Guidance for Replicable Use of the Model
Contents
1. We managed to get the current status of the city mapped in terms of energy usage because ERDF was involved as For a successful transformation of a city it is absolutely partner within TRANSFORM and GRDF was interested in necessary to understand existing barriers and to break following our e2. pu 11 501 11 03 11 D3 HUT 11 05 11 06 A k Ey humon TR NSFORM Br B J s a a lt ga 26 DG T1701 17 92 11503 ruga 11 05 11000 gt accenture 5 2 1 Database structure Step 2 Set scenarios tables p id bigint ES scenarioname varchar 7 5 ES description varchar 7 5 S username varchar 7 5 B cityname varchar 7 5 A trnsfrm meta scenario pkey Create Edit a scenario Name the scenario and its description y Name id hin Description e id bigint Scenan o Descnption S scenarioid bigint A factorid bigint Ada factors to scenario and customize them by edit button Ali factors Scenario A trnsfrm meta scenariotofactor pkey craro o Selected factor description Decreasing Electricity price Decreasing Heat price Add to Scenario gt Decreasing gas price 4 Remove from Scenario Create a new factor l Edit tactor accenture E JAME eeuse 5 2 1 Database structure Step3 Allocate measures tables 1 2 B sequencename varchar 7 5 A cityname varchar 7 5 ES description varchar 7 5 ES id bigint S user name warchar 75 A trnsfrm meta sequence pkey 1 CreaterEdit a measure portialio Name the new measure portfolio and its description sequenceid sequence id N 9 ILS Ec Solar PV Description There ls no descripbon of this portatelio 2 sequencetomeasureid bigint D sequenceid bigint E n measures
3. 2 Select experiment to view results finis hed with errors finis hed with errors finished Total City Impact selected Area Impact gt accenture Create experiment Select Scenario Select Measure Portfolio Select simulation start date and simulation end date Click Add experiment The created experiment is now sent to the simulation engine and the results are being calculated Select experiment s to view results Select the experiment you want to view the results for Click Total City Impact Selected Area Impact or Impact to view the actual results on KPIs changes in KPIs View results in Transform Dashboard or as Geographical Data p TR NSFORM Contents of the User Manual uS qixix disi i d d d d d d id d d d Rad uxeeiiedei ei id d i iei e E e f a D accentur A a ee x a es 25 SERE PE IK E E IA SS ae at SEE po m po E pou M e e p o uu u c ccc cect m D SES E EE E UU UU UU A k TR NSFORM How to Add amp Adapt Measure Library KPI Definition In case a user wants to go more in depth and review the assumptions behind the calculations of the four KPIs or change the values of city parameters the user is referred to the KPI Definition tab of the Measure Library A mindmap like structure gives an easy insight in the relations between variables and th
4. Seci re any gt Choose the appropriate level of detail Choose map type Geotbok 10 1 block building network Select an area for implementation of the measure Endthe selection by double clicking on the map N AN O Allocate measure to area Map Control Navigate Select info Choose number of maps 1234 a e T SN lt ON Choose a filter criterion to select only certain r NICE zz E au ave selec bor vec selec bons cn types of buildings ra eonun Read an d Press Select to confirm the selection zt c c gt accenture ut BE uos 1 Analyze City Context 4 Determine Impacts In the last step the user can determine the impacts of a measure portfolio defined in step 3 given a certain scenario step 2 A combination of measure portfolio and scenario is called an experiment One experiment basically represents a possible future for the city The user can view and compare the outcomes of different experiments measured on four city KPls Create experiment Scenarios Measure portfolios Scenario Mame Measure Portfolio New scenario Heat Pumps Lokale warmtevoorziening New scenarioz New scenario3 New measure New scenariod New measure New scenarios New measures Test 1 As proposed ES Test 2 Scenario description Measure portfolio description Start Simulation_ Mate End _Simulatino Mate 01 16 m 04 16 m Add experiment
5. x WP3 TRANSFORM Decision Support Environment TR NSFORM P 60 4 Enabling cities to become Smart Energy Cities e E ES m Guidance for the replicable use of the model and or methodology and recommendations for further development c2 X B TR NSFORM gt accenture un About the Structure of the Deliverables D3 1 and D3 2 The two TRANSFORM WP3 deliverables D3 1 Finalised prototype quantitative decision support model ready for replication in other cities and D3 2 Guidance for the replicable use of the model and or methodology developed in this work package and recommendations for further development aim at different audiences and have to be seen as separate documents Where D3 1 describes the tool itself D3 2 is giving advice to cities which want to adopt the DSE in order to be able to use it in the future so some content of the deliverables has been duplicated in order to serve the different audiences and should not confuse the readers of the two documents Since these are public documents the WP3 team tried to make the deliverables as consumable as possible for future external readers BE uos 2 accenture Outline contents and target audience of deliverable 3 2 v Process Guidebook Content Document about the preparation process for cities that want to use the DSE Audience Parties interested in preparing the DSE for a new city gt accentur
6. trnsfrm meta measure pk measure id Measure id A B Li _ pkey id bigint measure id bigint Ed measure node id bigint trnsfrm meta measurenodetomeasure pkey Note For future update of the database is the id in the measure table the one that needs to be a PK and not the measure name field k k ES STOR _TRYNSFORM T gt accenture c 5 2 2 Measure library measure editor tables 4 5 value _id Value id table id bigint comba id bigint ES value varchar 5 ES units varchar 7 5 trnsfrm meta tablerow pkey table id bigint ES value id bigint trnsfrm meta tablevalue pkey JJ table id bigint combo id bigint JJ table key id bigint ES key value varchar 7 5 Key id TableKey id and attribute id buildingattribute id ES name warchar 7 5 JI key id bigint ES type varchar 7 5 attribute id bigint trnsfrm meta buildingattributetotablekey pkey value id Value id EH public trnsfrm meta constantvalue walue id bigint A value varchar 7 5 attribute id bigint ES value varchar 7 5 trnsfrm meta constantvalue pkey attribute id BuildingAttribute Id Note Building attribute value table is currently empty The values are stored now in the table city Tobuildingattribute In addition the buildingattribute table serves as a dictionary for the values in citytobuildingattribute table x x B o _
7. 2 Glossary Content 3 Hardware Requirements Document about the 4 Software Requirements required hardware for 4 1 Specification running the DSE 4 2 Installation 4 3 Data see attached Word document DSE Deployment Guide v1 0 docx Audience Parties interested in installing the DSE on their own servers gt accenture AT bon AUSTRIAN INSTITUTE 22 TR NSFORM Enabling Context Guide This document is also a supporting document for this deliverable to guide e the intended audience through the city context and the enabling measures It is not part of the official deliverable but is seen by the team as Content a valuable source of information for cities which are interested in the Description of TRANSFORM city contexts and guidance through enabling measures adoption of WP3 results the final project event the latest Audience Scientific community AWT os gt accenture The document is still under revision and will be available shortly before x a TR NSFORM
8. 4 Data Input Manual Pa Content 4 1 Data requirements 4 2 Data components Document about the data input requirements Audience 4 3 Data integration and aggregation 4 4 Data enrichment Parties responsible for the city data gt accenture AIT ETEL LAILL e B TR NSFORM 4 1 Data requirements There are three criteria for city data to be of maximum value for and to generate the maximum value out of the Decision Support Environment Granularity Data can be available on different resolutions from a city total value to data on a building level or even address level The desired granularity for the Decision Support Environment is building level This prevents the data from being privacy sensitive but still creates maximum value for analysis of the data e Date For valuable analysis it is important that the data is up to date Preferably the latest available dataset 1 year old is selected for upload in the Decision Support Environment Data can be easily updated every year e Coverage For the data and the insights from TR NSFORM the Decision Support Environment to be valuable to city decision makers it is important that data is available for the whole city Having Granularity City district Building limited coverage of the city can provide an Date opportunity for testing the Decision Support oe niis cie Environment but real value is cap
9. Elec Gas Elec Gas 60 096 0 0 43 2 0 0 32 9 0 0 60 0 0 0 27 3 0 0 46 8 0 0 19 7 0 0 29 6 0 0 37 1 0 0 5 0 2 0 3 9 0 0 0 0 8 8 0 0 0 0 1 3 1 3 0 0 0 0 0 0 0 0 3 1 0 0 1 9 1 7 16 0 0 0 46 4 0 0 39 9 0 0 30 0 0 0 53 2 0 0 37 3 0 0 67 9 0 0 44 2 0 0 43 6 0 0 Heating building 12 0 79 0 0 0 99 6 0 0 83 6 0 0 90 0 0 0 88 0 0 0 99 9 0 0 100 0 0 0 85 3 1 7 90 8 Heating tap water 0 6 3 8 2 2 0 4 0 3 1 5 0 0 10 0 0 2 2 1 0 7 0 1 1 0 0 0 0 1 2 9 0 7 1 6 Showering Cooling 2 4 15 2 0 0 0 0 1 0 6 1 0 0 0 0 0 8 8 5 0 0 0 0 0 0 0 0 0 4 11 8 0 7 5 9 4 0 0 0 0 6 0 0 16 0 0 0 5 0 0 0 5 9 0 0 9 4 0 0 8 1 0 0 0 0 0 0 6 3 0 0 Ventilation systems 0 0 0 0 3 7 0 0 10 0 0 0 5 0 0 0 11 2 0 0 5 7 0 0 3 3 0 0 22 6 0 0 8 1 0 0 amp TR NSFORM 4 4 Data enrichment 4 6 a Energy consumption per purpose Assumptions need to be set about the different energy consumption purpose as to whether they are dependent on the energy label of a building or not Most commonly and logically the building related purposes are dependent on energy label depicted in red below and the other purposes are not depicted in blue Appliances Cooking Lightin
10. Equation Final Energy Consumption Local Energy Produced by Renewables Transpon Efficiency of Energy Note City specific variables are located in the cityvariable table in the db Their values in the cityvariablevalue table DI gt accenture C k AT AO AUSTRIAN ING ITUTE 25 TRYNSFORM 5 2 2 Measure library KPI definition tables 2 2 Measure Editar Di EEE varchar 75 nodename varchar 7 5 nodetype varchar 75 formulacomponent varchar 1500 A trnsfrm meta formula pkey c2 AUSTRIAN INSTITUTE OF TECHNO TRyNSFORM_ 6 n gt accenture 5 2 2 Measure library measure editor tables 1 5 iiinn cc ti BB nnn secre Ml Renee rae The name of the variables user friendly names in the GUI differ from the names that appear in the DB The names given in the db to these variables is as follows Affected Variables gt Measure Node Variable gt Building Attribute Auxiliary Variable Purpose Node Input Variable gt Purpose Node DIU gt accenture X amp Eg HNSFOR TR NSFORM 5 2 2 Measure library measure editor tables 2 5 id bigint E3 name varchar 7 5 node id bigint ES carrier id bigint ES indicator varchar 7 5 I trnsfrm meta kpinode pkey EH cityname varchar 5 ES measurename varchar 7 5 trnsfrm meta basenode p
11. Technical Documentation Content Technical details regarding the development of the DSE software Audience Technicians IT specialists interested in the software architecture of the DSE E gt accenture 5 1 General architecture 5 2 User interface 5 2 1 Database structure Step 1 Analyse city context tables Step 2 Set scenarios tables Step 3 Allocate measures tables Step 4 Determine impact tables 5 2 2 Measure library tables 5 2 3 Factor library tables 5 3 Package Diagram 5 4 Simulation model 5 4 1 Conceptual model TRANSFORM 5 4 2 Package overview 5 4 3 Internal data structure 5 4 4 Simulation scheduler AWT os x x a TR NSFORM 5 1 General Architecture City context explorer Scenario editor A vaadin gt Sequence editor Measure editor E LIFERAY remue TR ET feet fm nemo bam scm ant m gt u ru F Trmnaterm Map Compare Aresperdam i A iio e e i p ee e IM p 7 Cees rub quam ee oe m 4 gt accenture Database layer Separates the databases from the user interface This allows from a flexible architecture in which the system is independent from different types of databases GIS visual component lt gt Creates interactive maps from the city data and geographical database These maps can be used to visualize data and for the user selections Sequences Measures City data Scenarios Postare
12. accenture DTI Ede inci Aa d c Viewing of the city specific data that describes the state of the city on the specified KPI s Viewing of the city specific data in a maps functionality with the option to select via freehand polygon specific areas A potential future state of a city described through a set of factors e g population gas price electricity price economic conditions An independent market that provides the context for any future city transformation plans e g gas price oil price population growth Specified interventions that are applied by stakeholders to a city Technique of method that supports the implementation effectiveness of a measure A set of measures each allocated to a certain geographic area in time forming a transformation plan for a city The combination of a scenario plus its measures measure portfolio on a city or city area resulting in outcomes on the predetermined KPI s Area where all measures are stored made visible and are adaptable Area for adapting measure in structure in values or both Future to be value of a building attribute Current value of a building attribute A constant value that represents an assumption parameter A node that serves as intermediate step in an equation used to simplify equations A node that connects a group of nodes to be used in another node Area where all factors are stored made visible and are adaptable BE vos
13. MRE 3 Allocate measures 4 Determine impact Measure library Factor library In this step one or more city scenarios can be created by setting factors for future city characteristics EZ CS EE nario o Ove rview Create Edit a scenario Scenario Name Name the scenario and its description Fossil fuel favoured Name Fossil fuel opposed PR Baseline Hew scenano e Add factors to scenario and customize them by edit button All factors Scenario Factors in this scenario Increasing electricity price Constant electricity price Decreasing electricity price Constant electricity price Increasing gas price Decreasing gas price Constant gas price Interest rate Energy Savings heat Exchanger Increasing heat price Decreasing heat pce Go to Factor library 1 10 15 k k ac TR NSFORM gt accenture Factor library These steps show how the factor Increasing electricity price is created 1 Analyze the city context 2 Set scenarios 3 Allocate measures 4 Determine impact Measure library A AiAD jOAGL Initial factor value omm O me _ a la DIE TO MNR Ta ctc Factor Mame Emissions from Gas Emissions from Muclear Emissions from Chil Emissions from Solar Emissions from Wind Emissions from Central Electricity Production Emissions from Central Gas Production missions from Central Heat
14. Production Heat fram other renewables Electricity from Hydro Efficiency of Electricity Production from Hydro Emissions from Hydro Remove Select date to remove o Remove y s Ha I GA Initial factor value increasing electricity price l Description There is no description of this factor Create Add a value to the factor Increasing electricity price values Return to step 2 k k TR NSFORM c gt accenture un 2 Set Scenarios Make sure all three scenarios are created and filled out 1 Analyze the city context 3 Allocate measures 4 Determine impact Scenario 1 Baseline Scenario Overview Create Edit a scenario fenetre sonar agg ns een Fossil fuel favoured Fossil fuel opposed Baseline Mew scena o Description Scenan o Description e Add factors to scenario and customize them by edit button Increasing gas price Decreasing gas price Constant gas price Interest rate Energy Savings heat Exchanger Increasing heat price Decreasing heat price 1 10 15 DTU gt accenture un TR NSFORM The municipality considers four major alternatives for transforming the area A Energy Saving B Max Renewables A Pile ML D All Electric A TR NSFORM Solar PV panels roof facade Wind turbines The municipality has provided realistic tim
15. measure application a recalculation will be done on the attributes of these entities The recalculation is done using a calculate that evaluates the formulas that the users input in the measures and assigns the values to the entities The calculator will eventually calculate the formulas that are made to determine the KPIs which are finally outputted to the output database my gt accenture eg I NSFOR _TRYNSFORM 5 4 2 Package overview e Src nl macomi transform data A data representation of the data loaded from the database e Src nl macomi transform database Classes to support database loading and outputting e Src nl macomi transform equations Classes to represent an equation and its components e Src nl macomi transform measure Classes to represent a measure e Src nl macomi transform model Classes built from atomic and coupled models that is the actual representation of the simulation model e Src nl macomi transform calculator Classes to evaluate equations e Src nl macomi transform model data The internal data structure of the simulation model e Src nl macomi transform model modelbuilder classes that use automatically generate the simulation model from input data e Src nl macomi transform model utils Various utils functions that we need in other classes x eg iumon TR NSFORM DTU gt accenture 5 4 3 Internal data st
16. or what would be needed Instead of renewables in absolute values have stacked area graphs for electricity and heat mix so one can see how the electricity and heat mix change over time e Clearer impact graphs of how much is contributed by which measure Maybe even the option to switch on and off certain measures and see what changes in the result e Abatement curves how much money per ton of CO is reduced e lt A tutorial phase in the beginning should be added e Data harmonization is needed so a data update will be possible Make clear when a user has finished with a sub step gt Continue to next step indications pg TR NSFORM gt accenture 6 3 Suggested Improvements by the development team The DSE development team identified at least these improvements of the tool which could not be implemented during the project itself and need to be considered for future work e The factor values are not interpolated yet i e do not change gradually between provided values Experiments cannot be deleted through the UI e It should be possible to define more than one filter criterion e Instructions need to be added for hiding carriers in the charts An Energy balance overview of consumption and potential after selecting an area is needed pg TR NSFORM O gt accenture ut Ge ne Res m m 7 Deployment Guide 1 Introduction 3j 1 1 Intended audience
17. plans Simulation runs are scenarios interpreting done to answer the city s questions as specified in the context phase as the results and making objectives for the Decision Support process actionable plans Y What are the most realistic alternatives for transforming the city Y Are these alternatives applicable across the whole city or to certain areas buildings only Y Where are the high potential areas located for each of these alternatives Y What are realistic time frames for implementation of the transformation alternatives in the corresponding areas Y How are the impacts of the relevant measures calculated Which parameters need to be used Y What are the costs for realizing these different measures Y What are the relevant uncertainty factors that influence the decision making process Y What are realistic boundaries to the development of these uncertainty factors Y How is ensured that the simulation results provide the desired insights regarding the city s questions challenges and climate goals k k B TR NSFORM gt accenture Tool Creation For the South East region of Amsterdam we have used the Decision Support Environment deliverable of WP3 within TRANSFORM to simulate the impact of several energy scenario s and test the robustness of the energy plans in combination with external factors Bob Mantel is the City Coordinator of Amsterdam Elisabeth Kongsmark is the City Coordin
18. t the porilio and customize them by the edit buttons measurename varchar 75 0 ES priority bigint Click ona measure lo view the description ensure inis me nodespercentage double precision MN Ecency Ales Tine Add tn porfolia siam A trnsfrm_meta_sequencetomeasures_pkey 4 Dete tomponisio Create new measure Edit measure tue Al A accenture avery _TRSNSFORM_ ES percentage double precision E f measure boolean Allocate measure to time From Measure Efficiency 41414 n From Penetration rate Penetration rate 09 01 2020 1004 Lo gt a Remove x APER ES TRYNSFORM c2 Al gt accenture n 5 2 1 Database structure Step4 Determine impact tables 1 3 cityname varchar 7 5 A startdate timestamp enddate timestamp A user name varchar 7 5 kpiname varchar 7 5 2 timestamp timestamp fuel varchar 75 ES expid bigint EH scenarioid bigint j sequenceid bigint value double precision affectedentityvalue double precision Jp trnsfrm meta mainkpioutput pkey gt accenture GB cold id 1 electricity id 1 BB gas id 1 Bl heat id 1 Monthly consumption from 2014 to 2021 200k 150k a Dmm Sa ee Tr r ee 100k x 50k num Esq Am m E E E E 14 01 14 02 14 03 14 04 14 05 14 06 Use logarithmic scale Emissions e GB cold id 1 Bl electric
19. 001 433000 qd cuu T MA 137001 245000 ll soot 137000 Scenario 1 Baseline Factor name Change Constant electricity price 096 Constant gas price 096 Scenario 2 Increasing prices Factor name Change Increasing electricity price 2 year Increasing gas price 2 year Scenario 3 Decreasing prices Factor name Change Decreasing electricity price 296 year Decreasing gas price 296 year DI gt accenture E No one knows what the future brings and different futures can mean different outcomes for plans that we make now We can test the plans we make under different future scenarios The aim is to find the most cost effective way for reducing emissions and this cost effectiveness is highly dependent on energy prices Therefore the uncertainty in energy prices is where the municipality is most interested in We create three scenarios that together represent a range of possible future energy prices Go to step 2 BE vos 2 Set Scenarios Start creating a scenario by clicking on Create Give the scenario a name e g Baseline and start adding factors to the scenario f the factor list is empty create factors in the factor library see next slide When you re finished with the Baseline scenario continue with the Increasing prices and Decreasing prices scenarios and add the corresponding factors to these scenarios 1 Analyze the city context
20. 3 Objectives in TRANSFORM To develop a prototype Decision Support Environment DSE which enables decision makers to evaluate the impacts of different transformation plans under varying scenarios based on open energy data In addition to the prototype DSE documentation materials are developed for dissemination of the DSE to other cities The Deliverable 3 2 contains all the developed documentation material required for replication of the Decision Support Environment in other cities The structure of the document is set up in the form of Guidebooks which can be used together or separately depending on the type of audience interested in the DSE c res TR NSFORM ul gt accenture 2 Glossary The Smart Energy City is highly energy and resource efficient and is increasingly powered by renewable energy sources it relies on integrated and resilient resource systems as well as insight driven and innovative approaches to strategic planning The application of information communication and technology are commonly a means to meet these objectives The Smart Energy City as a core to the concept of the Smart City provides its users with a livable affordable climate friendly and engaging environment that supports the needs and interests of its users and is based on a sustainable economy A specific subject that a city has chosen to focus on for the duration of the Transform project e g district heating urban refur
21. 4 Determine Impact 1 Analyze the city context 2 Set scenarios 3 Allocate measures Measure libra Factor library m Create experiment Geographical Data Scenarios Measure portfolios Click on an item in the legend to add remove data from the graphs Scenario Mame Measure Portfolio po a OO IT a Use loganthmic scale All Electric BB cold id 1 B electricity id 1 B gas id 1 B heat id 1 Decreasing prices Monthly consumption from 2015 to 2018 1 000M Scenario description Click on a scenano fo view ite descnptian Start Simulation Date 01 15 Measure portfolio description Pii F There is no descnption of As measure portfolio ar SOOM i S ARIADNA THLE HHHH 15 01 15 05 15 09 16 01 16 05 16 09 17 01 17 05 17 09 18 15 02 15 06 15 10 16 02 16 06 16 10 17 02 17 06 17 10 16 0632 15 07 15 11 16 02 16 07 1656 11 17 3 17 07 17711 Emissions kWh A Electric Use logarithmic scale Energy Saving BB cold tid 1 B electricity id 1 B gas id 1 Bl heat id 1 Selected Area Impact mA Ml ad Monthly emissions from 2015 to 2018 400M 300M MN 16 05 After step 5 wait till experiment is finished then view results Use loganthmic scale Bl cold id 1 Bl electricity id 1 Bl heat id 1 Monthly renewables from 2015 to 2018 200M 150M 100M 50M Fs age 524031 LLN il 15 01 15 05 15 09 16 01 16 05 16 09 17 01 17 05 17 09 18 15 02 15 06 1
22. 5 10 16 02 16 06 16 10 17 02 17 06 17 10 16 02 15 07 15 11 16 02 16 07 16 11 17 02 17 57 17 11 Costs Use loganthmic scale cold id 1 Bl electricity id 1 Bl gas id 1 B heat id 1 Bl investments id 1 Monthly costs from 2015 to 2018 100M 75M B EM E E NND NND NND a DAHER LUE EE EE E d d E E E M UI 15 01 15 05 15 09 16 01 16 05 16 09 l7 01 17 05 17 09 18 Continue with the other measure portfolios or create your custom experiment A Energy Saving B Max Renewables Window replacement Solar PV panels roof facade Shower Heat Exchanger Wind turbines LED lighting Insulation Scenario 1 Baseline Factor name Change O Constant electricity price 0 Constant gas price 0 C City Grids Factor name Change Increasing electricity price 296 year Increasing gas price 296 year District cooling grid Scenario 3 Decreasing prices ee Factor name Change District heating grid Decreasing electricity price 296 year Decreasing gas price 296 year E TR NSFORM gt accenture Contents of the User Manual oM DOS XAK TRyNSFORM 1 Analyze City Data 2 Set Scenarios 3 Allocate Measures 4 Determine Impact Measure Library City Data Scenario Factor Measure Enabler Portfolio Experiment Measure Editor Affected Variable Variable Input Variable Auxiliary Variable Factor Library gt
23. FORM How to Start How to get your City Smart How to Add amp Adapt How to Use How to Understand DTI gt accenture E Log In Analyze City Context Set Scenarios Allocate Measures Determine Impacts Measure Library Factor Library Case study Glossary BE vos Contents of the User Manual S GERI RICRIORIOO OUR SICURA RI RIO OR RERERERERRH RE RE ACRIOR RE RTRERERERERE RE RERERERERERE RE RE RERERERERH RE RL RERERERERH RE REE te ene TR NSFORM The Decision Support Environment can be accessed through the internet and test accounts are available for new users that want to explore the options and get familiar with the DSE 1 i accenture TRyNSFORM iswara AET ese Login Username Passwotd s Select City x accenture TRyNSFORM forsee i AAT es Login Username Transformer A Password ETETETT select City Amsterdam T ox gt accenture Access the Decision Support Environment Go to sbc1 ait ac at web mfumarola dst via Google Chrome Type Username and Password Click on the field enter your details If no login details are provided login with username test and password test Select the City City for which the scenario planning will be made Click OK Opens the Decision Support Environment TR HAFORM AIT dr X A p TR NSFORM Contents of the User Manual ae es ee s 2 ee
24. For the data and the insights from the Decision Support Environment to be valuable to city decision makers it is important that data is available for the whole city Having limited coverage of the city can provide an opportunity for testing the Decision Support Environment but real value is captured when the complete city is covered ng TR NSFORM DI gt accenture E 6 2 Inputs from the testing session questionnaires 1 2 Selected comments from the city user during the testing session of the DSE in Amsterdam in February 2015 For them the DSE is e a highly developed expert too e user friendly but technical knowledge required is very high Training for data entry is necessary reported 2x e useful and simple reported 2x a good start and a way to address smart cities but the cities are not fully ready and the same goes for the tool Joined development is needed DSE Cities e very flexible outcome and results highly depend on the quality of integrated data pg TR NSFORM c E gt accenture ut 6 2 Inputs from the testing session questionnaires 2 2 The users attending the DSE testing session in Amsterdam in February 2015 identified the following requirements for improvements of the tool e Aclear overview on available measures their impacts and parameter values used is needed A Data overview is required What is available
25. SOL Simulation components Simulation model initiator Instantiates for each record in the city data e g house a simulation component and connects it through a network to the right producers In a typical simulation experiment about 300 000 consumers are instantiated Contains the standard software components to simulate producers network and consumers Simulation engine Output analyzer Using a model as input the simulation engine will calculate every key performance indicator for every month for the selected period of interest e g 2013 to 2025 Eg SFOR TR NSFORM 5 2 User Interface This part of the documentation provides an overview of the database tables and classes used to create the web user interface c2 X SEE Esi TR NSFORM Al gt accenture un 5 2 1 Database structure Step 1 Analyse city context tables 1 3 Jp city varchar 7 5 2 kpiname varchar 75 fuel varchar 75 ES value double precision ES unit varchar 7 5 A trnsfrm meta startingconditions pkey Cee sie tioes bn Sumsberdans Bemewobis bn fieeberdiane City amma A i 16 m 1004 mmm Las lagarifur r ocaie Lipa puro ca a Cou a A BB Gectriciry WE Gas HH ac Eneissions im Nae Euer dann City Costs bn Ameberdians Dily c2 ANY pex Ps AUSTRIAN INSTITUTE OF TECHNO TR NSFORM gt accenture MN 5 2 1 Data
26. Suggestions for improvement of the DSE prototype Audience Potential future developers of the DSE gt accenture 6 1 Suggested improvements 6 2 Inputs from the testing session questionnaires 6 3 Suggested improvements by the development team AIT a TR NSFORM 6 1 Suggested Improvements Open and harmonise the city energy data throughout Europe The most important finding of WP3 was the need for harmonised standardised energy data preferably delivered via standard web protocols e g semantic web technologies Linked Open Data to enable the easy integration of data into applications like the DSE and a comparison of measures between the cities Concerning content of the data We repeat the three critical criteria for city data to be of maximum value for and to generate the maximum value out of the Decision Support Environment Granularity Data can be available on different resolutions from a city total value to data on a building level or even address level The desired granularity for the Decision Support Environment is building level This prevents the data from being privacy sensitive but still creates maximum value for analysis of the data Date For valuable analysis it is important that the data is up to date Preferably the latest available dataset 1 year old is selected for upload in the Decision Support Environment Data can be easily updated every year Coverage
27. TRi NSFORM DT gt accenture c 5 2 2 Measure library measure editor tables 5 5 The meaning of the fields is as follows id bigint ES cityname varchar 5 ES attributerealname varchar 5 ES unit varchar 7 5 2 attributerealname is the name of column found in the table referenced in 5 every time the name of this ES buildingattributeid bigint attribute is changed in the db or a different table is used This field needs to be updated ES tablename varchar 7 5 ES function varchar 7 5 3 unit units ES measurevariable boolean A trnsirm meta citytobuildingattribute pkey 4 Buildingattributeid the id in the building attribute table to which the attribute realname is linked to 1 cityname the name of the city 5 tablename The name of a table in the public schema This table is the one that shall contain the city data if a new table is used in the db This field needs to be updated as well 6 function Sometimes the cities give aggregated information BLOCKS or disaggregated BUILDING In case this information is aggregated cities need to give a attributerealname i e area typeofbuilding or function from which the calculation could be disaggregated by the simulation and aggregated back again for the ouput In that case the field measurevariable needs to be put to FALSE See example below 31 132 Amsterdam gas consumption cooking m3 22 trnsfrm ams tmp v IRUE 32 133 Amsterdam gas consu
28. ator of Copenhagen One of the outcomes of working with Transform partners is that we became much more aware of the importance of the political support and commitment in the context of Climate planning and transformation Else Kloppenborg is a senior adviser for the city of Copenhagen accenture DTU ie ale ES We wanted to be able to make scenario projections for the future energy use in the city our focal area being the district Part Dieu After the first phase of collecting data to aggregate it in the Energy Atlas we built 4 scenarios on the energy efficiency of carriers The objective was challenging because first we needed to build a methodology and this required a dedicated effort and a special skillset It was the first time we proposed energy scenarios previously we simply did not have the competency or the tooling Now with the new stature of Lyon as metropole it is part of our direct policy therefore we have been able to attract the manpower and the competencies to bring scenario planning a step further B atrice Couturier is the City Representative and Coordinator of Grand Lyon BE uos 3 2 Zoom into Phases 4 Calibration 4 Calibration After the first results have been obtained collaboration between specialists pou and city stakeholders is required for reviewing and validating the results The Reviewing and Me 7 relevance and reliability of the results needs to be e
29. base structure Step 1 Analyse city context tables 2 3 B3 theme warchar 7 5 dashboard varchar 7 5 ES name varchar 7 5 unit varchar 7 5 ES city varchar 7 5 year bigint ES components varchar 7 5 BH value varchar 7 5 ES source varchar 7 5 49 trnsfrm meta citylevelmultvalue pkey c2 l i AUSTRIAN INSTITUTE OF TECHNOLOGY n gt accenture Ak LE iNSFOR TR NSFORM 5 2 1 Database structure Step 1 Analyse city context tables 3 3 4 username varchar 7 5 JP city varchar 7 5 targetname varchar 7 5 A value double precision A trnsfrm meta scenariotarget pkey WB cold od 1 M election id 1 MC gas id 1 na ad 11 B slectricay 0d 11 BB haar od 11 Monthly consumption from 2011 to 2020 Monthly renewables from 2011 to 2020 q ApEn AEE Lr A ee es anam uranium am EE M 2 Set targets of current city data 8 M m mm Carbon dioxide emission reduction 520 ius l i Final energy consumption reduction 55 0 T nio 11 03 11 03 11 04 11 05 11 08 M n 1 02 11 03 i 104 11 05 11 06 Increase in renewable energy sources BRO gt Usa legati scala a EI Una loganitimic scala pu a Energy con sumption cost reduction 5210 fe Bl cold od i B electricity ed BI as d B n d tl Bl stectricey id 1 Bl gas ied 1 BB hear od 1 Monthly emissions fram 2011 to 2020 Monthly costs from 2011 to 2020 Bug NIU Ioina EET 219 z
30. bishment renewables smart grids etc A specified intervention applied in a district or on the city level by a stakeholder or a group of stakeholders A potential future state of a district and or city described through a set of factors e g population gas price electricity price economic conditions City specific information that describes the state of the city in accordance with the specified Key Performance Indicators KPI s amp MERE DES TR NSFORM accenture Al ETEL LAILL m mem m m 3 Process Guidebook al 3 1 The DSE Process Framework The four C s Content 3 2 Zoom into the 4 phases with true stories from TRANSFORM cities Document about the preparation process for cities that want to use the DSE Audience Parties interested in preparing the DSE for a new city gt accenture x MAT Mi AP TERON B TR NSFORM 3 1 The DSE Process Framework The four C s Understanding the current state of your City Reviewing and Y maintaining the accuracy and relevance of the vmi Calibration N Defining responsibilities ALA NE NAGY Creation Commitment scenarios interpreting the allocating process owners results and making and collecting the right actionable plans data and content x B TR NSFORM gt accenture 3 2 Zoom into Phase 1 Context e Conte The first phase serve
31. d fs eee quee v p ee ue o k A k TR NSFORM gt Case study Instructions In the following slides background information is provided about an area of Amsterdam which will serve as a case study area This background information is interspersed with specific instructions on how to move through the Decision Support Environment successfully in order to generate insightful results Current and target CO emissions 390 kt year 176 kt year 74 kt year meras A SO a gt accenture TR NSFORM 4 TY E P j C y S 5 Ji ip A T f F WETTER E i 3 UT Jimsefaam E q 1 yu EED s A hs E y E s st Mos a h Mss F a a T 4 m i T D y RU 4 s i J y A ge e d E P y edelaan ND E Quderkerk de Amat N D di Yo Diemen Amsterdam Zuid Oost is a mixed used area with low prices and little restrictions which makes it suitable for urban innovation and experiments Current and target CO2 emissions 2025 A 55 176 kt year Da 2040 I 81 74 kt year yu Become a self sufficient neighborhood where energy is produced locally from renewable sources and where energy losses are minimized Go to step 1 1 1 1 Analyze city data Be T SET OLEI def Login with the test account manes Fis and look up different maps of the Zuid in the city of Amsterdam senio Ans s Oos
32. d in bar charts and on a map through an interactive geographical interface This provides a clear insight in the as is situation in the corresponding city and enables the user to identify areas with opportunities for improvement Next to exploring the current status of a city the user can set sustainability targets referring to the to be situation of the city Analyze city data A aan Analyze the city data yy o ee Transform Dashboard Data from Transform Constant costs m El Costs eds Extended Dashboard Data from other sources Production Emissions reduction in carbon dioxide emissions Geographical Data Selection of city area Electricity consumption per purpose Gas consumption per consumer type Electricity consumption per consumer type Electricity consumption per year bucket kwh 1 10 13 kwh Selected City Amsterdam Set targets re Future city targets as a function of the current city data 2 Set targets of current city data Carbon dioxide emission reduction 20 0 Final energy consumption reduction 20 0 Increase in 5s renewable energy sources 20 0 Energy consumption cost reduction 20 0 gt accenture BE uos H oy E o A ooo oo S UU o S SU S a SS S S 2 Set Scenarios 4 Alocate Measures Determine impacts In the second step different futures for the city can be defined with regard to the uncertain uncontrollable factors for a city actor E
33. e v Data Input Manual Content Document about the data input requirements Audience Parties responsible for the city data c c un Y User Manual Content Instructions for using the DSE through the user interface including a case study Audience Users of the Decision Support Environment Y Technical Documentation Content Technical details regarding the development of the DSE software Audience Technicians IT specialists interested in the software architecture of the DSE Y Recommendations for Further Development Content Suggestions for improvement of the DSE prototype Audience Potential future developers of the DSE Y Deployment Guide Content Document about the required hardware for running the DSE Audience Parties interested in installing the DSE on their own servers Y Enabling Context Guide Content Description of TRANSFORM city contexts and guidance through enabling measures Audience Scientific community p TR NSFORM Table of Contents 1 Introduction 5 2 Glossary 6 3 Process Guidebook 7 16 4 Data Input Manual 17 26 4 User manual 27 62 5 Technical Documentation 63 91 6 Recommendations for further development 92 96 7 Appendix Deployment Guide Word doc 97 8 Appendix Enabling Context Guide 98 The headlines are clickable hyperlinks k k B TR NSFORM c gt accenture WP
34. e mathematical relations behind can be reviewed by double clicking nodes in the mindmap LPI Definition Measure Editor 5 r k k B TR NSFORM gt accenture In case a user wants to go more in depth and review the assumptions behind the measure impact calculations the user is referred to the Measure Editor tab of the Measure Library A mindmap like structure gives an easy insight in the relations between variables and the mathematical relations behind can be reviewed and modified by double clicking nodes in the mindmap A user can also create new measures by itself using the Measure Editor interface KPI Definition Measure List Facade PV panem Insulation LED lighting and sensors Micro GHP Mew measure Shower Heat Exchanger Solar PV panels Wind turbines Window replacement inca ban gt accenture Name Mew measure Description Create Remove Visualize Create a measure Name the measure and add a description Review Edit a measure Visualize an existing measure Double click on existing nodes to view and change the equations Add new nodes by clicking on one of the colored buttons in the top menu See glossary for the meanings of the different types of nodes or variables EST TR NSFORM How to Add amp Adapt Factor Library In the factor l
35. eat Pump Allocate Time Allocate rea Solar PV panels Allocate Time Allocate Ares Select a measure and choose Allocate Time Facade PV panels Allocate Time Allocate rea Wind turbines Allocate Time Allocate Ares Allocate a start and end date for implementation of Aquifer Thermal Storage op Allocate Time Allocate Ares this measure Use slider to set a penetration rate E Allocate measure in time Please select the time penod from date to date in which the measure has to be implemented for a TOTAL percentage given by the penetration rate The model will distribute this percentage linearty over the complete period on a monthly basis From jM 01 15 Ll To 04 18 Pa LJ Total penetration rate E gt accenture E TR NSFORM Fu A A AE que Analyze Cily Conlexl CC Sel Scenarios 3 Allocate Measures Uciernine impacts Step 3 is dedicated to the design of transformation plans or measure portfolios These refer to factors that city actors do have control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementation C J Allocate measure to area Measure LED lighting and sensors Allocate area Map Control Navigate 9f amp ct info Choose number of maps 1 23 4 e Choose level of detail je select m E re
36. eating tap water Electricity consumption for Showering Electricity consumption Gas consumption Electricity consumption for Cooling Energy consumption per purpose Heat consumption Electricity consumption for Ventilation systems Energy label Gas consumption for Cooking Current insulation grades U values Building function Gas consumption for Heating building T Gas consumption for Heating tap water 3 Building dimensions Gas consumption for Showering Heat consumption for Heating building Heat consumption for Heating tap water Heat consumption for Showering Building attributes Building attributes Construction year U value Roof U value Facade Building attributes 3 Building attributes U value Floor Shapefile Roof area U value Windows Floor area Facade area Building function Windows area Number of floors B TR NSFORM gt accenture 4 4 Data enrichment 3 6 e Energy consumption per purpose City statistics need to be gathered about the average division of energy consumption between different purposes and how this differs between building functions Function Domestic Domestic Education Education Hotel Hotel Industry Industry Medical Medical Office Office Shop Shop Sport Sport Other Other gt accenture Carrier Appliances Cooking Lighting Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas Elec Gas
37. eframes and implementation details for the measures within Air source Heat Pump the All Electric portfolio our Solar PV panels 1 July 2015 1January 2016 70 elec kwh 1000 Air source Heat Pump 1 January 2016 1July 2016 8096 use of building office Facade PV panels 1 July 2016 1January 2017 100 use of building office ae atone 1 January 2017 1 July 2017 70 use of building office Wind turbines 1 July 2017 1January 2018 10096 Go to steps 3 1 3 3 ree TR NSFORM gt accenture 3 1 3 3 Allocate measures Create the All electric measure portfolio and start adding measures to it repeat step 3 5 for each measure Allocate the corresponding timeframes to each measure step 6 9 1 Analyze the city context 2 Set scenarios AAA 4 Determine impact Measure library Factor library Measure Portfolio Overview Create Edit a measure portfolio Measure Portfolio Name the new measure portfolio and its description xmize them by the edit buttons Add to portfolio Delete from portfolio All measures Measure portfolio New measure partfalia Shower Heat Exchanger LED fighting and sensors Mquifer Thermal Storage open system Solar PV panels Facade PY panels District Heating Grid Allocate measure in time Please select the time pe od from date to date in which the measure has to be implemented for a TOTAL percentage g
38. fort is required for enriching the elu Geade data to enable more sophisticated analysis on TR Vocem the impacts of measures A method was developed for converting 10 data points into 32 data points by using city statistics U value windows Energy data Electricity consumption for Appliances Electricity consumption for Cooking Building attributes Energy data kWh year Electricity consumption for Lighting GIS Shapefile Electricity consumption Electricity consumption for Heating building Electricity consumption for Heating tap water Construction year Gas consumption Electricity consumption for Showering Building function Heat consumption Electricity consumption for Cooling Floor area Energy label Electricity consumption for Ventilation systems Gas consumption for Cooking Number of storeys Gas consumption for Heating building Ownership Gas consumption for Heating tap water Gas consumption for Showering Heat consumption for Heating building Heat consumption for Heating tap water Heat consumption for Showering xk ree TRNSFORM gt accenture 4 4 Data enrichment 2 6 Electricity consumption for Appliances Electricity consumption for Cooking This data enrichment method consists of three parts In each part a category of data points is estimated from available data Electricity consumption for Lighting Electricity consumption for Heating building e Electricity consumption for H
39. g Heating tap water Showering Heating building Cooling Ventilation systems Label A B C D E F G k k E TR NSFORM c gt accenture un 4 4 Data enrichment 5 6 Available statistics are used to split up the energy consumption data into different purposes Different energy profiles are created for every combination of building function and energy label Building function domestic Gas consumption per purpose Electricity consumption per purpose 100 100 RE 80 E Cooling 60 m Showering 60 Heating tap water 40 m Heating tap water 40 m Heating building 209 amp Heating building 2096 E Lighting Cooking m Cooking 096 096 l A B C D E F G A B C D E F g Appliances Energy Label Energy Label Building function hotel Gas consumption per purpose Electricity consumption per purpose 100 100 80 80 E Ventilation systems 60 60 m Heating tap water m Cooling 4096 40 m Heating building Heating tap water 2076 Cooking 20 m Lighting 0 0 Appliances A B C D E g G A B C D E F G Energy Label Energy Label Cc uc UT LU CoC U J JT UU LU E E LJ LJ E w Uu U 5 y J JT JUTLUUSTC A xX amp m penny E TR NSFORM gt accenture 4 4 Data enrichment 6 6 a Current insulation grades U values The current insulation grades or U values for every surface of a building are based on averages per construction yea
40. gation serves to avoid privacy issues with the energy data Getting the shape files of buildings The municipality of Amsterdam has a dataset with GIS shape files and data about construction year intended use of a building etcetera basic administrational data Each building has an identification number these IDs follow a logical order through the city i e buildings next to each other have a consecutive ID gt accenture Integrating the energy consumption data The grid operator Alliander receives the basic administration data from municipality and uses a GIS program to add the most recent annual energy consumption data as extra columns to the GIS data files If there are more connections within one building the consumption data of all connections are summed The number of connections within a building is also saved as an extra column Aggregation of energy data All buildings with less than six connections are selected for privacy reasons the data of these buildings need to be aggregated A query is run in the GIS program that averages the consumption values of a group of six buildings that lay close to each other and then gives them all the same average value Groups of buildings are made based on the identification number see step 1 o TR NSFORM 4 4 Data enrichment 1 6 Building attributes When the data is integrated and aggregated Ve t oe additional ef
41. ibrary the user can review and modify the assumptions about different futures of a city These assumptions are stored in factors different evolutions of variables with a high uncertainty Next to reviewing and customizing these factors a user can also create new factors itself 1 Analyze the city context 2 Set scenarios 3 Allocate measures 4 Determine impact TAI Emssons trom Gas Emssons from Nuclear Emssons from Ol Emssons from Soler Emssons from Wind Emssons from Central Electnoty Producton mssons from Central Gas Production vct mssons from Centra est Pro Gas Pree Heat Pros Cold Price esr fom other renrewedies Electroty from Hydro Efficency of Electnoty Producton from Hydro Emssons from Hydro input Varad e Remove a val e ot the tactor Variable to link factor Name Electroity Prce increasing electroty pros inereese g electnoty pros _ _ Description There 1 o descnpto of ths factor Create Remove Add a value to the factor increasing electricity price values ime stamp T 9 ey do Mon Tu ved n Set Remove a value of the factor 817 10 111 12 Remove apao 16 112 118 ao Ol 29 21 22 29 24 28 26 27 20 20 90 31 Remove 8 4 1 8 r A k B TR NSFORM gt accenture Contents of the User Manual ES E en um WA E il E E a d Se ee Eee n c p
42. ighting and sensors Vind turbines Allocate Time Allocate Area Map Control Navigate Select Info Choose number of maps 123 4 7 Help fac sun Max energy Saved selections frac wind energy mwh gas consumption cooking gas consumption heating building gas consumption heating tap water gas consumption showering gas kwh gas m3 heat consumption heating building heat consumption heating tap water heat consumption showering identifica name perimeter_m status sun max energy b d ar JA NoOr Sur mia anar use of building WITT POW ET ITI wind total energy qwh Driemond Quderkerk aan de 4mste Repeat for each measure fer ven Senex Scenarios Measure portfolio Scenario 1 Baseline All Electric Factor name Change m T Constant electricity price 096 Solar PV panels Constant gas price 096 roof facade Scenario 2 Increasing prices Factor name Change Wind turbines Increasing electricity price 2 year Increasing gas price 2 year Air source Heat Pump Scenario 3 Decreasing prices Factor name Change UIS UN UTI Decreasing elec price 296 year storage open system Decreasing gas price 296 year Continue with initiating simulation runs and obtaining results AX Ak DT TR NSFORM gt accenture c
43. ity id 1 BB gas id 1 Bl heat id 1 Monthly emissions from 2014 to 2021 electricity id 1 Bl heat id 1 Monthly renewables from 2014 to 2021 14 01 _ Use logarithmic scale 14 02 14 03 Costs 14 04 14 05 14 06 electricity id 1 Bl gas id 1 BB heat id 1 Monthly costs from 2014 to 2021 14 01 14 02 14 03 14 04 14 05 14 06 14 01 14 02 14 03 14 04 14 05 14 06 _TRYNSFORM 5 2 1 Database structure Step4 Determine impact tables 2 3 A startdate timestamp gt accenture B cold Bl Electriciry cas MN Hear B Electricity MN Hear B enddate timestamp 300 250000 50 5 20000 A city varchar 7 5 ud s ox 15000 D user Name varchar 7 5 ve TM E 50 10000 H 1 m 0s 100000 A kpiname varchar 7 5 m a fuel varchar 7 5 100 X 50000 x 2200 150 LU A timestamp timestamp Mai ma ve measure varchar 75 expid bigint scenarioid bigint o m o sequenceid bigint um Bl cold MN electricity Bl Gas B Hear Bl electricity Ml cas NU Heat ES relativechange double precision ES absolutechange double precision 50 20000 300 2500000 ES relativechangeaffected double precision PT 200 2000000 totalvalue double precision q Po 1500000 A trnsfrm meta measureimpactoutput pkey k T ive as i 100 X 500000 150 200 Xo DIU _TRYNSFORM 5 2 1 Database structure Step4 Determi
44. iven by the penetration rate The model will distribute this percentage linearty over the complete period on a monthly basis Measure Solar PV panels Penetration rate 0701 2015 01 01 2018 From 07 15 To 01 18 Go to step 3 4 Total penetration rate January 2016 oo 3 4 Allocate measures to area Find the Zuid Oost area south east and select the corresponding area and filter criterion for each measure Allocate measure to area Measure LED lighting and sensors Map Control O Naviga loo efect Info Choose number of maps 1234 Please select ase select Ti F 2 AE I e La pe oh we Ouderkerk aan oeArnstel 200 m 1000 ft Measure Solar PV panels Allocate Time Allocate Area Facade PV panels Allocate Time Allocate Ares Aquifer Thermal Stor Allocate Time Allocate Area Wind turbines Allocate Time Allocate Ares 7 Help Saved selections y E huurl LL T xa E F i t i TEA V Che 5k Walburgdres Please continue k k 49 OpenstreetMap contributors 3 TRyNSFORM 3 4 Allocate measures to area Measure 1 Time 2 Area Find the Zuid Oost area south east and select the Fr POTES ee corresponding area and filter criterion for each measure Facade PV panels Allocate Time Allocate Ares Aquifer Thermal Stor Allocate Time Allocate Area Allocate measure to area Measure LED l
45. key node id basenode id B id bigint l ES name warchar 7 5 ES tableuistatus boolean ES parent warchar 7 5 8 trnsfrm meta value pkey node id bigint node id bigint ME update_id bigint ES value id bigint 1683 update type varchar 7 5 EH equation id bigint 2 trnsfrm meta node pkey trnsfrm meta measurenode pkey id bigint amp EH equation varchar 1500 EH equation type varchar 7 5 ES indicator varchar 5 ES nodetype varchar 7 5 Jp node id bigint ES nodename varchar 7 5 trnsfrm meta groupnode pkey e rep woe IT er ES cityname varchar 5 l i A purpose node id bigint trnsfrm meta purpasetogroup pkey trnsfrm meta equation pkey node id bigint E3 name warchar 7 5 ES purpose warchar 7 5 I trnsfrm meta purposenode pkey Note the group node table is not used it was initially thought as a way to give the same equations to different nodes k k ES iaon TR NSFORM DT gt accenture c 5 2 2 Measure library measure editor tables 3 5 Variable id2CityVariable id city name varchar 7 5 wariable id bigint id bigint ES name varchar 7 5 ES city varchar 7 5 ES value varchar 7 5 ES startime timestamp 2 cityname varchar 5 A measurename varchar 7 5 trnsfrm meta city plcew A city varchar 7 5 EH id bigint ES measuredescription varchar 7 5 ES geolevelapplication varchar 5
46. mption heating building m3 19 trnsfrm ams tmp e TRUE 33 34 Amsterdam gas consumption heating tap water m3 20 trnsfrm ams tmp vite TRUE 34 135 Amsterdam gas consumption showering m3 21 trnsfrm ams tmp re TRUE 35 136 Amsterdam heat consumption heating building Kwh 23 trnsfrm ams tmp Es IRUE 36 137 Amsterdam heat_consumption_heating tap water Kwh 24 trnsfrm ams tmp s IRUE 37 8 Amsterdam heat consumption showering Kwh 25 trnsfrm ams tmp de TRUE a_residential trnsfrm block vie Residential a office trnsfrm block vie Office a commerce trnsfrm block vie Commerce a industrialhall trnsfrm block vie IndustrialHall a trade service trnsfrm block vie Trade Service a social trnsfrm block vie Social a culture trnsfrm block vie Culture k k gt DTU kk accenture e TR NSFORM i 5 2 3 Factor library tables id bigint ES factorname varchar 7 5 ES factordescription varchar 7 5 ES city variable name varchar 7 5 ES user name varchar 7 5 EH cityname varchar 75 trnsfrm meta factor pkey N Warinde lo ink factor Heme miniai fac pes wal Bleciicity Price T Decraaxmq Feo os aJa Description Ej public trnsfrm meta factorentry Pi secs in 4B factorname varchar 7 5 aS oben arcs a pacta cin Wii bom Ua Jp timestamp timestamp pem CES ape Same int cade s Add Value ti 2 ES value double precision A trnsfrm meta factorentry pkey Diane Note Instead of the factor name as a FK in the fac
47. ne impact tables 3 3 1 ji 24 sequencetomeasureid bigint mg P fk geometry bigint A the geom bigint A trnsfrm meta sequencetogeometries pkey wba v vy cy HONOR On t ec Me Teros Rue dAubon A 4 gt 3 i P Lj a 3 A xm id i 9 ALAS y 2E w AB t a Im peace Guichard t ane n l i 2 n 20 10 E n At 4 ALI p iG tS Com gt s Avenue Fl ioe ul e ur 2 go pe um sr CS imo ie L e Riu 7 A A 3 2 E Eg xc TE Meis i FILUM s d t A ES BUM E 4o 0 y fae eo haponnay g 1 2 9 MZ x 5 pue Hr nM 11 A fe gmt AT t E MR BIN _ 20 40 2111 zn D E e k rt X gii MATA EAN g E Re Rue Vetet Manu B sow ui Ls r 1 Jo 20 ARA t la NO Eu E mue oe V de a e Dole y Y A Y fy i aas Pon r 3 Re a lone y j0 20 ES NE a Ba 8 v B n ln Rue Pind Bert NA uu 20 40 t em t Rue Pod Bet g ue td t Y Mz Mi e C a dp s T d Er wap uw a NT e i us gt 1 d lo 40 60 q T J Oe la n i AD A i p MA oie ex E Y ip TIT ed med TEAMS CEL E yg r e Ci tue Ruedes Rancy O zl Ir 8 Wm Rue des Rany E primare y 2 A A MA z ary E Lk PAS Mu A k swap contrbtors NB 80 100 is 3 e t Y ibutors Y We gt accenture Ak AMT Insror MEME TR NSFORM 5 2 2 Measure library KPI definition tables 1 2 Central Production Ox
48. nsured and additionally maintaining the accuracy DN and relevance of the required datasets need to be identified information Y What are the most important insights from the results for city decision makers Y To what extent do the results answer the city s questions as defined in phase 1 Y How relevant are the results for overcoming the restrictions and barriers to informed decision making Y Are the results reliable and valid Y How can the validity of the results be improved Y Which additional datasets would improve the quality of the insights from the Decision Support Tool Y How can these additional datasets be acquired Y What are the most important insights from the results for revising the city s questions and objectives k k B TR NSFORM gt accenture ia Ina Homeier is the City Representative and Coordinator of Vienna It is always helpful to observe and learn from other cities taking some best practice examples as inspirations and impulses for our local projects From my point of view it is absolutely necessary to take a critical and at the same time open look at internal structure and processes and to work in the most integrative manner possible To make these insights sustainable we have worked together the last 2 years and we have prefigured the governance of partners working on the future energy master plan After the TRANSFORM program ends it will be c
49. ontinued via new stakeholder mappings and working groups so the benefits will be continued into the future B atrice Couturier is the City Representative and Coordinator of Grand Lyon gt accenture L 2 p TR NSFORM mem
50. r and climatic zone U values W m K Built before 1975 Roof 0 50 Facade 0 50 Floor 0 50 Windows 3 00 Roof 1 50 Facade 1 50 Floor 1 20 Windows 3 50 Roof 3 40 Facade 2 60 Floor 3 40 Windows 4 20 Built 1975 1990 Built 1991 2002 0 20 0 30 0 20 2 00 0 50 1 00 0 80 3 50 0 80 1 20 0 80 4 20 Cold climatic zone 0 15 0 20 0 18 1 60 Moderate climatic zone 0 40 0 50 0 50 2 00 Warm climatic zone 0 50 0 60 0 55 3 50 Built 2003 2006 0 15 0 18 0 18 1 42 0 25 0 41 0 44 1 84 0 50 0 60 0 55 3 04 Built after 2006 0 13 0 17 0 17 1 33 0 23 0 38 0 41 1 68 0 43 0 48 0 48 2 71 From http www ecofys com files files ecofys 2005 costeffectiveclimateprotectionbuildingstock pdf 3 Building dimensions The roof facade and window area are calculated from the GIS shapefiles and based on average window to wall ratios for different building functions gt accenture DI Eme llo E Building function Domestic Education Hotel Industry Medical Office Shop Sport Other Window to wall ratio 0 16 0 22 0 34 0 06 0 27 0 35 0 11 0 22 0 22 TR NSFORM 4 DSE Full User Manual E Link to the tool http sbc1 alt ac at web mtumarola dst Content Instructions for using the DSE through the user interface including a case study c2 AWT sos gt accenture un x amp pss TR NS
51. ructure e Src nl macomi transform data This package contains the internal representation of the data from the database it contains the following classes whose name corresponds to the data it contains e AggregatedEntity e BuildingAttribute e BuildingAttributeValue e Carrier e CityVariable e ConstantValue e Entity this contains data from the city tables e Equation e Groupnode e KeyValuePair e KPINode e Measure e MeasureApplication e MeasureNode e MeasureUpdatableNode e Node e NodeValue e PurposeNode e TableKey e TableKeyCombination e TablueValue e Value x ES kuvon _TRYNSFORM T gt accenture E 5 4 4 Simulation scheduler The simulation scheduler is a java application that is continuously running and reads the experiments table in the database to check whether there is an experiment that has not been executed yet As soon as it finds an experiment that has not been executed it will simply spawn the simulation model with the appropriate sequence and scenario ID The simulation scheduler consists of 3 classes e Dataservice to read the database table containing the experiments e Scheduler the main class that runs the program that will check the table regularly e Tunnel a helper class to start a SSH tunnel programmatically eg kuvon TR NSFORM gt accenture Recommendations for further development zx Content
52. s Y Who is responsible for stakeholder management Y Who ensures the continuous flow of new information k k B TR NSFORM gt accenture In order for all the plans to materialize in the city context we have reorganized the dialogue on the full energy chain We made it an open choice for stakeholders to decide where to co invest or not to invest at all in new energy efficient initiatives This helped spark cooperation on the plans for the South Eastern region of Amsterdam where local businesses hospital stadion utilities have defined a joint agenda they are committed to work on Bob Mantel is the City Coordinator of Amsterdam After the first phase of collecting data to aggregate it in the Energy Atlas we built 4 scenarios on the energy efficiency of carriers The objective was challenging because first we needed to build a methodology and this required a dedicated effort and a special skillset It was the first time we proposed energy scenarios previously we simply did not have the competency or the tooling B atrice Couturier is the City Representative and Coordinator of Grand Lyon accenture apes TUTE res TR NSFORM 3 2 Zoom into Phases 3 Creation 3 Creatioh Once the first results from data acquisition are successful insights can be generated from the available data in the creation phase An appointed team Running the energy of specialists creates scenarios and transformation
53. s to define the desired objectives for the Decision Support process as well as mapping the involved stakeholders and useful data sources The current status and objectives for the city are analyzed in detail to assure correct scoping of the Decision Support process and prevent asymmetries of information Understanding the current state of your City Y How is the city performing on sustainability indicators Y How is the city performing with regard to policy insights from data and informed decision making Y What are the city s climate goals Y How is the achievement of these goals measured tracked presently Y What are the main challenges of the city in the energy transition process Y Which questions need to be answered by the Decision Support Tool Y Who are the relevant decision makers and what information is required for what decisions Y What are the current restrictions and barriers to informed decision making Y Are there any political commitments or priorities concerning transformational measures Y Who are the stakeholders in ownership of necessary data Y Which of these data is already openly available Y What is the current mindset of data owners with regard to open data k k pe TR NSFORM gt accenture
54. t area under Geographical data OK 1 Analyze the city context 2 Set scenarios 3 Allocate measures 4 Determine impact Measure library Factor library Selected City Amsterdam Analyze city data Transform Dashboard Extended Dashboard Fa EJ set targets of current city data Map Control Navigate Select Info Choose number of maps 32 Please select Carbon dioxide emission reduction 20 0 ris Final energy consumption reduction 20 0 rm Increase in renewable energy sources 20 0 c Amsterdam Energy consumption cost reduction 20 0 2 Choose map type Geofabrik A Help Please select ACE ICE Reset set Save selection Saved selcgsimg onsumpt Choose map typel AMS Gas C OSM Mapnik bw OSM Publ Transp L AMS Construction Year AMS El Consumption Applicances AMS El Consumption Cooling AMS El Consumption Heating AMS El Consumption Lighting 1 AMS ElL Consumption kWh AMS Gas Consumption Heating AMS Gas Consum tion m3 AMS Wind Potential Google Hybrid Google Maj Lh 4 A t Cons m3 J Lesom 5850000 a Bl 821000 1856000 cat Y o US co gy 433000 821000 awe s DU 245001 433000 d S ge E 137001 245000 IET i 4 gt o 65001 137000 ER MEN gg 20801 66000 B 1 20200 0 OpenStreetMap contributors Push for District grids me Re
55. tion Measure Portfolio Overview Create Measure Portfolio Heat pumps Name the new measure portfolio and add a description Lokale warmtevoorziening Portfolio Shower Heat Exchanger Create Thermal Heat Grid Remove Create Edit a measure portfolio Add measures to the portfolio Q Name the new measure portfolio and its description Name District Heating extension Select measures from the dropdown list Description Either Add to portfolio or C Add measures to the portfolio and customize them by the edit buttons Edit Create a new measure All measures Click on a measure to view the description Add to portfolio Delete from portfolio n E MANO Instead of creating a new measure portfolio an existing measure portfolio can be selected and either edited or applied via the outlined steps E TR NSFORM Create new measure Edit measure gt accenture tru gU UT Analyze City Context 4 Set Scenarios 3 Allocate Measures Otte rr ne m Step 3 is dedicated to the design of transformation plans or measure portfolios These refer to factors that city actors do have control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementation Mea sure portfol lo Measure 1 lime Allocate time and penetration rate EN Air source H
56. tor entry table the factor id should be used DIU gt accenture AIT teg AUSTRIAN INSTITUTE OF TECHNO TR NSFORM 5 3 Package Diagram 1 4 gt accenture ss 5 3 Package Diagram 2 4 E 1 pz ____ AN mindmapcomponent all measurelib windows n BEL LL measurelib webui factorlib i y nea a measureportlet_service_impl l Lanes a aste _ _sUse _ _ lise me i mE Al c2 x X x dae Esi TR NSFORM gt accenture un 5 3 Package Diagram 3 4 c2 A k AUSTRIAN INSTITUTE 25 TR NSFORM AN gt accenture un 5 3 Package Diagram 4 4 x x k D TR NSFORM accenture AN 5 4 Simulation model This part of the documentation provides an overview of the simulation model its internal data structure and the simulation scheduler c2 X x E k AUSTRIAN INGIITUTE 25 TR NSFORM Al gt accenture un 5 4 1 Conceptual model TRANSFORM The model distinguishes consumers network and producers Consumers and producers are entities in the system that contain attributes e g consumption values At each event in time i e scenario change or
57. trofit old buildings gt accenture Current and target CO2 emissions 2025 AS 55 176 kt year 2000 MN 81 74 kt year What is the most cost effective way for reducing CO2 emissions in this area of the city taking into consideration the local characteristics of the area Go to step 1 2 B rzinsrorm 1 2 Set targets Current and target CO emissions Set reduction targets for the area 2025 MS 55 176 kt year 2040 Ms 2 kt year Kr the city context _ 2 Set scenarios 3 Allocate measures 4 Determine impact Measure library Factor library Selected City Amsterdam GD Analyze city data o Transform Dashboard Extended Dashboard E Geographical data Map Control Navigate Select Info Choose number of maps 1234 A Help Please select Please select T v Geofabrik OSM Mapnik b w 1 OSM Publ Transp L AMS Construction Year AMS El Consumption Applicances AMS El Consumption Cooling AMS El Consumption Heating 4 AMS El Consumption Lighting AMS ElL Consumption kWh AMS Gas EARN oe 2 Set targets of current city data Carbon dioxide emission reduction Final energy consumption reduction Increase in renewable energy sources Amsterdam Energy consumption cost reduction AMS Py Potential AMS Wind Potential E EH Cons ma gg 155001 5850000 t LA gl 821000 1856000 E r T E oa s S B 433000 821000 PAA as E D 245
58. tured when Coverage the complete city is covered Some buildings City district B TR NSFORM DTU gt accenture 4 2 Data components Two components of city data are needed ae Building shapefiles GIS files that contain information about the shapes and location of buildings within the city See http en wikipedia org wiki Shapefile B Building attributes Properties of the buildings that can be saved in two formats Option 1 dbf Option 2 xls As columnar The dbf file and xls must both attributes for contain a unique ID column each shape where these IDs refer to the same buildings gt accenture Open data available within municipalities shp Shx dbf Typically available within energy grid companies Electricity consumption kWh year Gas consumption m3 year Other types of energy consumption kWh year prj Sbn and sbx Typically available within municipalities Building function office house Building floor area m Construction year Energy label Ownership Available within diverse institutions or not yet available Roof area suitable for solar panels m Underground heat storage potential depths or kWh year Wind potential TR NSFORM 4 3 Data integration and aggregation These three steps show how the data integration and aggregation process was done in the case of the city of Amsterdam Data aggre
59. uus A ee LUE LUE Dr bkn aes ILI OD ee x TR NSFORM LLL LLL LLL UUE D SESION 1 ooo panes os cc cc ci CLICS EA E A NN NN NN NN NN NN ae e 2 rr 2 z o ss ES PS SSES A SES 3 SSI E SESE SE a SS SESE SS SES SS SECS SS SES p SSS SRS SASSER SSE SSS E S ES SES p D SS S S T SES SS p SS SES SS E S ES S SES SS E S ES S SES SS SS SS S T Qu D S SD OQ OO SSSR p SSSR o RS IO o rr gt 2 E i gt 58 Z o z T SS Bee O Le 2 O OE 2 Le O O 2 2 How to get your city smart Outline The Decision Support Environment consist of four main steps A user can 1 analyse the current energy performance of a city based on the available data 2 set scenarios containing assumptions about the future state of a city 3 mimic the transformation of an area by allocating measures and 4 test the local or city wide impact of such a transformation under the various future assumptions scenarios 1 Analyze the city context 2 Set scenarios 3 Allocate measures 4 Determine impact Analyze City Context Design transformation plans for the city via measure portfolios in certain areas and for certain time frames gt accenture 13 Analyze City Context 3 Sel Scenarios 3 Alocate Measures e Determine impacts The first step is a representation of the available city data in the Decision Support Environment that can be viewe
60. xamples of these factors are energy prices and interest rate Scenario Overview Scenario Name Test 1 As proposed Test 2 bbb Create halla test 3 Remove what happens now Create Edit a scenario Fa i inti J Name the scenario and its description Name New scenaria1 Description a Add factors to scenario and customize them by edit button Set Scenarios Create new scenario Select Existing Scenario Delete existing scenario Create New Scenario follow steps below to validate accuracy select scenario from list and remove Name the Scenario Add a description This makes the scenario traceable and explicable to others All factors Scenario Select a factor fo add it to fhis scenario o Factors in this scenario Select 2 Add to Scenario Remove from Scenario Create a new factor Edit factor e DTU A d accenture Sd Add factors to the Scenario Customize by adding under which factors the scenario will be run BE uos Analyze City Context Sel Scenarios 3 Allocate Measures Jeiermine Impacts Step 3 is dedicated to the design of transformation plans or measure portfolios These refer to factors that city actors do have control over Each measure portfolio contains a set of measures allocated to certain entities in the city e g buildings and to a specific time frame for implementa
61. xperimentation they had a task and a up established structures We need to define a common purpose to help realize Grand Lyon s plans approach which will open the way for transformation Ina Homeier is the City Representative B atrice Couturier is the City Representative and Coordinator of Grand Lyon and Coordinator of Vienna tue AIT accenture ser BH 2 3 2 Zoom into Phases 2 Commitment e Commitment The commitment phase is dedicated to setting targets with regard to data acquisition and appointing the responsibilities for reaching these targets to Defining responsibilities the right people It is very important to have commitment from all parties allocating process owners involved to ensure collective effort and motivation which is essential for and collecting the right obtaining the required data data and content Y Who in your city is responsible for data acquisition Y Who is responsible for data integration in the tool Y Who is responsible for installation of the tool for the city Y Who are the specialists responsible for appropriate creation of scenarios and simulation runs Y Who represents the requirements and needs of the city decision makers Y How is dealt with political commitments or priorities of the city while creating scenarios and simulation runs Y Who is committed to gathering expert information on city specific measures Y Who is committed to assuring validity of the result
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