The document describes a decision support system for operators of energy efficient buildings developed by Emilio L. Cano and Javier M. Moguerza of the University Rey Juan Carlos. It presents an R implementation for modeling decision making under uncertainty that symbolically specifies optimization problems, generates solver input files, and analyzes solutions to support strategic and operational energy management decisions in public buildings.

1. Decision Making under Uncertainty:
R implementation for Energy Efficient Buildings
Emilio L. Cano1 Javier M. Moguerza1
1 Department of Statistics and Operations Research
University Rey Juan Carlos, Spain
The 8th International R Users Meeting
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 1/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

2. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

3. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

4. Introduction
The model described in this talk has been developed within
the project EnRiMa: Energy Efficiency and Risk Management
in Public Buildings, funded by the EC.
The overall objective of EnRiMa is to develop a
decision-support system (DSS) for operators of
energy-efficient buildings and spaces of public use.
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 4/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

5. Introduction
The model described in this talk has been developed within
the project EnRiMa: Energy Efficiency and Risk Management
in Public Buildings, funded by the EC.
The overall objective of EnRiMa is to develop a
decision-support system (DSS) for operators of
energy-efficient buildings and spaces of public use.
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 4/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

6. Consortium
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

7. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

8. EnRiMa DSS
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

9. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

10. Optimization Scope
Strategic Model Interaction
Strategic decisions concerning The strategic model includes
which technologies to install a simplified version of
and/or decommission in the long operational energy-balance
term constraints
The operational model
Operational Model includes the realisation of
Energy portfolio selection in the the strategic decisions as
short term parameters
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

11. Optimization Scope
Strategic Model Interaction
Strategic decisions concerning The strategic model includes
which technologies to install a simplified version of
and/or decommission in the long operational energy-balance
term constraints
The operational model
Operational Model includes the realisation of
Energy portfolio selection in the the strategic decisions as
short term parameters
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

12. Optimization Scope
Strategic Model Interaction
Strategic decisions concerning The strategic model includes
which technologies to install a simplified version of
and/or decommission in the long operational energy-balance
term constraints
The operational model
Operational Model includes the realisation of
Energy portfolio selection in the the strategic decisions as
short term parameters
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

13. Scheme of the Project
EnRiMaDSS
Strate ic
g
Strategic DVs
Module
Strategic Upper-Level
Constraints Operational DVs
Upper-Level Lower-Level
Energy-Balance Operational DVs
Constraints
Lower-Level
Energy-Balance
Operational Constraints
Module
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

14. Scenario trees
Stage 1 Stage 2 Stage 3
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
1 2 3 4 5 6 7 8 9
Decision Time
Illustrative scenario tree
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

15. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

16. Objective Function (example)

min  CIip,0 · Gi · siip CISjp,0 · GSj · xijp
p∈P i∈I j ∈J
   
p p p p
+ Gi  CDip−a1 sdia1,a2  +  CDSjp−a1 xdja1,a2 
i∈I a1=0 a2=a1+1 j ∈J a1=0 a2=a1+1
p p,m,t p,m,t
+ DMm COi,k · zi,k
m∈M i∈I k ∈K t∈T
p p,m,t p,m,t
+ DMm COSk ,j · rk ,j
m∈M j ∈J k ∈K t∈T
p p,m,t p,m,t,mm
− DMm PPi,k ,n · uk ,n
m∈M i∈I k ∈K n∈NS (k ) mm∈MA t∈T
p p,m,t p,m,t,mm
− DMm SPi,k ,n · wk ,n
m∈M i∈I k ∈K n∈NS (k ) mm∈MS t∈T

− SUip · Gi · siip 
i∈I
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

17. Constraints (two examples)
Energy Balance (operational):
p,m,t p,m,t,mm
zi,k + uk ,n
i∈I n∈NB(k ) mm∈MA
p,m,t p,m,t,mm p,m,t p,m,t
− yi,k − wk ,n qik ,j ≥ Dk
i∈I mm∈MS n∈NS (k ) j ∈JS
p,m,t
− qok ,j − Φp,m,t
j − p,m,t
ODk ,j · xjp · Dk
j ∈JS j ∈JPS j ∈JPU
p ∈ P, m ∈ M, t ∈ T, k ∈ K
Emissions limit (strategic):
 
p,m,t p,m,t,mm 
p
DMm  Hi,k ,l · yi,k Ci,l,n · uk ,n ≤ PLp
l
m∈M i∈I k ∈K t∈T n∈N k ∈K t∈T
p ∈ P, l ∈ L
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

18. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

19. Symbolic Model Specification
The formulation reached models complex systems
Moreover, the Symbolic Model Specification should be:
Flexible
Replicable
Reproducible
Scalable
Portable
Thus, a suitable structure is needed
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

20. Data model
Model and Instance Classes, data attributes, input/output methods
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

21. Outline
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

22. Algebraic Languages
Needs
Statistical Software
Data Visualization
Data Analysis
Mathematical
Representation
Solver Input
Generation
Output
Documentation
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

23. R as an Integrated Environment
Advantages
Open Source
Reproducible Research and Literate Programming capabilities.
Integrated framework for SMS, data, equations and solvers.
Data Analysis (pre- and post-), graphics and reporting.
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

24. R Code Example
> cat ( getEq ( mySMS , 1 , format = " gams " ) , " n " )
genTechAvail (p , i ) .. s (i , p ) = e = G ( i ) * Sum (( a ) , AG (i , a ) * (
si (i , p ) - Sum (( q ) , sd (i ,p , q ) ) ) ;
> cat ( getEq ( mySMS , 1 , format = " tex " ) , " n " )
mathit { s } _ { i }^{ p } = mathit { G } _ { i }^{} cdot sum _ { a
in mathcal { A }} mathit { AG } _ { i }^{ a } cdot left (
mathit { si } _ { i }^{ p } - sum _ { q in mathcal { Q }}
mathit { sd } _ { i }^{ p , q } right ) qquad forall ; p in
mathcal { P } ,; i in mathcal { I }
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

25. Solution and report
Sweave file example:
%
documentclass [ a4paper ]{ article }
usepackage { Sweave }
title { Example Symbolic Model Specification }
author { urjc }
begin { document }
maketitle
section { Data analysis }
< < > >=
# Some code for importing the
# Symbolic Model and analyzing the
# input data ...
# Generate tex file
wProblem ( myImplem ,
filename = " myImplem . tex " ,
format = " tex " ,
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

26. Solution and report (cont.)
solver = " lp " )
# generate gams file
wProblem ( initStochImplem ,
filename = " myImplem . gms " ,
format = " gams " ,
solver = " lp " )
@
section { Symbolic Model Specification }
% Write the LaTeX equations
input { myImplem }
section { Call to solver }
< < > >=
require ( gdxrrw )
gams ( " myImplem . gms -- outfile = mySol . gdx " )
@
section { Solution Analysis }
< < > >=
lst <- list ( name = ' solvestat ' , form = ' full ' , compress = TRUE )
solverResults <- rgdx ( " mySol . gdx " , lst )
# Some analysis and charts over solverResults object
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

27. Solution and report (cont.)
@
end { document }
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

28. Summary
In this presentation the models developed for the EnRiMa
DSS have been described
An integrated framework allows to integrate analysis,
representation and solution of optimization problems
Examples of use have been presented
Outlook
Integration of scenarios for stochastic optimization
Extend representation formats: HTML, ODF, . . .
Further formats: AMPL, MPS, XML, . . .
A contributed package?
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 25/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

29. Summary
In this presentation the models developed for the EnRiMa
DSS have been described
An integrated framework allows to integrate analysis,
representation and solution of optimization problems
Examples of use have been presented
Outlook
Integration of scenarios for stochastic optimization
Extend representation formats: HTML, ODF, . . .
Further formats: AMPL, MPS, XML, . . .
A contributed package?
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 25/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

30. References
[1] Michel Berkelaar and others. lpSolve: Interface to Lp solve v. 5.5 to solve
linear/integer programs, 2011. URL
http://CRAN.R-project.org/package=lpSolve. R package version 5.6.6.
[2] COIN-OR Foundation. Internet, 2012. URL http://www.coin-or.org/. retrieved
2012-06-12.
[3] A.J. Conejo, M. Carri´n, and J.M. Morales. Decision Making Under Uncertainty in
o
Electricity Markets. International Series in Operations Research and Management
Science Series. Springer, 2010. ISBN 9781441974204. URL
http://books.google.es/books?id=zta0qWS_W98C.
[4] EnRiMa. Energy efficiency and risk management in public buildings.
www.enrima-project.eu, 2012.
[5] GAMS. gdxrrw: interfacing gams and R. Internet, 2012. URL
http://support.gams-software.com/doku.php?id=gdxrrw:
interfacing_gams_and_r. retrieved 2012-03-06.
[6] Chris Marnay, Joseph Chard, Kristina Hamachi, Tim Lipman, Mithra Moezzi,
Boubekeur Ouaglal, and Afzal Siddiqui. Modeling of customer adoption of
distributed energy resources. Technical report, Lawrence Berkeley National
Laboratory, 2001. URL http://der.lbl.gov/publications/
modeling-customer-adoption-distributed-energy-resources.
Use R! 2012, Vanderbilt University, Nashville, June 14 2012 26/1
Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

31. References (cont.)
[7] R Development Core Team. R: A Language and Environment for Statistical
Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012. URL
http://www.R-project.org/. ISBN 3-900051-07-0.
[8] Afzal S. Siddiqui, Chris Marnay, Jennifer L. Edwards, Ryan Firestone, Srijay
Ghosh, and Michael Stadler. Effects of carbon tax on microgrid combined heat
and power adoption. Journal of Energy Engineering, 131(1):2–25, 2005. doi:
10.1061/(ASCE)0733-9402(2005)131:1(2). URL
http://link.aip.org/link/?QEY/131/2/1.
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

32. Acknowledgements
R-project
GAMS Software
EnRiMa project partners
Project RIESGOS-CM: code S2009/ESP-1685
This work has been partially funded by the projects:
Energy Efficiency and Risk Management in Public Buildings (EnRiMa) EC’s FP7
project (number 260041)
AGORANET project (IPT-430000-2010-32)
HAUS: IPT-2011-1049-430000
EDUCALAB: IPT-2011-1071-430000
DEMOCRACY4ALL: IPT-2011-0869-430000
CORPORATE COMMUNITY: IPT-2011-0871-430000
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

33. Discussion
Thanks for your attention !
emilio.lopez@urjc.es
@emilopezcano
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Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation