Functional and Spatial System Model for City Infrastructure Systems: A City.Net IES Case Study

esses within a system, overlooking the interdependencies thatare critical in establishing system requirements, determiningsystem behaviors, and evaluating system performances. Citysystems such as the energy, water, transportation, building,and waste systems are increasingly interdependent, and it isimportant to take these interdependencies into account whilemodeling city systems. As a result, system modeling methodsthat incorporate system interdependencies are required.

In addition, interdependencies are significant in systemrepresentation since the bidirectional requirements betweensystems determine the level of system integration, and conse-quently the probability of cascading failures [Carreras et al.,2007]. Critical infrastructure interdependency research tendsto focus on these failures, but there is also a need to considerthe unexpected effects of design decisions within each systemand between systems in a system of systems.

This paper describes a system modeling framework suitedto infrastructure systems. This modeling framework com-prises a systematic method for breaking down and connectingsystem components as well as a spatial modeling frameworkfor representing these system components. The systematicprocess of breaking down and connecting system componentsenables the analysis of the system in three dimensions: analy-sis of the system’s inputs, analysis of the system’s hierarchy,and analysis of the system’s interdependencies. The modelingframework adopts the hierarchical system decompositionmethod presented in Alfaris et al. [2010], and, therefore,system models developed using this framework have a lay-ered structure ranging from viewing the system as a whole tofundamental system parameters. In addition, the multidomainformulation approach [Alfaris et al., 2010] adopted in thismodeling framework enables the integration of the systeminterdependencies. The spatial aspect of the modeling frame-work incorporates the geographical orientations and physicallocations of system infrastructure facilities, thus further en-hancing the modeling results.

Furthermore, the modeling framework is suited for use inthe development of Decision Support Systems (DSSs), withthe aim of informing administrative design decisions relatedto city infrastructures without having to explore advancedengineering processes. In order to demonstrate the advantagesof system development using the functional and spatial sys-tem representation model, an Integrated Energy System (IES)model has been developed [Adepetu et al., 2012] and exe-cuted in the form of a case study.

The contributions of this paper are:

• Development of a method for modeling city infrastruc-ture systems that incorporates the functional and spatialfeatures of the infrastructure systems in order to im-prove the system modeling process and results. Themethod builds on previous work on system decompo-sition [Alfaris et al., 2010], spatial topologies appliedin Geographic Information System (GIS) environ-ments, and functional layers discussed in Tolone et al.[2004] and Grogan and de Weck [2012].

• Integration of the critical system interdependencies inthe modeling framework since, typically, city systemsdo not operate in isolation. These interdependenciesensure that the impact of a system on the other systems,

and vice versa, is taken into consideration during themodeling process. This is an application of criticalinfrastructure interdependency studies done by de Por-cellinis et al. [2008] and Tolone et al. [2004].

• Application of the system modeling framework to thedevelopment of an Integrated Energy System (IES)model. The advantages of the IES over conventionalenergy modeling frameworks include the application ofan hourly load flow analysis, Distributed Generation(DG) modeling, integration of critical system interde-pendencies, and incorporation of spatial features inscenario analysis. The load flow analysis and distrib-uted generation modeling are implemented in the IESusing MATPOWER developed by Zimmerman et al.[2011].

• Implementation and analysis of a sustainable city casestudy that shows the application of the IES model andillustrates the meaning and viability of the obtainedresults.

Section 2 examines similar models and studies on interdepen-dency analysis. Section 3 gives a detailed explanation of thesystem development and modeling method. Section 4 gives adetailed description of the IES. Section 5 presents an applica-tion of the energy model. Section 6 summarizes the systemdevelopment model and highlights areas of future work.

2. BACKGROUND AND RELATED WORK

This paper is based on prior research work on modelingcomplex sustainable systems [Alfaris et al., 2010] and theapplication of this modeling approach in developing a city-scale energy system model [Adepetu et al., 2012]. Alfaris etal. [2010] provide a systematic multidomain approach thatcan be used to simultaneously break down a complex systemand integrate the resulting subsystems. Adepetu et al. [2012]present the IES, an integrated energy model and briefly dis-cusses other City.Net systems, i.e., water, waste, transporta-tion, and building. The current energy model in this papertakes some steps forward from Adepetu et al. [2012] such asthe conversion of the modeling approach from the annualscale to the diurnal scale, the introduction of a load flowanalysis (using MATPOWER), etc.

Energy models and simulations that have objectives simi-lar to either City.Net or the IES include Systems AdvisorModel (SAM), Homer, Energis, SynCity, Urban Infrastruc-ture Suite (UIS), Land Use Evolution and Impact AssessmentModel (LEAM), and UrbanSim. The structures of these mod-els and the contribution of this work to existing models aresubsequently discussed.

SAM was developed by the National Renewable EnergyLaboratory (NREL) and simulates different energy genera-tion methods such as Photovoltaics (PV), Concentrated SolarPower (CSP), solar water heating, wind, and geothermal.SAM presents energy-related and extensive financial outputsbased on the values of variables provided by the user but doesnot incorporate multiple energy source-load interactions.Homer (also developed by NREL) is similar to the SAM butexecutes multiple energy source-load optimizations. How-

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Systems Engineering DOI 10.1002/sys

Functional and Spatial System Model for CityInfrastructure Systems: A City.Net IES CaseStudyAdedamola Adepetu,1 Paul Grogan,2 Anas Alfaris,2 Davor Svetinovic,3,* and Olivier L. de Weck2, 4

1Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada2Engineering Systems Division, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 021393Computing and Information Science, Masdar Institute of Science and Technology, PO Box 54224, Abu Dhabi, United Arab Emirates4Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA02139

SYSTEM MODEL FOR CITY INFRASTRUCTURE SYSTEMS: A CITY.NET IES CASE STUDY

Received 3 July 2012; Revised 13 October 2012; Accepted 13 October 2012, after one or more revisionsPublished online 24 May 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/sys.21251

ABSTRACT

City infrastructure systems have distinct functions but are not isolated from one another, with interactionsexisting between these systems. Modeling these systems requires a focus on the system functions andinterdependencies. Most models focus on system failures rather than the unexpected effects of designdecisions in these systems. This paper presents a functional and spatial modeling framework suited forthe representation of city infrastructure systems. This framework comprises a systematic process forbreaking down the system into fundamental components and defining the relations between the systemcomponents. In addition, the spatial feature of the framework facilitates the synthesis, analysis, andevaluation of infrastructures based on their geographical locations and spatial orientations. This systemmodeling approach is used to design an Integrated Energy System (IES) model in order to exhibit thefeatures of this framework. The IES consists of standard energy system estimation techniques and toolssuch as MATPOWER for load flow analysis, and is also used to execute a city case study. As a result, theadvantages of the functional and spatial framework for modeling city infrastructures are presented. © 2013Wiley Periodicals, Inc. Syst Eng 17: 62–76, 2014

Key words: power system modeling; modeling; decision support systems; load flow analysis; distributedpower generation

1. INTRODUCTION

Designing city infrastructure system models requires exten-sive visualization and estimation methods capable of ade-quately representing the interactions within and between thecity systems. This interaction between two systems, i.e.,interdependency, is “a bidirectional relationship between twoinfrastructures through which the state of each infrastructureinfluences or is correlated to the state of the other” [Rinaldiet al., 2001: 14]. System models typically focus on the proc-

Contract grant sponsor: MIT-Masdar Institute of Science and Technologycollaborative research grant, Project Code 400030.

*Author to whom all correspondence should be addressed (e-mail: [email protected]; [email protected]; [email protected];[email protected]; [email protected]).

Systems Engineering Vol. 17, No. 1, 2014© 2013 Wiley Periodicals, Inc.

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esses within a system, overlooking the interdependencies thatare critical in establishing system requirements, determiningsystem behaviors, and evaluating system performances. Citysystems such as the energy, water, transportation, building,and waste systems are increasingly interdependent, and it isimportant to take these interdependencies into account whilemodeling city systems. As a result, system modeling methodsthat incorporate system interdependencies are required.

In addition, interdependencies are significant in systemrepresentation since the bidirectional requirements betweensystems determine the level of system integration, and conse-quently the probability of cascading failures [Carreras et al.,2007]. Critical infrastructure interdependency research tendsto focus on these failures, but there is also a need to considerthe unexpected effects of design decisions within each systemand between systems in a system of systems.

This paper describes a system modeling framework suitedto infrastructure systems. This modeling framework com-prises a systematic method for breaking down and connectingsystem components as well as a spatial modeling frameworkfor representing these system components. The systematicprocess of breaking down and connecting system componentsenables the analysis of the system in three dimensions: analy-sis of the system’s inputs, analysis of the system’s hierarchy,and analysis of the system’s interdependencies. The modelingframework adopts the hierarchical system decompositionmethod presented in Alfaris et al. [2010], and, therefore,system models developed using this framework have a lay-ered structure ranging from viewing the system as a whole tofundamental system parameters. In addition, the multidomainformulation approach [Alfaris et al., 2010] adopted in thismodeling framework enables the integration of the systeminterdependencies. The spatial aspect of the modeling frame-work incorporates the geographical orientations and physicallocations of system infrastructure facilities, thus further en-hancing the modeling results.

Furthermore, the modeling framework is suited for use inthe development of Decision Support Systems (DSSs), withthe aim of informing administrative design decisions relatedto city infrastructures without having to explore advancedengineering processes. In order to demonstrate the advantagesof system development using the functional and spatial sys-tem representation model, an Integrated Energy System (IES)model has been developed [Adepetu et al., 2012] and exe-cuted in the form of a case study.

The contributions of this paper are:

• Development of a method for modeling city infrastruc-ture systems that incorporates the functional and spatialfeatures of the infrastructure systems in order to im-prove the system modeling process and results. Themethod builds on previous work on system decompo-sition [Alfaris et al., 2010], spatial topologies appliedin Geographic Information System (GIS) environ-ments, and functional layers discussed in Tolone et al.[2004] and Grogan and de Weck [2012].

• Integration of the critical system interdependencies inthe modeling framework since, typically, city systemsdo not operate in isolation. These interdependenciesensure that the impact of a system on the other systems,

and vice versa, is taken into consideration during themodeling process. This is an application of criticalinfrastructure interdependency studies done by de Por-cellinis et al. [2008] and Tolone et al. [2004].

• Application of the system modeling framework to thedevelopment of an Integrated Energy System (IES)model. The advantages of the IES over conventionalenergy modeling frameworks include the application ofan hourly load flow analysis, Distributed Generation(DG) modeling, integration of critical system interde-pendencies, and incorporation of spatial features inscenario analysis. The load flow analysis and distrib-uted generation modeling are implemented in the IESusing MATPOWER developed by Zimmerman et al.[2011].

• Implementation and analysis of a sustainable city casestudy that shows the application of the IES model andillustrates the meaning and viability of the obtainedresults.

Section 2 examines similar models and studies on interdepen-dency analysis. Section 3 gives a detailed explanation of thesystem development and modeling method. Section 4 gives adetailed description of the IES. Section 5 presents an applica-tion of the energy model. Section 6 summarizes the systemdevelopment model and highlights areas of future work.

2. BACKGROUND AND RELATED WORK

This paper is based on prior research work on modelingcomplex sustainable systems [Alfaris et al., 2010] and theapplication of this modeling approach in developing a city-scale energy system model [Adepetu et al., 2012]. Alfaris etal. [2010] provide a systematic multidomain approach thatcan be used to simultaneously break down a complex systemand integrate the resulting subsystems. Adepetu et al. [2012]present the IES, an integrated energy model and briefly dis-cusses other City.Net systems, i.e., water, waste, transporta-tion, and building. The current energy model in this papertakes some steps forward from Adepetu et al. [2012] such asthe conversion of the modeling approach from the annualscale to the diurnal scale, the introduction of a load flowanalysis (using MATPOWER), etc.

Energy models and simulations that have objectives simi-lar to either City.Net or the IES include Systems AdvisorModel (SAM), Homer, Energis, SynCity, Urban Infrastruc-ture Suite (UIS), Land Use Evolution and Impact AssessmentModel (LEAM), and UrbanSim. The structures of these mod-els and the contribution of this work to existing models aresubsequently discussed.

SAM was developed by the National Renewable EnergyLaboratory (NREL) and simulates different energy genera-tion methods such as Photovoltaics (PV), Concentrated SolarPower (CSP), solar water heating, wind, and geothermal.SAM presents energy-related and extensive financial outputsbased on the values of variables provided by the user but doesnot incorporate multiple energy source-load interactions.Homer (also developed by NREL) is similar to the SAM butexecutes multiple energy source-load optimizations. How-

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tant to clarify that the layers in the system are only conceptualand functional, and do not correspond to physical layers.Conversely, the locations of the nodes and edges in the modelrepresent the geographical locations of the represented systemcomponents. A cell represents a fundamental unit of space,and a combination of cells form a city layout.

Nodes represent points of interest in the infrastructuresystem while an edge is a connection between two nodes,carrying the “ flow” between the nodes. For example, in theenergy system, nodes include power stations, distributionsubstations, buses, and transformers while the edges are thepower lines with different voltage levels that connect thesenodes. It should also be pointed out that multiple nodes andmultiple edges can occupy the same functional layer. Thepurpose of the functional layers is to clearly identify andgroup the functionalities of the nodes and edges in a system.Furthermore, the spatial modeling has the advantage of in-cluding the spatial parameters such as distance in the synthe-

sis and analysis stages of the model, which makes the model-ing process more comprehensive.

3.3. Model Structure

City.Net has a three-stage model structure [Adepetu et al.,2012] as seen in Figure 1. These stages are synthesis, analysis,and evaluation. These are the stages at which the resulting IESmodel can be used.

a. Synthesis is the process of defining a custom systemconfiguration for a scenario by defining the values ofthe FPs and the spatial properties of system compo-nents. For example, synthesizing a wind farm wouldinvolve defining the values of FPs such as number ofwind turbines, turbine cut-in speed, turbine rated speed,turbine cut-out speed, etc. In addition, a spatial propertyof the plant such as its location would be defined inorder to account for the distance of the wind farm to thevarious loads.

b. Analysis is the stage of the defined model where thebehavior of the system is obtained based on its definedconfiguration; i.e., the values of the BPs are obtainedbased on the defined values of the FPs.

c. Evaluation involves rating the behaviors of the user-de-fined system using performance benchmarks, i.e.,KPIs. In order words, the user-defined system is evalu-ated.

4. IES

The IES is defined according to the research approach speci-fied in Section 3. The FPs, BPs, and KPIs are outlined, andparameter relations are defined.

4.1. Conceptualization

The concept behind each aspect of the IES is detailed here.This includes subsystems in energy generation, transmission,and distribution.

Figure 6. Wind power generation analysis. [Color figure can beviewed in the online issue, which is available at wileyonlineli-brary.com.]

Figure 5. Decomposition of electricity generation. [Color figure canbe viewed in the online issue, which is available at wileyonlineli-brary.com.]

Figure 7. City infrastructure spatial framework. [Color figure canbe viewed in the online issue, which is available at wileyonlineli-brary.com.]

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feeding the output from one stage as an input to the next stage.In general, decomposition determines the Form Parameters(FPs), Behavior Parameters (BPs) and Key Performance In-dicators (KPIs), and these are the parameters at the synthesis,analysis, and evaluation stages, respectively. Decompositionwith respect to system synthesis is displayed in Figure 2,logically separating the main structures in a system (nodes)from the links within and between these structures (edges).The decomposition process is hierarchical, breaking down asystem one layer after another until the fundamental compo-nents of the system (represented by FPs) are defined.

Figures 3, 4, and 5 show a sample decomposition processin progression. Figure 3 shows the decomposition of the windfarm with respect to the synthesis of the wind farm and howthe fundamental FPs of the wind farm are defined. Figure 4shows the second level of the system hierarchy with respectto the behavior analysis. The generation-consumption ap-proach is employed since city infrastructure systems are eithersupplying or utilizing some resources, finances, etc. Based onthe nature of city systems, four categories of generation-con-sumption behaviors were specified: resources, finances, emis-sions, and utilities/services. Looking downwards in thesystem hierarchy, Figure 5 shows the electricity generationbehavior and how decomposition proceeds from resourcegeneration to the different modes of electricity generation.

3.1.3. FormulationFormulation comprises the identification of the parameterrelations and the energy system’s governing equations, i.e.,relationships between the FPs, BPs and KPIs and the con-straints involved. The formulation process is applied acrossevery level of the system hierarchy as defined in the systemdecomposition and establishes the relationships between thedifferent levels of the hierarchy. The formulation of windelectricity generation is seen in Figure 6, resulting in Eq. (1).This formulation is a continuation of the decomposition proc-ess in Figure 5.

3.1.4. SimulationSimulation is the development of a software tool that modelsthe system parameters, relations, and interdependencies. The

BPs and KPIs are computed, and the results are presented asnumbers and figures.

3.2. Spatial Modeling Framework

The spatial modeling framework, used to represent infrastruc-ture system components, comprises nodes, edges, cells, andlayers. The focus of this work is on city infrastructure systems,and, as a result, the geographical orientation of the systemcomponents contributes to the system behaviors and perform-ances. This framework is similar to the framework used inGeographic Information System (GIS) applications [ESRI,2012]. Figure 7 [Grogan and de Weck, 2012] shows a graphicdescription of the nodes, edges, cells, and layers. It is impor-

Figure 2. Decomposition (synthesis). [Color figure can be viewedin the online issue, which is available at wileyonlinelibrary.com.]

Figure 3. Decomposition of wind farm (synthesis). [Color figurecan be viewed in the online issue, which is available at wileyonlineli-brary.com.]

Figure 4. Decomposition of system behaviors. [Color figure can beviewed in the online issue, which is available at wileyonlineli-brary.com.]

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ever, neither SAM nor Homer incorporate system interde-pendencies.

MetroQuest is an application targeted at stakeholders andenables its users to observe and understand the future effectof current policies. MetroQuest is based on the integration ofhealth, sustainability, and air quality models, and it is appliedas a tool for estimating greenhouse gas reduction, regionalgrowth administration, and transportation-related develop-ment [MetroQuest, 2012].

EnerGis [Girardin et al., 2010] is a GIS that models energysystem processes in urban areas. EnerGis is used for findingmeans to improve the efficiency of energy systems, and toadvance the integration of renewable energy technologies asmodes of distributed generation. In particular, EnerGis in-cludes the heating and cooling requirements of geographicalregions using GIS information.

Another model similar to the IES is SynCity [Keirstead etal., 2009], which is also used to model energy systems inurban areas. SynCity incorporates the city layout and socio-economic factors such as population activity in the energymodeling process.

Urban Infrastructure Suite (UIS), developed by the USNational Infrastructure Simulation and Analysis Center (NI-SAC) comprises seven integrated modules that model urbanpopulations and infrastructures [NISAC, 2012a]. These sevenmodules represent transportation, energy, water, telecommu-nications, mobility, epidemiology, and finance. The Interde-pendent Energy Infrastructure Simulation System (IEISS)module [NISAC, 2012b] focuses on the energy system, mod-eling the energy-related interdependencies with nonlinearcomplexity. The objective of IEISS is to determine the serv-ice-providing capability of an electric grid and natural gasnetwork.

The Land Use Evolution and Impact Assessment Model(LEAM) [LEAMgroup, 2012] employs a dynamic modelingapproach, with autonomous submodels that represent the landuse transformation being executed concurrently after calibra-tion. LEAM computes the economic, environmental, andsocial impacts of these transformations in land use, and cantherefore compare and evaluate different scenarios resultingfrom different land use policies.

UrbanSim, an open source decision support system tar-geted at Metropolitan Planning Organizations (MPOs), mod-els land use, finances, transportation and the environment inurban areas as a result of selected policies and infrastructures[UrbanSim, 2012]. As a result, MPOs can compare and assessgrowth management policies for urban areas. The UrbanSimmodel is based on the interdependencies between different butvital infrastructures.

The above-mentioned models, which are state-of-the-artmodels albeit for different purposes, either do not incorporatesystem interdependencies or lack a structured spatial frame-work as described in this paper. Critical infrastructure studiesthat focus on system failures include [de Porcellinis et al.,2008] and [Tolone et al., 2004]. Porcellinis et al. show amethod of modeling heterogeneous and interdependent criti-cal infrastructures that accounts for possible failures in infra-structure with different levels of accuracy. Tolone et al. alsofocus on understanding cross-dependencies in infrastructure,thereby estimating acceleration and chain reaction of failures

in infrastructures. This paper presents interdependencies witha focus on determining the effect of design decisions, ratherthan system failure that some of the aforementioned systemsare designed to model.

Furthermore, a spatial modeling framework that enhancessystem design and could prove to be a powerful tool in systemrepresentation models is introduced in this paper. These twomajor aspects of the system model are further described inSection 3.

3. SYSTEM MODELING METHOD

The functional and spatial system model comprises two re-search methods that complement each other. The first is asystem functional representation that uses hierarchical de-composition and the multidomain formulation for modelingsystems [Alfaris et al., 2010], comprising four stages: concep-tualization, decomposition, formulation, and simulation. Thesecond aspect of the research method is a spatial modelingframework that provides the infrastructure components witha geographical orientation, and informs the synthesis of com-ponents, analysis of behaviors, and evaluation of perform-ances.

3.1. System Functional Representation

The four stages comprising the system functional repre-sentation process are described as follows.

3.1.1. ConceptualizationConceptualization is the process of elaborating the fundamen-tal ideas of the system. Conceptualization involves elicitingand specifying the requirements for the system to achieve itsobjective. One of the fundamental ideas of the system is thesystem process in Figure 1, specifying the function of eachstage and the information flow between different stages in thedeveloped system model.

3.1.2. DecompositionDecomposition involves breaking down the system into com-ponents and logical phases, using a hierarchical approach and

Figure 1. System process.













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4.1.1. Wind FarmA wind farm consists of a number of wind turbines arrangedfor optimal energy generation. For wind farm arrangement,the distance between wind turbines on the same row shouldbe about three to five turbine diameters and the distancebetween wind turbines on the same column should be aboutfive to eight diameters [Goebel, 2010b].

4.1.2. PVThe PV station comprises several PV panels with the impor-tant factors affecting electricity generation being the total PVpanel area, the panel efficiency at different temperatures andlight intensities, and the hourly Direct Normal Irradiation(DNI) at the selected geographical location. The effect oftemperature on PV power is important as temperature varieswith time and location.

4.1.3. CSPThe different types of CSP stations include the parabolictrough, central tower, parabolic dish, and Fresnel mirrors.However, the parabolic trough type is the only CSP typecurrently synthesized in the IES.

4.1.4. HydropowerThe generation of energy from a hydropower station involvesbuilding the plant across a river, creating a head to yieldpotential energy and possibly harnessing kinetic energy fromthe river flow. The three major types of hydropower stationsare impoundment, run-of-river, and pumped storage. Theimpoundment type taps into the potential energy while therun-of-river takes advantage of the river’s kinetic energy[Tester et al., 2005].

4.1.5. BiomassThe biomass power station in the IES can be used in twomodes: combustion or gasification of biomass. The combus-tion process uses a steam turbine for energy generation whilethe gasification process uses a combined cycle [Caputo et al.,2005]. As detailed in Caputo et al. [2005], the efficiency ofthe plant is dependent on the power capacity of the biomassstation. Typically, higher power capacities have higher energyconversion efficiencies.

4.1.6. Natural GasThe IES natural gas station follows the template applied bySpath and Mann [2000] and consists of a combined cycle. Thecombined cycle consists of a gas turbine and a steam turbine.The steam turbine capacity is typically about half the capacityof the gas turbine.

4.1.7. Transmission and DistributionThe IES utilizes load flow analysis algorithm for modelingpower transmission and distribution. The user defines theelectric grid configuration that comprises the bus, line, andtransformer parameters. The load at each bus, which can bedefined by the user based on a seasonal profile, is analyzed inconjunction with the seasonal variation of the generator ca-pacities and the grid capacity. The load flow analysis isexecuted using MATPOWER [Zimmerman et al., 2011], a setof MATLAB files that use standard load flow analysis meth-

ods in analyzing power systems. Load flow analysis methodsinclude the Newton-Raphson method and the Gauss-Seidelmethod.

The power grid structure and the load flow analysis ap-proach applied in the IES ensure that distributed generationsources can be incorporated in the power grid. The load flowanalysis is executed each hour of the year based on thevariation of the capacity of the intermittent power sources(wind, PV, CSP) and the user-defined operating schedule ofother power stations. As a result, the model captures theeffects of the intermittences on meeting the load demand, thevoltage balance of the power grid, and the effectiveness ofdistributed generation sources.

4.1.8. Revenue GenerationRevenue can be generated in the energy system by sellingelectricity to residential, commercial, and industrial consum-ers, which are represented in the other City.Net systems.Energy generated but not consumed locally by the load isinjected to the external grid if the use case being modeled hasa distribution system connected to the grid. Otherwise, excessenergy is dumped.

4.1.9. Finance ConsumptionExpenses in the energy system are generally represented inthe form of initial capital expenditures and annual costs suchas operational, maintenance, and fuel costs.

4.1.10. EmissionsThere are generally two concepts of emissions: lifecycleemissions and actual emissions that occur during energygeneration. The lifecycle emissions are used in estimatingemissions from renewable energy methods such as PV andwind. These lifecycle emissions are the Greenhouse Gases(GHGs), usually represented in a CO2 equivalent, producedwhile manufacturing the equipment used in the power plant[Evans et al., 2009]. Typically, the lifecycle emissions areestimated per unit energy generated by a power plant, whilethose for the actual emissions (CO2, CO, CH4, and NOx) areestimated based on the amount of fuel used.

4.1.11. Resource ConsumptionResource consumption in the energy system is, for the mostpart, in the form of water use and land requirements:

a. Water Consumption: In the energy system, water isused in the CSP, hydropower, natural gas, and biomasspower plants. In the hydropower station, the water usedis more of a case of water reserved and evaporated dueto the water storage built in a hydropower station[Caputo et al., 2005].

b. Land Requirements: Infrastructure such as powerplants and substations require dedicated land space.This is the land space that is estimated in the IES andnot the lifecycle land requirements. Lifecycle land re-quirements is a way of estimating the land footprint ofenergy generation technologies, ranging from the landused for manufacturing of the power station equipmentto the disposal or recycling of this equipment afterdecommissioning the power station. This is the ap-

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4.1.1. Wind FarmA wind farm consists of a number of wind turbines arrangedfor optimal energy generation. For wind farm arrangement,the distance between wind turbines on the same row shouldbe about three to five turbine diameters and the distancebetween wind turbines on the same column should be aboutfive to eight diameters [Goebel, 2010b].

4.1.2. PVThe PV station comprises several PV panels with the impor-tant factors affecting electricity generation being the total PVpanel area, the panel efficiency at different temperatures andlight intensities, and the hourly Direct Normal Irradiation(DNI) at the selected geographical location. The effect oftemperature on PV power is important as temperature varieswith time and location.

4.1.3. CSPThe different types of CSP stations include the parabolictrough, central tower, parabolic dish, and Fresnel mirrors.However, the parabolic trough type is the only CSP typecurrently synthesized in the IES.

4.1.4. HydropowerThe generation of energy from a hydropower station involvesbuilding the plant across a river, creating a head to yieldpotential energy and possibly harnessing kinetic energy fromthe river flow. The three major types of hydropower stationsare impoundment, run-of-river, and pumped storage. Theimpoundment type taps into the potential energy while therun-of-river takes advantage of the river’s kinetic energy[Tester et al., 2005].

4.1.5. BiomassThe biomass power station in the IES can be used in twomodes: combustion or gasification of biomass. The combus-tion process uses a steam turbine for energy generation whilethe gasification process uses a combined cycle [Caputo et al.,2005]. As detailed in Caputo et al. [2005], the efficiency ofthe plant is dependent on the power capacity of the biomassstation. Typically, higher power capacities have higher energyconversion efficiencies.

4.1.6. Natural GasThe IES natural gas station follows the template applied bySpath and Mann [2000] and consists of a combined cycle. Thecombined cycle consists of a gas turbine and a steam turbine.The steam turbine capacity is typically about half the capacityof the gas turbine.

4.1.7. Transmission and DistributionThe IES utilizes load flow analysis algorithm for modelingpower transmission and distribution. The user defines theelectric grid configuration that comprises the bus, line, andtransformer parameters. The load at each bus, which can bedefined by the user based on a seasonal profile, is analyzed inconjunction with the seasonal variation of the generator ca-pacities and the grid capacity. The load flow analysis isexecuted using MATPOWER [Zimmerman et al., 2011], a setof MATLAB files that use standard load flow analysis meth-

ods in analyzing power systems. Load flow analysis methodsinclude the Newton-Raphson method and the Gauss-Seidelmethod.

The power grid structure and the load flow analysis ap-proach applied in the IES ensure that distributed generationsources can be incorporated in the power grid. The load flowanalysis is executed each hour of the year based on thevariation of the capacity of the intermittent power sources(wind, PV, CSP) and the user-defined operating schedule ofother power stations. As a result, the model captures theeffects of the intermittences on meeting the load demand, thevoltage balance of the power grid, and the effectiveness ofdistributed generation sources.

4.1.8. Revenue GenerationRevenue can be generated in the energy system by sellingelectricity to residential, commercial, and industrial consum-ers, which are represented in the other City.Net systems.Energy generated but not consumed locally by the load isinjected to the external grid if the use case being modeled hasa distribution system connected to the grid. Otherwise, excessenergy is dumped.

4.1.9. Finance ConsumptionExpenses in the energy system are generally represented inthe form of initial capital expenditures and annual costs suchas operational, maintenance, and fuel costs.

4.1.10. EmissionsThere are generally two concepts of emissions: lifecycleemissions and actual emissions that occur during energygeneration. The lifecycle emissions are used in estimatingemissions from renewable energy methods such as PV andwind. These lifecycle emissions are the Greenhouse Gases(GHGs), usually represented in a CO2 equivalent, producedwhile manufacturing the equipment used in the power plant[Evans et al., 2009]. Typically, the lifecycle emissions areestimated per unit energy generated by a power plant, whilethose for the actual emissions (CO2, CO, CH4, and NOx) areestimated based on the amount of fuel used.

4.1.11. Resource ConsumptionResource consumption in the energy system is, for the mostpart, in the form of water use and land requirements:

a. Water Consumption: In the energy system, water isused in the CSP, hydropower, natural gas, and biomasspower plants. In the hydropower station, the water usedis more of a case of water reserved and evaporated dueto the water storage built in a hydropower station[Caputo et al., 2005].

b. Land Requirements: Infrastructure such as powerplants and substations require dedicated land space.This is the land space that is estimated in the IES andnot the lifecycle land requirements. Lifecycle land re-quirements is a way of estimating the land footprint ofenergy generation technologies, ranging from the landused for manufacturing of the power station equipmentto the disposal or recycling of this equipment afterdecommissioning the power station. This is the ap-

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4. IES

















4. IES









67

Pstation = 1

106 ρgh +

12

ρ(vout2 − vin

2 ) × Q × η, (5)

E = Pstation × CF × 8760 hours (6)

4.2.5. Biomass [Caputo et al., 2005] and Natural Gas [Spathand Mann, 2000]The same set of parameters are used in estimating the energygenerated E by the biomass and natural gas power stations.The difference is the type of fuel used, i.e., biomass andnatural gas as suggested by the power station names. Theparameters used for estimating energy generation are: steamturbine capacity Pst (MW), gas turbine capacity Pgt (MW),and the hours of operation OH (h). In reality, the capacity ofthe turbines and the capacity of the power block are matched.Both steam and gas turbines are used in combined cycleswhile only one of either type of turbine is used in a singlecycle.

E = (Pst + Pgt) × OH (7)

Furthermore, the fuel flow rate, i.e., the mass of fuelconsumed by the power station M (tonnes/year) is determinedbased on the generated energy, the fuel heating value HV(kJ/kg), annual operating full load hours OH (h), and plantenergy-conversion efficiency at rated power ηp:

M = E × 3600ηp × HV

(8)

4.2.6. Finances [NREL, 2008; Tester et al., 2005]In the IES, revenue R ($/year) is generated from the sale ofenergy. The energy sold is determined based on the energyactually used by consumers EC (MWh/year) and not thegenerated energy as there are some losses in the grid. Theother FP included in the revenue estimation is the unit sellingprice of electricity SP ($/MWh).

R = EC × SP (9)

On the other hand, costs are estimated based on unit capitalcost Cunit Cap ($/MW), unit operational and maintenance(O&M) cost Cunit O&M ($/MW), unit fuel cost Cunit F ($/kg),plant capacity P (MW), mass of fuel consumed annually M(kg), capital cost CCap ($), annual O&M cost CO&M ($/year),and the annual fuel cost CF ($/year). CF applies to powerstations that use fuels such as the biomass and the natural gas.

CCap = P × Cunit Cap, (10)

CO&M = P × Cunit O&M, (11)

CF = M × Cunit F, (12)

These costs by themselves are not particularly useful fordeducing the actual cost of generating energy, and this iswhere the levelized cost LEC ($/kWh) is applicable. Theadditional parameters used in estimating the LEC are: ex-pected power station lifetime T (years), net cost in year t, Ct($/yr), energy generated in year t, Et (kWh), and discount rated.

LEC = ∑t=0

N Ct

(1 + d)t

∑t=1N

Et

(1 + d)t

(13)

where t is the year and Ct=0 is the same as CCap.

4.2.7. EmissionsEmissions are estimated linearly from the energy generatedby each power station, Eplant (kWh), and the fuel used per year,Mfuel (kg) (if applicable). The key FPs are the mass of CO2 perunit energy, CO2/kWh (kg/kWh), and the emissions per unitmass of fuel GHG/kg. As a result, the lifecycle emissionsGHGLC (kg) and the actual emissions during generation,GHGGen (kg), can be obtained:

GHGLC = CO2/kWh × Eplant(14)

GHGGen = GHG/kg × Mfuel. (15)

4.2.8. Resource ConsumptionThe resource consumption BPs in the IES are the land occu-pied by power plants Lplant (sq-km) and water consumed bypower plants Wplant (tonnes). These BPs are linearly estimatedfrom the energy generated per year in the power plant, Eplant(kWh), water used per unit energy in the plant, Wper kWh(tonnes/kWh), land required per unit kW in plant, Lper kW(sq-km/kW), and infrastructure capacity Pplant (kW):

Wplant = Wper kWh × Eplant(16)

Lplant = Lper kW × Pplant(17)

4.2.9. Transmission and Distribution [Zimmerman et al.,2011; Grainger and Stevenson, 1994]Equation (18) is the Gauss-Seidel equation that finds a bal-ance in the values of the voltage at each bus in a power grid.The FPs used are: the net load Pk + jQk (VA) at each bus, busvoltage Vk (V), and number of buses, N.

The admittance matrix Y (Ω−1) is obtained from the imped-ances between buses. It is a product of the feeder cableimpedance per unit distance and the length of the cablebetween buses. Y is calculated by the MATPOWER module.

Vki+1 =

1Ykk

Pk − jQk

Vki∗ − ∑

n=1,n≠k

k−1

YknVni+1 − ∑

n=k+1,n≠k

N

YknVni

, (18)

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proach taken by Evans et al. [2009] in describing theland use as a sustainability indicator. The model in thispaper focuses only on the land occupied by the powerstation during its years in operation. For infrastructureequipment, such as PV stations, CSP stations, and windfarms, the land required per unit power capacity is beestimated based on the spatial arrangement of PV pan-els, solar mirrors, and wind turbines, respectively.

4.2. Decomposition and Formulation

System parameters, parameter relations, and interdependen-cies of the IES are presented and discussed in this section. Itis important to point out that certain assumptions have beenmade in order to make the model generally applicable andcomprehensive at the same time. One broad assumption thathas been applied to the modeling process is that of linearestimation; i.e., the behavior parameters such as the landoccupied and costs vary linearly with the capacity of thepower stations. According to the IES model, if a 5-MW planthas a capital cost of $10,000, then a 10-MW plant has a capitalcost of $20,000, while this is not necessarily true in reality.However, the assumption might be reasonable taking intoaccount the fact that the cost per MW ratio drops as plantcapacity increases due to the economies of scale.

Another assumption is that of efficiency and operationhours in the plants such as the biomass power plant or naturalgas plant. The efficiency typically increases (but at a decreas-ing rate) with the plant capacity increase and then hits athreshold. Moreover, the IES model assumes that the biomassor natural gas stations are either working at full capacity ornot at all, but, in reality, they could work at any other capacity,e.g., 50% capacity or 60% capacity. Assumptions are alsomade in the case of the intermittent power sources such assolar and wind since the exact availability of these resourcescannot be determined in advance.

The IES model does not apply a stochastic modelingapproach. Therefore, these assumptions are necessary and aidthe simplicity of the IES model. Sensitivity analysis forparameters such as the specific land use, specific emissions,specific water consumption, hourly wind speeds, and hourlysolar irradiation can be varied in order to get a range ofperformance of modeled energy system scenarios. Theseseem to be the most fragile parameters for which it is neces-sary to perform extensive uncertainty analysis. In addition,the load can be used as the parameter of interest in a sensitivityanalysis in order to estimate the impact of population growthin the city. As a result, the corresponding energy infrastructuredevelopment requirements can be determined with more pre-cision.

The system FPs and BPs, and the relations between theseparameters are as follows.

4.2.1. Wind Farm [Jahanbani-Ardakani et al., 2010]The primary BP of the wind farm is the electricity generatedE (MWh), and it is estimated based on the variation of thewind turbine power with wind speeds as seen in Eq. (1). Thewind farm FPs are: wind speed v (m/s), duration of operationT (s), turbine cut-in speed vin (m/s), turbine cut-out speed vco(m/s), turbine rated speed vrated (m/s), turbine blade length r

(m), turbine rated capacity Prated (MW), number of turbinesin wind farm, N, and wind variation exponent m. The windfarm capacity Pstation (MW) is another BP that is determinedbased on the sum total of the wind turbine power ratings.

E = NT ×

0,

Pr × (v − vci

vr − vvi

)m,

Pr + Pco − Pr

vco − vr

,

v < vci, v > vco

vci ≤ v ≤ vco

vr ≤ v ≤ vco

(1)

4.2.2. PV Station [Tester et al., 2005; NREL, 2012;Tamizhmani et al., 2003]The electricity generated by the PV station E (MWh) iscalculated based on the hourly DNI through the course of theyear. One of the important factors that affects the efficiencyof the PV panel η is the module temperature Tmod (oC). Tmodis estimated hourly using the following FPs: ambient tem-perature Tamb (oC), hourly DNI (kWh/m2), and hourly windspeeds vwind (m/s):

Tmod = 0.943Tamb + 28DNI − 1.528vwind + 4.3 (2)

In addition to Tmod, the energy generated per hour is calculatedfrom the overall DC-to-AC derate factor df, PV panel lengthl (m), PV panel width w (m), PV panel efficiency, PV panelcapacity Prated (kW), temperature coefficient of rated power,δP/T (%/oC), and number of PV panels N. df is used to estimatelosses from panel rating errors, wiring, inverters, transform-ers, etc.

E = df × DNI1000

× lwN × η 1 +

(Tmod − 25) × δP/T

100

(3)

4.2.3. CSP Station [Tester et al., 2005; Goebel, 2010a]The energy generation BP E (MWh) calculation of the CSPstation is similar to that of the PV station but without tempera-ture dependency. The FPs are: mirror aperture area A (m2),number of mirrors, N, plant solar-to-electric efficiency ηS-E,and hourly DNI (kWh/m2). ηS-E represents the plant efficiencyfrom the solar field to the point of energy output (typicallyelectricity).

E = ηS−E × N × A × DNI (4)

4.2.4. Hydropower [Tester et al., 2005]The energy generated E (MWh) by the hydropower station iscalculated from the hydropower station rated capacity Pstation(MW) and the capacity factor CF, which represents how oftenthe station is used at its full capacity. The other FPs are: thehead h (m), inlet water speed vin (m/s), outlet water speed vout(m/s), volumetric flow rate Q (m3/s), plant efficiency η,capacity factor CF, water density ρ (kg/m3), and accelerationdue to gravity g (m/s2):













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68

Pstation = 1

106 ρgh +

12

ρ(vout2 − vin

2 ) × Q × η, (5)

E = Pstation × CF × 8760 hours (6)

4.2.5. Biomass [Caputo et al., 2005] and Natural Gas [Spathand Mann, 2000]The same set of parameters are used in estimating the energygenerated E by the biomass and natural gas power stations.The difference is the type of fuel used, i.e., biomass andnatural gas as suggested by the power station names. Theparameters used for estimating energy generation are: steamturbine capacity Pst (MW), gas turbine capacity Pgt (MW),and the hours of operation OH (h). In reality, the capacity ofthe turbines and the capacity of the power block are matched.Both steam and gas turbines are used in combined cycleswhile only one of either type of turbine is used in a singlecycle.

E = (Pst + Pgt) × OH (7)

Furthermore, the fuel flow rate, i.e., the mass of fuelconsumed by the power station M (tonnes/year) is determinedbased on the generated energy, the fuel heating value HV(kJ/kg), annual operating full load hours OH (h), and plantenergy-conversion efficiency at rated power ηp:

M = E × 3600ηp × HV

(8)

4.2.6. Finances [NREL, 2008; Tester et al., 2005]In the IES, revenue R ($/year) is generated from the sale ofenergy. The energy sold is determined based on the energyactually used by consumers EC (MWh/year) and not thegenerated energy as there are some losses in the grid. Theother FP included in the revenue estimation is the unit sellingprice of electricity SP ($/MWh).

R = EC × SP (9)

On the other hand, costs are estimated based on unit capitalcost Cunit Cap ($/MW), unit operational and maintenance(O&M) cost Cunit O&M ($/MW), unit fuel cost Cunit F ($/kg),plant capacity P (MW), mass of fuel consumed annually M(kg), capital cost CCap ($), annual O&M cost CO&M ($/year),and the annual fuel cost CF ($/year). CF applies to powerstations that use fuels such as the biomass and the natural gas.

CCap = P × Cunit Cap, (10)

CO&M = P × Cunit O&M, (11)

CF = M × Cunit F, (12)

These costs by themselves are not particularly useful fordeducing the actual cost of generating energy, and this iswhere the levelized cost LEC ($/kWh) is applicable. Theadditional parameters used in estimating the LEC are: ex-pected power station lifetime T (years), net cost in year t, Ct($/yr), energy generated in year t, Et (kWh), and discount rated.

LEC = ∑t=0

N Ct

(1 + d)t

∑t=1N

Et

(1 + d)t

(13)

where t is the year and Ct=0 is the same as CCap.

4.2.7. EmissionsEmissions are estimated linearly from the energy generatedby each power station, Eplant (kWh), and the fuel used per year,Mfuel (kg) (if applicable). The key FPs are the mass of CO2 perunit energy, CO2/kWh (kg/kWh), and the emissions per unitmass of fuel GHG/kg. As a result, the lifecycle emissionsGHGLC (kg) and the actual emissions during generation,GHGGen (kg), can be obtained:

GHGLC = CO2/kWh × Eplant(14)

GHGGen = GHG/kg × Mfuel. (15)

4.2.8. Resource ConsumptionThe resource consumption BPs in the IES are the land occu-pied by power plants Lplant (sq-km) and water consumed bypower plants Wplant (tonnes). These BPs are linearly estimatedfrom the energy generated per year in the power plant, Eplant(kWh), water used per unit energy in the plant, Wper kWh(tonnes/kWh), land required per unit kW in plant, Lper kW(sq-km/kW), and infrastructure capacity Pplant (kW):

Wplant = Wper kWh × Eplant(16)

Lplant = Lper kW × Pplant(17)

4.2.9. Transmission and Distribution [Zimmerman et al.,2011; Grainger and Stevenson, 1994]Equation (18) is the Gauss-Seidel equation that finds a bal-ance in the values of the voltage at each bus in a power grid.The FPs used are: the net load Pk + jQk (VA) at each bus, busvoltage Vk (V), and number of buses, N.

The admittance matrix Y (Ω−1) is obtained from the imped-ances between buses. It is a product of the feeder cableimpedance per unit distance and the length of the cablebetween buses. Y is calculated by the MATPOWER module.

Vki+1 =

1Ykk

Pk − jQk

Vki∗ − ∑

n=1,n≠k

k−1

YknVni+1 − ∑

n=k+1,n≠k

N

YknVni

, (18)

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4. IES









69

5. CASE STUDY: MASDAR CITY

Masdar City is located in the outskirts of Abu Dhabi, UnitedArab Emirates (UAE) with coordinates 24o 26′ 2.55" N and54o 36′ 14.44" E. Masdar City is still under construction andaims to be the world’s first sustainable and carbon neutral city.The city is home to the Masdar Institute of Science andTechnology and will house 50,000 people [Carvalho, 2009].This case study illustrates a simplified application of the IES.

5.1. Synthesis

The energy infrastructure simulated are a wind farm, biomasspower station, PV station, and alternating current (AC) distri-bution system.

5.1.1. Wind FarmThe wind farm has a capacity of 11 MW with four GE2.75–103 [General Electric Power and Water, 2011] windturbines. The hourly wind data for the Masdar City area wasnot available, but an hourly dataset with similar aggregatewind speeds was used.

5.1.2. PV StationThe PV station has a capacity of 10 MW and comprises solelySuntech STP280-24/Vd [Suntech, 2012] polycrystalline PVmodules. The PV modules have a Standard Test Condition(STC) efficiency of 14.4%. Abu Dhabi has an annual DNI ofabout 2000 kWh/m2 and is therefore a good location for a PVstation (the hourly solar radiation data was not available buta dataset with a total DNI of about 2000 kWh/m2 was used).In addition, the temperature variation in the Masdar City areawas included in the synthesis of the PV station.

5.1.3. Biomass StationThe biomass power station has a capacity of 47 MW with agas turbine of 30 MW and a steam turbine of 17 MW. A powerplant of this output capacity and configuration is expected tohave an efficiency of 45% [Caputo et al., 2005]. Also, aschedule is used to determine when the biomass power stationis in operation or offline.

5.1.4. Distribution SystemAn AC distribution system is simulated with a mesh networkas seen in Figure 9. Five buses are configured in the distribu-tion system: one slack bus (Bus 1), two load buses (Bus 2 andBus 4), and two voltage control buses (Bus 3 and Bus 5). Theloads at the various buses use the IEEE RTS load model buthave different peak values. The peak loads at the various busesused in the simulation are as follows: 10 MW at Bus 2, 55MW at Bus 3, 8 MW at Bus 4, and 9 MW at Bus 5. By

inspection, it can be observed that the generators cannot meetthe above-mentioned loads. Therefore, the city is dependenton the utility to meet its energy demands. Figure 10 shows theconnections between the generators and the buses in differentlayers.

Since the Masdar City case study is a small city applicationof the IES model, there is no transmission layer as seen in

Figure 9. Distribution system (layer 4).

Table II. IES Layers

Figure 10. Masdar City energy system layers. [Color figure can beviewed in the online issue, which is available at wileyonlineli-brary.com.]

10 ADEPETU ET AL.


where k is the number of the bus in consideration.

4.2.10. Energy-Related InterdependenciesTable I shows a summary of energy-related interdependenciesin other systems, i.e., energy generation and energy consump-tion in other systems. These relations help in defining the rolethe energy system plays in other city infrastructure systems.This paper focuses on the IES alone and the interdependenciesonly show the first-order impacts of the system requirements.However, the nth-order impact, where n is the number ofsystem BPs, of these interdependencies can be visualized inan integrated city model that includes parameters from all fiveCity.Net systems. These nth-order impacts can be measuredusing multidomain matrices as defined in Alfaris et al. [2010].This is an area for future research.

Figure 8 provides an overview of the City.Net system fromthe energy system perspective. Figure 8 shows energy-relateddependencies in the city infrastructure system following thehierarchy used in the decomposition template seen in Figure4. Generation and Consumption refer to the generation andconsumption of resources, finances, and emissions within theenergy system, hence, the “G” and “C” tags used in otherparts of Figure 8.

4.2.11. KPIsAt the evaluation stage in IES, KPIs are used to estimate andrate the performance of the energy system. These KPIs arecombinations of environmental, social, and financial indica-tors and they follow the equations in [Adepetu et al., 2012].They are listed as follows:

a. Renewable Energy Fraction (REF) [Economist Intelli-gence Unit and Siemens, 2009]

b. Energy Cost Indicator (ECI) [Afgan and Carvalho,2004]

c. Capital Cost Indicator (CCI) [Afgan and Carvalho,2004]

d. Energy Consumption per Head (ECH) [Economist In-telligence Unit and Siemens, 2009]

e. Energy Intensity (EI) [Economist Intelligence Unit andSiemens, 2009]

f. CO2 Emissions per Head [Economist Intelligence Unitand Siemens, 2009]

g. CO2 Savingsh. Area per MW [Afgan and Carvalho, 2004].

4.2.12. IES LayersThe nodes and edges in the energy system are classifiedaccording to their functions in the energy system. The layers

are not strictly ordered. For example, transmission substationson layer 2 can be eliminated in the cases of distributedgeneration. The classifications are listed in Table II.

4.3. Simulation

Simulation of the IES comprises a Graphic User Interface(GUI), Microsoft Excel spreadsheets, a MATLAB Applica-tion Programming Interface (API), and MATLAB *.m filesat the simulation stage. The synthesis process is executed withthe GUI and spreadsheets, while analysis and evaluation isexecuted on MATLAB. The diurnal simulation time approachis applied in the IES. This way, the variations in the solarirradiation and wind speeds and the effects of these intermit-tences on the power grid are adequately incorporated in theIES modeling process. Schedules are also applied to thebiomass and natural gas power stations where they are eitherin operation or not.

Table I. Energy Generation and Consumption in Other City.Net Systems

Figure 8. Energy system perspective of City.Net. [Color figure canbe viewed in the online issue, which is available at wileyonlineli-brary.com.]













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70

5. CASE STUDY: MASDAR CITY

Masdar City is located in the outskirts of Abu Dhabi, UnitedArab Emirates (UAE) with coordinates 24o 26′ 2.55" N and54o 36′ 14.44" E. Masdar City is still under construction andaims to be the world’s first sustainable and carbon neutral city.The city is home to the Masdar Institute of Science andTechnology and will house 50,000 people [Carvalho, 2009].This case study illustrates a simplified application of the IES.

5.1. Synthesis

The energy infrastructure simulated are a wind farm, biomasspower station, PV station, and alternating current (AC) distri-bution system.

5.1.1. Wind FarmThe wind farm has a capacity of 11 MW with four GE2.75–103 [General Electric Power and Water, 2011] windturbines. The hourly wind data for the Masdar City area wasnot available, but an hourly dataset with similar aggregatewind speeds was used.

5.1.2. PV StationThe PV station has a capacity of 10 MW and comprises solelySuntech STP280-24/Vd [Suntech, 2012] polycrystalline PVmodules. The PV modules have a Standard Test Condition(STC) efficiency of 14.4%. Abu Dhabi has an annual DNI ofabout 2000 kWh/m2 and is therefore a good location for a PVstation (the hourly solar radiation data was not available buta dataset with a total DNI of about 2000 kWh/m2 was used).In addition, the temperature variation in the Masdar City areawas included in the synthesis of the PV station.

5.1.3. Biomass StationThe biomass power station has a capacity of 47 MW with agas turbine of 30 MW and a steam turbine of 17 MW. A powerplant of this output capacity and configuration is expected tohave an efficiency of 45% [Caputo et al., 2005]. Also, aschedule is used to determine when the biomass power stationis in operation or offline.

5.1.4. Distribution SystemAn AC distribution system is simulated with a mesh networkas seen in Figure 9. Five buses are configured in the distribu-tion system: one slack bus (Bus 1), two load buses (Bus 2 andBus 4), and two voltage control buses (Bus 3 and Bus 5). Theloads at the various buses use the IEEE RTS load model buthave different peak values. The peak loads at the various busesused in the simulation are as follows: 10 MW at Bus 2, 55MW at Bus 3, 8 MW at Bus 4, and 9 MW at Bus 5. By

inspection, it can be observed that the generators cannot meetthe above-mentioned loads. Therefore, the city is dependenton the utility to meet its energy demands. Figure 10 shows theconnections between the generators and the buses in differentlayers.

Since the Masdar City case study is a small city applicationof the IES model, there is no transmission layer as seen in

Figure 9. Distribution system (layer 4).

Table II. IES Layers

Figure 10. Masdar City energy system layers. [Color figure can beviewed in the online issue, which is available at wileyonlineli-brary.com.]

10 ADEPETU ET AL.










4. IES

















4. IES









71

mented in the case study would have to be increased andstorage would be required to balance intermittences in windand PV. Furthermore, for larger cities and regions, the DGsources would have to be of higher capacities in order to meetthe scaled-up energy demands.

One major setback is the absence of energy generation andconsumption metrics in other City.Net systems due to thisstand-alone execution of the IES. As a result, the impact ofother systems on the energy system cannot be known. Thisreveals the importance of having complete system interdepen-dency estimations.

5.3. Validation of Results

Estimations of similar proportions in the Masdar City casestudy were carried out using Homer and SAM, which areenergy simulation applications. The results for the energygenerated and levelized costs obtained are compared in TableVIII. Homer and SAM are used to validate the energy genera-

tion results obtained in the IES simulation. A significantnumber of other parameters such as LEC, water use, busvoltages, power system losses, and emissions are estimatedbased on the generated energy. As a result, it was importantto verify that the energy generation values were indeed accu-rate. In addition, the LEC values are validated as well as thereare typically different methods for estimating levelized costs.

The results obtained are similar, and this aids in validatingsome of the results obtained in the IES. It is important to pointout that the input parameters could not be exact due to thedifferent approaches taken by the different energy models,and this partly explains the different values obtained. Also,the simulation for biomass power in the SAM had manyhigh-detail parameters and, as a result, was not suitable forcomparison with the IES.

Additional validation of the results can be achieved byperforming sensitivity analysis and uncertainty analysis as apart of the city modeling process. As mentioned previouslyin the paper, assumptions were made with respect to someparameter values and relations. Sensitivity and uncertaintyanalyses would establish a range of results obtainable basedon the assumptions made in the model, thus improving thecredibility of the IES model and the ability of the IES modelto support decision making. Validation by sensitivity analysisand uncertainty analysis, and their efficient incorporation inthe modeling process, is an area of consideration for futureresearch work. As a promising direction, this validation couldinclude the application of the one-factor-at-a-time (OAT)analysis approach in order to evaluate the impact of singularparameters of interest. It could also include execution of theanalysis with a focus on combinations of closely linked pa-rameters of interest.

The primary limitation of any conclusions drawn from theMasdar City case study is that the energy demand used in thesimulation does not represent the actual energy consumptionwithin Masdar City at the time when the city is fully com-pleted. The city is still under construction, and, as a result, theactual energy demand patterns of the city at full operationcannot be fully determined. This extends to the analysis andsharing of any economic issues and conclusions based on thisstudy. However, the main purpose of the case study is to showhow City.Net IES can be used to estimate energy systeminteractions based on the expected energy demands.

6. CONCLUSION AND FUTURE WORK

A functional and spatial modeling framework suited for therepresentation of city infrastructure systems is described in

Table VII. Masdar City KPIs Table VIII. Comparison of Simulation Results

Figure 11. Hourly energy generation. [Color figure can be viewedin the online issue, which is available at wileyonlinelibrary.com.]

12 ADEPETU ET AL.


Figure 10. That is, there are no transmission lines, and thecity’s electrical grid connects directly to the distribution levelof the national grid. In case studies with larger cities, thetransmission layer could be included depending on the gridstructure.

5.2. Analysis and Evaluation

The analysis and evaluation results are shown in TablesIII–VII. The results show a REF of 64.7%, resulting in CO2emission savings of 66,609 tonnes/year. According to theSiemens European Green City Index [Economist IntelligenceUnit and Siemens, 2009] compiled in 2009, the CO2 emis-sions per head across Europe ranged from 2.19 tonnes/year inOslo to 9.72 tonnes/year in Dublin. Therefore, CO2 savingsper head of 1.33 tonnes/year obtained from the simulationrepresents a significant contribution to the reduction of GHGemissions. This result is, however, dependent on the assump-tion that the load profile used in the simulation would besufficient to represent the energy demand of 50,000 people inMasdar City.

The distribution system is also seen to be stable with aminimum voltage of 0.9846 p.u. and a maximum voltage of1 p.u. at the voltage control buses. The total loss in thedistribution system is 12.37 GWh/year and this is about 3%of the energy generated within the city. Figure 11 shows theenergy generated by the wind farm and PV station throughthe course of the year. The peaks and troughs of the wind andPV stations can be visualized. However, the load flow analy-sis shows that the grid maintains a stable voltage with avoltage range of 0.98– 0.99 p.u. at Bus 2 and a stable voltageof 0.99 p.u. at Bus 4. As a result, the intermittencies in thepower supply of the wind and PV stations can be adequatelymanaged in the modeled power system configuration.

It should be pointed out that the city is dependent on theutility for meeting the local energy demands as specified inthe simulation. As seen in Table VI, 215.83 GWh is obtainedfrom the utility grid and only 3.04 GWh is supplied in returnto the utility grid annually. In order for the city to be inde-pendent of the utility grid, the capacities of the DG imple-

Table III. Wind Farm FPs and BPs

Table IV. PV Station FPs and BPs

Table V. Biomass Station FPs and BPs

Table VI. Distribution System FPs and BPs













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72

mented in the case study would have to be increased andstorage would be required to balance intermittences in windand PV. Furthermore, for larger cities and regions, the DGsources would have to be of higher capacities in order to meetthe scaled-up energy demands.

One major setback is the absence of energy generation andconsumption metrics in other City.Net systems due to thisstand-alone execution of the IES. As a result, the impact ofother systems on the energy system cannot be known. Thisreveals the importance of having complete system interdepen-dency estimations.

5.3. Validation of Results

Estimations of similar proportions in the Masdar City casestudy were carried out using Homer and SAM, which areenergy simulation applications. The results for the energygenerated and levelized costs obtained are compared in TableVIII. Homer and SAM are used to validate the energy genera-

tion results obtained in the IES simulation. A significantnumber of other parameters such as LEC, water use, busvoltages, power system losses, and emissions are estimatedbased on the generated energy. As a result, it was importantto verify that the energy generation values were indeed accu-rate. In addition, the LEC values are validated as well as thereare typically different methods for estimating levelized costs.

The results obtained are similar, and this aids in validatingsome of the results obtained in the IES. It is important to pointout that the input parameters could not be exact due to thedifferent approaches taken by the different energy models,and this partly explains the different values obtained. Also,the simulation for biomass power in the SAM had manyhigh-detail parameters and, as a result, was not suitable forcomparison with the IES.

Additional validation of the results can be achieved byperforming sensitivity analysis and uncertainty analysis as apart of the city modeling process. As mentioned previouslyin the paper, assumptions were made with respect to someparameter values and relations. Sensitivity and uncertaintyanalyses would establish a range of results obtainable basedon the assumptions made in the model, thus improving thecredibility of the IES model and the ability of the IES modelto support decision making. Validation by sensitivity analysisand uncertainty analysis, and their efficient incorporation inthe modeling process, is an area of consideration for futureresearch work. As a promising direction, this validation couldinclude the application of the one-factor-at-a-time (OAT)analysis approach in order to evaluate the impact of singularparameters of interest. It could also include execution of theanalysis with a focus on combinations of closely linked pa-rameters of interest.

The primary limitation of any conclusions drawn from theMasdar City case study is that the energy demand used in thesimulation does not represent the actual energy consumptionwithin Masdar City at the time when the city is fully com-pleted. The city is still under construction, and, as a result, theactual energy demand patterns of the city at full operationcannot be fully determined. This extends to the analysis andsharing of any economic issues and conclusions based on thisstudy. However, the main purpose of the case study is to showhow City.Net IES can be used to estimate energy systeminteractions based on the expected energy demands.

6. CONCLUSION AND FUTURE WORK

A functional and spatial modeling framework suited for therepresentation of city infrastructure systems is described in

Table VII. Masdar City KPIs Table VIII. Comparison of Simulation Results

Figure 11. Hourly energy generation. [Color figure can be viewedin the online issue, which is available at wileyonlinelibrary.com.]

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(IEISS), http://www.sandia.gov/nisac/capabilities/interdepend-ent-energy-infrastructure-simulation-system-ieiss/, accessedMay 15, 2012b.

National Renewable Energy Laboratory (NREL), Solar Advisormodel user guide 2.0 , Colorado, 2008,https://www.nrel.gov/analysis/sam/pdfs/sam_userguide.pdf, ac-cessed May 11, 2012.

National Renewable Energy Laboratory (NREL), Renewable Re-source Data Center : PV watts , Colorado,http://www.nrel.gov/rredc/pvwatts/, accessed April 15, 2012..

S.M. Rinaldi, J.P. Peerenboom, and T.K. Kelly, Identifying, under-standing, and analyzing critical infrastructure interdependencies,IEEE Contr Syst Mag 21(6) (December 2001), 11–25.

P.L. Spath and M.K. Mann, Life-cycle assessment of a natural gascombined-cycle power generation system, National RenewableEnergy Laboratory, Colorado, 2000.

Suntech, STP280-24/Vd: 280W polycrystalline solar module,Schaffhausen, http://eu.suntech-power.com/images/sto-ries/pdf/datasheets_feb_2012/EN/20120125_Vd280(H4_280_275)_EN_Web.pdf, accessed May 1, 2012.

G. Tamizhmani, L. Ji, Y. Tang, L. Petacci, and C. Osterwald, Photo-voltaic module thermal/wind performance: Long-term monitor-ing and model development for energy rating, In National Centerfor PhotoVoltaics (NCPV) and solar program review meeting(2003), 936–939.

J.W. Tester, E.M. Drake, M.J. Driscoll, M.W. Golay, and W.A.Peters, Sustainable energy: choosing among options, MIT Press,Cambridge, MA, 2005, http:/ /books.goo-gle.com/books?id=AIbLqsJrW-QC.

W. Tolone, D. Wilson, A. Raja, W. Xiang, H. Hao, S. Phelps, and E.Johnson, Critical infrastructure integration modeling and simu-lation, intelligence and security informatics, Lecture Notes inComputer Science 3073, Springer, Heidelberg, 2004, pp. 214–225.

UrbanSim, UrbanSim community Web portal, California,http://www.urbansim.org/Main/WebHome, accessed May 15,2012.

R.D. Zimmerman, C.E. Murillo-Sanchez, and R.J. Thomas, MAT-POWER: Steady-State operations, planning, and analysis toolsfor power systems research and education, IEEE Trans PowerSyst 26(1) (February 2011), 12–19.

Adedamola Adepetu is a Ph.D. student in the Cheriton School of Computer Science at the University of Waterloo,Canada. His research interests include energy systems, system modeling, and crowd sourcing. His current work aimsto understand seasonality in electricity and gas loads using machine learning methods. He holds a master’s degree inComputing and Information Science from Masdar Institute of Science and Technology, UAE, and a bachelor’s degreein Electronic and Electrical Engineering from ObafemiAwolowo University, Ile-Ife, Nigeria.

Paul Grogan is a Ph.D. candidate in the MIT Engineering Systems Division. His research interests include informationsystem design, systems analysis, and simulation. His dissertation seeks to develop interactive simulation games forinfrastructure system-of-systems design with an emphasis on supporting collaboration among independent decision-makers. He holds a master’s degree in Aeronautics and Astronautics from MIT and a bachelor’s degree in EngineeringMechanics and Astronautics from the University of Wisconsin, Madison.

Anas Alfaris is currently an Assistant Professor at King Abdulaziz City for Science and Technology (KACST) as wellas a visiting Assistant Professor at MIT. He is currently the codirector of the Center for Complex Engineering Systemsat KACST and MIT. Prior to that, he was a Research Scientist in the Engineering Systems Division (ESD) at MIT.There he has led several research initiatives, advised several graduate students, and taught several courses focusing onMultidisciplinary System Design Optimization (MSDO) as well as Computer Aided Design (CAD). Dr. Alfaris receivedhis training in several disciplines including civil architecture, engineering, and computer science. He started his careerby earning a bachelor degree in Architecture and Building Engineering from King Saud University in Riyadh, SaudiArabia. He then received a Master in Building Technology followed by a Master of Science in Architectural Studieswith a focus on computational design systems, both from the University of Pennsylvania, Philadelphia. He subsequentlyreceived a Master of Science in Computation for Design and Optimization from the Center for ComputationalEngineering while completing his Ph.D. in Design Computation, both from MIT. His research experience spans severalfields including Systems Architecture & Engineering, Generative Synthesis Systems, Integrated Modeling andSimulation, Multidisciplinary Analysis and Optimization as well as Decision Support Systems. His current researchfocuses on the development of Computational Design Systems for the design of Complex Engineered Systems.Furthermore, He has been part of several multidisciplinary research teams including the Design Lab and the SmartCities Group at the Media Lab, MIT, Massachusetts and the Strategic Engineering Group at the Engineering SystemsDivision, MIT, Massachusetts.

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this paper. The functional aspect of the framework is basedon a hierarchical decomposition and multidomain formula-tion approach for the modeling of complex sustainable sys-tems. The spatial aspect of the framework comprises a spatialrepresentation structure that enables the synthesis, analysis,and evaluation of infrastructures based on their geographicallocations and spatial orientations. The properties of thisframework are exhibited in the development of the IES and aMasdar City case study. The IES consists of standard energygeneration technologies and utilizes MATPOWER as a loadflow analysis tool. As a result, the IES incorporates thesimulation of active generation networks, which is currentlyan area of interest in the power systems field, particularly inthe grid integration of renewable energy technologies.

One area of future interest is the development of theCity.Net model into an executable DSS. As mentioned pre-viously, the functional and spatial model can be used todevelop a system model for a DSS, and Georgilakis [2006]provides benchmarks for state-of-the-art energy DSS. An-other area of future work is the integration of the functionaland spatial system model with GIS applications. This is madefeasible by the spatial framework that makes up part of themodel.

Concisely, the modeling framework introduced in thispaper is a modeling approach useful for modeling city infra-structure systems since it systematically defines the funda-mental system components, accounts for interdependencies,and utilizes a spatial system representation.

ACKNOWLEDGMENTS

This work was supported by funding from a MIT-MasdarInstitute of Science and Technology collaborative researchgrant, Project Code 400030.

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S.M. Rinaldi, J.P. Peerenboom, and T.K. Kelly, Identifying, under-standing, and analyzing critical infrastructure interdependencies,IEEE Contr Syst Mag 21(6) (December 2001), 11–25.

P.L. Spath and M.K. Mann, Life-cycle assessment of a natural gascombined-cycle power generation system, National RenewableEnergy Laboratory, Colorado, 2000.

Suntech, STP280-24/Vd: 280W polycrystalline solar module,Schaffhausen, http://eu.suntech-power.com/images/sto-ries/pdf/datasheets_feb_2012/EN/20120125_Vd280(H4_280_275)_EN_Web.pdf, accessed May 1, 2012.

G. Tamizhmani, L. Ji, Y. Tang, L. Petacci, and C. Osterwald, Photo-voltaic module thermal/wind performance: Long-term monitor-ing and model development for energy rating, In National Centerfor PhotoVoltaics (NCPV) and solar program review meeting(2003), 936–939.

J.W. Tester, E.M. Drake, M.J. Driscoll, M.W. Golay, and W.A.Peters, Sustainable energy: choosing among options, MIT Press,Cambridge, MA, 2005, http:/ /books.goo-gle.com/books?id=AIbLqsJrW-QC.

W. Tolone, D. Wilson, A. Raja, W. Xiang, H. Hao, S. Phelps, and E.Johnson, Critical infrastructure integration modeling and simu-lation, intelligence and security informatics, Lecture Notes inComputer Science 3073, Springer, Heidelberg, 2004, pp. 214–225.

UrbanSim, UrbanSim community Web portal, California,http://www.urbansim.org/Main/WebHome, accessed May 15,2012.

R.D. Zimmerman, C.E. Murillo-Sanchez, and R.J. Thomas, MAT-POWER: Steady-State operations, planning, and analysis toolsfor power systems research and education, IEEE Trans PowerSyst 26(1) (February 2011), 12–19.

Adedamola Adepetu is a Ph.D. student in the Cheriton School of Computer Science at the University of Waterloo,Canada. His research interests include energy systems, system modeling, and crowd sourcing. His current work aimsto understand seasonality in electricity and gas loads using machine learning methods. He holds a master’s degree inComputing and Information Science from Masdar Institute of Science and Technology, UAE, and a bachelor’s degreein Electronic and Electrical Engineering from ObafemiAwolowo University, Ile-Ife, Nigeria.

Paul Grogan is a Ph.D. candidate in the MIT Engineering Systems Division. His research interests include informationsystem design, systems analysis, and simulation. His dissertation seeks to develop interactive simulation games forinfrastructure system-of-systems design with an emphasis on supporting collaboration among independent decision-makers. He holds a master’s degree in Aeronautics and Astronautics from MIT and a bachelor’s degree in EngineeringMechanics and Astronautics from the University of Wisconsin, Madison.

Anas Alfaris is currently an Assistant Professor at King Abdulaziz City for Science and Technology (KACST) as wellas a visiting Assistant Professor at MIT. He is currently the codirector of the Center for Complex Engineering Systemsat KACST and MIT. Prior to that, he was a Research Scientist in the Engineering Systems Division (ESD) at MIT.There he has led several research initiatives, advised several graduate students, and taught several courses focusing onMultidisciplinary System Design Optimization (MSDO) as well as Computer Aided Design (CAD). Dr. Alfaris receivedhis training in several disciplines including civil architecture, engineering, and computer science. He started his careerby earning a bachelor degree in Architecture and Building Engineering from King Saud University in Riyadh, SaudiArabia. He then received a Master in Building Technology followed by a Master of Science in Architectural Studieswith a focus on computational design systems, both from the University of Pennsylvania, Philadelphia. He subsequentlyreceived a Master of Science in Computation for Design and Optimization from the Center for ComputationalEngineering while completing his Ph.D. in Design Computation, both from MIT. His research experience spans severalfields including Systems Architecture & Engineering, Generative Synthesis Systems, Integrated Modeling andSimulation, Multidisciplinary Analysis and Optimization as well as Decision Support Systems. His current researchfocuses on the development of Computational Design Systems for the design of Complex Engineered Systems.Furthermore, He has been part of several multidisciplinary research teams including the Design Lab and the SmartCities Group at the Media Lab, MIT, Massachusetts and the Strategic Engineering Group at the Engineering SystemsDivision, MIT, Massachusetts.

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Davor Svetinovic is an assistant professor at Masdar Institute of Science and Technology, UAE, and a research affiliateat MIT. His current research interests include: strategic requirements engineering, systems architecture with emphasison software and smart grids, and sustainable development from the systems security perspective. Previously, he workedat Lero—the Irish Software Engineering Center, Limerick, Ireland, and Vienna University of Technology, Austria. Hereceived his Ph.D. and MMath degrees in Computer Science from University of Waterloo, Canada.

Olivier L. de Weck focuses on how complex man-made systems such as aircraft, spacecraft, automobiles, printers, andcritical infrastructures are designed and how they evolve over time. His main emphasis is on strategic properties thathave the potential to maximize lifecycle value (aka the “ lities” ). Since 2001 his group has developed novel quantitativemethods and tools that explicitly consider manufacturability, flexibility, robustness, and sustainability among othercharacteristics. Professor de Weck’s teaching emphasizes excellence, innovation, and bridging of theory and practice.He is a Fellow of INCOSE and an Associate Fellow of AIAA. He serves as Associate Editor for the Journal of Spacecraftand Rockets and the Journal of Mechanical Design. He won six best paper awards since 2004, including the 2008 and2011 best paper awards from the journal Systems Engineering. He won the 2006 Frank E. Perkins Award for Excellencein Graduate Advising, the 2010 Marion MacDonald Award for Excellence in Mentoring and Advising, and a 2012 AIAATeaching Award. Since early 2011 he serves as Executive Director of the new MIT Production in the Innovation Economy(PIE) initiative. He has authored two books and about 200 papers.













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Documents

Functional and Spatial System Model for City Infrastructure Systems: A City.Net IES Case Study