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Atlanta

Business-as-usual development

development Pagegrowth Environmental 1 of 27 More compact Science & Technology

Air Emissions

CCHP in new and existing buildings w/net metering

40

NOX (103 tonnes)

30

-50% 20 -90%

106 gallons per day 400 Water-for-energy

2005-2030

CO2 (106 tonnes)

300

-93%

200

ACS Paragon Plus Environment 10 0

100 0

Environmental Science & Technology

1

Impacts of Combined Cooling, Heating and Power Systems and

2

Rainwater Harvesting on Water Demand, Carbon Dioxide and NOx

3

Emissions for Atlanta

4 Authors: Jean-Ann James†*, Sangwoo Sung±, Hyunju Jeongͼ, Osvaldo A. Broesicke†, Steve P. French‡, Duo Li§, John C. Crittenden†§

5 6 7

†

8

Georgia Institute of Technology, Atlanta, Georgia 30332, United States

9

±

Brook Byers Institute for Sustainable Systems, School of Civil and Environmental Engineering,

The Department of Geography, Planning and Environment, East Carolina University,

10

Greenville, North Carolina, 27858, United States

11

ͼ College

12

‡

13

30332, United States

14

§

15

Chaoyang District, Beijing 100102, China

16

*Corresponding author ([email protected], T: 404-894-7895, Address: 828 West Peachtree Street, Suite 320,

17

Atlanta, Georgia 30332, USA)

18

Abstract

19

The purpose of this study is to explore the potential water, CO2 and NOx emission, and cost

20

savings that the deployment of decentralized water and energy technologies within two urban

21

growth scenarios can achieve. We assess the effectiveness of urban growth, technological, and

22

political strategies to reduce these burdens in the 13-county Atlanta metropolitan region. The

of Engineering, Arkansas State University, Jonesboro Arkansas 72467, United States

School of City and Regional Planning, Georgia Institute of Technology, Atlanta, Georgia

Crittenden and Associates, C-305, Building E, Wangjing High-Tech Park, Lizezhong Er Road,

ACS Paragon Plus Environment

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urban growth between 2005 and 2030 was modeled for a business as usual (BAU) scenario and a

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more compact growth (MCG) scenario. We considered combined cooling, heating and power

25

(CCHP) systems using microturbines for our decentralized energy technology and rooftop

26

rainwater harvesting and low flow fixtures for the decentralized water technologies.

27

Decentralized water and energy technologies had more of an impact in reducing the CO2 and

28

NOx emissions and water withdrawal and consumption than an MCG growth scenario (which

29

does not consider energy for transit). Decentralized energy can reduce the CO2 and NOx

30

emissions by 8% and 63%, respectively. Decentralized energy and water technologies can reduce

31

the water withdrawal and consumption in the MCG scenario by 49% and 50% respectively.

32

Installing CCHP systems on both the existing and new building stocks with a net metering policy

33

could reduce the CO2, NOx, and water consumption by 50%, 90%, and 75% respectively.

34

Introduction

35

By 2030 cities are expected to house 90% of the United States’ population.1 To accommodate

36

this growth requires 427 billion square feet of building infrastructure2 and heavy investment into

37

water and energy infrastructure. Both water and energy, the two main growth-limiting resources

38

of an urban region, are highly interdependent.3 Water collection and treatment requires energy

39

and most energy-generation facilities require water for system operation and raw fuel processing.

40

Subsequently, both the amount of water needed for energy generation and the energy needed to

41

treat water will increase with population growth. Half of the total water withdrawal of the US in

42

2005 was used for energy generation, while 4% of the electricity generated nationally was used

43

to treat and distribute water.3,4 To meet the additional water demand, the energy required by

44

municipal plants to treat surface water will increase by an estimated 5-10%.3 Therefore, it is



45

important to consider the interactions between water and energy when determining how to meet

46

the future demand of these resources.

47

As population increases, regions are prone to urban sprawl, which leads to water and

48

energy losses that are amplified by inefficiencies in the treatment, generation, and distribution

49

systems..5 In 2005 leaks in the water distribution system led to daily potable water losses of

50

approximately 16% (7 billion gallons) of total potable water supply.4,6 The aging and

51

deteriorating electrical infrastructure results in energy losses within the system.6 In 2011

52

approximately 40% of the primary energy use in the US was for electricity generation.7 From the

53

total US electricity generated, major losses manifested as heat (67%) as well as transmission and

54

distribution inefficiencies (6.5%).8 Losses in the electrical distribution system result in

55

approximately 0.13 gallons of water loss per kWh from the average U.S. power production

56

plant.9

57

Compact growth strategies may help mitigate many of the negative effects of urban

58

sprawl since they promote the development of walkable, transit-oriented, and mixed-used

59

neighborhoods.10,11 The decreased urban and automobile footprint improves air and water

60

quality, reduces emissions, and conserves open space,11 thereby reducing linked health risks12,13

61

and improving the quality of life. Compact growth also enables decentralized water and energy

62

technology deployment, such as low-impact development (LID) and combined cooling, heating

63

and power (CCHP), to meet user needs and reduce system inefficiencies. Additionally,

64

decentralizing these infrastructures may help cities curtail water consumption and emissions

65

(CO2 and NOx).

66

In this study we examine the potential that decentralized water and energy technologies

67

have in reducing water consumption, emissions – specifically carbon dioxide (CO2) and nitrous


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oxides (NOx) – and costs under two urban growth scenarios: (1) business as usual (BAU) and (2)

69

more compact growth (MCG). Accordingly, we also compare the effectiveness of two policy

70

strategies for increasing the sustainability of growing cities. The study scope is the 13-county

71

Atlanta metropolitan region, one of the fastest growing urban regions in the US.14 The impacts of

72

transportation changes were not included in this study. By coupling models for energy

73

generation, water management, and land-use projections, this study also serves as a proof-of-

74

concept for model integration to examine the dynamics and complexity of urban systems.

75

Methods

76

We modeled the water and energy consumption for BAU15 and MCG for the 13-county Atlanta

77

metropolitan region.16 We then considered changes to the regional water and energy

78

consumption if decentralized water and energy systems and a net metering energy policy were

79

implemented. A spatial growth model was used to predict the potential residential and

80

commercial growth between 2005 and 2030 under the two urban growth scenarios, BAU and

81

MCG. The energy demand for five prototype buildings (3-commercial, 2-residential) in the

82

Atlanta metropolitan region was obtained from the Open Energy Information (OpenEI)

83

database.17 Based on the heating, cooling and hot water energy demands of a building we sized

84

CCHP systems to meet these energy demands.

85

The maps in this paper were created using ArcGIS® software by Esri. ArcGIS® and

86

ArcMapTM are the intellectual property of Esri and are used herein under license. Copyright

87

©Esri. All rights reserved. For more information about Esri® software, please visit

88

www.esri.com.18



89

Spatial growth model

90

“What If?”, a community land use planning support system (PSS), was used to predict the future

91

land-use patterns of the 13-county Atlanta Metropolitan region.19 “What If?” is a rule-based20

92

and projections-for-urban-planning (PUP) model21 that has been used widely as a PSS to

93

compare urban growth and land use planning scenarios.19,22–27 “What If?” was selected as the

94

most suitable urban growth testing platform because of its transparency, flexibility, user-friendly

95

interface and deterministic procedures in the spatial growth modeling procedure. The procedures

96

involve three steps: (1) suitability analysis, (2) land demand analysis, and (3) allocation analysis.

97

The suitability analysis generates suitability maps using spatial datasets by adopting overlay

98

techniques28,29 and weighted linear combination (WLC) multiple criteria analysis (MCA)

99

techniques.30 The land demand analysis projects future land requirement using exogenous socio-

100

economic data of population and employment growth estimates. The allocation analysis assigns

101

population and employment to the future land based on the suitability maps and the land demand

102

analysis projection result. For more details, see Klosterman (1999)19 and Pettit and Pullar

103

(2004)22. Limitations and uncertainties are discussed in Klosterman et al. (2005)20 and

104

Klosterman (2012).31

105

Our model integrated GIS datasets for population, employment, and housing projections

106

published by the Atlanta Regional Commission (ARC) quinquennially.32 The ARC projection

107

was generated prior to 2010 and was not modified for the 2005-2017 timeframe. Accordingly,

108

we expect a discrepancy between the 2005-2017 projection results and the actual population and

109

employment of Atlanta. The focus in this land-use simulation was to develop scenarios to

110

visualize different spatial growth maps related to population-employment densities.


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111


To generate the suitability maps, our suitability analysis combined thirteen factor layers:

112

(1) distance from major roads, (2) distance from floodplains, (3) distance to parks, (4) distance to

113

highway ramps, (5) distance to rail, (6) distance to town centers, (7) distance from lakes and

114

rivers, (8) distance to “not-in-my-back-yard” facilities(e.g., wastewater treatment plants and

115

landfill sites), (9) distance to existing industry, (10) city boundaries, (11) public land and

116

national parks, and (13) slope of the land. The factor layers were given weights and rankings to

117

create the residential and employment land-suitability scores from 10 (least suitable) to 90 (most

118

suitable), following the WLC and MCA technique. The assigned weights and rankings of each

119

suitability factor were initially determined to reflect the relationship between urban development

120

potential and land location or price, which is normally a reversed function of distance to existing

121

urban services and physical landscape. The rationality of the weights and rankings was checked

122

through the consultation with planning students and faculty group. This “focus group approach”

123

to determine the relative importance of multi-criteria is one of multiple methods.33,34 Allen and

124

Lu (2003)34 suggested that proper use of a focus group such as local experts, planners, and

125

interest groups improve the reliability of growth prediction. Details of the GIS attributes, weights

126

and rankings can be found in Tables S 1a-c of the supporting information (S.I.).

127

Our land demand analysis used the 2005 residential and employment density values

128

provided by the ARC GIS dataset.32 The county-by-county land demand and land-allocation

129

projection require the different application of conditions and parameters – associated with spatial

130

growth patterns, land requirements, housing profiles, and density values – for both the BAU and

131

MCG scenarios. The BAU and MCG scenarios had four major differences in the input of spatial

132

and non-spatial parameters. In contrast to the BAU scenario, the MCG scenario (1) stimulated

133

urban growth near preexisting public transit-subway and bus-stop corridors; (2) increased the



134

residential and employment density (50-100%); (3) increased the allocation of multi-family

135

housing units for future residential use by 17%, and; (4) assumed higher infill rates in the land

136

demand analysis. Details of the inputs used in the “What If?” model for each scenario can be

137

found in Table S 2 of the S.I.

138

Finally, in the land allocation analysis, the locations of new developments were

139

prioritized based on the suitability scores for each scenario. We combined GIS layers that were

140

generated in the previous steps (i.e., ARC LandPro 2005 land use data, population and

141

employment projections, US Census block group data and suitability maps) to create uniform

142

analysis zones (UAZs). The UAZs are homogenous-minimum-permissible-size land units (1

143

acre) that “What If?” uses to allocate future land use. The allocation analysis is first performed

144

within each separate county prior to combining allocation the 2005 to 2030 results or each

145

projection year. Figure S 1 in the SI summarizes the urban growth modeling procedure.

146

Water demand and consumption

147

The total residential water demand was calculated using the population growth estimates and the

148

gallons per capita per day (gpcd) water-use coefficients for each county from the North Georgia

149

Metropolitan Planning District (MNGWD).35 The non-residential water demand was calculated

150

using employment estimates and the gallons used per employee per day (GED) coefficients from

151

literature (S.I. Table S 3).36 The MCG scenario assumed that all new residential buildings would

152

be built with EPA WaterSense low-flow fixtures.37 Consequently, all 13 counties decreased

153

residential indoor water use by 20.7% in the MCG scenario. The single-family and multifamily

154

residential outdoor water uses for each county were modified for the MCG scenario using the

155

BAU-to-MCG residential density ratio. The ratios were multiplied by the current outdoor gpcd to

156

get a modified gpcd value for the MCG scenario. The BAU and MCG residential indoor and


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outdoor water-use coefficients for each county are presented in Table S 4 of the S.I. Previous

158

studies indicate a 21-50% potential efficiency improvements of water devices in non-residential

159

sectors.36–40 In this study, we assumed an efficiency improvement of 20% as a reasonable GED

160

reduction for local and regional water demand for the MCG scenario. We used modified water-

161

use coefficients (See S.I. Table S5) to calculate the total water use for each employment sector.

162

The water consumption was calculated for each scenario using the weighted average of the

163

outdoor water use to the total use for each residential building type.35

164

Low Impact Development (LID)

165

The LID technology considered in this study was rooftop rainwater harvesting. LID technologies

166

can control stormwater runoff and harvest rainwater for non-potable water use, which reduces

167

the amount of water needed from the centralized water treatment systems for non-potable

168

purposes.41 Various regression models were tested to estimate the future residential and

169

commercial roofing areas. The model was used to estimate the future roofing area in future.

170

Further detail on the regression model can be found in the S.I.

171

The estimated roofing area and the 30-year average annual rainfall (49.7 inches in

172

Atlanta) was used to calculate the average daily rainwater harvesting potential from new

173

development using Equation 1. We assumed a collection efficiency of 0.5. We assumed no

174

difference of rainwater harvesting from buildings constructed before 2005 between either BAU

175

and MCG with rainwater harvesting (RWH) scenario. The estimated water demand in the MCG

176

scenario was calculated by subtracting the volume of water saved by implementing rainwater

177

harvesting from the estimated regional water demand.

178 179

Average daily water saving potential (gal) = [49.7 inches of annual rainfall predicted area of roof surface * 0.5 collection efficiency ÷365 days. (1)



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Building energy simulation

181

We used the energy demand for 5 prototype buildings (Table 1) and assumed that all future

182

residential and commercial growth will be met by these five buildings. The energy consumption

183

of the buildings was obtained from OpenEI, which used Energyplus and the Department of

184

Energy’s commercial reference building models.17 These prototypes were chosen to provide an

185

estimation of how typical residential and commercial energy demand would unfold over the

186

projection lifetime, similar to a previous study.42

187

Table 1. Residential and commercial building characteristics. Adapted from James et al (2016).43

Building Types Small Office

Medium Office

Large Office

Multifamily Residential

Single Family Residential

Square footage (ft2)

5,500

53,628

500,000

33,740

2,546

Number of floors

1

3

12

4

1

Building electrical demand (kWh) [cooling+ plug load]

68,171

728,547

6,963,487

258,790

2,548

Building heating demand(kWh)

7,447

18,019

419,346

107,795

2,068

188

Combined cooling, heating and power (CCHP)

189

The CCHP system is composed of a Capstone air-cooled microturbine as the primary generation

190

unit (PGU), a heat recovery unit and an absorption chiller. The CCHP system was designed to

191

follow the hourly thermal load (FTL). The operational parameters, thermal outputs, and electrical

192

outputs of the microturbine were compiled from the Capstone technical reference assuming

193

natural gas as its primary energy source.44 Capstone commercially manufactures three turbines

194

sizes (30kW, 65kW, and 200kW) but a larger system size can be determined by operating

195

multiple turbines.


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The thermal load was considered to be the energy required for heating, cooling, and hot

197

water.43 During the winter, the outputs of the CCHP system are electricity, hot water, and space

198

heating, whereas in summer the outputs are electricity, hot water, and space cooling. Space

199

cooling is provided when the microturbine’s waste heat is converted to cooling by the absorption

200

chiller.

201

Estimating energy demand, water-for-energy withdrawal and consumption,

202

emissions (CO2 and NOx), and cost for the growth scenarios

203

The SMARTRAQ (Strategies for the Metro Atlanta Region’s Transportation and Air Quality)

204

project, initiated in 1998, was a multidisciplinary and multi-institutional research effort to

205

examine the impacts of land use on transportation choice, vehicular emissions, and physical

206

activity.45,46 The SMARTRAQ database generated by contains the land use data and attributes of

207

an area (see the Health & Community Design Lab at the University of British Columbia).47 We

208

used the SMARTRAQ46 database estimates to quantify the total commercial building square

209

footage in the small (less than 10,000 ft2), medium (10,000 ft2 to 100,000 ft2) and large (100,000

210

ft2) office building categories for 2005 (Table S 6). The calculated percentages were applied to

211

the 5-year “What If?” growth increment for each county to determine the square footage of each

212

building type. We used data from our previous study to determine the change in emissions, and

213

water–for-energy consumption within all office buildings with and without a CCHP system

214

between 2005 and 2030 (Table S7).43 We also considered the impact a net metering policy,

215

which is a mechanism that allows the excess CCHP-generated electricity to be used by grid-

216

connected buildings. This policy can reduce overall emissions since centralized power plants can

217

redirect this excess electricity to meet demand rather than ramp up centralized electricity

218

production.



219

In the case of the multifamily residential building, the number of housing units estimated

220

was translated to total square feet by multiplying the number of housing units by the average size

221

of a unit in the South.48 Using the total square footage growth in each scenario along with the

222

per square foot estimates for the emissions and water consumption (Table S7), we were able to

223

estimate the change in emissions and water consumption with and without a CCHP system.

224

The sizing of the residential CCHP systems was determined using the 5-year growth

225

outputs of the “What If?” model and the number of new housing units that would be needed for

226

each census tract. We assumed that all new buildings for each census tract within a growth

227

period would be a new community that would have a CCHP system sized to meet the demand of

228

the community. A MATLAB model was designed to determine the maximum CCHP system size

229

that would be needed for a new community based on the maximum hourly thermal load of the

230

community.43 Since the CCHP system is composed of multiple microturbine units, a large

231

community can have multiple smaller units distributed throughout the community without the

232

need for a centralized CCHP unit. . Upon discussion with the management of the St. Paul, MN

233

district energy system, we considered thermal losses in the system negligible. We did not

234

account for the cost of the thermal grid because this would require unique designs for each

235

community. The emissions produced and water consumption for energy generation were

236

determined for each community using the equations and emissions factors from our previous

237

study.43

238

The total emissions and water-for-energy generation for the growth in the residential and

239

commercial buildings in the Atlanta Metropolitan region under the two growth scenarios (BAU

240

and MCG) and three energy scenarios (No CCHP, CCHP, CCHP with net metering) were

241

determined. We calculated the water loss for electricity generation using Georgia Power’s


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estimates of the 10% consumptive water loss of all water withdrawn.49 Using the total square

243

footage for all building types and the per square foot average annual cost estimates of grid

244

energy and the CCHP system we were able to estimate the total annual cost of energy under the

245

two growth scenario and three energy. We also estimated the range of CCHP system costs based

246

on the maximum and minimum costs of the individual components in our previous study.43

247 248

Estimating the impact of CCHPs on old buildings

249

We determined a best-case scenario in which we estimated the impact of installing CCHP

250

systems in all new and existing commercial and residential buildings. The scenario assumed that

251

all buildings prior to 2005 would fit the five building prototypes previously discussed.

252

Accordingly, we assume that new and existing buildings have the equivalent efficiency. The total

253

emissions and water consumption for the old buildings with CCHP systems was determined

254

using the total estimated square footage of all the office buildings and multifamily buildings in

255

2005 along with the emissions and water consumption estimates (See Table S7). The emissions

256

and water consumption for existing single-family buildings was determined using the results

257

from the 2005 to 2030 projections. Based on the projected community size, we determined how

258

the per-building emissions and water consumption changes with community growth. We used

259

the maximum emissions and water consumption values to be conservative, along with the

260

number of buildings in the base year to determine the impact of installing CCHPs on the existing

261

single family building stock.43



262

Results and Discussion

263

In our results, we examine how changing the regional growth pattern and selected decentralized

264

technologies would affect the water-for-energy, municipal water demand, and emissions (CO2

265

and NOx) of a region. The results indicate that decentralized energy alternatives can be more

266

impactful in reducing the water (consumption and withdrawal) and emissions impacts than

267

changing the growth pattern of a region. It is important to note that these results do not account

268

for the water and emissions impacts of transportation. Our study framework incorporates urban

269

growth models, water use, and energy use to provide useful insights on the impact of various

270

policy decisions on the urban region.


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Spatial growth implications

272

Figure 1 shows the land-use differences between the BAU and MCG scenarios (enlarged in

273

Figures S2-S4). The results indicate that the largest portion of the new land demand is from

274

single-family residential homes. The BAU scenario requires 663.7 thousand acres of new land to

275

meet future (2030) development needs for residential and employment uses. In comparison, the

276

MCG scenario requires 299.1 thousand acres to meet future development and employment needs.

277

The percentage of multifamily households in the MCG scenario increases to 31% of the total

278

number of residential units in 2030 as compared to 26.7% in the BAU scenario (Table 2 and S8).

279

The increase in the number of multifamily units in the MCG scenario was most likely not as

280

significant as expected because of the “What If?” land use parameters and constraints. The

281

validation of the “What If?” model to actual land-use allocation is presented and discussed in the

282

S.I. (Figures S6 – S7 and Table S9) for 2010.

283 284 285 286 287 288 289

Figure 1. Land use/land cover changes from the base year 2005 (center) to 2030 for both BAU (left) and MCG (right) scenarios. The BAU scenario is dominated by the sprawl of single family residential units, which displaces much of the forest cover. Because multifamily residential units displace single family residential units in the MCG scenario, much of the forest cover is conserved. Accordingly, much of the development is constrained to major roads and highways. Larger land use/cover figures are available in the SI for the base year (Figure S2), BAU (Figure S3), and MCG (Figure S4). 50 Notes: W.S = Whole Sale; Pub. = Public; Res = Residential. Adapted from James (2015) .



290 291 292

Table 2. Estimated square footage (office buildings) or number of buildings (residential) in the base year and the estimated growth between 2030 and the base year (Table S8 have county-by-county estimated square footage and residential building results).

2005

2005-2030 BAU

Large Office Multifamily Residential Single Family Residential

Units

Medium Office

106 ft2

Small Office

MCG

106

343

343

249

279

279

204

445

445

458,207 192,459 305,707 1,170,283 614,617 504,205

293 294

Water Demand and CO2 and NOx Emissions

295

Municipal water demand is responsible for 29%, 17%, and 14% of the total water withdrawal

296

and 46%, 30%, and 19% of the total water consumed for the base year, BAU and MCG

297

scenarios, respectively. The population and employment allocation resulting from the BAU

298

scenario leads to approximately 57% increase in the projected water withdrawal from 2005

299

(506.9 MGD) to 2030 (800.3 MGD) (Figure 2a). The municipal water demand in the MCG

300

scenario, which includes low flow fixtures and LID, increases by 33% to 667 MGD in 2030.

301

Therefore, there is approximately a 16% (133 MGD) reduction in the municipal water

302

withdrawal between BAU and MCG scenarios in 2030. Implementing low flow fixtures and LID

303

systems in the MCG scenario resulted in a 34% (55.5MGD) reduction in the municipal water

304

consumed when compared to the BAU scenario (Figure 2b).


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305 306 307 308 309

Figure 2. Estimated municipal and energy generation water demand for the base year (2005) and projected new commercial and residential growth (between 2005 and 2030) for business-as-usual (BAU) and more compact growth (MCG) with low-impact development scenarios assuming grid energy, a CCHP system, and a net metering policy: a) Water-for-energy withdrawal and domestic use; b) Water-for-energy consumption and domestic use.

310 311

Most of the water withdrawal and evaporative losses result from energy generation. We

312

ran two simulations when considering electricity from the grid. The first simulation assumed that

313

grid electricity comes from the current and projected grid mix in Atlanta – see Choi and

314

Thomas51 – and the second assumes the electricity from the grid is provided only by combined

315

cycle natural gas plant (CCNG). Adding a CCHP system, in the grid mix simulation, can reduce

316

the water-for-energy (withdrawal and consumption) by 86% in the BAU case and 85% in the

317

MCG in comparison to their respective “No CCHP” scenario (Figure 2). Incorporating a net



318

metering policy may further reduce the water-for-energy by 95% and 94% in the BAU and MCG

319

scenarios, respectively. If the grid were composed solely of CCNG plants, then the water-for-

320

energy would be 12% of the demand with the grid mix for both the BAU and MCG scenarios

321

with and without CCHP. The reductions in a generation scenario with CCNG and CCHP are

322

similar to that of the grid mix scenario (S.I. Figure S 8 and Table S10). Overall, combining

323

MCG, LID, CCHP systems and net metering can reduce the water withdrawal and consumption.

324

While an MCG scenario can reduce the water withdrawal and consumption of a region,

325

modifying the energy generation scheme is more effective in reducing regional water

326

consumption.

327

As shown in Figure 3, CO2 emissions in the BAU grid mix scenario decrease by 9.5%

328

and 24.8% without and with net metering, respectively. In the MCG scenario, the CO2 emissions

329

decrease by 7.2% and 25.4% without and with net metering, respectively. When we consider the

330

electricity from the grid coming from CCNG plants, the CO2 emissions without and with net

331

metering decrease by 9.4% and 36.4%, respectively, in the BAU scenario and 6.9% and 34.7% in

332

the MCG scenario, respectively (Figure 3). Other studies have found similar CO2 emissions

333

reduction within these ranges.42,52,53 Howard et al. (2013) simulated an aggregate CO2 emissions

334

reduction of 4% and 9% in New York City for individual building systems and microgrid

335

systems, respectively.42 Similarly, Lee et al. (2013) estimate a 4-5% reduction in CO2 emissions

336

in Boston over 20 years.53 Duquette et al. (2013) estimated approximately 24% and 32% CO2

337

emissions reduction in Ontario using two different CHPs; however, this study also analyzed a

338

widespread district energy system.52 In comparing our results to these studies a few caveats

339

should be considered: (1) these three studies take place in colder climates, which increases CHP

340

systems’ efficiency; (2) the generation and dispatch strategies in these studies differ in that they


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follow the electrical load, optimize for pricing, or make differing assumptions on energy needs,

342

and; (3) the CHP system design differs from the design considered in this study.

343

The BAU NOx emissions decrease by approximately 61% without net metering and 73%

344

with net metering under the grid mix (Figure 3). In the MCG scenario, the NOx emissions

345

decrease by 63% without net metering and 82% with net metering. If grid electricity comes

346

solely from CCNG plants, the NOx emissions can be further reduced 24-40%, depending on the

347

growth scenario and if there is net metering (Figure 3). This can have interesting policy

348

implications for the benefits of converting coal-fired power plants to natural gas. Switching to

349

CCHP systems would result in a greater decrease in the emissions and water consumption than

350

solely switching to CCNG power plants.

351

352 353 354 355

Figure 3: Projected annual 2030 CO2 and NOX emissions from energy consumption commercial and residential buildings for two growth scenarios. These values only represent emissions from new construction between 2005-2030 assuming electricity is provided by either the grid or a CCNG in conjunction with a CCHP.

356 357

The average annual cost of energy in 2030 for the commercial and residential buildings

358

built between 2005 and 2030 will be slightly lower, 1% (~$51M) in the BAU scenario and 3%

359

($150M) in the MCG, when a CCHP system is implemented (Figure 4a). Net metering further



360

reduces the cost of energy associated with CCHP systems to 7% (BAU) and 8% (MCG). As

361

shown by the upper bound cost bar, the costs would be higher than the cost from the

362

conventional grid if the maximum CCHP systems cost is used. Lower system costs would make

363

implementing CCHP systems more economically feasible (Figure 4a). Increased system

364

efficiencies would also improve the financial viability of CCHP systems as more electricity

365

would be produced reducing the electricity required from the grid. Lower fuel costs could also

366

significantly increase the economic feasibility of the system as shown in our previous work.43

367 368 369 370 371

Figure 4: The approximate annual energy-generation cost in 2030 (includes the cost of energy required for the grid and cost of the HVAC system) versus the average cost of the CCHP systems, for the BAU and MCG scenarios. A) Energy costs to accommodate the 2005 to 2030 population and employment growth. B) Energy costs in 2030 assuming CCHP installation of existing (2005) and new building stocks.

372

Best case scenario: CCHP in existing and new buildings

373

Compared to their respective “No CCHP” scenario with the grid mix, installing CCHPs in all

374

residential and commercial buildings decreases the CO2 emissions by 9% (without net metering)


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375

and 49% (with net metering) in the BAU scenario and by 8% (without net metering) and 50%

376

(with net metering) in the MCG scenario (Figure 5). Similarly, NOx emissions decrease by 61%

377

(without net metering) and 86% (with net metering) in the BAU scenario and 63% (without net

378

metering) and 90% (with net metering) in the MCG scenario (Figure 5). Furthermore, when

379

compared to the “No CCHP” scenario, the average cost with a CCHP system would increase by

380

1% and 0.14% in the BAU and MCG “CCHP” scenarios, respectively. If net metering is

381

considered, the cost would be lower than a “No CCHP” scenario in both growth cases by

382

approximately 7% (Figure 4b). As shown by the lower bound of the cost bar, if minimum CCHP

383

system cost is assumed, implementing a CCHP system will always be less expensive than a “No

384

CCHP” scenario. The emissions (CO2 and NOx) (Figure 5) and water consumption reductions

385

(Figure 6) differ from the projections when we consider CCHPs in new and existing buildings

386

because the ratio of the different types of buildings has changed. The estimates of the single-

387

family residential emissions also influence the reductions. Similarly, if the maximum CCHP

388

system cost is assumed, it will always be more expensive to implement these systems (Figure

389

4b).

390 391 392 393

Figure 5: Total annual CO2 and NOx emissions of the 13-county Atlanta Metropolitan region, from energy consumption commercial and residential buildings for two growth scenarios. The dashed line represents the CO2 and NOX emission levels in the base year (2005). The solid bars represent the total emissions for existing and new buildings when only new



394 395

buildings have CCHP systems. The crosshatched pattern indicates the CO2 and NOX emission levels if all buildings, existing and new, have CCHP systems.

396

397 398 399 400 401 402 403

Figure 6: Water-for-energy withdrawal and consumption in 2030 for the 13-county Atlanta metropolitan region. The dashed line represents the water-for-energy withdrawals and consumption of the base year (dashed line). The solid bars represent the total withdrawals and consumption for existing and new buildings when only new buildings have CCHP systems. The crosshatched pattern indicates the total withdrawals and consumption levels if all buildings, existing and new, have CCHP systems.

Overall, this study has demonstrated three factors that could impact the water-for-energy

404

and emissions (CO2 and NOx) of the Atlanta region as it grows. These are: 1) Moving to an

405

MCG scenario does not significantly reduce the water demand nor emissions. However, these

406

results do not account for how an MCG scenario would affect energy for transit. 2)

407

Decentralized water and energy systems reduce, water-for-energy, water from a centralized

408

plant, cost and emissions (CO2 and NOx) of a region more so than increasing the density of

409

residential communities. 3) Net metering policy for distributed energy generation systems can

410

significantly aid municipalities in reducing their water impact by incentivizing the adoption of

411

decentralized generation. However, while this study integrates various models to assess the co-

412

benefits of infrastructure investments it has three main limitations: 1) the assumption that the

413

energy demand of all prototype buildings match that of the actual demand of in-situ buildings; 2)


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414

transportation changes due to growth scenario differences can significantly affect the emissions;

415

and 3) Consumer preference and metrics, such as quality of life, can significantly influence urban

416

growth scenarios and technology options. Further discussion of the limitations is included in the

417

S.I.

418

The results demonstrated by our study are intended to inform policy on potential trends

419

and outcomes when considering technology, development, or policy schemes. Since this study

420

was performed, the City of Atlanta has pledged to meet all building energy demand with

421

renewable energy generation by the year 2035.54,55 Accordingly, newer studies should focus on

422

identifying barriers and vulnerabilities of a fully renewable grid in Atlanta. The also suggest that

423

compact growth is ineffective at reducing environmental impacts; however, this study did not

424

incorporate transit choice and the subsequent emissions nor the improvements to human health

425

and the quality of life that compact growth provides. Moreover, the results presented did not

426

assess the co-benefits of reduced land development. This paper weighted the direct cost, water,

427

and emissions impacts more heavily than potential co-benefits from land use. Finally, the

428

methodology employed herein does not incorporate consumer choice, adoption, and fits an

429

energy model to a projected land use model. Accordingly, the results showcase the potential

430

improvements that LID, compact growth, and CCHP systems can provide, but do not incorporate

431

all of the interdependencies and interactions that exist between the water-energy-transportation

432

(WET) nexus. Nevertheless, this paper provides the framework to integrate infrastructure

433

systems and aid municipalities with a holistic approach to urban development.

434

Acknowledgments

435 436 437

The Brook Byers Institute sponsored this research for Sustainable Systems, Hightower Chair, and the Georgia Research Alliance at the Georgia Institute of Technology. This work was also supported by a grant for “Resilient Interdependent Infrastructure Processes and Systems (RIPS) Type 2: Participatory Modeling of



438 439 440 441 442

Complex Urban Infrastructure Systems (Model Urban SysTems),” (#1441208) and “Resilient and Sustainable Infrastructure: Sustainable Infrastructures for Energy and Water Supply (SINEWS),” (#0836046) from National Science Foundation, Division of Emerging Frontiers in Research and Innovations (EFRI). The views and ideas expressed herein are solely of the authors and do not represent the ideas of the funding agencies in any form.

443

Supporting Information

444 445

Further information regarding the land use, water, and energy modeling, as well as expanded maps and results associated with this paper is available free of charge via the ACS Publications website at http://pubs.acs.org.

446

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Nomenclature

565

ARC – Atlanta regional commission

566

BAU - Business as usual

567

CCHP - Combined cooling, heating and power

568

CCNG - Combined cycle natural gas

569

FTL – Follow thermal load

570

Gpcd - Gallons per capita per day

571

GED - gallons per employee per day

572

LID - Low impact development

573

PGU – Primary generating unit

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MCG - More compact growth

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RWH - Rainwater harvesting

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UAZs – Uniform analysis zones

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TOC abstract

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Impacts of Combined Cooling, Heating and Power ... - ACS Publications

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