Participatory Modeling of Complex Urban Infrastructure Systems (Model

Participatory Modeling of Complex Urban Infrastructure Systems (Model

Participatory Modeling of Complex Urban Infrastructure Systems (Model Urban SysTems, MUST) John C. Crittenden, Ph.D., P.E., NAE (US & China) Zhongming Lu, Ph.D. Brook Byers Institute for Sustainable Systems, Georgia Institute of Technology, Atlanta, GA E-Mail: [email protected] Conference website: http://www.icsi2016.org/ Introduction of MUST Food Research Elements: 1. Systems dynamics modeling of complex urban infrastructure systems (i.e., the Metamodel). 2. Quantifying the resilience of urban infrastructure systems. 3. Social, Behavioral, and Economic decision making. 4. Optimizing between resilience and sustainability. MUST Investigators Name Ashuri, Baabak

Bras, Bert Clark, Jennifer Crittenden, John Fujimoto, Richard Grijalva, Santiago Guhathakurta, Subhrajit Leigh, Nancey Green McDermott, Tom Thomas, Valerie Weissburg, Marc Affiliation Associate Professor, School of Building Construction; Director, Economics of the Sustainable Built Environment (ESBE) Lab Professor, George W. Woodruff School of Mechanical Engineering. Associate Professor in the School of Public Policy and Director of the Center for Urban Innovation in the Ivan Allen College Director, Brook Byers Institute for Sustainable Systems, Hightower Chair and Georgia Research Alliance Eminent Scholar in Environmental Technologies; School of Civil and Environmental Engineering Regents Professor in the School of Computational Science and Engineering Associate Professor; Associate Director for Electricity Strategic Energy Institute (SEI); Georgia Power Distinguished Professor Professor, School of City and Regional Planning; Director, Center for Geographic Information Systems Associate Dean For Research, College of Architecture; Professor, School of City

and Regional Planning; Director of Technology Policy Research, Georgia Tech Research Institute Anderson Interface Professor of Natural Systems, School of Industrial and Systems Engineering Professor, School of Biology MUST Investigators: Expertise Name Ashuri, Baabak Bras, Bert Clark, Jennifer Crittenden, John Fujimoto, Richard Grijalva, Santiago Guhathakurta, Subhrajit Leigh, Nancey Green McDermott, Tom Thomas, Valerie Weissburg, Marc Expertise / Role in Project Infrastructure Project Finance & Investment Science, Systems Engineering, Infrastructure project development processes & delivery systems, Risk Management, & Operations Research (Business Analytics & Data Mining) Computer-aided engineering, design and manufacturing; environmentally conscious design, design for recycling and robust design Regional economic development, manufacturing, industry clusters and innovation Sustainable systems, pollution prevention

Execution of discrete-event simulation programs on parallel and distributed computing platforms Power system and smart grid computation; De-centralized and autonomous power control architectures; Ultra-reliable electricity internetworks; Seamless integration of large-scale renewable energy; Electricity markets design and power system economics Geographic information systems, planning support systems, sustainability Economic development planning, sustainable development, urban andregional theory, industrial restructuring, income inequality Modeling dynamic systems, systems thinking, organizational and team behavior, management of technology Energy and materials efficiency, sustainability, industrial ecology, technology assessment, international security, science and technology policy Ecology, community ecology, biologically Inspired design methodology and pedagogy Interdependences of Urban Infrastructure Systems Water Land Use Fresh Surface Transport Residential Water Supply

Fresh Ground Energy for Water Commercial Saline Surface Rain Water Industrial Agriculture Oil Natural Gas Water for Energy Coal Biomass Geothermal Water Energy Carbon

Fuel Electricity Water Evaporation Hydro Wind/Solar Energy Energy for Water Waste water Treatment & Discharge Carbon Emissions Defining Infrastructure Ecology Developing a Science of Infrastructure Ecology for Sustainable Urban Systems (2012, 46 (15), pp 79287929) Ming Xu, Marc Weissburg, Joshua P. Newell, and John C. Crittenden

Infrastructure Ecology is an emerging transdiscipline: Infrastructure Ecology alters and reorganizes energy and resource flows and considers the potential synergistic effects arising from infrastructure symbiosis. Understanding the city as an ecosystem requires knowledge of how human and natural infrastructure systems interact to create emergent properties. These infrastructures include physical infrastructure systems and their interactions (e.g., water-transportationenergy nexus), as well as ecological infrastructure, information and communications technology (ICT) infrastructure, socio-economic infrastructure (e.g., banking, finance) and social network infrastructure. It is Transdisciplinary. - It creates a body of knowledge distinct from its antecedents (engineering, ecology) that fundamentally changes the questions that are asked, and the tools used to answer them. SI2100 Infrastructure Symbiosis: Systembased Design Recognizing the Interdependence Water Resource Withdrawal Profile in the United States Decentralized Energy Production Thermoelectric; 39% Industrial; 5% Urban Farming Irrigation; 40%

Public Supply; 13% Aquaculture; 1% Livestock; 1% Mining; 1% Domestic; 1% Decentralized Water Production Low Impact Development (Reducing Storm Water Runoff, Erosion and Surface Water Contamination) LID Best Management Practices (BMPs) Typical Greywater Reclamation System at the Household Level Note: N.S.W. denotes New South Wales, Australia Source: http://www.environmentwriter.com/wp-content/uploads/greywater1.jpg Water Flows with LID and Reclamation a 2-story apartment unit of Atlanta, GA Smaller Water Treatment Plant Potential of off-grid water

supply Same Size Waste Water Treatment Plant Smaller Flow, More Concentrated; Smaller Plant: Better energy and nutrient recovery. Decentralized Energy Production at Perkins + Will, Atlanta Office Microturbines are used to for heating and cooling using Absorption Chillers and supply 40% of the total electricity. Adding Distributed Generation as part of the Grid: Water Reduction: >50% CO2 Reduction: 40% NOX Reduction: 15 ~90% Adsorption Chiller

65 kW Microturbine Perkins+Will Office Building Future Research: Expanding the Current CCHP System 2.0 Wind Dispatch Optimization of Electric Energy Output Minimize the Generation Cost and Maximize the Environmental Benefits Electric Energy from Grid (No MT and No PV) Office MT Office Size Small Number of Floors 1 Floor Area in ft2

5,500 Electric and Thermal Peaks 19.4 kW (July/07) Number of Buildings 22 Number of 65-kW Capstone MTs 1 Penetration % of MT from Peak [4-5] Total Capacity [4-5] PV Electric Energy from Grid, MT, and PV 65 kW Penetration % of PV from Peak PV Location and

Lifetime Atlanta (Facing South) and 30 Years Peak reduction by PV and MT Credit: Insu Kim Cooling load (up to 80.7 kW) is covered by chillers of MT Closing the Urban Water, Nutrient, Energy and Carbon Loops On-site Energy and Nutrient Recovery Stormwater treated through LID Harvested Rainwater Urban Agriculture (Aquaponics, Urban Farming, Greenhouse Farm)

Fertilizer for Farms, Food for Aquaponics Natural Gas from Anaerobic Digestion More Concentrated Wastewater Source of Fertilizer Stormwater Management with Low-Impact Development CO2 Injection Local Composting Heat Heat and Energy Natural Gas from Compost Landfill Natural Gas Natural Gas

Combined Carbon Capture, Cooling, Heating and Power (Air-cooled microturbines) Natural Gas from Landfill Heat and Energy Water Fertilizer CO2 The Design of Decentralized Water, Energy and Food Systems in Rural Baoting, Hainan The installation cost: 50,000 ($8,000) Credit: Baolong Han, RCEES One single family with 5 people Land use (including housing and farming) Water use Chemical fertilizer use Pesticide use Net household income

Conventional Decentralized Change More than 400 m2 Less than 100 m2 -75% More than 200 tones/year More than 40 kg/acre/year More than 1kg/acre/year Less than 40,000/year Less than 120 tones/year Less than 10 kg/acre/year Less than 0.1 kg/acre/year More than 50,000/year -40% -75% -90% +20% The Synergistic Effects of Infrastructural

Symbiosis Low-Impact Development The accumulated synergistic effects : Greywater Reclamation Rainwater Harvesting Air-cooled Microturbine Residential PV, Wind, etc Thermal and Energy Storage Vehicle-to-Grid (V2G) Transit oriented Development Autonomous Vehicles Decentralized Water Infrastructure reduced water and

Preferred Neighborhood Decentralized Energy Infrastructure Increased Neighborhood Amenities Mixed Land Use Compact Growth energy consumption, lower dependence

on centralized systems, larger share of renewables in the electricity mix, reduced vehiclemiles travelled, & an increase in tax revenue. enhanced system resilience Manage the Complexity in Infrastructure Systems Urban Systems Complexity Emergence of desirable amenities (high Tax Revenue and Quality of Life) & undesirable amenities (e.g., poor air quality, low tax revenue, traffic congestion, flooding, etc.) Macro Infrastructure Systems Micro SocioEconomic Environment

Big Data for Social Decision and Complexity Modeling Topic Modeling Collect Social Media Blogs Twitter News Product Reviews Analyze Enrich and prepare social media content with metadata Modeling Agent-based urban model and visualization Sentiment Analysis on the Topic of

Green Roof We synthesized the topics of 1.2 millions sustainabilityrelated tweets collected from April, 2016 to June, 2016. The topic modeling algorithm takes less than 40 minutes. In 6054 tweets about green roof topics: 4543 tweets are neutral We do green roof systems for todays homeowners and building owners 1422 tweets are positive 4 best reason to grow a living roof! Beautiful, beneficial, efficient, green living rooftops 89 tweets are negative Green roof garage has been nixed. Too expensive. :( Agent-based Housing Market Simulation Apartment vs. Single-family Transaction price Estate sale Prospective homebuyers Socioeconomic attributes Preference 1. 2. 3.

Evaluate candidate houses Decide the biding house Determine willingness to pay Homeowners Property value Living community Green space Transportation accessibility Property Tax House demand for next period Bid Price Developers Asking price House inventory: New house apartment, single-family investment decision Infrastructure service New houses Consider LID Stormwater management(1stst yr)

options if impact fee Transportation improvement (5thth yr) exists Housing Market Bid Success Infrastructure improvement Government Collect property tax Distribute property tax for infrastructure improvement Asking Price Impact fee ? Agent-based Modeling: Simulating the Adoption Rate for More Sustainable Urban Development 100% Business As Usual (BAU)

80% Principal Agents: Prospective 65% Homebuyer, Homeowners, Developers, Government 60% 40% 35% 20% 0% 0 5 10 15 Year 20 25

30 Percentage of households as compared to total households after 30 years Percentage of households in single-family houses as compared to total households after 30 years Percentage of households in apartments as compared to total households after 30 years 100% More Sustainable Development (MSD) 80% 59% 60% Impact fee for Low Impact Development non-compliance penalty: $13,000 per unit for single-family house $1,500 per unit for apartment home Policy Implementation Effect After 30 years:

41% 40% 20% 0% Implemented Policy Tool 0 5 10 15 Year 20 25 30 40% reduction in potable water demand from centralized plant in MSD

as compared to BAU 36% increase in net property tax revenue generation in MSD as compared to BAU Source: Lu. et al., ES&T, 2013 Integrated Simulation Models to Study Infrastructure Dependencies: Approach Integrated (Federated) Simulations System Specification Common Object, Data Models, Simulations Speed(MPH) Comparison of Measured and Simulated Vehicle Speeds 70 60 Measured Simulated

50 40 30 20 10 0 Visualization and Analysis Arterials Cloud-Based Model execution Specification of common data model in SysML Automated generation of federated simulations Fast Runtime Infrastructure (RTI) software to interconnect models Leverage industry standards, computational tools when available SPATIAL DATABASES FOR URBAN MODELING

The SMARTRAQ project Supports research on land use impact on transportation and air quality 1.3 million parcels in the 13 metropolitan Atlanta nonattainment counties SMARTRAQ DATA AND ATTRIBUTES

Land Use Type Number of Units X,Y Coordinate Estimated Sq Feet Total Sq Feet Address Road Type City Zip Code Owner Occupied Commercial/Residential Zoning

Sale Price Sale Date Tax Value Assessed Value Improvement Value Land Value Year Built No. of Stories Bedrooms Parking Acreage Projected Growth Scenarios for Atlanta Business As Usual Year 2030 More Compact Development Year 2030 ForeSEE: An Integrated Water-EnergyCost Modeling Tool with Hourly, Daily, Monthly and Annual Forecasting Hourly demand data used to project grid electricity and water savings from use of distributed water and energy technologies Source: C. Golin and M. Cox (Credit: V. Thomas) Atlanta Water Demand for New Residential and Commercial Buildings in More Compact Growth Scenario (with low flow fixtures + decentralized CCHP system)

Installation of Air Cooled Microturbines save 2.4 times the amount of water used for domestic consumption Potential GHG and Cost Reductions in 2030 By 2030, implementation of CHP in all new residential and commercial buildings will reduce the CO2 emissions by~ 0.007 Gt CO2, NOx emissions by ~ 15000 Tons ,and the energy costs by $680 million per year for the Metro Atlanta region. NOX Emissions CO2 Emissions Energy Cost -25% -8% -23% - 65% Summary Infrastructure Systems Are All Connected and Greater

Sustainability Gains Can be Achieved by Looking at Their Interactions Decentralized Water / Low Impact Development Can Save Water, Energy and Money Decentralized Energy and Combined Heat and Power Can Save Energy, Water and Money Transportation and Land Use/ Planning Is Vital in Reducing the Impact Of Urban Systems and Examining Their Interactions Complexity Models May Be Useful to Examine the Adoption Rate of Policy Instruments Caveat: We need to test the ideas that were presented Emerging Engineering Solutions for Water and Human Sustainability Performance Monitoring Transit-oriented Development Bike Friendly Neighborhood Energy Independent Buildings Social-Media Data Analytics

Network of Wireless Sensors Network of Things Understand Stakeholder Preference Water and Human Sustainability Shared Autonomous Vehicles Efficient Water Use Tele-commute to work Decentralized Water Infrastructure Living Buildings

High Performance Buildings Decentralized Energy Infrastructure Flow Batteries Grid Scale Energy Storage Solar Powered Public transit Super Capacitors THANK YOU! John C Crittenden, Ph.D., P.E., U.S. and Chinese N.A.E. E-Mail: [email protected] Zhongming Lu, Ph.D. E-Mail: [email protected] Web Site: http://www.sustainable.gatech.edu/

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