Dynamic Uncertainty Analysis of the Building Energy Performance in ...
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts 4ème université de la Chaire éco-conception PERFORMANCE DES OUTILS D’ECO-CONCEPTION Sebastian Stinner
E.ON Energy Research Center June 2006: the largest research
co-operation in Europe between a private company and a university was signed Five new professorships in the
field of energy technology were defined across 4 faculties Main Research Areas: Grids and Storage Buildings and City Districts Heat and Power Generation
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 2
Research Foci EBC
E.ON Energy Research Center Energy Concepts for Buildings and Communities Room Air Flows, Thermal Comfort and Indoor Air Quality
Heating and Air-conditioning components
Generation and Storage Systems
Energy Efficient Cities
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 3
Wind power in MW
Future Power Generation – „Energiewende“
hours
Wind power max. 20.000 MW, min. 270 MW Source: Erneuerbare Energien 2010. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), 2010 / DENA – presentation at the energy operators meeting 2010 in Heiligendamm Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 4
Energy Storage Systems in Germany
Gas Grid and Gas Storage (200 TWh)
High Voltage
Medium Voltage
Pump Storage – 40 GWh
Low Voltage
Batteries
Buildings (DSM)
E – Mobility (5 kWh / Vehicle)
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 5
Energy Demand in Germany
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 6
Why are we interested in City Districts?
70 %
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 7
What did we analyze? Weather conditions (hot, medium, cold)
Indoor air temperature Air exchange rate Inner loads
Dynamics Thickness of insulation Material properties
100
Nr. of buildings in class Refurbishment status
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 8
100
Basics – Quasi Monte Carlo (QMC) Method Classical Monte Carlo Estimate Uncertainties in Parameters
Draw Random Numbers
Do a High Number of Simulations
Get Uncertainty in Target Figure Air exchange rate in 1/h
Estimate Uncertainties in Parameters
Sampling Algorithm
Quasi Monte Carlo Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 9
Do a Low Number of Simulations
Challenge – Total Annual Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 10
Challenge – Dynamic Heat Demand („Summer“ Day)
Quartiles Quartiles
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 11
Median of Dynamic Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 12
RIQR of Dynamic Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 13
Influence of Heat Demand on RIQR
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 14
Integration of HVAC technologies
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 15
Offering flexibility to the electrical grid
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 16
Median of Flexibility
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 17
IQR of Flexibility
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 18
Conclusions & Further Work
Conclusion
Extension of global uncertainty analysis to city district level Dynamic uncertainty analysis Decreasing relative uncertainty with higher heat demand Uncertainties do not disappear due to the analysis of city districts Quantification of uncertainties in flexibility important for future integration of renewables
Further Work
Statistical tests Influence of composition of district Integrate City District and Flexibility applications Influence on available storage capacity
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 19
We gratefully acknowledge the financial support for this project by BMWi (German Federal Ministry for Economic Affairs and Energy) under promotional Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner reference 03ET1111B. Slide 20
Application to Every Type of Distribution
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 21
E.ON Energy Research Center June 2006: the largest research
co-operation in Europe between a private company and a university was signed Five new professorships in the
field of energy technology were defined across 4 faculties Main Research Areas: Grids and Storage Buildings and City Districts Heat and Power Generation
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 2
Research Foci EBC
E.ON Energy Research Center Energy Concepts for Buildings and Communities Room Air Flows, Thermal Comfort and Indoor Air Quality
Heating and Air-conditioning components
Generation and Storage Systems
Energy Efficient Cities
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 3
Wind power in MW
Future Power Generation – „Energiewende“
hours
Wind power max. 20.000 MW, min. 270 MW Source: Erneuerbare Energien 2010. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), 2010 / DENA – presentation at the energy operators meeting 2010 in Heiligendamm Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 4
Energy Storage Systems in Germany
Gas Grid and Gas Storage (200 TWh)
High Voltage
Medium Voltage
Pump Storage – 40 GWh
Low Voltage
Batteries
Buildings (DSM)
E – Mobility (5 kWh / Vehicle)
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 5
Energy Demand in Germany
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 6
Why are we interested in City Districts?
70 %
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 7
What did we analyze? Weather conditions (hot, medium, cold)
Indoor air temperature Air exchange rate Inner loads
Dynamics Thickness of insulation Material properties
100
Nr. of buildings in class Refurbishment status
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 8
100
Basics – Quasi Monte Carlo (QMC) Method Classical Monte Carlo Estimate Uncertainties in Parameters
Draw Random Numbers
Do a High Number of Simulations
Get Uncertainty in Target Figure Air exchange rate in 1/h
Estimate Uncertainties in Parameters
Sampling Algorithm
Quasi Monte Carlo Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 9
Do a Low Number of Simulations
Challenge – Total Annual Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 10
Challenge – Dynamic Heat Demand („Summer“ Day)
Quartiles Quartiles
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 11
Median of Dynamic Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 12
RIQR of Dynamic Heat Demand
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 13
Influence of Heat Demand on RIQR
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 14
Integration of HVAC technologies
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 15
Offering flexibility to the electrical grid
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 16
Median of Flexibility
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 17
IQR of Flexibility
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 18
Conclusions & Further Work
Conclusion
Extension of global uncertainty analysis to city district level Dynamic uncertainty analysis Decreasing relative uncertainty with higher heat demand Uncertainties do not disappear due to the analysis of city districts Quantification of uncertainties in flexibility important for future integration of renewables
Further Work
Statistical tests Influence of composition of district Integrate City District and Flexibility applications Influence on available storage capacity
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 19
We gratefully acknowledge the financial support for this project by BMWi (German Federal Ministry for Economic Affairs and Energy) under promotional Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner reference 03ET1111B. Slide 20
Application to Every Type of Distribution
Dynamic Uncertainty Analysis of the Building Energy Performance in City Districts | Sebastian Stinner Slide 21
