Model Predictive Control of thermal energy storage in building cooling systems

Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Packard, Andrew; Bortoff, Scott A.

doi:10.1109/cdc.2009.5400677

Cited by 134 publications

(118 citation statements)

References 14 publications

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“…A formal optimization approach can incorporate and address a variety of such features. Often, the result is a nonlinear optimization problem, relying on mixed integer nonlinear programming [27,28]. Such techniques are not only computationally difficult, they might produce impractical solutions (e.g., one with numerous turn-downs and start-ups for the chillers) unless a full set of transient dynamic models are incorporated; a task that is exceedingly difficult.…”

Section: Chiller Dispatch Optimization Through Linearizationmentioning

confidence: 99%

Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study

McLarty

Sabate

Brouwer

et al. 2015

International Journal of Electrical Power & Energy Systems

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a b s t r a c tDistributed power and energy resources are now being used to meet the combined electric power, heating, and cooling demands of many buildings. The addition of on-site renewables and their accompanying intermittency and non-coincidence requires even greater dynamic performance from the distributed power and energy system. Load following generators, energy storage devices, and predictive energy management are increasingly important to achieve the simultaneous goals of increased efficiency, reduced emissions, and sustainable economics. This paper presents two optimization strategies for the dispatch of a multi-chiller cooling plant with cold-water thermal storage. The optimizations aim to reduce both costs and emissions while considering real operational constraints of a plant. The UC Irvine campus micro-grid operation between January 2009 and December 2013 serves as a case study for how improved utilization of energy storage can buffer demand transients, reduce costs and improve plant efficiency. A predictive control strategy which forecasts campus demands from weather predictions, optimizes the plant dispatch, and applies feedback control to modify the plant dispatch in real-time is compared to best-practices manual operation. The dispatch optimization and predictive control algorithms are shown to reduce annual utility bill costs by 12.0%, net energy costs by 3.61%, and improve energy efficiency by 1.56%.Ó 2014 Elsevier Ltd. All rights reserved. IntroductionIncreasing concerns regarding electricity costs, energy reliability, and emissions are encouraging businesses and campuses to consider self-generation and district heating/cooling. Deployment of a combination of electric generators, energy storage devices, district heating/cooling, and electrical circuit infrastructure to meet the energy demands of several buildings comprises a micro-grid [1]. Micro-grids are typically connected to a regional electric utility network that provides supplemental electricity through a single high voltage interconnection. Regional utilities rely upon the temporal smoothing effect of aggregating thousands of dynamic consumer demands. At the micro-grid scale, i.e. 250 kW-50 MW, energy management similarly relies to some extent upon aggregation to temporally smooth demand, but primarily micro-grids must remain highly responsive to demand variations arising from building energy demands and energy supply dynamics caused by on-site renewable generators. High efficiency and low cost operation requires a continuous optimization and dispatch of resources and effective management of energy storage devices to balance power under all circumstances of dynamic load, dynamic generation, renewable intermittency or other perturbations [2]. Thermal energy storage (TES) technology, i.e. cold-water storage, ice, or molten salts, can assist to decouple demand from production. Use of any storage technologies introduces a time horizon to the dispatch optimization, which can be further complicated by physical and operational constraints. ...

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Section: Chiller Dispatch Optimization Through Linearizationmentioning

confidence: 99%

Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study

McLarty

Sabate

Brouwer

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…In building control level, model predictive based controllers (MPC) have been recently proposed as powerful tools to optimize the operation of smart buildings, to improve human comfort and improve grid stability [28,29]. Recently, a "Ten Questions" paper was published that covered in detail how MPC can improve the energy efficiency of buildings [30].…”

Section: Can Integration Of a Smart Grid And Smart Buildings Improve mentioning

confidence: 99%

Ten questions concerning integrating smart buildings into the smart grid

Lawrence

Boudreau

Helsen

et al. 2016

Building and Environment

121

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Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be 'prosumers' on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and nontechnical areas.

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“…The formulation of the room temperature constraints is shown in detail, other constraints can be formulated in a similar fashion. Denote with x t,1 the first element of the state vector x of (14), which is the room temperature at time t. Since the model (12) has been linearized, the predicted room temperature at t time steps in the future is a linear function of the state now (time t = 0) and the intervening inputs u k and weather w k acting on the building. As a result, the room temperature x t,1 at time t in the future can be written as…”

Section: Construct Constraintsmentioning

confidence: 99%

“…The predicted variables included ambient air temperature, relative humidity, and solar radiation. In [12] the implementation of MPC for a chilled water plant was investigated. A predictive control strategy using a forecasting model of outdoor air temperature was investigated in [6] for intermittently heated radiant floor heating systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of model predictive control and weather forecasts for energy efficient building climate control

Oldewurtel

Parisio

Jones

et al. 2012

Energy and Buildings

1,004

501

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a b s t r a c tThis paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO 2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions.As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

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Model Predictive Control of thermal energy storage in building cooling systems

Cited by 134 publications

References 14 publications

Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study

Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study

Ten questions concerning integrating smart buildings into the smart grid

Use of model predictive control and weather forecasts for energy efficient building climate control

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