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Vienna’s Smart City Project as foundation for the development ofa migration path to a smart urban energy system

Research Project Seestadt Aspern

© Siemens AG 2015 All rights reserved.2015-11-04Page 2

Smart City – Framework strategy of the City of Vienna

• The Vision – Smart City Vienna 2050(decided and driven by the Vienna town council)

• Smart City frame work strategy is focusing on:

• Resources (energy, mobility, infrastructure,buildings)

• Quality of life (social field, health,environment)

• Innovation (education, research, technology,economy)

• Positioning of Vienna as solution provider withsocial responsibility


Seestadt Aspern – Facts and Figures

• 20,000 jobs regional centre

• Apartments, offices, shop-, sciences-, andresearch facilities, education, trade, publicareas, park areas

• Development period 20 years (until 2028)

• 10.500 apartments for 20,000

inhabitants

• Planning gross floor area 2.2 million m

• Planning area 2,4 km

• Net building area 1 km

• Outside area 0.9 km

• Traffic area 0.5 km

2

2

2

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Seestadt Aspern – public transportation and building utilization

Appartments

Different categoriesof mixed utilization

Small & medium business

Research & Development

Social infrastructure(including education)

Culture

Water

Park area

Bus

Tramway

Underground

Railway

Public Transportation

Building Utilization


Aspern Smart City Research (ASCR)Joint Venture Partners (1)

Wien Energie GmbHThe largest energy service company of Austria

• Supply of 2 million people with electricity, gas,district heating and telecom services

Wiener Netze GmbH• The largest distribution network operator in

Austria for electricity, gas and district heating

Siemens AG Österreich, in collaboration withHeadquarters (CT & EM)

• Unique Know-how and access to internationalexpertise in the areas of energy (productiondistribution, storage management), buildingtechnologies (energy efficiency, managementsystems, security), mobility, project management, IT& Consulting

Wien 3420 Aspern Development AG• Owner of extensive properties and

project development in the Seestadt Aspern;• Local marketing and branding• Infrastructural development

Business Agency Wien• Developing the business position of Vienna• First point of contact for national and international

companies

Project duration: 5 years


ASCR TestbedSmart Building Infrastructure

D18 BIG Nursery / primary school• 7 heat pumps (800 kW)• Solar heat (90 kW) + hybrid (60 kWpth)• PV (15 kWp) + hybrid (20 kWpel)• Ground heat storage (40 MWh)• Hot water storage• Battery (20 kWh)• Smart automation• Room automation

D12 EBG 213 flats• 2 Heat pumps (510 kW)• PV (29 kWp)• Solar heat(90 kW)• Electrical water heating (70 kW)• Smart automation

D5b GPA Student hostel for 300 students• PV (250 kWp)• Battery (120 kWh)• Electrical water heating (2 x 8 kW)• Smart automation

D10 ÖVW/EGW mixed utilizationReference (benchmark) building

C4 WAB officesReference (benchmark) building


ASCR TestbedSmart Grid and Smart ICT Infrastructure

Smart Grid Testbed• 12 smart transformer stations (prototypes)• 23 transformers with different technologies

(amorphous core, ester midel, aluminium,tap changer transformer)

• Grid monitoring devices (LV grid)• Smart Meters from 2 building blocks

delivering consumption and grid data• LV grid control center

Smart ICT Testbed• Data warehouse

(Teradata )• Data integration• Business analytics


Smart ICT• Data collection, integration and provisioning for business processes and system operation• System optimization

An optimized Energy System is the foundation forsustainable Smart City Concepts – Research Domains

• Production increasinglydependent on weather

• Demand for improvedproduction and load forecasts

• Demand for short termenergy pricing according topresent production volume

• Platform for flexibility trading

Smart Markets• Own energy production• Heat pumps + thermal storage• Batteries• Future:• Flexible energy tariffs• Flexibility offerings• Self optimizing buildings with

an interface to market partners

Smart Homes / Buildings

Smart Grid as facilitator for smart energy system (Vision )

• Improved load and generation forecasts• Flexibility management and grid protection• additional services for market partners

Provisioning of:• Power quality and grid availability

under fast changing requirements

Smart User

• Optimized energycosts

• Extended market &consumptioninformation

• Home automation

Complementaryrequirements








Smart Markets




under fast changing requiremts


• Own energy production• Heat pumps + thermal storage• Batteries• Future:• Flexible energy tariffs• Flexibility offerings• Self optimizing buildings with



Smart User



• Home automation


Smart Building Research Topics

• Integration of all stakeholders into the system• Standardized communication interfaces• Development of an overall system architecture• Simplified provisioning, configuration and

management over the system’s life cycle

Buildings provideflexibility

Standardizedinterfaces

Buildings aspart of ahigher-leveloptimization

• Central or de-central data analytics• Data Mining• User behavior modeling to increase the

accuracy of energy forecasting algorithms• Data correlation for fraud detection and

predictive maintenance

Generate newinformation fromdata analytics

Context-awareinformation

systems

Dataanalysis

Use energy atthe right time

(Model)Predictive

Optimization

Predictiveoptimization

Challenges

• Prediction of generation & consumption• Predictive optimization using external

information (e.g., weather forecasts)• Multi-modal optimization (HVAC, electricity)• Simulation and model based optimization

Technology & Innovation


Self-Consumption Optimization

Reduce total energy costs atbuilding level by maximizing self-consumption of generatedenergy

Customer benefit

§ Forecasting of energy generation and consumption at building level§ Predictive optimization of self-consumption using energy storage models

Innovation


Predictive Maintenance

Costs for maintenance oftechnical infrastructure inside thebuilding get minimized and canbe scheduled, while increasingavailability

Customer benefit

§ Adaptive (self learning) system to increase energy forecast accuracy§ Analysis of deviations between forecasts and actual consumption to support predictive

maintenance

Innovation

Energy forecasts

Sensor values

Increase forecast accuracy

Adaptive system Maintenancerecommendations


Interaction with Smart User

By evaluation and changing ofcustomer’s behavior, customercan gain benefits, e.g. smartenergy consumption.

Customer benefit

§ Split in three aspects: social, technical and product solution§ Use case deal with the aspects of behaviour and flexibility of the end-user in regards to

his energy consumption – Such as tariff models will be investigated and evaluated.

Innovation


Smart Building System Concept

S7Desigo TRA Desigo PX

Building MeteringRoom Automation HVAC Renewable Energy& Storage

Building EnergyManagement

Smart ICT

Smar

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pera

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Flex

ibilit

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Smart UserInteraction








Smart Markets• Own energy production• Heat pumps + thermal storage• Batteries• Future:• Flexible energy tariffs• Flexibility offerings• Self optimizing buildings with



Smart User



• Home automation







What are the challenges for distribution grids to cope with

110KV

20kV

400/230V

20kV

400/230V

20kV

Substation

Tran

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Bid

irect

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PV

PV

PV

e-vehiclee-vehicle

• Distributed Generation → U problem (rural area), I problem (urban areas)• Flexible Tariffs → “synchronized” consumption behavior• Implemented protection concepts become obsolete• Flexibility Trading & e-mobility → load problems combined with U/I challenges• High amount of inverters connected to the grid → Grid stability

Physical effects

• Which effect causes where problems in the LV/MV Grid → Lack ofinformation

• Passive consumers become highly dynamic & active prosumers → Gridplanning rules loose their validity

• Fast changing requirements increase capabilities of existing infrastructure

Challenges for distribution grid operators

• Efficient utilization of existing infrastructure, optimized grid operation• Demand for more information to support efficiency of 3rd parties (TSO’s,

Market partners, energy consumer)

Strong demand to increase efficiency


The Smart Grid Migration PathGuideline for our R&D activities

Functional dependencies

17Alfred Einfalt

• Continuous provisioning ofgrid operation data throughdistributed devices (sensors,meters) and load estimation

• Monitoring of faults andthreshold violations

• Alarm generation

Data provisioning &grid monitoringè grid operation

„passive“ grid optimization,analysis of events andeffects

• Migration of planning processfrom “worst caseassumptions” to “realrequirements” based onmeasured data

• Grid and process optimizationthrough business analytics

• Decentralized voltage/loadmanagement

• Flexibility management(interaction with buildings)

• Load dependend gridconfiguration

• Automated fault isolation

Big data &business analyticsè back office grid

optimization

Active grid managementè distributed intelligent

devices

Where does the infrastructurereach its limits?

„active“ grid optimization,platform for new services


Grid Monitoring within Testbed AspernStep 1 of the migration path

Selected research targets

• What is the optimal ratiobetween measured andestimated data?

• Which accuracy ofmeasurement values isnecessary?

• Acquisition of grid topology

• Contribution of Smart Meters

• Alarm generation and filtering


Business Analytics for Grid PlanningStep 2 of the migration path

Calculation offuture grid loads

(Set of analytic app’s)

Grid planning tool(SINCAL)

Solutionassessment

Data Warehouse

Critical grid areas + Data

Solution scenarios

Parameter setting

Measurement Data

Optional:SCADAthresholds

Integrationinto a HMI

Analytics App‘s: Estimation of future grid loadsbased on

• Historical Data• Prosumer models• According to the market development

modified prosumer models (→ Scenarioevaluation)

• Export of critical grid areas to a planning tool

Grid Planning Tool:• Problem verification• Generation of possible solution scenarios• Export of solution scenarios for evaluation

Solution assesment:• Evaluation of solution scenarios (costs,

sustainability)• Export of optimal scenarios to DWH and if

necessary generation of implementationorders

Target:

Optimal support for operative

and strategic grid planning

Service Team


• Estimation of future gridloadsbased on historical dataand/or on changedprosumer models

• Fault analysis: Correlationof grid events and effectswith other data (e.g.,weather, asset data)


Plug & Automate supporting active grid managementStep 3 of the migration path

Focus topic 1: reduction of operating costs for distributed intelligent devicesà Plug and Automate functionalities

Focus topic 2: flexibility operationàFlexibility management to coordinate grid, market and customer requirements


• Robust and fault tolerantdesign of control andregulation devices

• Plug and Automatefunctionalities

• Automated configuration andadaption to topologychanges

• Comprehensive device andapplication management

• Energy consumption andflexibility trading becomessynchronized à possibleeffects on grid operation dueto increasing peak loads







Smart User



• Home automation










Smart Markets


Making Flexibility available for the market

Energy markets

Flexibility Aggregator

Use Case: Participation in Energy Markets

Use Case: Smart User Interaction • Smart Meters enable energy offerings withflexible price for residential customers

• Home and building automation devices areable to handle flexible tariffs and provideflexibility to the market

àFlexibility becomes a value for the market

Thesis

Flexible prices are a lever for Smart Buildings to optimize energy costs

Flexibility aggregation enables trading on energy stock exchanges or compensation ofenergy forecast deviations

Research topics

• Process optimization in order to keep operating costs low• Analysis: Flexibility costs versus benefit for Smart Users and Smart Markets


Making Flexibility available for the Market:Involvement of Grid Operators to ensure Quality of Supply

Grid operationFlexibilityOperator

Use Case: Decentralized LV grid management

Energy markets

Flexibility Aggregator

Use Case: Participation inEnergy Markets

Use Case: Smart User Interaction• Energy consumption and

flexibility trading becomes moreand more synchronized withenergy price changes

àGrid peak loads increaseàFlexibility can be used to reduce

peak loads and thereforereduce grid refurbishment costs

àA flexibility management tocoordinate grid, market andcustomer requirements isneeded

Thesis







• Demand for flexibilities

Smart Markets

Smart User



• Home automation


• Improved load and generation forecasts• Platform for flexibility trading and management• additional services for market partners








City Data: A World full of Silos…

Traffic WastePublic

AdministrationLoad

ForecastingSmart

Building

Mobility Water HealthcareOpen Data

(Stats,Wiki,…)Grid

Planning...

Multiple Visualization Tools and Applications

Platform-bound Stack with Physical Data Model Silos without Integration

Multiple Loading & Streaming Tools

Applications from other projects/domains ASCR focus

Operational Source Systems (SCADA/DMS, MDMS, GIS, WFMS, BEMS, etc.)


Exploring Aspern Smart City data:Traditional Business Intelligence AND New Data Discovery

BusinessSpecifies requirements and defines

business questions

ITStructures data to answer existing

business questions

IT + SMEsProvide platform and domain expertise (!)to easily query data from various sources

BusinessExplores data to identify and harvesthidden value and find new questions

Traditional Business IntelligenceStructured and repeatable

Data DiscoveryMulti-structured and iterative


Smart ICT at a glance

Analytic Demo Apps

Extract,Transform,Load

ASCR Infrastructure

Benchmarks

Grid Planning

Load Forecast

Grid OperationSmart CitizenApp

City Data Information Ecosystem Management and Operation(API Store, Privacy, Access Gateway)

DataPublishers

ApplicationDevelopers

SmartCitizens

UtilityProviders

CityAdministration

DataOwners

DataMerchandisers

Examples

ofdatasources

ina

city

Smart ICT

Platform

Building data

Building topology

Water and heating

Forecasts

Weather

Events

Grid data

Runtime data

http://www.nrel.gov/news/features/images/20121008_analysis_pix22292_large.jpg

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Smart ICT Research Areas

Estimate potential ofvarious operations

modes withsimulation

Valididation andmutual impact of

Optimizationstrategies

§ Creating a digital twin of the grid for futureGrid Planning§ Consideration of external factors§ Model-based Optimization

Simulationand

Optimization

Independentstakeholder fromdifferent areas

Interfaces andInteractions across

domains

§ Involve all stakeholder in the overall system§ Standardization of all communication links§ Simple provisioning of data via APIs§ Support for a App and API economy

Smart ICT ina Smart City

Context

Cross-Domain DataIntegration using

central & distributeddata models

Complex DataAnalytics

and Identification ofCorrelations

§ Distributed Data Integration from multiplesources§ Simple and efficient data access§ Data Science and Discovery to generate new

knowledge from data

DataIntegration

andAnalytics

Challenges Research Approaches


Holistic AND domain specific system optimization strategiesas well as scalable and future-proof solutionsare the key success factors

Our research program reflects that mission

Thank you for your attention

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Vienna’s Smart City Project as foundation for the ...€¢ Positioning of Vienna as solution provider with ... storage management), building ... Generate new information from data