A Strategic Planning Model for Energy Efficiency in Public Buildings

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Presentation at the XXXIII Congreso Nacional de Estadística e Investigación Operativa (Madrid, April 2012)

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A Strategic Planning Model for Energy Efficiency in Public Buildings

  1. 1. Introduction Modelling Symbolic Model Specification A Strategic Planning Model for Energy Efficiency in Public Buildings Emilio L. Cano1 Javier M. Moguerza1 1 Department of Statistics and Operations Research University Rey Juan Carlos, Spain XXXIII Congreso Nacional de Estad´ ıstica e Investigaci´n Operativa oSEIO 2012, Madrid, April 20 2012 1/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  2. 2. Introduction Modelling Symbolic Model Specification Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 2/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  3. 3. Introduction Modelling EnRiMa Project Symbolic Model Specification Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 3/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  4. 4. Introduction Modelling EnRiMa Project Symbolic Model Specification 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.SEIO 2012, Madrid, April 20 2012 4/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  5. 5. Introduction Modelling EnRiMa Project Symbolic Model Specification 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.SEIO 2012, Madrid, April 20 2012 4/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  6. 6. Introduction Modelling EnRiMa Project Symbolic Model Specification EnRiMa DSSSEIO 2012, Madrid, April 20 2012 5/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  7. 7. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 6/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  8. 8. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives 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 ModuleSEIO 2012, Madrid, April 20 2012 7/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  9. 9. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Model The strategic model is used in order to make strategic decisions concerning which technologies to install and/or de- commission in the long term. In an attempt to tackle short- and long-term decisions as a whole, the strategic model includes a simplified version of operational energy-balance constraints, and the operational model, in turn, includes the realisation of the strategic decisions as parameters. In this way, both models feed back to each other.SEIO 2012, Madrid, April 20 2012 8/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  10. 10. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Embedded Operational Model Time resolution m Mid-term period; m ∈ M. p Long-term period; p ∈ P. t Short-term period; t ∈ T . The model includes the realisation of short-term decisions (t) that are scaled to a long-term period (p) through a mid-term representative profile (m).SEIO 2012, Madrid, April 20 2012 9/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  11. 11. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Energy Technologies and Markets Other Relevant Sets i Energy-creating technology; i ∈ I. j Energy-absorbing technology; j ∈ J . k Energy type; k ∈ K. n Energy market; n ∈ N . l Pollutant; l ∈ L.SEIO 2012, Madrid, April 20 2012 10/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  12. 12. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 11/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  13. 13. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Decisions Long Term Energy-creating technologies (i ) sip Available capacity (kW ) sdip,q Number of devices to be decommissioned siip Number of devices to be installed We denote by energy-creating technologies those technologies that provide the energy demanded by the building (e.g. Combined Heat and Power, Photovoltaic, etc.)SEIO 2012, Madrid, April 20 2012 12/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  14. 14. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Decisions Long Term Energy-absorbing technologies (j ) xjp Available capacity (kWh) xdjp,q Capacity to be decommissioned xijp Capacity to be installed We denote by energy-absorbing technologies those technologies that allow the building to demand less input energy. These technologies can be storing technologies (e.g. batteries) or passive measures (e.g. isolation).SEIO 2012, Madrid, April 20 2012 13/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  15. 15. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Embedded Operational Decisions Short Term Basic variables∗ p,m,t,mm uk ,n Purchase of energy (kWh) p,m,t,mm wk ,n Sale of energy (kWh) p,m,t yi,k Input of energy k to technology i (kWh) p,m,t qik ,j Energy type k added to storage technology j (kWh) p,m,t qok ,j Energy type k released from storage technology j (kWh) ∗ mm = m deals with energy traded in forward markets.SEIO 2012, Madrid, April 20 2012 14/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  16. 16. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Decisions Short Term Calculated variables p,m,t zi,k Output of energy type k from technology i (kWh) p,m,t rk ,j Energy type k to be stored in technology j (kWh) e p,m,t Energy consumption (kWh)SEIO 2012, Madrid, April 20 2012 15/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  17. 17. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Energy-dispatching Decision Flow Generation Sales w Technologies K y I w qi Fictitious u u Storage z Market u qi Technologies J N u qo Purchases Demand KSEIO 2012, Madrid, April 20 2012 16/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  18. 18. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 17/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  19. 19. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Constraints Strategic Available generating technologies calculation Available storing technologies calculation Generation technologies decommissioning limit Storage technologies decommissioning limit Budget limit Emissions limit Physical limit for energy-creation technologies installation Physical limit for energy-absorbing technologies installation Efficiency constraint Primary energy calculationSEIO 2012, Madrid, April 20 2012 18/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  20. 20. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Constraints Embedded Operational Constraints Energy balance Technologies short-term availability Energy output calculation Energy stored calculation Energy discharging limit Energy storage lower limit Energy storage upper limitSEIO 2012, Madrid, April 20 2012 19/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  21. 21. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Available generating technologies calculation The available capacity to generate a type of energy for each technology in a long-term period equals the number of total devices available times their nominal capacity, corrected by the aging factor. p p sip = Gi AGip−a1 siia1 − sdia1,a2 p ∈ P, i ∈ I. a1=0 a2=a1+1SEIO 2012, Madrid, April 20 2012 20/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  22. 22. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Available storing technologies calculation The available capacity to store a type of energy for each technology in a long-term period equals the number of total devices available times their nominal capacity, corrected by the aging factor. p p xjp = GSj ASip−a1 xija1 − xdja1,a2 p ∈ P, j ∈ J a1=0 a2=a1+1SEIO 2012, Madrid, April 20 2012 21/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  23. 23. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Generation technologies decommissioning limit The number of devices to be decommissioned must be less than the number of devices previously installed. sdip,a1 ≤ siip p ∈ P, i ∈ I a1>pSEIO 2012, Madrid, April 20 2012 22/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  24. 24. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Storage technologies decommissioning limit The number of devices to be decommissioned must be less than the number of devices previously installed. xdjp,a1 ≤ xijp p ∈ P, j ∈ J a1>pSEIO 2012, Madrid, April 20 2012 23/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  25. 25. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Budget limit The total investment in technologies must be lower than a specified budget limit. This includes installation and decommissioning costs. CIip,0 · Gi · siip + CISjp,0 · GSj · xijp i∈I j ∈J p p + Gi CDip−a1 sdia1,a2 i∈I a1=0 a2=a1+1 p p + CDSjp−a1 xdja1,a2 ≤ ILp p∈P j ∈J a1=0 a2=a1+1SEIO 2012, Madrid, April 20 2012 24/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  26. 26. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Emissions limit The total emissions must be lower than the allowed limit per year. p p,m,t p,m,t,mm DMm Hi,k ,l · yi,k Ci,l,n · uk ,n m∈M i∈I k ∈K t∈T n∈N k ∈K t∈T ≤ PLp l p ∈ P, l ∈ LSEIO 2012, Madrid, April 20 2012 25/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  27. 27. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Physical limit for energy-creation technologies installation The available capacity of a given energy-creation technology, is limited by the physical space needed. This is a function of both building and technology configuration. For example, for PV technologies it would be the ratio roof/technology surfaces. Note that the function must return the appropriate units. sip ≤ f (BuildPars, TechPars i ) p ∈ P, i ∈ ISEIO 2012, Madrid, April 20 2012 26/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  28. 28. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Physical limit for energy-absorbing technologies installation The available capacity of a given energy-absorbing technology is limited by the physical space needed. This is a function of both building and technology configuration. Note that the function must return the appropriate units. xjp ≤ f (BuildPars, TechPars j ) p ∈ P, j ∈ JSEIO 2012, Madrid, April 20 2012 27/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  29. 29. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Efficiency constraint Minimum efficiency required in the building. p,m,t p,m,t,mm Dk + wk ,n p∈P m∈M t∈T k ∈K m∈M n∈N ≥ EF · e p,m,t p∈P m∈M t∈TSEIO 2012, Madrid, April 20 2012 28/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  30. 30. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Strategic Constraints Primary energy calculation The primary energy (not from a fictitious market) consumed is the sum of the processed energy of each type and the one used as an input fuel. p,m,t,mm p,m,t,mm e p,m,t = uk ,n · Bk ,n + uk ,n m∈M k ∈K n∈N n∈N p ∈ P, m ∈ M, t ∈ TSEIO 2012, Madrid, April 20 2012 29/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  31. 31. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy Balance The energy supplied must meet the energy demand minus the energy saved due to absorbing technologies. It is composed of the energy produced with energy-creating technologies plus the energy purchased in the market minus the energy for sale, energy for storage and energy for production. On the demand side, the energy released from storage and the energy saved with passive technologies diminish the total demand.SEIO 2012, Madrid, April 20 2012 30/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  32. 32. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy Balance Equation 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 Passive technologies can be modelled in two ways: space-measurable ones, and unitary-measurable ones.SEIO 2012, Madrid, April 20 2012 31/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  33. 33. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Technologies short-term availability The energy that can be supplied by a technology is constrained by the availability of the technology. p,m,t p,m,t zi,k ≤ DT · Ai · sip p ∈ P, m ∈ M, t ∈ T, i ∈ I, k = KF (i)SEIO 2012, Madrid, April 20 2012 32/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  34. 34. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy output calculation The total energy output by each technology for a type of energy output is the sum of all the energy outputs over the energy inputs, computed as the energy input corrected by the conversion factor. p,m,t −1 p,m,t zi,kk = Ei,k ,kk · yi,k k ∈KI (i) p ∈ P, m ∈ M, t ∈ T, i ∈ I, kk ∈ KO(i)SEIO 2012, Madrid, April 20 2012 33/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  35. 35. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy stored calculation The energy stored each period is the energy stored in the previous period, plus the energy sent to storage, minus the energy released from storage. All flows are corrected by the losses ratio parameters. p,m,t p,m,t−1 p,m,t−1 p,m,t−1 rk ,j = OSk ,j · rk ,j + OIk ,j · qik ,j − OOk ,j · qok ,j p ∈ P, m ∈ M, t ∈ T, k ∈ K, j ∈ JSSEIO 2012, Madrid, April 20 2012 34/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  36. 36. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy discharging limit The amount of energy that may be discharged from any energy-storage technology is limited by the storage level. p,m,t p,m,t qok ,j ≤ ORk ,j · rk ,j p ∈ P, m ∈ M, t ∈ T, k ∈ K, j ∈ JSSEIO 2012, Madrid, April 20 2012 35/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  37. 37. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy storage lower limit The amount of energy that may be stored from any energy-storage technology must be greater than the capacity installed corrected by the minimum charge allowed. p,m,t rk ,j ≥ OAk ,j · xjp p ∈ P, m ∈ M, t ∈ T, k ∈ K, j ∈ JSSEIO 2012, Madrid, April 20 2012 36/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  38. 38. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Operational Constraints Energy storage upper limit The amount of energy that may be stored in any energy-storage technology must be lower than the capacity installed, corrected by the maximum charge allowed. p,m,t rk ,j ≤ OBk ,j · xjp p ∈ P, m ∈ M, t ∈ T, k ∈ K, j ∈ JSSEIO 2012, Madrid, April 20 2012 37/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  39. 39. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Objective Function Minimize total cost The objective is to minimize the total cost. It is composed of the cost of installing and maintaining technologies (both energy creating and energy absorbing), the operational cost of the technologies and the cost of purchasing energy in the market. The sales of energy and subsidies are incomes that we have to subtract from the total cost.SEIO 2012, Madrid, April 20 2012 38/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  40. 40. Introduction Model Description Modelling Decision Variables Symbolic Model Specification Constraints and Objectives Objective Function  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∈ISEIO 2012, Madrid, April 20 2012 39/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  41. 41. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Outline 1 Introduction EnRiMa Project 2 Modelling Model Description Decision Variables Constraints and Objectives 3 Symbolic Model Specification Model Generation Through RSEIO 2012, Madrid, April 20 2012 40/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  42. 42. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Algebraic Languages From Data to Models Needs Statistical Software Data Visualization Data Analysis Mathematical Representation Solver Input Generation Output DocumentationSEIO 2012, Madrid, April 20 2012 41/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  43. 43. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification 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.SEIO 2012, Madrid, April 20 2012 42/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  44. 44. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  45. 45. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  46. 46. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  47. 47. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  48. 48. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  49. 49. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  50. 50. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Summary In this presentation the strategic model for the EnRiMa DSS has been described. It has been developed in a sequential way from the “atomic” elements of the models. The Symbolic Model Specification generates output documentation and algebraic language definitions for solvers. Outlook Extension to a stochastic optimisation formulation. Symbolic Model Specification enhancement. GUI for the EnRiMa DSS.SEIO 2012, Madrid, April 20 2012 43/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.
  51. 51. Introduction Model Generation Through R Modelling Summary Symbolic Model Specification Discussion Thanks for your attention ! emilio.lopez@urjc.esSEIO 2012, Madrid, April 20 2012 44/44 Emilio L. Cano and Javier M. Moguerza Strategic Model Planning: Energy Efficiency & Risk Mgmt.

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