PROJECT: SUSTAINABLE

INFRASTRUCTURE FOR ENERGY

AND WATER SUPPLY (SINEWS)

PI: John C. Crittenden

Presented by: Arka Pandit

E-Mail: Arka.Pandit@gatech.edu

Participants in the RESIN Project

Arizona State

University

George G. Karady

Samuel Ariaratnam

Charles Perrings

Ke Li

John C. Crittenden

(PI)

Georgia Institute of Technology

Bert Bras

Doglas Noonan

Miroslav M. Begovic

Marilyn Brown

Steven P French

Outline

• Infrastructure Ecology

• Decentralized Water Resource Development: Low Impact

Development (LID)

• Decentralized Energy Production:

• Combined Heat and Power (CHP)

• Renewable Energy

• Policies for Adoption of Rain Water Harvesting and Compact

Living

• Large Scale Urban Development Simulation: Water Savings

and GHG Emission Reductions from LID and CHP

• Implications for Decision Making

• Summary

INFRASTRUCTURE

ECOLOGY

City

People

Economy

Transportation

Energy

Water

Waste

Buildings

Parks

Government

And many

more…

Industry

5

Q: With the next

generation of

infrastructure, what

are the implications

if we design, build,

and operate these

systems separately,

as we have done in

the past?

Interdependence of Different

Infrastructure Components

Land Use Energy

Transportation

Land Use Intensity & Type

Availability

Fuel Choice

Interconnection between Urban Infrastructure

System , Natural Environmental Systems and

Socio-Economic Systems

Incre

asin

g S

pati

ote

mp

ora

l S

cale

Why Infrastructure Ecology?

Analogies between UIS and Ecological Systems:

• are complex, dynamic and adaptive;

• are comprised of interconnected components;

• connect the natural and human environments;

• are scalable and show efficiency of scale;

• share some general architectural dynamics across time and space;

• create novelty; and

• cannot be evaluated by looking at any component element, but instead must

be examined as a system.

Problems with current paradigm of Urban Infrastructure Planning:

1. Compartmental Optimization resulting in:

1. Unintended Consequences

2. Sub-optimal System Level Performance

3. Non inclusion of macro-level emergent properties

The Synergistic Effects of

“Infrastructural Symbiosis” • Designing UIS using an infrastructure ecology approach alters and

reorganizes energy and resource flows, allowing one to consider the

potential synergistic effects arising from infrastructural symbiosis.

• The accumulated synergistic

effects of this particular model of

infrastructure ecology is significant:

• reduced water and energy

consumption,

• lower dependence on centralized

systems,

• larger share of renewables in the

electricity mix,

• reduced vehicle-miles travelled, &

• an increase in tax revenue.

DECENTRALIZED WATER

RESOURCE DEVELOPMENT:

LOW IMPACT DEVELOPMENT

Implementation of Low Impact Development

Techniques: Case Study of Atlanta, GA

• Benefits:

• For a 3-story apartment community,

• 40% reduction of potable water demand, about 12,000 Gal/cap/yr

• 37% reduction in impervious area

• 50% reduction in irrigated area

• 20% reduction of Life Cycle Impacts

Rain Barrel Rain Garden Pervious Space

Citywide implementation of Hybrid (LID + Centralized) System would save the city

~$1.2million per year in energy costs.

Rain gardens occupying 11% - 16% of community size can control 100% of stormwater

runoff generated in extreme rainfall events up to 8 in/24-hr.

DECENTRALIZED ENERGY

PRODUCTION: COMBINED

HEAT AND POWER (CHP)

Number of buildings= (Thermal output at max efficiency/Peak

thermal load)monthly

Summary: Building Energy Requirements

met by Air-Cooled Microturbines

R4

(12 Single

Family

homes)

RG4

(2 Apartment

buildings)

Heat

Cooling

Electricity

Thermal Load

Design Criteria

Electrical Load

Required

Excess

346.5 MWh

(100%)

106 MWh

(23%)

540 MWh

(100%)

360 MWh

(40%)

Required 778 MWh

(66%)

477 MWh

(54%)

Building Types

The design criteria for:

• R4 buildings is based on the usage of a 30kW microturbine

• RG4 buildings is based on the usage of a 60 kW microturbine

Summary of building energy requirements met

by CHP

30kW

MT

Electricity:

477MWh

(54%)

Thermal:

452.5 MWh

(123%)

60kW

MT

Electricity: 778

MWh (66%) Thermal: 900

MWh (140%)

Grid

Energy

Electricity:

218MWh (46%)

Electricity:

260MWh (34%) RG4: 2 6-story

apartment

buildings

R4: 12 Single

Family homes

Thermal load includes heating and cooling demand

437600 Gal 985300 Gal Water for

energy

savings

DECENTRALIZED ENERGY

PRODUCTION: RENEWABLE

ENERGY

NO-PV

40% PV

Simulation Results in Hourly

Resolution

The large variation in PV output

demands a higher capacity of

spinning reserves

Plug-in Hybrid Electric Vehicles (PHEVs) and Vehicle-to-Grid (V2G) power

Credit: Kempton and Tomić, 2005

PHEVs can send power back to the grid when parked, and function as distributed

storage for intermittent energy from renewable sources

US demand-supply balances during

maximum demand with various V2G

ratios in 2045

30% V2G penetration could reduce ~100 GW or about ⅓ of the total peak

demand of ~300 GW in US by 2045

Source: Modelling Load Shifting Using Electric Vehicles in a Smart Grid Environment – © OECD/IEA 2010

POLICIES FOR ADOPTION OF

RAIN WATER HARVESTING

AND COMPACT LIVING

Interactions between Infrastructure and

Socio-Economic Environment

Macro

Micro

Infrastructure

Systems

Water

Energy

Heating/

Cooling

Transportation

Socio-

Economic

Environment

Business

Neighborhood

Housing

Jobs

Emergent Properties: land use, water, energy and material

consumption, transportation quality, quality of life, and other

sustainability metrics.

Decisions, behaviors of Individual Agents and the relationship

of individual agents: location choice, traffic activity, and

willingness to pay

Housing Market

House inventory: apartment, single-family

Infrastructure service Stormwater management(1st yr)

Transportation improvement (5th yr)

Prospective homebuyers

• Socioeconomic attributes

• Preference 1. Evaluate candidate houses

2. Decide the biding house

3. Determine willingness to pay

Homeowners

Property value

Living community Green space

Transportation accessibility

Developers

• Asking price

• New house

investment decision

• Consider LID

options if impact fee

exists

Apartment

vs. Single-family Transaction price

Estate sale

Government • Collect property tax

• Distribute property tax for

infrastructure improvement

Bid

Price

Bid

Success

Asking Price

New houses

Impact

fee ?

House demand

for next period

Infrastructure

improvement

Agent-based Housing Market Simulation

Property

Tax

Land Use Pattern Between Business As Usual (BAU) and

More Sustainable Development (MSD)

: Single Family Homes : Apartment Homes

The total area for new development: 9 sq mi

More Sustainable Development

Business As Usual

Time: Year

Ho

us

eh

old

Nu

mb

er

Ho

us

eh

old

Nu

mb

er

0

5000

10000

15000

20000

25000

0 5 10 15 20 25 30

Total households

Households living insingle-family houses

Households living inapartments

0

5000

10000

15000

20000

25000

0 5 10 15 20 25 30

Sc

en

ari

os

Total

households

% of

households

living in

apartment

BAU 23,630 35.0%

MSD 24,475 58.0%

Impact fee for LID

non-compliance

penalty:

• $13,000 per unit

for single-family

house

• $1,500 per unit for

apartment home

Property Tax Revenues • BAU versus MSD

• Accumulation of property tax revenues for 30 years

• Surplus at time t = (property tax revenues at time t) – (new construction

cost for stormwater management at time t) + (Surplus at time t-1)

• Property tax revenues at time t = (house value at time t) × 0.4 × 0.01 ×

(number of households)

-$100

-$50

$0

$50

$100

$150

$200

$250

$300

$350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Mil

lio

ns

Time: Year

BAU

MSD

URBAN DEVELOPMENT

SIMULATION AND LARGE SCALE

WATER SAVINGS CARBON

EMISSION REDUCTIONS FROM

LID AND CHP

Projected Growth Scenarios for Atlanta Business As Usual

Year 2030

More Sustainable Development

Year 2030

59,531

90,116

86,444

22,906

34,741 33,110

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

Base year BAU MSD

2005 2030

GW

h

Energy from grid

Energy from grid w/ CHP

ATL Residential Energy Demand from Grid (with Air Cooled Microturbines in a Decentralized CHP system)

62% reduction

62% reduction

BAU= Business As Usual MSD= More Sustainable Development CHP: Combined Heat and Power

# of Households: Single Family: 1170283

Apartments: 458207

Single Family: 1783935

Apartments: 649907

Single Family: 1671596

Apartments: 763019

61% reduction

Note: The 2030 grid+CHP scenarios assumed all residential units in the base year were retrofitted with CHP systems

3198

1541

4874

2370

4637

2203

0

1000

2000

3000

4000

5000

6000

grid grid + CHP grid grid + CHP grid grid + CHP

Base year BAU MSD

2005 2030

MG

D (

10

6 g

all

on

pe

r d

ay)

Municipal water demand 'Water for energy' demand for residential energy production

ATL Water Demand Projection (Withdrawal) (with low flow fixtures + rooftop rainwater harvesting + CHP)

52% reduction 51% reduction

52% reduction

Note: The 2030 grid + CHP scenarios assumed all residential units in the base year were retrofitted with CHP

systems

370

207

569

323

496

259

0

100

200

300

400

500

600

grid grid + CHP grid grid + CHP grid grid + CHP

Base year BAU MSD

2005 2030

MG

D (

10

6 g

all

on

pe

r d

ay)

Industrial and domestic water consumption

Water consumption for residential energy production

ATL Water Consumption Projection (Evaporation) (with low flow fixtures + rooftop rainwater harvesting + CHP)

48% reduction 43% reduction

(Courtesy: Crittenden et al., Georgia Tech)

44% reduction

Note: The 2030 grid + CHP scenarios assumed all residential units in the base year were retrofitted with CHP

systems

149

164 163

89 99 98

0

20

40

60

80

100

120

140

160

180

Base year BAU MSD

2005 2030

Mil

lio

n T

on

s o

f C

O2e

w/o CHP

w/ CHP

Potential GHG reductions in 2030

• The reduction is based on the electricity produced by CHP replacing that from the centralized

power plant.

• Base year case assumes that all existing homes are retrofitted to facilitate a CHP system.

• The 2030 grid+CHP scenarios assumed residential units in the base year were also

retrofitted with CHP systems

• For MSD implementation the CO2e Savings is 0.065 Gt

~ 40% Reduction in all cases

Projected Domestic Water Demand (with low flow fixture + rooftop rainwater harvesting + reclamation)

507

800

729 707

102

163 108 108

0

100

200

300

400

500

600

700

800

900

2005 2030 2030(w/ low flow

fixture)

2030(w/ low flow

fixture +rooftop

rainwaterharvesting)

2005 2030 2030(w/ low flow

fixture)

2030(w/ low flow

fixture +rooftop

rainwaterharvesting)

Base year BAU MSD Base year BAU MSD

Water withdrawal Water consumption

MG

D (

10

6 G

all

on

pe

r d

ay)

57%

11.6% 8.8%

60 % 34 %

Water Withdrawal Water Consumption

328 65%

520 65%

456 63%

443 63%

Reclamation

IMPLICATIONS FOR

DECISION MAKING

Dimensions of Decision Making S

ho

rt

Local

Lo

ng

Global

Te

mp

ora

l S

ca

le

Spatial Scale

Adaptive

Management

Traditional

Engineering

Optimization

Sustainable and

Resilient Design

Sustainable and

Resilient Design

Sustainable and Resilient Design and

Adaptive Management

Sustainable and Resilient

Design

• Technological Intervention

• Increase the capacity of the

system to absorb shocks

from a perturbation and

minimize the damage.

• Decrease the response time

in the aftermath of a

perturbation

• Smart infrastructure:

wireless sensors to

communicate failure events

Adaptive Management

• Policy Intervention • To make the Urban

Infrastructure Systems more flexible and adaptable to changing scenarios.

• To increase and promote adoption of more “sustainable and resilient” alternatives (both technological and behavioral) across the urban System through policy tools.

SUMMARY

Summary

• Urban Systems Are All Connected and More Efficiency

Can be Achieved by Looking at Their Interactions

• Decentralized Energy and Combined Heat and Power

Can Save Energy and Water

• Decentralized Water / Low Impact Development Can

Save Water, Energy and Money

• Land Use/ Planning Is Vital in Reducing the Impact Of

Urban Systems and Examining Their Interactions

• Agent Based Models May Be Useful to Examine the

Adoption Rate of Policy Instruments

THANK YOU!!