings in EnergyPlus and real building equipment via BACnet.
  • Publication
    Co-Design of Anytime Computation and Robust Control
    (2015-01-01) Pant, Yash Vardhan; Mohta, Kartik; Abbas, Houssam; Nghiem, Truong; Deveitti, Joesph; Mangharam, Rahul
  • Publication
    Uncertainty Propagation from Sensing to Modeling and Control in Buildings - Technical Report
    (2013-10-19) Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    A fundamental problem in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. To provide optimal control for such closed-loop systems, model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the operational cost of model-based controllers. We present the Model-IQ toolbox which, given a plant model and real input data, automatically evaluates the effect of this uncertainty propagation from sensor data to model accuracy to controller performance. We apply the Model-IQ uncertainty analysis for model-based controls in buildings to demonstrate the cost-benefit of adding temporary sensors to capture a building model. Model-IQ's automated process lowers the cost of sensor deployment, model training and evaluation of advanced controls for small and medium sized buildings. Model-IQ provides recommendation of sensor placement and density to trade-off the cost of additional sensors with energy savings by the improved controller performance. Such end-to-end analysis of uncertainty propagation has the potential to lower the cost for CPS with closed-loop model based control. We demonstrate this with real building data in the Department of Energy's HUB.
  • Publication
    Event-based Green Scheduling of Radiant Systems in Buildings
    (2013-03-01) Nghiem, Truong X; Pappas, George; Mangharam, Rahul
    This paper looks at the problem of peak power demand reduction for intermittent operation of radiant systems in buildings. Uncoordinated operation of the circulation pumps of a multi-zone hydronic radiant system can cause temporally correlated electricity demand surges when multiple pumps are activated simultaneously. Under a demand-based electricity pricing policy, this uncoordinated behavior can result in high electricity costs and expensive system operation. We have previously presented Green Scheduling with the periodic scheduling approach for reducing the peak power demand of electric radiant heating systems while maintaining indoor thermal comfort. This paper develops an event-based state feedback scheduling strategy that, unlike periodic scheduling, directly takes into account the disturbances and is thus more suitable for building systems. The effectiveness of the new strategy is demonstrated through simulation in MATLAB.
  • Publication
    Green Scheduling of Control Systems for Peak Demand Reduction
    (2011-12-01) Nghiem, Truong X; Behl, Madhur; Mangharam, Rahul; Pappas, George J
    Building systems such as heating, air quality control and refrigeration operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. We present an approach to fine-grained coordination of energy demand by scheduling the control systems within a constrained peak while ensuring custom climate environments are facilitated. The peak constraint is minimized for energy efficiency, while we provide feasibility conditions for the constraint to be realizable by a scheduling policy for the control systems. The physical systems are then coordinated by the scheduling controller so as both the peak constraint and the climate/safety constraint are satisfied. We also introduce a simple scheduling approach called lazy scheduling. The proposed control and scheduling strategy is implemented in simulation examples from small to large scales, which show that it can achieve significant peak demand reduction while being efficient and scalable.
  • Publication
    Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings.
    (2014-04-01) Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    A fundamental problem in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing the dynamics of the underlying physical system. To provide optimal control for such closed-loop systems, model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the operational cost of model-based controllers. We present the Model-IQ toolbox which, given a plant model and real input data, automatically evaluates the effect of this uncertainty propagation from sensor data to model accuracy to controller performance. We apply the Model-IQ uncertainty analysis for model-based controls in buildings to demonstrate the cost-benefit of adding temporary sensors to capture a building model. We show how sensor placement and density bias training data. For the real building considered, a bias of 1% degrades model accuracy by 20%. Model-IQ's automated process lowers the cost of sensor deployment, model training and evaluation of advanced controls for small and medium sized buildings. Such end-to-end analysis of uncertainty propagation has the potential to lower the cost for CPS with closed-loop model based control. We demonstrate this with real building data in the Department of Energy's HUB.