I. Mezic: Integrated Energy Efficient Design

Dean’s Cabinet April, 2008

Dean’s Cabinet April 17, 2008 Center for Energy Efficient Design

•  •  •  • 

Integrated Building Systems Energy Efficiency in Transportation Energy Storage Energy Harvesting and Micropower (off-grid) Generation

•  Data Center Cooling •  Smart Grid Interdisciplinary. Unifying theme: Dynamics. Control. Computation. Bamieh, Chong, Bullo, El Abbadi, Gibou, Hespanha, Khammash, Homsy, Yuen, Matthys, Mezic, Moehlis, Pennathur, Wolsky, Yang, Madhow

Dean’s Cabinet April 17, 2008 Why Buildings?

U.S. Buildings Produce •  48 % of carbon emissions

•  •  •  • 

U.S. Buildings Consume 39 % of total U.S. energy 71% of U.S. electricity 54% of U.S. natural gas

Jeff Moehlis, I.M. Sources: High Performance Commercial Buildings: A Technology Roadmap, U.S. DOE., US GBC, DOE EIA CBECS Database, Table C2A and 5B.

What are we trying to do? Why does it ma2er?       Energy Breakdown by Sector     

What are we trying to do? Why does it ma2er?       Energy Breakdown by Sector     

Sensor Work:  Prof. Francesco Bullo,   Prof. Madhow Upamanyu 

What are we trying to do? Why does it ma2er?       Energy Breakdown by Sector     

Can we do 70% be5er in NEW buildings? 90% be5er? 

                      50% be5er in RETROFITS? 

Sensor Work:  Prof. Francesco Bullo,   Prof. Madhow Upamanyu 

How is it done today, and what are the limitaDons of current pracDce?  2("%&'#"%3(3%4#*%&#*0(#)(&5.(6787(9.:%*&;."&(#)(-".*/0 +))'<.(#)(-".*/0(-))'<'."<0(=(>.".?%43.(-".*/0

!

!"#$%&"'()*&*+",'*(-&*./0(1",%."#%.0( !""#$%&'#"()#*(+,*(-".*/0(1,&,*.(

@.<5"'<%3(>.:#*&(

1*22%&2(1*".&*4(H.%I(( ;"2*(J#94$*2(%H(J$K(( L$/7>M*.H%.I"&6*(:9$'4$&/2(

!)-1NOM>PPF>@GPQR( S9&*(RFFA(

!"#$%&'())*+*,#-"#!)(..,#/"#0(&1,#2"#3&*44*56,## 7"#8%+9,#:+;#<"#=1;>%44#

!)-1($2(%3*."#*4(,0(5$4+*2#()*2*".67(8&2#$#9#*(!(:"##*''*(((((;%&#."6#(!%<(=->?;@A>BB>CDEF@@G(

• “Properly applied offtheshelf or state‐of‐the‐shelf technologies are available to   achieve low‐energy buildings. However, these strategies must be applied together   and properly integrated in the design, installaDon, and operaDon to realize   energy savings. There is no single efficiency measure or checklist of measures   to achieve low‐energy buildings.”             ‐NEED FOR INTEGRATION OF BEST‐In‐CLAS COMPONENTS  • “‐There was oNen a lack of control soNware or appropriate control logic to allow the   technologies to work well together.     ‐Design teams were too opDmisDc about the behavior of the occupants and their      acceptance of systems.     ‐Energy savings from daylighDng were substanDal, but were generally less than     expected.     ‐Plug loads were oNen greater than design predicDons.     ‐EffecDve insulaDon values are oNen inflated when comparing the actual building        to the asdesigned building.     ‐PV systems experienced a range of operaDonal performance degradaDons.      Common degradaDon sources included snow, inverter faults, shading, and parasiDc      standby losses. “          ‐NEED INTEGRATED CONTROL SOFTWARE AND UNCERTAINTY ANALYSIS  • Each of these buildings saved energy, with energy  use 25% to 70% lower than code.   Although each building is a good energy performer, addiDonal energy efficiency   and on‐site generaDon is required for these buildings to reach DOE’s ZEB goal.              ‐NEED FOR FOR ENERGY EFFICIENT DESIGN BLUEPRINTS 

Faculty in CCDC 

How is it done today, and what are the limitaDons of current pracDce?  2("%&'#"%3(3%4#*%&#*0(#)(&5.(6787(9.:%*&;."&(#)(-".*/0 +))'<.(#)(-".*/0(-))'<'."<0(=(>.".?%43.(-".*/0

!

!"#$%&"'()*&*+",'*(-&*./0(1",%."#%.0( !""#$%&'#"()#*(+,*(-".*/0(1,&,*.(

• “Properly applied offtheshelf or state‐of‐the‐shelf technologies are available to   achieve low‐energy buildings. However, these strategies must be applied together   and properly integrated in the design, installaDon, and operaDon to realize   energy savings. There is no single efficiency measure or checklist of measures   to achieve low‐energy buildings.”             ‐NEED FOR INTEGRATION OF BEST‐In‐CLAS COMPONENTS  • “‐There was oNen a lack of control soNware or appropriate control logic to allow the   technologies to work well together.     ‐Design teams were too opDmisDc about the behavior of the occupants and their      acceptance of systems.     ‐Energy savings from daylighDng were substanDal, but were generally less than     expected.     ‐Plug loads were oNen greater than design predicDons.     ‐EffecDve insulaDon values are oNen inflated when comparing the actual building        to the asdesigned building.     ‐PV systems experienced a range of operaDonal performance degradaDons.      Common degradaDon sources included snow, inverter faults, shading, and parasiDc      standby losses. “          ‐NEED INTEGRATED CONTROL SOFTWARE AND UNCERTAINTY ANALYSIS  • Each of these buildings saved energy, with energy  use 25% to 70% lower than code.   Although each building is a good energy performer, addiDonal energy efficiency   and on‐site generaDon is required for these buildings to reach DOE’s ZEB goal.              ‐NEED FOR FOR ENERGY EFFICIENT DESIGN BLUEPRINTS 

What does the            DNA of a Zero Energy Building                                                     LOOK LIKE?  @.<5"'<%3(>.:#*&(

1*22%&2(1*".&*4(H.%I(( ;"2*(J#94$*2(%H(J$K(( L$/7>M*.H%.I"&6*(:9$'4$&/2(

!)-1NOM>PPF>@GPQR( S9&*(RFFA(

!"#$%&'())*+*,#-"#!)(..,#/"#0(&1,#2"#3&*44*56,## 7"#8%+9,#:+;#<"#=1;>%44#

!)-1($2(%3*."#*4(,0(5$4+*2#()*2*".67(8&2#$#9#*(!(:"##*''*(((((;%&#."6#(!%<(=->?;@A>BB>CDEF@@G(

Faculty in CCDC 

What is new in our approach / technology, and why do we think it will be successful?  

Lucid Design Group   Building Dashboard 

Agilewaves Building   Dashboard 

Dean’s Cabinet April 17, 2008

Uncertainty Management Tool 3: DSample Deterministic Test Vectors for Accurate Sampling

Sharp increase in accuracy with new Sampling tool (red) vs standard method (blue)

-Automatically produces test vectors for uncertainty analysis, beating the curse of dimensionality.

Example of use: to reduce cost of physical testing, perform model-based testing of a subsystem whose description contains 100 to 1000s of states and physical parameters that are not known exactly, but only within a range, such as outside temperature.

-The tool (DSample) produces a set of deterministic test vectors for such simulation. DSAMPLE precision does not depend on the number of dimensions and it beats the speed of the competing algorithms by orders of magnitude.

DyNARUM Program  • 

Develop analysis and design tools for Uncertainty Management in large  Dynamical Systems 

• 

Demonstrate complexity management tools in problems with 10,000+ states/ parameters. 

• 

Close collaboraDon with industrial partner (United Technologies CorporaDon) 

Dean’s Cabinet April 17, 2008

Uncertainty Management Tool 1: VERTool Simplification Using Graphical Decompositions

Layered system decomposition

-Automatically finds chains of influences in complex systems with 1000’s of variables Example of use: vendor change requests a small change in communication protocol linking two components. What are the possible negative consequences for system performance? Which other components will be affected?

-The tool (VERTool) produces a layered decomposition, that enables efficient system analysis.

Dean’s Cabinet April 17, 2008

Uncertainty Management Tool 2: COORTool Simplification Using Global Modes

G

time

Global (emergent) mode oscillation

-Automatically finds global description variables in complex systems with 1000’s of variables Example of use: Design of an system leads to unwanted oscillations that represent themselves on the scale of the system (i.e. state of every component oscillates in time), with no apparent cause from a single component. Which changes are necessary to remove oscillatory behavior?

-The tool (COORTool) produces a description of the system in global variables that reveal cause and effect relationships at system scale.

A Power Grid Model Classical

Alternative

DOE seed project (with LBL,UTC)

Energy Efficiency in a UC Merced building

The Classroom and Office Building at UC Merced

• 92000sq ft. Leed gold building

A small number of parameters affect energy output!

Dean’s April 17, 2008 LocalCabinet interactions

Dean’s CabinetBuilding April 17, 2008 Student Resources

Dean’s Cabinet April 17, 2008

• Secured $100K/year for 2 years from SEMPRA utility. • Looking for matching funds (total project cost: $250K/year)

Dean’s Cabinet April 17, 2008 Recreation Center

• 50% of all Divisions of Student Affairs energy costs • Relatively simple use of our modeling and optimization tools can improve energy efficiency substantially (e.g. just swimming pool thermal cover scheduling optimization can lead to up to 30% savings)

Dean’s April 17, 2008 LocalCabinet interactions

• Southern California Edison support for study of integrated system design: cost-benefit, engineering/economics/sustainability study

NaDonal laboratories 

Student Affairs 

Commercial partners 

FaciliDes  Funding agencies 

InternaDonal partnerships