Decision Making under Uncertainty: R implementation for Energy Efficient Buildings

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Presentation at the Use R! 2012 Conference (Nashville, TN, June 2012)

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Decision Making under Uncertainty: R implementation for Energy Efficient Buildings

  1. 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 MeetingUse R! 2012, Vanderbilt University, Nashville, June 14 2012 1/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  2. 2. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 2/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  3. 3. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 3/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  4. 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. 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. 6. ConsortiumUse R! 2012, Vanderbilt University, Nashville, June 14 2012 5/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  7. 7. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 6/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  8. 8. EnRiMa DSSUse R! 2012, Vanderbilt University, Nashville, June 14 2012 7/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  9. 9. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 8/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  10. 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 parametersUse R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  11. 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 parametersUse R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  12. 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 parametersUse R! 2012, Vanderbilt University, Nashville, June 14 2012 9/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  13. 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 ModuleUse R! 2012, Vanderbilt University, Nashville, June 14 2012 10/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  14. 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 treeUse R! 2012, Vanderbilt University, Nashville, June 14 2012 11/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  15. 15. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 12/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  16. 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∈IUse R! 2012, Vanderbilt University, Nashville, June 14 2012 13/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  17. 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 ∈ LUse R! 2012, Vanderbilt University, Nashville, June 14 2012 14/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  18. 18. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 15/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  19. 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 neededUse R! 2012, Vanderbilt University, Nashville, June 14 2012 16/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  20. 20. Data model Model and Instance Classes, data attributes, input/output methodsUse R! 2012, Vanderbilt University, Nashville, June 14 2012 17/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  21. 21. OutlineUse R! 2012, Vanderbilt University, Nashville, June 14 2012 18/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  22. 22. Algebraic Languages Needs Statistical Software Data Visualization Data Analysis Mathematical Representation Solver Input Generation Output DocumentationUse R! 2012, Vanderbilt University, Nashville, June 14 2012 19/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  23. 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.Use R! 2012, Vanderbilt University, Nashville, June 14 2012 20/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  24. 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 }Use R! 2012, Vanderbilt University, Nashville, June 14 2012 21/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  25. 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 " ,Use R! 2012, Vanderbilt University, Nashville, June 14 2012 22/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  26. 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 objectUse R! 2012, Vanderbilt University, Nashville, June 14 2012 23/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  27. 27. Solution and report (cont.) @ end { document }Use R! 2012, Vanderbilt University, Nashville, June 14 2012 24/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  28. 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. 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. 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. 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.Use R! 2012, Vanderbilt University, Nashville, June 14 2012 27/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  32. 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-2011-0871-430000Use R! 2012, Vanderbilt University, Nashville, June 14 2012 28/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation
  33. 33. Discussion Thanks for your attention ! emilio.lopez@urjc.es @emilopezcanoUse R! 2012, Vanderbilt University, Nashville, June 14 2012 29/1 Emilio L. Cano and Javier M. Moguerza Decision Making under Uncertainty: R implementation

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