Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings ☆

Conventional design of chiller plant is typically based on the peak cooling loads of buildings, while the cooling load reaches its peak level for only a small proportion of time in a year. This results in that even a perfectly designed chiller plant could be very significantly oversized in actual operation and it thus causes significant energy wastes. In this paper, an uncertainty-based optimal design based on probabilistic approach is proposed to optimize the chiller plant design. It ensures that the chiller plant operate at a high efficiency and the minimum annual total cost (including annual operational cost and annual capital cost) could be achieved under various possible cooling load conditions, considering the uncertain variables in cooling load calculation (i.e., weather conditions). On the premise of determining the minimum sufficient number of Monte Carlo simulation, this method maximizes the operating COP (coefficient of performance) and minimizing the annual total cost. A case study on the chiller plant of a building in Hong Kong is conducted to demonstrate the design process and validate the uncertainty-based optimal design method.

[1]  Fu Xiao,et al.  Quantitative energy performance assessment methods for existing buildings , 2012 .

[2]  W. L. Lee,et al.  Simplifying energy performance assessment in the Hong Kong Building Environmental Assessment Method , 2001 .

[3]  Ignacio E. Grossmann,et al.  Systematic Methods of Chemical Process Design , 1997 .

[4]  Gregor P. Henze,et al.  Stochastic control optimization for a mixed mode building considering occupant window opening behaviour , 2014 .

[5]  Jose Manuel Cejudo-Lopez,et al.  Uncertainty in peak cooling load calculations , 2010 .

[6]  N. Murray,et al.  Import risk analysis: animals and animal products. , 2002 .

[7]  Michael J. Benz,et al.  Sensitivity analysis for robust design of building energy systems , 2014 .

[8]  Leslie Daryl Danny Harvey,et al.  A Handbook on Low-Energy Buildings and District-Energy Systems : Fundamentals, Techniques and Examples , 2012 .

[9]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[10]  Warren E. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .

[11]  W. L. Lee,et al.  Developing a simplified model for evaluating chiller-system configurations , 2007 .

[12]  Le Yang,et al.  Data and analytics to inform energy retrofit of high performance buildings , 2014 .

[13]  H. Christopher Frey,et al.  Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs , 1999 .

[14]  Deng Shiming,et al.  Sizing Replacement Chiller Plants , 2002 .

[15]  Henry W. Altland,et al.  Engineering Methods for Robust Product Design , 1996 .

[16]  Frøydis Gillund,et al.  Do uncertainty analyses reveal uncertainties? Using the introduction of DNA vaccines to aquaculture as a case. , 2008, The Science of the total environment.

[17]  G. Towler,et al.  Chapter 7 – Capital Cost Estimating , 2012 .

[18]  Keith Beven,et al.  Environmental Modelling , 2007 .

[19]  J Burnett BEng PhD CEng Rpe Fcibse Fhkie Fiee,et al.  Chiller Plant Sizing by Cooling Load Simulation as a Means to Avoid Oversized Plant , 1999 .

[20]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[21]  Dale Hattis,et al.  A. Uncertainty and Variability , 1999 .

[22]  Godfried Augenbroe,et al.  SIZING HVAC SYSTEMS UNDER UNCERTAINTY , 2014 .

[23]  Gregor P. Henze,et al.  Uncertainty quantification for combined building performance and cost-benefit analyses , 2013 .

[24]  Igor Bulatov,et al.  Cost estimation and energy price forecasts for economic evaluation of retrofit projects , 2003 .

[25]  Mustafa Y. Ata.  A convergence criterion for the Monte Carlo estimates , 2007, Simul. Model. Pract. Theory.

[26]  Michael C. Georgiadis,et al.  A two-stage stochastic programming model for the optimal design of distributed energy systems , 2013 .

[27]  Sanford Klein,et al.  Applications of optimal control to chilled water systems without storage , 1989 .

[28]  Janet M. Carey,et al.  Linguistic Uncertainty in Qualitative Risk Analysis and How to Minimize It , 2008, Annals of the New York Academy of Sciences.

[29]  Panagiota Karava,et al.  Stochastic Model Predictive Control of Mixed- mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds , 2014 .

[30]  John E. Mottershead,et al.  A review of robust optimal design and its application in dynamics , 2005 .

[31]  F. W. Yu,et al.  Strategy for designing more energy efficient chiller plants serving air-conditioned buildings , 2007 .

[32]  Thomas Hartman,et al.  All-Variable Speed Centrifugal Chiller Plants , 2001 .

[33]  Jeffrey Celuch.  Hybrid chilled water plant , 2001 .

[34]  Savvas A. Tassou,et al.  Comparative performance evaluation of positive displacement compressors in variable-speed refrigeration applications , 1998 .

[35]  B. Marcot,et al.  Bayesian belief networks: applications in ecology and natural resource management , 2006 .

[36]  Jennifer L. Gorter,et al.  HVAC Equipment Right-sizing: Occupant Comfort and Energy Savings Potential , 2012 .

[37]  Pedro J. Mago,et al.  Analysis of a combined cooling, heating, and power system model under different operating strategies with input and model data uncertainty , 2010 .

[38]  A. Kaya.  Improving efficiency in existing chillers with optimization technology , 1991 .

[39]  Yung-Chung Chang,et al.  Optimal chiller loading by genetic algorithm for reducing energy consumption , 2005 .

[40]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[41]  Manfred Morari,et al.  Flexibility and resiliency of process systems , 1983 .

[42]  Warren D. Seider,et al.  Product and Process Design Principles: Synthesis, Analysis, and Evaluation , 1998 .

[43]  Shengwei Wang,et al.  Impacts of cooling load calculation uncertainties on the design optimization of building cooling systems , 2015 .

[44]  Fu Xiao,et al.  In situ performance comparison and evaluation of three chiller sequencing control strategies in a super high-rise building , 2013 .

[45]  J. Gentle.  Random number generation and Monte Carlo methods , 1998 .

[46]  Godfried Augenbroe,et al.  Exploring HVAC system sizing under uncertainty , 2014 .

[47]  Staf Roels,et al.  Effective And Robust Measures For Energy Efficient Dwellings: Probabilistic Determination , 2013, Building Simulation Conference Proceedings.

[48]  Ricardo Nicolau Nassar Koury,et al.  Numerical simulation of a variable speed refrigeration system , 2001 .

[49]  W. L. Lee,et al.  Energy saving by realistic design data for commercial buildings in Hong Kong , 2001 .

[50]  B. N. Gidwani.  Optimization of chilled water systems , 1987 .

[51]  Y. M. Shi,et al.  Sensitivity analysis of energy demands on performance of CCHP system , 2008 .

[52]  Philip C.H. Yu,et al.  Sizing of air-conditioning plant for commercial buildings in Hong Kong , 2000 .

[53]  Joseph Fiksel,et al.  Designing resilient, sustainable systems. , 2003, Environmental science & technology.

[54]  Zhenjun Ma,et al.  Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm , 2011 .