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Stochastic optimization and risk management for an efficient planning of buildings' energy systems

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Talk at the 20th Conference of the International Federation of Operational Research Societies, Barcelona, Spain

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Stochastic optimization and risk management for an efficient planning of buildings' energy systems

  1. 1. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Stochastic Optimization and Risk Management for an ecient planning of buildings' energy systems Emilio L. Cano, Javier M. Moguerza and Antonio Alonso-Ayuso Department of Computer Science and Statistics Rey Juan Carlos University 20th Conference of the International Federation of Operational Research Societies Barcelona, July 17, 2014 20th Conference of the International Federation of Operational Research Societies 1/36
  2. 2. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Outline 1 Introduction The problem Background 2 Modeling Deterministic Modelling Stochastic Modelling Risk Management 3 Conclusions Summary 20th Conference of the International Federation of Operational Research Societies 2/36
  3. 3. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Outline 1 Introduction The problem Background 2 Modeling Deterministic Modelling Stochastic Modelling Risk Management 3 Conclusions Summary 20th Conference of the International Federation of Operational Research Societies 3/36
  4. 4. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Global changes, local challenges Global Regulations: emissions, eciency De-regulations: market Global warming Resources scarcity Global markets 20th Conference of the International Federation of Operational Research Societies 4/36
  5. 5. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Global changes, local challenges Global Regulations: emissions, eciency De-regulations: market Global warming Resources scarcity Global markets Local Users' comfort Security Availability Limited budget New options 20th Conference of the International Federation of Operational Research Societies 4/36
  6. 6. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Global changes, local challenges Global Regulations: emissions, eciency De-regulations: market Global warming Resources scarcity Global markets Local Users' comfort Security Availability Limited budget New options 20th Conference of the International Federation of Operational Research Societies 4/36
  7. 7. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Global changes, local challenges Global Regulations: emissions, eciency De-regulations: market Global warming Resources scarcity Global markets Local Users' comfort Security Availability Limited budget New options 20th Conference of the International Federation of Operational Research Societies 4/36
  8. 8. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Global changes, local challenges Global Regulations: emissions, eciency De-regulations: market Global warming Resources scarcity Global markets Local Users' comfort Security Availability Limited budget New options 20th Conference of the International Federation of Operational Research Societies 4/36
  9. 9. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Energy Systems 20th Conference of the International Federation of Operational Research Societies 5/36
  10. 10. Building systems energy ow: Sankey diagram Campus Pinkafeld test site
  11. 11. Building systems energy ow: Sankey diagram Demand side: requirements, uncertainty
  12. 12. Building systems energy ow: Sankey diagram Supply side: Markets, renewables
  13. 13. Building systems energy ow: Sankey diagram Strategic decisions are the goal
  14. 14. Building systems energy ow: Sankey diagram Operational performance interdependent with strategic decisions
  15. 15. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Outline 1 Introduction The problem Background 2 Modeling Deterministic Modelling Stochastic Modelling Risk Management 3 Conclusions Summary 20th Conference of the International Federation of Operational Research Societies 7/36
  16. 16. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary EnRiMa Project 20th Conference of the International Federation of Operational Research Societies 8/36
  17. 17. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary EnRiMa Models EnRiMa DSS Strategic Module Strategic DVs Strategic Constraints Upper-Level Operational DVs Upper-Level Energy-Balance Constraints Operational Module Lower-Level Operational DVs Lower-Level Energy-Balance Constraints 20th Conference of the International Federation of Operational Research Societies 9/36
  18. 18. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Decision Support Systems (DSS) 20th Conference of the International Federation of Operational Research Societies 10/36
  19. 19. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Decision Support Systems (DSS) Model: Symbolic Model Speci
  20. 20. cation (SMS) 20th Conference of the International Federation of Operational Research Societies 10/36
  21. 21. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Decision Support Systems (DSS) Model: Symbolic Model Speci
  22. 22. cation (SMS) Data: Statistical analysis 20th Conference of the International Federation of Operational Research Societies 10/36
  23. 23. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Decision Support Systems (DSS) Model: Symbolic Model Speci
  24. 24. cation (SMS) Data: Statistical analysis Framework: Stakeholders dialog 20th Conference of the International Federation of Operational Research Societies 10/36
  25. 25. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Decision Support Systems (DSS) Algorithms Model Symbolic model Variables, relations Underlying theory Methodology, technique Uncertainty modelling Data Deterministic data Uncertain data - Stochastic processes Data analysis Solution Data treatment Analysis Visualization DSS Stakeholders Dialog Interpretation Model: Symbolic Model Speci
  26. 26. cation (SMS) Data: Statistical analysis Framework: Stakeholders dialog 20th Conference of the International Federation of Operational Research Societies 10/36
  27. 27. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Outline 1 Introduction The problem Background 2 Modeling Deterministic Modelling Stochastic Modelling Risk Management 3 Conclusions Summary 20th Conference of the International Federation of Operational Research Societies 11/36
  28. 28. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Time Resolution Representative short-term periods within long-term periods 20th Conference of the International Federation of Operational Research Societies 12/36
  29. 29. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Time Resolution Strategic decisions: horizon 15-20 years 20th Conference of the International Federation of Operational Research Societies 12/36
  30. 30. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Time Resolution Operational decisions (energy ows): hours 20th Conference of the International Federation of Operational Research Societies 12/36
  31. 31. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Sets Time resolution p Long-term period; p 2 P m Mid-term representative period; m 2M t Short-term period; t 2 T The model includes the realization of short-term decisions (t) that are scaled to a long-term period (p) through a mid-term representative pro
  32. 32. le (m). 20th Conference of the International Federation of Operational Research Societies 13/36
  33. 33. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Sets Time resolution p Long-term period; p 2 P m Mid-term representative period; m 2M t Short-term period; t 2 T The model includes the realization of short-term decisions (t) that are scaled to a long-term period (p) through a mid-term representative pro
  34. 34. le (m). Energy, technologies, markets, emissions i Technology (generators, storage, passive); i 2 I k Energy type; k 2 K n Energy market (contract taris); n 2 N l Pollutant; l 2 L 20th Conference of the International Federation of Operational Research Societies 13/36
  35. 35. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level 20th Conference of the International Federation of Operational Research Societies 14/36
  36. 36. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning 20th Conference of the International Federation of Operational Research Societies 14/36
  37. 37. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) 20th Conference of the International Federation of Operational Research Societies 14/36
  38. 38. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index 20th Conference of the International Federation of Operational Research Societies 14/36
  39. 39. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index Emissions 20th Conference of the International Federation of Operational Research Societies 14/36
  40. 40. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index Emissions Eciency 20th Conference of the International Federation of Operational Research Societies 14/36
  41. 41. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index Emissions Eciency Dierent energy types 20th Conference of the International Federation of Operational Research Societies 14/36
  42. 42. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index Emissions Eciency Dierent energy types Dierent technology types: generation, storage, passive measures 20th Conference of the International Federation of Operational Research Societies 14/36
  43. 43. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Model Features Modelling at the building level Technologies installation and decommissioning Energy ows (short term) along with investment (long term) Technologies aging through the a index Emissions Eciency Dierent energy types Dierent technology types: generation, storage, passive measures Objective: minimize total discounted cost 20th Conference of the International Federation of Operational Research Societies 14/36
  44. 44. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Energy-dispatching Decision Flow Renewables Market Demand Purchases Generation Storage N K I I Sales K y u u u w u w z ri ri ro Technologies Technologies r 20th Conference of the International Federation of Operational Research Societies 15/36
  45. 45. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Energy-dispatching Decision Flow Renewables Market Demand Purchases Generation Storage N K I I Sales K y u u u w u w z ri ri ro Technologies Technologies r Cano EL, Groissbock M, Moguerza JM and Stadler M (2014). A Strategic Optimization Model for Energy Systems Planning. Energy and Buildings. http://dx.doi.org/10.1016/j.enbuild.2014.06.030. 20th Conference of the International Federation of Operational Research Societies 15/36
  46. 46. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Outline 1 Introduction The problem Background 2 Modeling Deterministic Modelling Stochastic Modelling Risk Management 3 Conclusions Summary 20th Conference of the International Federation of Operational Research Societies 16/36
  47. 47. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Deterministic vs. Stochastic Five periods, two technologies (CHP, PV), only electricity. 100 scenarios simulation 80 60 40 20 2013 2014 2015 2016 Demand level (kW) Energy demand 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 CHP PV RTE 2013 2014 2015 2016 2017 EUR/kW Investment cost 0.3 0.2 0.1 0.3 0.2 0.1 CHP RTE 2013 2014 2015 2016 EUR/kWh Scenario 100 75 50 25 Energy price 20th Conference of the International Federation of Operational Research Societies 17/36
  48. 48. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Deterministic vs. Stochastic Five periods, two technologies (CHP, PV), only electricity. 100 scenarios simulation 80 60 40 20 2013 2014 2015 2016 Demand level (kW) Energy demand 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 CHP PV RTE 2013 2014 2015 2016 2017 EUR/kW Investment cost 0.3 0.2 0.1 0.3 0.2 0.1 CHP RTE 2013 2014 2015 2016 EUR/kWh Scenario 100 75 50 25 Energy price Fdet (x det ) = 66; 920 EUR. 20th Conference of the International Federation of Operational Research Societies 17/36
  49. 49. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Deterministic vs. Stochastic Five periods, two technologies (CHP, PV), only electricity. 100 scenarios simulation 80 60 40 20 2013 2014 2015 2016 Demand level (kW) Energy demand 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 CHP PV RTE 2013 2014 2015 2016 2017 EUR/kW Investment cost 0.3 0.2 0.1 0.3 0.2 0.1 CHP RTE 2013 2014 2015 2016 EUR/kWh Scenario 100 75 50 25 Energy price Fdet (x det ) = 66; 920 EUR. Fsto(x sto) = 68; 595 EUR. 20th Conference of the International Federation of Operational Research Societies 17/36
  50. 50. Risk Manag. planning energy systems IFORS 2014 July 17 E.L. Cano Introduction The problem Background Modeling Deterministic Modelling Stochastic Modelling Risk Management Conclusions Summary Deterministic vs. Stochastic Five periods, two technologies (CHP, PV), only electricity. 100 scenarios simulation 80 60 40 20 2013 2014 2015 2016 Demand level (kW) Energy demand 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 2500 2000 1500 1000 500 0 CHP PV RTE 2013 2014 2015 2016 2017 EUR/kW Investment cost 0.3 0.2 0.1 0.3 0.2 0.1 CHP RTE 2013 2014 2015 2016 EUR/kWh Scenario 100 75 50 25 Energy price Fdet (x det ) = 66; 920 EUR. Fsto(x sto) = 68; 595 EUR. VSS = Fsto(x det )

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