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ENERGY AND BUILDINGS

Prof. Gregor P. Henze, Ph.D., P.E.University of ColoradoBoulder

Building sector energy use

¨ 120 million buildings consume

¨ 42% of primary energy

¨ 72% of electricity¨ 34% of natural gas

US buildings third largest consumer

Commercial building stock

Building energy use and area

Predicted 30% increase by 2050

Energy end use consumption splitsProfiles of Building Sector Energy Use

Energy Use in Commercial Buildings The way energy is used in a commercial buildings has a large ef-fect on energy efficiency strategies. The most important energy end-use across the stock of commercial buildings is lighting, which accounts for one-quarter of total primary energy use.

Heating and cooling are next in importance, each with about one-seventh of the total. Equal in magnitude—though not well-defined by the U.S. DOE Energy Information Administration—is an aggregate category of miscellaneous “other uses,” such as service station equipment, ATM machines, medical equipment and telecommunications equipment. Ventilation uses another 7% of energy, making HVAC as a whole the largest user of en-ergy in commercial buildings at nearly 32%.

Water heating and office equipment (not counting personal computers) use similar amounts of energy (6%–7.5%), and re-frigeration, computer use and cooking consuming the least.

Energy Use in Residential Buildings Space heating comprises the largest energy use in a home, at one quarter—almost twice any other end use. Space cooling, water heating and lighting all use roughly the same percentage of energy in a home (12%–13%), followed by another set of uses —electronics, refrigeration and wet cleaning—sharing similar lev-els of use from 6% to 8%.

16 U.S. DEPARTMENT OF ENERGY

Figure 17 Commercial Primary Energy End-Use Splits, 2006

24.8%

12.7%

12.1% 6.8%

2.0%

13.2%

6.3%

5.7%

4.1%

3.8%

7.5%

Lighting Space Cooling Space Heating Ventilation Electronics Water Heating Refrigeration Computers Cooking Other * Energy Adjustment

Source: 2009 Buildings Energy Data Book, U.S. Department of Energy, Table 3.1.4 <http://buildingsdatabook.eere.energy.gov/docs/xls_pdf/3.1.4.pdf>

* Energy adjustment U.S. Department of Energy, Energy Information Administration uses to adjust for discrepencies between data sources. Energy attributed to the commercial buildings sector, but not directly to any specific end-use.

Figure 18 Residential Primary Energy End-Use Splits, 2006

26.4%

13.0%

12.4%11.6%

1.0%

8.1%

3.6% 5.7%

7.2%

6.2%

4.7%

Space Heating Space Cooling Water Heating Lighting Electronics Refrigeration Wet Clean Cooking Computers Other * Energy Adjustment


* Energy adjustment U.S. Department of Energy, Energy Information Administration uses to adjust for discrepencies between data sources. Energy attributed to the residential buildings sector, but not directly to any specific end-use.

Profiles of Building Sector Energy Use

Energy Use in Commercial Buildings The way energy is used in a commercial buildings has a large ef-fect on energy efficiency strategies. The most important energy end-use across the stock of commercial buildings is lighting, which accounts for one-quarter of total primary energy use.

Heating and cooling are next in importance, each with about one-seventh of the total. Equal in magnitude—though not well-defined by the U.S. DOE Energy Information Administration—is an aggregate category of miscellaneous “other uses,” such as service station equipment, ATM machines, medical equipment and telecommunications equipment. Ventilation uses another 7% of energy, making HVAC as a whole the largest user of en-ergy in commercial buildings at nearly 32%.

Water heating and office equipment (not counting personal computers) use similar amounts of energy (6%–7.5%), and re-frigeration, computer use and cooking consuming the least.

Energy Use in Residential Buildings Space heating comprises the largest energy use in a home, at one quarter—almost twice any other end use. Space cooling, water heating and lighting all use roughly the same percentage of energy in a home (12%–13%), followed by another set of uses —electronics, refrigeration and wet cleaning—sharing similar lev-els of use from 6% to 8%.

16 U.S. DEPARTMENT OF ENERGY

Figure 17 Commercial Primary Energy End-Use Splits, 2006

24.8%

12.7%

12.1% 6.8%

2.0%

13.2%

6.3%

5.7%

4.1%

3.8%

7.5%

Lighting Space Cooling Space Heating Ventilation Electronics Water Heating Refrigeration Computers Cooking Other * Energy Adjustment


* Energy adjustment U.S. Department of Energy, Energy Information Administration uses to adjust for discrepencies between data sources. Energy attributed to the commercial buildings sector, but not directly to any specific end-use.

Figure 18 Residential Primary Energy End-Use Splits, 2006

26.4%

13.0%

12.4%11.6%

1.0%

8.1%

3.6% 5.7%

7.2%

6.2%

4.7%

Space Heating Space Cooling Water Heating Lighting Electronics Refrigeration Wet Clean Cooking Computers Other * Energy Adjustment


* Energy adjustment U.S. Department of Energy, Energy Information Administration uses to adjust for discrepencies between data sources. Energy attributed to the residential buildings sector, but not directly to any specific end-use.

Building Energy UseCommercial Consumption by Type

Source: WBCSD 2008

Cooling Tower

Chiller

Chilled Water Loop

Chilled Water

Cold Water

Evaporator

CondenserExpa

nsio

n V

alve

Com

pres

sor

Bypass Pipe

Bypass Pipe

Splitter

Splitter

Mixer

Mixer

Plant Loop Supply Side

Plant Loop Demand Side

Pum

p

Bypass PipeMixer

Splitter

Pump

Supply Outlet Pipe

Chilled Water Condenser Loop

Fan

Perimeter Zone Core ZonePeople, Lights & Plugs People, Lights & Plugs

Cooling Coil

Zone Splitter

Zone Mixer

Outside Air Mixer

Hot Water LoopBoiler

PumpBypass Pipe

BypassMixer

Splitter

Plant Loop Supply Side

Plant Loop Demand Side

ß Single Duct VAV

Air System

ß Reheat Coil

Large Central System Equipment

Retail System With Rooftop Units

Packaged Rooftop Unit (RTU)

Central System for Midsize Building

Packaged System for Midsize Buildings

Building-to-Grid Integration through CommercialBuilding Portfolios Participating in Energy and

Frequency Regulation Markets

Gregory S. Pavlak & Gregor P. Henze

University of Colorado Boulder

April 15, 2015





April 15, 2015





April 15, 2015

Motivation Modeling Multi-Market Multi-Building Portfolio Conclusions

Section 1

Motivation


The Opportunity for Efficiency in Buildings

IEA: 32% of global final delivered energy [16].

more than any other sector

EIA: building energy demand projected to increase at 1.6%/year [7].

faster than any other sector

PNNL: save 1 quad primary energy through improved control [3].

6% of 2002 U.S. commercial building primary energy consumption

NREL: 50% net site energy savings over ASHRAE 90.1-2004 [15].

Achieved through holistic integration of efficiency measures


Energy in Transition

IEA: global electricity demand growth projected at ≈ 2%/ year [1].

more than any other final form of energy (2011 to 2035)

U.S. consumed 78 quadsof fossil (82%) in 2012.

U.S. consumed 17 quadsof nuclear and renewables(18%) in 2012 [6].

Sustainability seems tonecessitate transitioning tomore renewables.

Biomass = 71%

Solar = 4%Geothermal = 4%

Wind = 22%

Petroleum

Coal

Natural Gas

NuclearHydro

Non−Hydro0

20

40

60

80

Fossil Nuclear Renewable

Ener

gy C

onsu

mpt

ion

[qua

drilli

on B

TU]


Challenges with Transition

Electric grid primarily developed around predictable generation.

Majority of future renewables is likely wind and solar.

Wind and solar variability makes scheduling resources difficult [18].

Improper integration of large quantities of variable generation [11]:

power quality issuespower flow imbalancesgrid stability issues

Solutions

Grid Storage: pumped-hydro, compressed air, batteries

Demand Response: modulation of HVAC, deferrable loads

NREL: demand response and load participation critical in achievinghigher penetrations of variable resources [10].


Electricity System Overview

Continuously balance supply and demand

Diurnal load patterns caused by human activity and weather effects

Day-ahead energy market involves least-cost scheduling of base load,load following, and peaking plants → time dependent energy price

Ancillary services: frequency regulation, voltage control, and reserves

4

Co-optimization presents significant challenges for loads supplying ancillary services and especially spinning reserve. As discussed above, a critical reason some loads are better suited to supplying spinning reserve rather than peak reduction is because load response duration is limited. Co-optimization can extend the response duration unacceptably and force the load to withdraw from the market, harming power system reliability.

Co-optimization (also called joint optimization, simultaneous optimization, or rational buying) minimizes the total cost of energy, regulation, and contingency reserves by allowing the substitution of “higher value” services for “lower value” services. If a generator offers spinning reserve at $8/MW-hr, for example, and other generators are offering replacement reserve at $12/MW-hr the co-optimizer will use the spinning reserve resource for replacement reserve (instead of the replacement reserve offered) and pay it the spinning reserve clearing price. Co-optimization has many benefits. It encourages generators to bid in with their actual costs for energy and each of the ancillary services. When they do so the co-optimizer is able to simultaneously minimize overall system costs and maximize individual generator profits.

Unfortunately, co-optimization can effectively bar responsive loads as well as emissions-limited generators and water-limited hydro generators from offering to provide ancillary services.

Fundamentally the problem is that the co-optimizer is unable to deal with a rising cost curve. Many responsive loads differ from most generators in that the cost of response rises with response duration. An air conditioning load, for example, incurs almost no cost when it provides a ten minute interruption but incurs unacceptable costs when it provides a six hour interruption. Conversely a generator typically incurs startup and shutdown costs even for short responses but only has ongoing fuel costs associated with its response duration. In fact, many generators have minimum run times and minimum shutdown times. This low-cost-for-short-duration-response (coupled with fast response speed) makes some responsive loads ideal for providing spinning reserve but less well suited for providing energy response or peak reduction. A generator benefits economically when response duration is extended but a load is hurt. The co-optimizer assumes that all offering entities behave like generators and benefit from longer response.

Unfortunately current market rules in New York and New England let the ISOs dispatch capacity assigned to reserves for economic reasons as well as reliability purposes. As long as the ISO has enough spinning and non-spinning reserve capacity to cover contingencies, it will dispatch any remaining resources economically regardless of whether that capacity is labeled as contingency reserve or not. Ancillary service and energy suppliers are automatically co-optimized.

This policy works well for most generators but causes severe problems for loads that need to limit the duration or frequency of their response to occasional contingency conditions. Loads can submit very high energy bids in an

attempt to be the last resource called but this is still no guarantee that they will not be used as a multi-hour energy resource. Submitting a high cost energy bid also means that the load will be used less frequently for contingency response than is economically optimal. Price caps on energy bids further limit the ability of the loads to control how long they are deployed for.

Fortunately there is a simple solution. California had this problem with their rational buyer but changed their market rules and now allows resources to flag themselves as available for contingency response only. PJM allows resources to establish different prices for each service and energy providing a partial solution. ERCOT does not have the problem because most energy is supplied through bilateral arrangements that the ISO is not part of; energy and ancillary service markets are separate. Possibly as a consequence half of ERCOT’s contingency response comes from responsive load (the maximum currently allowed) while no loads offer to supply balancing energy.

B. Some Loads May Be Able To Provide Better Regulation Than Generators

Regulation, the minute-to-minute varying of generation or consumption at the system operator’s command in order to maintain the control area’s generation/load balance, is the most difficult ancillary service for loads to provide. Automatic generation control (AGC) commands are typically sent from the system operator to the regulating generators about every four seconds (Figure 6). Regulation is also the most expensive ancillary service so it may be the most attractive service to sell for loads that are capable of supplying it.

Fig. 6. Regulation provides the minute-to-minute balancing of generation and load.

Some loads may have the inherent capability to provide

regulation. Loads that are electronically controlled potentially could follow automatic generation control commands. Loads with large adjustable speed drives or solid state power supplies are candidates. Product quality must be independent of the rate of electricity consumption to allow the power system operator to adjust the load’s consumption. Energy efficiency can be impacted by the rate of electricity consumption. Efficiency reductions simply impact the cost of

15000

17500

20000

22500

25000

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Sys

tem

Lo

ad (

MW

)

22200

22250

22300

22350

22400

8:00 8:15 8:30 8:45 9:00

Regulation

15000

17500

20000

22500

25000

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Sys

tem

Lo

ad (

MW

)

22200

22250

22300

22350

22400

8:00 8:15 8:30 8:45 9:00

Regulation

B. Kirby, Load Response Fundamentally Matches Power SystemReliability Requirements (2007)

5

3. Regulation

Regulation and load following (which, in competitive spot markets, are provided by the intra-hour workings of the real-time energy market) are the two services required to continuously balance generation and load under normal conditions (Kirby and Hirst 2000). Figure 4 shows the morning ramp-up decomposed into base energy, load following, and regulation. Starting at a base energy of 3566 MW, the smooth load following ramp is shown rising to 4035 MW. Regulation consists of the rapid fluctuations in load around the underlying trend, shown here on an expanded scale to the right with a ±55 MW range. Combined, the three elements serve a load that ranges from 3539 to 4079 MW during the three hours depicted. In the PJM region, New York, New England, and Ontario, regulation is a 5-min service, defined as five times the ramp rate in megawatts per minute. In Texas it is a 15-min service, and in Alberta and California it is a 10-min service. Load following and regulation ensure that, under normal operating conditions, a control area is able to balance generation and load. Regulation is the use of on-line generation, storage, or load that is equipped with automatic generation control (AGC) and that can change output quickly (MW/min) to track the moment-to-moment fluctuations in customer loads and to correct for the unintended fluctuations in generation. Regulation helps to maintain interconnection frequency, manage differences between actual and scheduled power flows between control areas, and match generation to load within the control area. Load following is the use of on-line generation, storage, or load equipment to track the intra- and inter-hour changes in customer loads. Regulation and load following characteristics are summarized in Table 2.

3000

3200

3400

3600

3800

4000

4200

7:00 AM 8:00 AM 9:00 AM 10:00 AM

Load

and

Loa

d Fo

llow

ing

(MW

)

-60

-30

0

30

60

90

120

150

Reg

ulat

ion

(MW

)

Regulation

Total Load andLoad Following

Fig. 4. Regulation is a zero-energy service that compensates for minute-to-minute fluctuations in total system load and uncontrolled generation.

B. Kirby, Frequency Regulation Basics and Trends (2004)


Building-to-Grid Integration

Passive thermal storage can buffer varied HVAC operation

Optimize thermal storage operation around energy prices

Buildings can potentially supply ancillary services to the grid:

flexible loads may be more accurate, reliable, and prompt [14].potential benefits [13]:

increased system reliabilityrisk managementreduced environmental emissionsmarket power mitigationincreased system efficiencies

Optimize operations around ancillary services

Which takes priority?

co-optimize participation in energy and ancillary service markets.determine strategies that balance the severity of grid needs with thedesire for lower utility bills


Optimal Control of Building Portfolios

Past work has evaluated optimal building control from an individualperspective.

Buildings are connected to the same grid.

Individual perspective may be suboptimal.

By giving the optimizer the knowledge of all unique buildingcharacteristics available within a portfolio of buildings, variousfeatures may be exploited to orchestrate an optimal combinedoperation of all portfolio members.

Hypothesis:

Diversity among building characteristics and operations creates anopportunity for synergy.


Methodology and Organization

1 Motivation

2 Modeling

3 Multi-Market

4 Multi-Building

5 Portfolio

6 Conclusions


Section 2

Modeling


Introduction to Model Predictive ControlExplicit system model used to predict future plant dynamics

High-level MPC (setpoints) vs. low-level MPC (tracking)

Performance oriented formulation (energy, comfort, emissions)

Incorporation of various constraints (state, action, and plant)

Moving horizon implementation

(Fig. by Gregor Henze)


Building Modeling

Rearrange energy balance to develop zone temperature update:

Tz =

9∑l=2

S0(l)ut(l) +8∑

j=1

Sjut−j∆τ −8∑

j=1

ekQsh,t−j∆τ + 2Cz

∆τTz,t−∆τ

+minf Cput(2) + mSACpTSA

)(2Cz

∆τ− S0(1) + minf Cp + mSACp

)−1


Retail Building

Vintage: 1980

Floor Area: 2294 m2

U-value: 0.418 W/(m2 K)

ACH: 1.01 hour−1

Glazing: 7%

LPD: 32.3 W/m2

EPD: 5.23 W/m2

Occ. Density: 7.11 m2/person

HVAC: CV DX RTU

!!!!!!

ZONE!

SA!

Economizer! DX!Cooling!Coil!

Gas!Hea9ng!Coil!

CV!Fan!

RA!

OA!


Medium Office Building

Vintage: 2001

Floor Area: 14240 m2

U-value: 0.334 W/(m2 K)

ACH: 0.13 hour−1

Glazing: 40%

LPD: 7.16 W/m2

EPD: 4.5 W/m2


HVAC: DX VAV !!!!!!

ZONE!

SA

Air!Mixer!

DX!Cooling!Coil!

Hea5ng!Coil!

VAV!Fan!

RA

DOAS!Supply!Fan!

DOAS!Exhaust!Fan!

OA

Exhaust


Large Office Building

Vintage: 1980

Floor Area: 76659 m2

U-value: 0.339 W/(m2 K)

ACH: 0.17 hour−1

Glazing: 53%

LPD: 9.8 W/m2

EPD: 4.63 W/m2


HVAC: CHW VAV!!!!!!

ZONE!

SA

Economizer! CHW!Cooling!Coil!

VAV!Supply!Fan!

RA

OA!

Cooling!Tower!1!

Cooling!Tower!2!

Chiller!1!

Chiller!2!

CW!Pump!1!

CW!Pump!2!

CHW!Pump!2!

CHW!Pump!1!

Electric!BB!

VAV!Return!Fan!


Retail Model Validation

11p RC network

4760 4780 4800 4820 4840 4860 4880 4900 4920 494016

18

20

22

24

26

28

30

Tem

pera

ture

(C)

Retail Model Validation:Zone Temperature − Precool

setpoint EnergyPlus Simplified

4760 4780 4800 4820 4840 4860 4880 4900 4920 4940

1000

2000

3000

4000

Cumulative Temperature (C)

Time (hours)

Tem

pera

ture

(C)

RMSE = 0.24MBE = −0.04Cum. % Err = −0.19%

EnergyPlusSimplified

4760 4780 4800 4820 4840 4860 4880 4900 4920 49400

20

40

60

80

100

120

En

erg

y (k

Wh

)

Retail Model Validation:HVAC Electric Consumption ! Precool

EnergyPlus Simplified

4760 4780 4800 4820 4840 4860 4880 4900 4920 49400

2000

4000

6000

Cumulative Energy (kWh)

Time (hours)

En

erg

y (k

Wh

)

RMSE = 6.49MBE = !0.04Cum. % Err = !0.11%



Medium Office Model Validation

5p RC network

5260 5280 5300 5320 5340 5360 5380 5400 5420 544020

21

22

23

24

25

26

27

28

Mean A

ir T

em

p (

C)

Medium Office Model Validation:Zone Temperature ! Precool

Setpoint EnergyPlus Simplified

5260 5280 5300 5320 5340 5360 5380 5400 5420 5440

1000

2000

3000

4000

Cumulative Mean Air Temp (C)

Time (hours)

Mean A

ir T

em

p (

C)

RMSE = 0.52MBE = 0.35Cum. % Err = 1.42%


5260 5280 5300 5320 5340 5360 5380 5400 5420 54400

100

200

300

400

500

600

En

erg

y (k

Wh

)

Medium Office Model Validation:HVAC Electric Consumption ! Precool


5260 5280 5300 5320 5340 5360 5380 5400 5420 54400

5000

10000

15000

Cumulative Energy (kWh)

Time (hours)

En

erg

y (k

Wh

)

RMSE = 28.18MBE = 9.77Cum. % Err = 10.81%



Large Office Model Validation

21p RC network

4740 4760 4780 4800 4820 4840 4860 4880 4900 4920 494018

20

22

24

26

28

30

32

Tem

pera

ture

(C)

Large Office Model Validation:Zone Temperature − Precool

setpoint EnergyPlus Simplified

4740 4760 4780 4800 4820 4840 4860 4880 4900 4920 4940

1000

2000

3000

4000

5000

Cumulative Temperature (C)

Time (hours)

Tem

pera

ture

(C)

RMSE = 0.39MBE = 0.04Cum. % Err = 0.14%


4740 4760 4780 4800 4820 4840 4860 4880 4900 4920 49400

500

1000

1500

2000

2500

3000

3500

4000

Elec

tric

Con

sum

ptio

n (k

Wh)

Large Office Model Validation:HVAC Electric Consumption − Precool


4740 4760 4780 4800 4820 4840 4860 4880 4900 4920 49400

0.5

1

1.5

2x 105 Cumulative Electric Consumption (kWh)

Time (hours)Elec

tric

Con

sum

ptio

n (k

Wh)

RMSE = 267.54MBE = −14.50Cum. % Err = −1.43%



Section 3

Multi-Market


Background

Well established for traditional generation resources

Typically linear models solved with DP or MILP [5, 9, 2]

Vehicle-to-grid optimal charging algorithms [17]

Linear models solved using LP

Frequency regulation in commercial buildings:

Zhao et al. FR via duct SP and zone setpoint modulation [19]Hao et al. FR via direct fan speed modulation [12]

Challenges of multi-market scheduling in buildings

occupant thermal, visual, and IAQ constraintspassive thermal storage vs. direct electricHVAC system capacity constraints and staging

Whole-building energy model perturbation approach proposed


FR Estimation

An illustrative example: How much FR at 13:00?

Baseline setpoint of 23 ◦C

Fans 50%, DX Coil: On-On-Cyc-Off, 85% Occupied

22.0

22.5

23.0

23.5

24.0

Zone

Tem

pera

ture

[°C

]

350

400

450

500

550

600

06:00 09:00 12:00 15:00 18:00

Faci

lity

Elec

tric

[kW

]


FR Estimation

●●

●●−100

−50

0

50

100

22.5 23.0 23.5Zone Temperature [°C]

Δ P

ower

[kW

]

CoolingStagesON● 2

34

Group power response based on number of active cooling stages.

Max: ±100 kW, 22.3 ◦C to 23.8 ◦C


FR Estimation

Occupied Tmax

Occupied Tmin

Reg. DownReg. Up

22

23

24

25

26

27Zo

ne T

empe

ratu

re [°

C]

Baseline Setpoint Temp. Limits Zone Temp.

Regulating Band

Reg. Down

Reg. Up−50

0

50

06:00 09:00 12:00 15:00 18:00

Δ P

ower

[kW

] Regulating Band

Repeat perturbation for desired hours (9am to 4pm in this example).


Multi-Market Optimization

Problem:min J (~x) s.t.: ~x ∈ [~xmin, ~xmax ]

Cost function:

J (~x) = Ecost + Pdemand − Rreg

Energy cost:

Ecost =

tCH∑t=1

rDA (t)Euse (t)

Demand penalty:

Pdemand = max [M (max (ElecDemandpeak)− TDL) , 0]

Regulation revenue:

Rreg =

tCH∑t=1

∆power (t) rreg (t)


Multi-Market Optimization Overview

simulate(~x + �1) . . . simulate(~x + �n)

simulate(~x)

objfun

optimizer

J (~x)

~xPdemand

Energy

�power

FR estimation

chiller(~x + �1, X) . . . chiller(~x + �n, X)

simulate(~x)

objfun

optimizer

J (~x)

~x

Pdemand

X

Energy

�power

FR estimation

1

Perturbations limited to those that do not increase the demand penalty.


Medium Office ResultsLow Target Demand Limit

●

●

●

●

●

●

●

●

●

●●

●

●

●●

● ● ●

●● ●

Occupied Tmax

Occupied Tmin

On−Peak:

$5.50/kWdemand penalty

for all kW's> 325 kW

22

24

26Zo

ne T

empe

ratu

re [°

C]

NSU Set.NSU Temp.OPT+FR Set.OPT+FR Temp. OPT+FR ● OPT Temp.

●

● ●

●● ●

●

●

●

●

●

●

●● ●

● ● ●

●●

●

0

100

200

300

400

500

Elec

tric

Dem

and

[kW

]

NSU Power OPT+FR Power ● OPT Power OPT+FR

050

100150200

03:00 06:00 09:00 12:00 15:00 18:00

Pric

e [$

/MW

h] DA Energy Regulation

Available 5/9 hours

±8 kW to ±60 kW

$12 reg. revenue

38% → 39.5%


Medium Office ResultsLow Target Demand Limit

Summary:

Available 5/9 hours

±8 kW to ±60 kW

$12 reg. revenue

38% → 39.5%

●

●

●

●

●

●

●

●

●

●●

●

●

●●

● ● ●

●● ●

Occupied Tmax

Occupied Tmin

On−Peak:

$5.50/kWdemand penalty

for all kW's> 325 kW

22

24

26

Zone

Tem

pera

ture

[°C

]

NSU Set.NSU Temp.OPT+FR Set.OPT+FR Temp. OPT+FR ● OPT Temp.

●

● ●

●● ●

●

●

●

●

●

●

●● ●

● ● ●

●●

●

0

100

200

300

400

500

Elec

tric

Dem

and

[kW

]


050

100150200

03:00 06:00 09:00 12:00 15:00 18:00

Pric

e [$

/MW

h] DA Energy Regulation


Medium Office ResultsHigh Target Demand Limit

●●

●

●

●

●

● ●●

●

●

●● ● ● ● ● ●

●

● ●

Occupied Tmax

Occupied Tmin22

23

24

25

26

27

Zone

Tem

pera

ture

[°C]

OPT+FR ● OPT Temp. NSU SetpointOPT+FR Set.OPT+FR Temp.

● ●

● ●

● ●●

●

●●

●

● ●●

●● ● ●

●●

●

0

100

200

300

400

500

Elec

tric

Dem

and

[kW

]


050

100150200

03:00 06:00 09:00 12:00 15:00 18:00

Price

[$/M

Wh] DA Energy Regulation

Available 8/9 hours

277 kWh > OPT

$23 > energy expense

±85 kW on average

$56 reg. revenue

2.3% → 15.7%


Medium Office ResultsHigh Target Demand Limit

Summary:

Available 8/9 hours

277 kWh > OPT

$23 > energy expense

±85 kW on average

$56 reg. revenue

2.3% → 15.7%

●●

●

●

●

●

● ●●

●

●

●● ● ● ● ● ●

●

● ●

Occupied Tmax

Occupied Tmin22

23

24

25

26

27

Zone

Tem

pera

ture

[°C]

OPT+FR ● OPT Temp. NSU SetpointOPT+FR Set.OPT+FR Temp.

● ●

● ●

● ●●

●

●●

●

● ●●

●● ● ●

●●

●

0

100

200

300

400

500

Elec

tric

Dem

and

[kW

]


050

100150200

03:00 06:00 09:00 12:00 15:00 18:00

Price

[$/M

Wh] DA Energy Regulation


Chiller Power Response

CHW Supply Temp (C)

Qev

ap/Q

rate

d

4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P/Pr

ated

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

Condenser water temperature is a constant 26.7 ◦C.

State 1 State 2 State 3Tchw,supply 7 ◦C 6 ◦C 6 ◦CTchw,return 9.1 ◦C 9.1 ◦C 8.1 ◦CCOP 3.18 3.52 3.13



X = 7Y = 0.3069Z = 0.425

CHW Supply Temp (C)

Qev

ap/Q

rate

d

4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P/Pr

ated

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1




X = 7Y = 0.3069Z = 0.425

X = 6Y = 0.4531Z = 0.5661

CHW Supply Temp (C)

Qev

ap/Q

rate

d

4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P/Pr

ated

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1

2




CHW Supply Temp (C)

Qev

ap/Q

rate

d

X = 7Y = 0.3069Z = 0.425

X = 6Y = 0.4531Z = 0.5661

X = 6Y = 0.3069Z = 0.4315

4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

P/Pr

ated

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

13

2



Section 4

Multi-Building


Multi-Building Optimization

Need to extend MPC environment originally developed by Corbin et al. [8]

Multi-building is a generalization of single building problem.

1 Initialization

2 Optimization

3 Execution


Multi-Building OptimizationInitialization

(1) load(globalParams)(2) for n = 1 to N(3)

(4) load(localParams(n));(5) load(weather(n));(6) load(utilityData(n));(7) load(Model(n));(8) warmUp(Model(n));(9)

(10) end

1

Bldg 1

Bldg 2

Bldg N

...

1


Multi-Building OptimizationOptimization: Objective Function

Problem:

min J (~x) s.t.: ~x ∈ [~xmin, ~xmax ]

Cost function:

J (~x) = Ecost + Pdemand − Rreg

Control vector ~x now contains decision variables for all buildings

~x = [~xB1,S1, . . . , ~xBN,Sj ]

~xB1,S1 is a vector in time of control variables for Building 1, Schedule 1


Multi-Building OptimizationOptimization: Overview

complete?

J (~x)

~x

[~xB1,S1, . . . , ~xBN,Sj ]

[~xB1,S1, . . . , ~xB1,Sh] [~xB2,S1, . . . , ~xB2,Si] [~xBN,S1, . . . , ~xBN,Sj ]

Bldg 1 Bldg 2 Bldg N. . .

. . .

. . .ROM E+ ROM

FRFR

out1 (~x, �) out2 (~x, �) outN (~x)

Determine Optimal FR Calc. Demand Penalty Calc. Cost

Objective FunctionRreg + Pdemand + Ecost

OPT+FR 1

OPT+FR 2

OPT N

no yes ...

1


Multi-Building OptimizationOptimization: Overview

complete?

J (~x)

~x

[~xB1,S1, . . . , ~xBN,Sj ]

[~xB1,S1, . . . , ~xB1,Sh] [~xB2,S1, . . . , ~xB2,Si] [~xBN,S1, . . . , ~xBN,Sj ]

Bldg 1 Bldg 2 Bldg N. . .

. . .

. . .ROM E+ ROM

FRFR

out1 (~x, �) out2 (~x, �) outN (~x)

Determine Optimal FR Calc. Demand Penalty Calc. Cost

Objective FunctionRreg + Pdemand + Ecost

OPT+FR 1

OPT+FR 2

OPT N

no yes ...

1


Section 5

Portfolio


Portfolio

Target Demand Limit

Scenario Rs Ls (RL)p Units

a 16 419 3013 19 432 kWb 16 826 2606 19 432 kWc 17 227 2205 19 432 kW

TDL

Min. peak achievable

5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL

Minimum peak achievable

Max. NSU

18500

19000

19500

20000

20500

a b cScenario

1


Scenario a: RetailNo Frequency Regulation

Occupied Tmax

Occupied Tmin

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

●●

●●

● ● ●●

● ● ● ● ● ● ● ● ● ● ● ● ●●

●●

On−Peak

15

20

25

30

Zone

Tem

pera

ture

[°C

]

● ● ● ● ● ● ●

●

●

●● ●

●● ● ● ● ●

●

●

●

●

● ●● ● ● ● ● ● ●

●

●

●

● ●

●

●●

●●

●

●

●

●

●

● ●

TDL

0

5000

10000

15000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]

● ●NSU Rp NSU Rs OPT Rp OPT Rs

Rp

Rs

Rp

Rs

$0 $5,000 $10,000Energy Cost [$]

Sim

ulat

ion

Stat

s

0 100 200Energy Use [MWh]

NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Scenario a: Large OfficeNo Frequency Regulation

Occupied Tmax

Occupied Tmin

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ●

● ●● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ●

On−Peak

22

24

26

28

30

Zone

Tem

pera

ture

[°C

]

TDL

● ● ● ● ● ●

●

●

● ● ● ●●

● ● ●● ●

● ● ●● ● ●

●● ● ● ● ●

●

●

● ●● ●

●

●●

●

● ●

● ●●

● ● ●

0

1000

2000

3000

4000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]

● ●NSU Lp NSU Ls OPT Lp OPT Ls

Lp

Ls

Lp

Ls

$0 $900 $1,800Energy Cost [$]

Sim

ulat

ion

Stat

s

0 10 20 30 40 50Energy Use [MWh]

NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Scenario a: PortfolioNo Frequency Regulation

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c

198.29 42.86

198.29 42.86

198.29 45.14

254.64 40.53

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

0%

0%

19.47%

26.98%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0% 10% 20% 30%Percent Savings [%]

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Net Dem. Pen.Ecost


Scenario a: PortfolioNo Frequency Regulation

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c

198.29 42.86

198.29 42.86

198.29 45.14

254.64 40.53

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

0%

0%

19.47%

26.98%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Net Dem. Pen.Ecost

!!


Portfolio: RetailNo Frequency Regulation

Occupied Tmax

Occupied Tmin

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

●●

●●

● ● ●●

● ● ● ● ● ● ● ● ● ● ● ● ●●

●●

On−Peak

15

20

25

30

Zone

Tem

pera

ture

[°C

]

● ● ● ● ● ● ●

●

●

●● ●

●● ● ● ● ●

●

●

●

●

● ●● ● ● ● ● ● ●

●

●

●

● ●

●

●●

●●

●

●

●

●

●

● ●

TDL

0

5000

10000

15000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Rp

Rs

Rp

Rs

$0 $5,000 $10,000Energy Cost [$]

Sim

ulat

ion

Stat

s


NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



Occupied Tmax

Occupied Tmin

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

●●

●●

● ● ●●

● ● ● ● ● ● ● ● ● ● ● ● ●●

●●

On−Peak

15

20

25

30

Zone

Tem

pera

ture

[°C

]

● ● ● ● ● ● ●

●

●

●● ●

●● ● ● ● ●

●

●

●

●

● ●● ● ● ● ● ● ●

●

●

●

● ●

●

●●

●●

●

●

●

●

●

● ●

TDL

0

5000

10000

15000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Rp

Rs

Rp

Rs

$0 $5,000 $10,000Energy Cost [$]

Sim

ulat

ion

Stat

s


NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



Occupied Tmax

Occupied Tmin

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

●●

●●

● ● ●●

● ● ● ● ● ● ● ● ● ● ● ● ●●

●●

On−Peak

15

20

25

30

Zone

Tem

pera

ture

[°C

]

● ● ● ● ● ● ●

●

●

●● ●

●● ● ● ● ●

●

●

●

●

● ●● ● ● ● ● ● ●

●

●

●

● ●

●

●●

●●

●

●

●

●

●

● ●

TDL

0

5000

10000

15000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Rp

Rs

Rp

Rs

$0 $5,000 $10,000Energy Cost [$]

Sim

ulat

ion

Stat

s


NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Portfolio: Large OfficeNo Frequency Regulation

Occupied Tmax

Occupied Tmin

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ●

● ●● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ●

On−Peak

22

24

26

28

30

Zone

Tem

pera

ture

[°C

]

TDL

● ● ● ● ● ●

●

●

● ● ● ●●

● ● ●● ●

● ● ●● ● ●

●● ● ● ● ●

●

●

● ●● ●

●

●●

●

● ●

● ●●

● ● ●

0

1000

2000

3000

4000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Lp

Ls

Lp

Ls

$0 $900 $1,800Energy Cost [$]

Sim

ulat

ion

Stat

s

0 10 20 30 40 50Energy Use [MWh]

NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



Occupied Tmax

Occupied Tmin

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ●

● ●● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ●

On−Peak

22

24

26

28

30

Zone

Tem

pera

ture

[°C

]

TDL

● ● ● ● ● ●

●

●

● ● ● ●●

● ● ●● ●

● ● ●● ● ●

●● ● ● ● ●

●

●

● ●● ●

●

●●

●

● ●

● ●●

● ● ●

0

1000

2000

3000

4000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Lp

Ls

Lp

Ls

$0 $900 $1,800Energy Cost [$]

Sim

ulat

ion

Stat

s

0 10 20 30 40 50Energy Use [MWh]

NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



Occupied Tmax

Occupied Tmin

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ●

● ●● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ●

On−Peak

22

24

26

28

30

Zone

Tem

pera

ture

[°C

]

TDL

● ● ● ● ● ●

●

●

● ● ● ●●

● ● ●● ●

● ● ●● ● ●

●● ● ● ● ●

●

●

● ●● ●

●

●●

●

● ●

● ●●

● ● ●

0

1000

2000

3000

4000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Con

sum

ptio

n [k

Wh]


Lp

Ls

Lp

Ls

$0 $900 $1,800Energy Cost [$]

Sim

ulat

ion

Stat

s

0 10 20 30 40 50Energy Use [MWh]

NSU

OPT

$0Reg. Rev. [$]

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Portfolio ResultsNo Frequency Regulation

198.29 42.86

198.29 42.86

198.29 45.14

254.64 40.53

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

0%

0%

19.47%

26.98%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Net Dem. Pen.Ecost

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



198.29 42.86

198.29 42.86

198.29 45.14

213.66 41.46

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

0%

0%

24.76%

26.98%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Net Dem. Pen.Ecost

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



198.29 42.86

198.29 42.86

198.29 45.14

198.29 49.58

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

0%

0%

26.54%

26.98%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Net Dem. Pen.Ecost

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Portfolio ResultsWith Frequency Regulation

198.29 42.86

198.29 42.86

198.29 46.72

254.64 44.62

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

1.21%

1.6%

*Relative to NSU w/o FR*

21.15%

28.44%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]

Dem. Pen.EcostReg. Rev.

Net

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



198.29 42.86

198.29 42.86

198.29 46.72

213.66 45.6

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

1.21%

1.6%


26.2%

28.44%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]


Net

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c



198.29 42.86

198.29 42.86

198.29 46.72

198.29 49.72

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

0 100 200 300Energy [MWh]

LR

1.21%

1.6%


27.83%

28.44%

(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT


(RL)p

Rs+Ls

(RL)p

Rs+Ls

NSU

OPT

$0 $5,000 $10,000 $15,000 $20,000J(x) [$]


Net

TDL


5000

10000

15000

20000

03:00 06:00 09:00 12:00 15:00 18:00 21:00

Elec

tric

Dem

and

[kW

]

NSU

120R

120R

L

L

TDL


Max. NSU

18500

19000

19500

20000

20500

a b cScenario

a b c


Portfolio SummaryIn general, retail buildings were less efficient at load shifting

21% less thermal capacitance23% higher average construction U-value6x greater ACH2.6x internal gains

Energy savings over aggregated individual solutions:Scenario a: 17.5%Scenario b: 5%Scenario c: 1.6%

Percent savings summary for Portfolio:

No FR FRScenario Rs+Ls (RL)p Diff. Rs+Ls (RL)p Diff.

a -19.47% -26.98% 7.51 -21.15% -28.44% 7.29b -24.76% -26.98% 2.22 -26.20% -28.44% 2.24c -26.54% -26.98% 0.44 -27.83% -28.44% 0.60


Section 6

Conclusions


got synergy?

What was the nature of observed synergistic effect?

1 Synergy was dependent on individual building opt. conditions

2 Synergy was dependent on portfolio construction

3 Synergy was dependent on grid market design (i.e. demand charge)


Contributions to Building-to-Grid Integration

1 Model-based method for estimating commercial building frequencyregulation capability

2 Thermal mass optimization considering ancillary service revenueopportunities along with energy price and demand

3 Centralized building portfolio optimization approach

4 Identified several opportunities for synergy among portfolios


Thank you.

Questions?


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