Sustainable Infrastructure for Energy and Water Supply (SINEWS) Arizona State University, Georgia Institute of Technology, The University of Georgia - Athens National Science Foundation, EFRI RESIN Project Steve French, Ke Li George Karady, Eric Williams, Miroslav Begovic, Bert Bras John Crittenden, Eric Williams, Sam Ariaratnam Dynamic life cycle energy of multicrystalline-Silicon Photovoltaic EIOLCA = economic input-output LCA Process LCA = bottom up materials flow based LCA Aqua Conserve Weathermatic HydroPoint Data Systems Life cycle CO 2 savings (kg/yr) 39 to 92 43 to 122 -2 to 190 Annual water bill savings -$3 to $62 $7 to $66 -$37 to $95 Life cycle CO 2 and cost for different models of smart irrigation controllers for Phoenix households Reliability of Energy Production System Life Cycle Assessment of Decentralized Energy Production and Electrified Transportation Reliability of Water Distribution System 0 1 2 3 4 5 6 7 8 Energy Use Per Passenger Distance (MJ/person-km) -50 0 50 100 150 200 250 300 350 400 450 500 CO 2 Output Per Passenger Distance (g/person-km) PTW CO2 Output Per Passenger Distance WTP CO2 Output Per Passenger Distance • Poor environmental performance of electric vehicles, all sizes, due to coal fired powerplants (Georgia Power’s Plant Bowen emits about 0.9kgCO 2 /kWh_ • Marta rail & bus performance bad due to low ridership • Renewable distributed generators (such as PV) may be located at several locations across distribution feeders or microgrids • At strategic locations, reclosers are installed to allow the possibility or system separation into islands • Islanded operation within zones with balanced generation and load is expected to be allowed under future standards, such as IEEE 1547.4, currently undergoing balloting • In such cases, faults within any of the islands (outlined by dashed lines) would only affect the loads within the island and not the entire feeder • That would create positive impact on overall reliability of the feeder, but requires that the topology of the feeder (or microgrid), distributed generators and recloser(s) be optimized (ongoing work) Land Use and Policy Land Use Scenarios and Forecasting Unit : kWh/10 6 gal Raw Water Acquisition Treatment Distribution Surface Water 0 ~ 9,200 (depending on the conveyance distance) ~1,200 (can be up to 5,200 for desalination) ~ 1,100 (varies depending on the topography and distance) Groundwater 500 – 2,000 (depending on depth) 100 – 5,000 (depending on water quality) Wastewater Typically gravity flow ~ 2,500 N/A Energy for Water Water for Energy Membrane Bioreactor (MBR) Centralized Wastewater Treatment with MBR Decentralized Stormwater Management - Bioretention Area Future Failure Rate Prediction Water Main Break Data Break/Mile/Year (1991-96) Decade ACP DIP CIP RCP GALV STL.CYL PVC STL 1900 0 0 0 0 0 0 0 0 1910 0 0 0 0 1.58 0 0 0 1920 0.38 5.11 2.72 0 9.53 0 0 0 1930 0.23 1.06 0.31 0.09 0.82 0 0 0 1940 1 1 0.48 0.38 3.71 0 0 0.98 1950 0.19 0.72 0.38 0.04 3.67 0.02 0 0.36 1960 0.19 0.77 0.25 0.05 3.16 0 0 4.16 1970 0.13 0.37 0.27 0.03 2.83 0.72 1.37 0.09 1980 0.1 0.24 0.2 0.03 5.08 0 0 0.47 1990 0.13 0.19 0.89 0.01 0 0 0 0 Legend: ACP: Asbestos Cement Pipe, DIP: Ductile Iron Pipe, CIP: Cast Iron Pipe, RCP: Reinforced Concrete Pipe, GALV: Galvanized Steel Pipe, PVC: Polyvinyl Chloride Pipe, STL.CYL.: Steel Cylinder pipe STL: Steel pipe Past and Current Rate Reliability can be defined as “the probability that the system performs its specified tasks under specified conditions in specified time” (Kaufmann et al. 1977) Life Cycle Assessment of Centralized and Decentralized Water/Wastewater Systems Energy Source Gallons Per kWh (Evaporative loss) Hydro 18.27 Nuclear 0.62 Coal 0.49 Oil 0.43 PV Solar 0.030 Wind 0.001 Household Wastewater Effluent to Dosing/Distribution Network Discharge to subsurface Septic Tank Intermittent Sand Filter (Single Pass) Decentralized Wastewater Treatment Smart Irrigation Controller Phoenix growth scenarios (above) and urban form indicator(below) Atlanta growth scenarios (above) and employment location(below) Employees /Acre POWER FLOW ENGINE (MATLAB) Input (Feeder Information) - LOAD profiles - Voltage Controls Power Flow Solutions - Voltages - Currents - Power, loss, power factor,… METHODOLOGY MONTE CARLO SIMULATION - Impact of PV penetration - Inverter control strategies - Impact of DG placement - Voltage profile, power factor, losses, reliability improvement Input - Random DG size and locations - Transformable feeder topologies - Random DG generation Load Profiles PV Output Boundaries of Islands • ASU developing a design method for design Urban, Electrical Micro-grid with Distributed Generation • The first step is the determination of the capacity of the existing infrastructure: – How many kW the water, gas etc system can support • Preliminary results: – In a community which has 81 houses and 475 kW maximum electrical load the water is supplied by a 6” pipe – The capacity of this pipe is: < 415 gal/min – The present water surplus is: > 22.4 gal/min – The available surplus water can support: • 112 kW combined cycle gas turbine • > 7465 kW Fuel cell – Similar analysis has been done for the natural gas and sewer Mobility System Design & Assessment: Initial Energy & CO 2 Results for Atlanta The relationship between local policy, urban structure, and actual consumption is being explored by examining two decades of 'planning for quality growth' in communities in the Atlanta, Georgia area www.georgiaencyclopedia.org 2030 2030 #0836046 City Mean House Price Increase in Plant Richness Increasing distance to water course WTP change % METRO AREA $167,344 2.37% -0.15% PEORIA $160,646 6.02% -0.78% SCOTTSDALE $302,579 -3.14% -0.49% PHOENIX $140,802 -6.77% -0.18% GLENDALE $145,922 -9.07% -0.22% MESA $146,538 TEMPE $178,749 -5.90% -0.58% AVONDALE $134,961 -35.3% -0.97% GILBERT $179,702 -0.66% CHANDLER $150,438 -0.46% SURPRISE $155,464 GOODYEAR $167,673 1.38% Willingness to pay for reliability of supply through a hedonic price function-Phoenix Vi Charles Perrings, Doug Noonan, Marilyn Brown Hedonic Price Estimation for Infrastructure Reliability Hedonic price analysis Z X 0 lnP – Determine how price affected by reliability of infrastructures Breakpoint analysis – Sup-Wald tests track price jump point – Compare to infrastructure changes. Where P: House sale price : Infrastructure reliability : Other factors affecting sale price ε: error term X Z sup-Wald test time Floods and house value in Atlanta Property Value Low High Flash point