Research Project Seestadt Aspern
Vienna’s Smart City Project as foundation for the development of a migration path to a smart urban energy system
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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 with social responsibility
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Seestadt Aspern – Facts and Figures
• 20,000 jobs regional centre • Apartments, offices, shop-, sciences-, and research facilities, education, trade, public areas, park areas • Development period 20 years (until 2028) • 10.500 apartments for 20,000 inhabitants • Planning gross floor area 2.2 million m 2 • Planning area 2,4 km 2 • Net building area 1 km2 • Outside area 0.9 km 2 • Traffic area 0.5 km 2
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Seestadt Aspern – public transportation and building utilization
Public Transportation Bus Tramway Underground Railway
Building Utilization Appartments
Different categories of mixed utilization
Small & medium business Research & Development Social infrastructure (including education) Culture Water Park area
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Aspern Smart City Research (ASCR) Joint Venture Partners (1)
Siemens AG Österreich, in collaboration with Headquarters (CT & EM) • Unique Know-how and access to international expertise in the areas of energy (production distribution, storage management), building technologies (energy efficiency, management systems, security), mobility, project management, IT & Consulting
Wien Energie GmbH The 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
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 © Siemens AG 2015 All rights reserved. Page 5
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ASCR Testbed Smart Building Infrastructure
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 utilization Reference (benchmark) building C4 WAB offices Reference (benchmark) building
D12 EBG 213 flats • 2 Heat pumps (510 kW) • PV (29 kWp) • Solar heat(90 kW) • Electrical water heating (70 kW) • Smart automation
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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
ASCR Testbed Smart 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
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An optimized Energy System is the foundation for sustainable Smart City Concepts – Research Domains Smart ICT • Data collection, integration and provisioning for business processes and system operation • System optimization
Smart Markets
Smart Homes / Buildings • • • •
Smart User
• Optimized energy costs • Extended market & consumption information • Home automation
Own energy production Heat pumps + thermal storage Batteries Future: • Flexible energy tariffs • Flexibility offerings • Self optimizing buildings with an interface to market partners
Complementary requirements
Smart Grid as facilitator for smart energy system Provisioning of: • Power quality and grid availability under fast changing requirements © Siemens AG 2015 All rights reserved. Page 8
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• Production increasingly dependent on weather • Demand for improved production and load forecasts • Demand for short term energy pricing according to present production volume • Platform for flexibility trading
(Vision )
• Improved load and generation forecasts • Flexibility management and grid protection • additional services for market partners
An optimized Energy System is the foundation for sustainable Smart City Concepts – Research Domains Smart ICT • Data collection, integration and provisioning for business processes and system operation • System optimization
Smart Markets
Smart Homes / Buildings • • • •
Smart User
• Optimized energy costs • Extended market & consumption information • Home automation
Own energy production Heat pumps + thermal storage Batteries Future: • Flexible energy tariffs • Flexibility offerings • Self optimizing buildings with an interface to market partners
Complementary requirements
Smart Grid as facilitator for smart energy system Provisioning of: • Power quality and grid availability under fast changing requiremts © Siemens AG 2015 All rights reserved. Page 9
2015-11-04
• Production increasingly dependent on weather • Demand for improved production and load forecasts • Demand for short term energy pricing according to present production volume • Platform for flexibility trading
(Vision )
• Improved load and generation forecasts • Flexibility management and grid protection • additional services for market partners
Smart Building Research Topics Technology & Innovation
Challenges
Predictive optimization
Buildings as part of a higher-level optimization
Data analysis
Use energy at the right time
Buildings provide flexibility
Generate new information from data analytics
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(Model) Predictive Optimization
Standardized interfaces
Context-aware information systems
• Prediction of generation & consumption • Predictive optimization using external information (e.g., weather forecasts) • Multi-modal optimization (HVAC, electricity) • Simulation and model based optimization
• • • •
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
• 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
Self-Consumption Optimization
Customer benefit Reduce total energy costs at building level by maximizing selfconsumption of generated energy
Innovation § §
Forecasting of energy generation and consumption at building level Predictive optimization of self-consumption using energy storage models
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Predictive Maintenance
Increase forecast accuracy
Energy forecasts
Sensor values
Adaptive system
Maintenance recommendations
Customer benefit Costs for maintenance of technical infrastructure inside the building get minimized and can be scheduled, while increasing availability
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Innovation § Adaptive (self learning) system to increase energy forecast accuracy § Analysis of deviations between forecasts and actual consumption to support predictive maintenance
Interaction with Smart User
Customer benefit Innovation By evaluation and changing of customer’s behavior, customer can gain benefits, e.g. smart energy consumption.
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§ §
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.
Smart Building System Concept Smart ICT
Room Automation
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S7
Building Metering
Desigo PX
HVAC
Renewable Energy & Storage
Smart Market
Desigo TRA
Building Energy Management
Flexibility Aggregator
Flexibility Operator
Smart Grid
Smart User Interaction
An optimized Energy System is the foundation for sustainable Smart City Concepts – Research Domains Smart ICT • Data collection, integration and provisioning for business processes and system operation • System optimization
Smart Markets
Smart Homes / Buildings • • • •
Smart User
• Optimized energy costs • Extended market & consumption information • Home automation
Own energy production Heat pumps + thermal storage Batteries Future: • Flexible energy tariffs • Flexibility offerings • Self optimizing buildings with an interface to market partners
Complementary requirements
Smart Grid as facilitator for smart energy system Provisioning of: • Power quality and grid availability under fast changing requirements © Siemens AG 2015 All rights reserved. Page 15
2015-11-04
• Production increasingly dependent on weather • Demand for improved production and load forecasts • Demand for short term energy pricing according to present production volume • Platform for flexibility trading
(Vision )
• Improved load and generation forecasts • Flexibility management and grid protection • additional services for market partners
What are the challenges for distribution grids to cope with Physical effects 110KV
Substation
• Distributed Generation → U problem (rural area), I problem (urban areas) • Flexible Tariffs → “synchronized” consumption behavior
20kV
• Implemented protection concepts become obsolete • Flexibility Trading & e-mobility → load problems combined with U/I challenges
station
Transformer
ststion
Transformer
Bidirectional load flow
• High amount of inverters connected to the grid → Grid stability
20kV
20kV
400/230V
400/230V
PV
PV
e-vehicle
PV
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Challenges for distribution grid operators • Which effect causes where problems in the LV/MV Grid → Lack of information • Passive consumers become highly dynamic & active prosumers → Grid planning rules loose their validity • Fast changing requirements increase capabilities of existing infrastructure
e-vehicle
Strong demand to increase efficiency • Efficient utilization of existing infrastructure, optimized grid operation • Demand for more information to support efficiency of 3rd parties (TSO’s, Market partners, energy consumer)
The Smart Grid Migration Path Guideline for our R&D activities
Functional dependencies
Data provisioning & grid monitoring è grid operation Where does the infrastructure reach its limits?
„passive“ grid optimization, analysis of events and effects
• Continuous provisioning of grid operation data through distributed devices (sensors, meters) and load estimation
• Migration of planning process from “worst case assumptions” to “real requirements” based on measured data
• Monitoring of faults and threshold violations • Alarm generation © Siemens AG 2015 All rights reserved. Page 17
Big data & business analytics Alfred Einfalt è back office grid optimization
2015-11-04
• Grid and process optimization through business analytics
Active grid management 17 è distributed intelligent devices „active“ grid optimization, platform for new services
• Decentralized voltage/load management • Flexibility management (interaction with buildings) • Load dependend grid configuration • Automated fault isolation
Grid Monitoring within Testbed Aspern Step 1 of the migration path
Selected research targets • What is the optimal ratio between measured and estimated data? • Which accuracy of measurement values is necessary? • Acquisition of grid topology • Contribution of Smart Meters • Alarm generation and filtering
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Business Analytics for Grid Planning Step 2 of the migration path Measurement Data
Optional: SCADA thresholds
Data Warehouse
Integration into a HMI
Calculation of future grid loads Parameter setting
(Set of analytic app’s)
Analytics App‘s: Estimation of future grid loads based on • Historical Data • Prosumer models • According to the market development modified prosumer models (→ Scenario evaluation) • Export of critical grid areas to a planning tool
Critical grid areas + Data
Target: Optimal support for operative and strategic grid planning
Grid planning tool (SINCAL)
Grid Planning Tool: • Problem verification • Generation of possible solution scenarios • Export of solution scenarios for evaluation
Solution scenarios
Solution assessment Service Team
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Solution assesment: • Evaluation of solution scenarios (costs, sustainability) • Export of optimal scenarios to DWH and if necessary generation of implementation orders
Selected research targets • Estimation of future grid loads based on historical data and/or on changed prosumer models • Fault analysis: Correlation of grid events and effects with other data (e.g., weather, asset data)
Plug & Automate supporting active grid management Step 3 of the migration path Focus topic 1: reduction of operating costs for distributed intelligent devices à Plug and Automate functionalities Focus topic 2: flexibility operation àFlexibility management to coordinate grid, market and customer requirements
Selected research targets • Robust and fault tolerant design of control and regulation devices • Plug and Automate functionalities • Automated configuration and adaption to topology changes • Comprehensive device and application management • Energy consumption and flexibility trading becomes synchronized à possible effects on grid operation due to increasing peak loads
© Siemens AG 2015 All rights reserved. Page 20
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An optimized Energy System is the foundation for sustainable Smart City Concepts – Research Domains Smart ICT • Data collection, integration and provisioning for business processes and system operation • System optimization
Smart Markets
Smart Homes / Buildings • • • •
Smart User
• Optimized energy costs • Extended market & consumption information • Home automation
Own energy production Heat pumps + thermal storage Batteries Future: • Flexible energy tariffs • Flexibility offerings • Self optimizing buildings with an interface to market partners
Complementary requirements
Smart Grid as facilitator for smart energy system Provisioning of: • Power quality and grid availability under fast changing requirements © Siemens AG 2015 All rights reserved. Page 21
2015-11-04
• Production increasingly dependent on weather • Demand for improved production and load forecasts • Demand for short term energy pricing according to present production volume • Platform for flexibility trading
(Vision )
• Improved load and generation forecasts • Flexibility management and grid protection • additional services for market partners
Making Flexibility available for the market
Flexible prices are a lever for Smart Buildings to optimize energy costs Flexibility aggregation enables trading on energy stock exchanges or compensation of energy forecast deviations Research topics • Process optimization in order to keep operating costs low • Analysis: Flexibility costs versus benefit for Smart Users and Smart Markets
Use Case: Smart User Interaction
Flexibility Aggregator
Energy markets Use Case: Participation in Energy Markets
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Thesis • Smart Meters enable energy offerings with flexible price for residential customers • Home and building automation devices are able to handle flexible tariffs and provide flexibility to the market àFlexibility becomes a value for the market
Making Flexibility available for the Market: Involvement of Grid Operators to ensure Quality of Supply
Use Case: Smart User Interaction
Flexibility Aggregator
Thesis • Energy consumption and flexibility trading becomes more and more synchronized with energy price changes
Energy markets Use Case: Participation in Energy Markets
Grid operation
Flexibility Operator
Use Case: Decentralized LV grid management
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àGrid peak loads increase àFlexibility can be used to reduce peak loads and therefore reduce grid refurbishment costs àA flexibility management to coordinate grid, market and customer requirements is needed
An optimized Energy System is the foundation for sustainable Smart City Concepts – Research Domains Smart ICT • Data collection, integration and provisioning for business processes and system operation • System optimization
Smart Markets
Smart Homes / Buildings • • • •
Smart User
• Optimized energy costs • Extended market & consumption information • Home automation
Own energy production Heat pumps + thermal storage Batteries Future: • Flexible energy tariffs • Flexibility offerings • Self optimizing buildings with an interface to market partners
Complementary requirements
Smart Grid as facilitator for smart energy system Provisioning of: • Power quality and grid availability under fast changing requirements © Siemens AG 2015 All rights reserved. Page 24
2015-11-04
• Production increasingly dependent on weather • Demand for improved production and load forecasts • Demand for short term energy pricing according to present production volume • Demand for flexibilities
(Vision )
• Improved load and generation forecasts • Platform for flexibility trading and management • additional services for market partners
City Data: A World full of Silos… Multiple Visualization Tools and Applications ASCR focus
Applications from other projects/domains
Mobility
Water
Traffic
Healthcare
Waste
Open Data (Stats,Wiki,…)
Public Administration
Grid Planning
Load Forecasting
...
Smart Building
Platform-bound Stack with Physical Data Model Silos without Integration Multiple Loading & Streaming Tools Operational Source Systems (SCADA/DMS, MDMS, GIS, WFMS, BEMS, etc.) © Siemens AG 2015 All rights reserved. Page 25
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Exploring Aspern Smart City data: Traditional Business Intelligence AND New Data Discovery
Business
IT
Specifies requirements and defines business questions
Structures data to answer existing business questions
Traditional Business Intelligence Structured and repeatable
IT + SMEs
Business
Provide platform and domain expertise (!) to easily query data from various sources
Explores data to identify and harvest hidden value and find new questions
Data Discovery Multi-structured and iterative © Siemens AG 2015 All rights reserved. Page 26
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Smart ICT at a glance Examples of data sources in a city
Building data
ASCR Infrastructure Extract, Transform, Load
Building topology Water and heating Forecasts Weather Events Grid data
Analytic Demo Apps Benchmarks
Smart ICT
Grid Planning Load Forecast
Platform Smart Citizen App
Runtime data
Grid Operation
City Data Information Ecosystem Management and Operation (API Store, Privacy, Access Gateway)
Data Owners
Data Publishers
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Data Merchandisers
Application Developers
City Administration
Utility Providers
Smart Citizens
Smart ICT Research Areas Challenges Simulation and Optimization
Estimate potential of various operations modes with simulation
Research Approaches Valididation and mutual impact of Optimization strategies
§ Creating a digital twin of the grid for future Grid Planning § Consideration of external factors § Model-based Optimization
Smart ICT in a Smart City Context
Independent stakeholder from different areas
Interfaces and Interactions across domains
§ § § §
Data Integration and Analytics
Cross-Domain Data Integration using central & distributed data models
Complex Data Analytics and Identification of Correlations
§ Distributed Data Integration from multiple sources § Simple and efficient data access § Data Science and Discovery to generate new knowledge from data
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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
Holistic AND domain specific system optimization strategies as well as scalable and future-proof solutions are the key success factors Our research program reflects that mission Thank you for your attention © Siemens AG 2015 All rights reserved. Page 29
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