Innovative Smart Grid Applications

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Innovative Smart Grid Applications Content: 1. Use Cases & Challenges 2. The Role of ICT 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5.

1 Innovative Smart Grid Applications Content: 1. Use Cases & Challenges 2. The Role of ICT 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5. Outlook Multi-modal Energy Systems EIT Smart Energy Summer School Paris 2013 Siemens AG Corporate Technology, Power & Actuators Michael Metzger Page 1 Corporate Technology Copyright Siemens AG All rights reserved.

2 Siemens has a long tradition in grid Siemens has a long tradition in grid automation, but but new new things things are happening are happening Page 2 Corporate Technology Copyright Siemens AG All rights reserved.

Significant changes in energy systems From centralized, unidirectional grid

Significant changes in energy systems to distributed energy and

Offshore Wind Parks Small Industrial Gas Turbine Private Solar Pumped

Smart Street Lighting Large Scale PV Plant Unidirectional Power Flow

4 Significant changes in energy systems to distributed energy and bidirectional energy balancing Hydrogen Storage Diesel Generator Biogas CHP Offshore Wind Parks Small Industrial Gas Turbine Private Solar Pumped Storage Power Plant Storage Solutions Storage Solutions Electrical Vehicles Smart Street Lighting Large Scale PV Plant Unidirectional Power Flow Bidirectional Power Flow Page 4 Corporate Technology Copyright Siemens AG All rights reserved.

5 Smart Grid / Generation Definitions Microgrid Microgrids are geographically restricted areas containing and controlling generation assets, storage and consumption. Microgrids manage the assets in a decentralized and smart way in order to generate the maximum customer benefits of the assets. The installed power of a Microgrid is usually up to 30 MW 1) and contains LV and MV equipment A Microgrid does have defined point(s) of common coupling May be pure electrical or optionally integrate heating and cooling equipment Island Grid 2) (sub-set of Microgrid) Island grids are Microgrids, which can run independently and which are able to maintain voltage and frequency (for AC) Island grid might be able to switch between off-grid and on-grid mode Island grid might be are able to black-start Nano Grid (sub-set of Microgrid) Nanogrids are small Microgrids up to 1 MW installed power and typically low voltage Typical: power networks in buildings or factories Virtual Powerplant Demand Response VPP and demand response describes an economic aggregation of (geographically distinct) generation and consumption assets in order to trade the produced energy (or shedded load). VPPs generation assets can be in broader geographical region (e.g. nationwide) VPP assets are always on-grid Page 7 1) Potentially even larger of up to 100 MW 2) Island grid also referred to as island mode Microgrids Siemens AG All rights reserved

6 There is not one global Smart Grid Regional drivers are different: some examples Aging infrastructure Non-technical losses Renewables Integration Electrification of Megacities Distributed Generation Page 8 Siemens AG All rights reserved

Key changes in distribution grids force optimal Smart Metering solutions Trends Non-technical losses Integration of renewables Technologies and standards Customer challenges

Non-technical losses: E.g. in Brazil (5.8%) due to non-technical causes, e.g. electricity theft.

7 Key changes in distribution grids force optimal Smart Metering solutions Trends Non-technical losses Integration of renewables Technologies and standards Customer challenges Integration of renewables: Share of renewables to increase dramatically in the next 2 decades, bringing urgent need for grid integration, e.g. in EU, share of renewables expected to rise to 38% in Non-technical losses: E.g. in Brazil (5.8%) due to non-technical causes, e.g. electricity theft. Market regulation: Regulation in key markets drives adoption; Europe, USA and China have regulations in place. Regulations Restricted Siemens AG 2013 All rights reserved. Page 9 8/26/2013 Infrastructure & Cities Sector - Smart Grid Division

Key challenges drive renewable integration Trends Customer challenges Renewable generation in distribution grids Increasing electrical loads in LV distribution grids

in 2030 1) High cost for integration of renewable generation through grid extension Limited transparency on distribution grid Distribution grids are not designed for

8 Key challenges drive renewable integration Trends Customer challenges Renewable generation in distribution grids Increasing electrical loads in LV distribution grids Aging and/or weak infrastructure Overload of distribution grids due to fluctuating renewable in-feed E.g. share of world renewable generation to triple from 4% to 13% in ) High cost for integration of renewable generation through grid extension Limited transparency on distribution grid Distribution grids are not designed for bidirectional energy Source: 1) Energy Trends Study Restricted Siemens AG 2013 All rights reserved. Page 10 8/26/2013 Infrastructure & Cities Sector - Smart Grid Division

9 Changing in-feed patterns challenge existing grid infrastructures Week burden of a transformer station in the rural area the LEW-Verteilnetz GmbH 2003 and today Load in kw Load profile 2003 Load profile today Distribution grid changes from load to source 12:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 Source: LEW Page 11 Corporate Technology Copyright Siemens AG All rights reserved.

10 Future Situation for Germany will be challenging Day 124, Year 2050: 73 GW PV according Leistudie 2011 A (BMU) Spatial Distribution like 2012 Eduard Gutschmidt, Allgäuer Überlandwerk GmbH Andreas Armstorfer, Hochschule Kempten

Key challenges drive implementation of the Microgrid Trends Cost optimized operation of remote grids

remote grids based on diesel generation.

11 Key challenges drive implementation of the Microgrid Trends Cost optimized operation of remote grids Power reliability and independence Load balancing Customer challenges High cost of operations for remote grids based on diesel generation. Integration & balance of the high proportion of cost-efficient renewable generation in remote grids. Power availability and reliability. Optimized operation through balancing generation, consumption and storage. Independence. Integration of renewables Restricted Siemens AG 2013 All rights reserved. Page 13 8/26/2013 Infrastructure & Cities Sector - Smart Grid Division

12 Smart Inverter are becoming a key component for our Energy System Micro-Grid- Controller Bi-directional electric vehicle charging station power Inverter information Inverter Inverter Transformer Switching -Station Smart Meter Inverter Inverter Inverter Inverter IT integration Page 14 Corporate Technology Copyright Siemens AG All rights reserved.

MV LOADs LV LOADs System design of Microgrids An example Demand: ~4 MWp PV Generator: ~5 MWp Battery: ~3 MW / 2 MWh Diesel Generator(s): 2 x 2 MWp Plant Management incl.

13 MV LOADs LV LOADs System design of Microgrids An example Demand: ~4 MWp PV Generator: ~5 MWp Battery: ~3 MW / 2 MWh Diesel Generator(s): 2 x 2 MWp Plant Management incl. Weather Forecast-Based Optimization Automated Generation Control (frequency, voltage and cos phi control) Remote Monitoring Interface G Diesel Gen Set(s) PV Plant ~ = PV Inverter(s) ~ Battery Inverter(s) Battery storage + Battery Management = local (islanded) MV distribution grid Corporate Technology, Smart Grid Siemens AG Confidential

14 Dynamic behaviour of a microgrid (current source inverters and Diesel Gensets) Corporate Technology, Smart Grid Siemens AG Confidential

Key challenges drive implementation of Demand Response & Virtual Power Plants Trends Customer challenges

Generation & network capacity bottlenecks: E.g. California, US.

prices. Increased grid stability through emergency load shed & selective load dispatch.

15 Key challenges drive implementation of Demand Response & Virtual Power Plants Trends Customer challenges Generation & network bottlenecks Increasing peak load prices Increasing distributed & renewable generation Generation & network capacity bottlenecks: E.g. California, US. Dispatch load as most economic power supply: Avoidance of generation and network bottlenecks and high peak load prices. Increased grid stability through emergency load shed & selective load dispatch. New market opportunities for distributed energy resources. Rising consumption Restricted Siemens AG 2013 All rights reserved. Page 17 8/26/2013 Infrastructure & Cities Sector - Smart Grid Division

The challenge decentralized generation: High complexity of engineering, algorithms and data models DENMARK 1980 AND 2005 Source: A*STAR Energy

16 The challenge decentralized generation: High complexity of engineering, algorithms and data models DENMARK 1980 AND 2005 Source: A*STAR Energy Technology R&D Program, Prof Ho Hiang Kwee Program Director, Page 19 Corporate Technology Copyright Siemens AG All rights reserved.

17 Innovative Smart Grid Applications 1. Use Cases & Challenges 2. The Role of Information and Communication Technology 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5. Outlook Multi-modal Energy Systems Page 21 Corporate Technology Copyright Siemens AG All rights reserved.

Information Layer Communication Layer Component Layer What Information is going to be exchanged?

18 System A Cross-Cutting Issues System B Interoperability the missing dimension Interoperability among Systems Business Layer Function Layer What business processes and regulatory constraints apply? Which functions are required? Information Layer Communication Layer Component Layer What Information is going to be exchanged? How is the information being exchanged? What Hardware is required? What issue affects all layers? Page 22 Corporate Technology Copyright Siemens AG All rights reserved.

Putting all together Smart Grid Architecture Model Business Objectives Polit.

. Framework Business Layer Function Layer Interoperability Layers Information Layer

Protocol Protocol Market Enterprise Component Layer Operation Generation Transmission

19 Putting all together Smart Grid Architecture Model Business Objectives Polit. / Regulat.. Framework Business Layer Function Layer Interoperability Layers Information Layer Outline of Usecase Functions/Services Data Model Data Model Communication Layer Protocol Protocol Market Enterprise Component Layer Operation Generation Transmission Distribution Domains DER Customer (Home, Building, Industry, EV, Mobility) Process Field Station Zones Page 23 Corporate Technology Copyright Siemens AG All rights reserved.

20 Smart Building in Smart Grid on SGAM: Business layer Market Operator Plant Control System Energy Wholesale Market Energy Management System Reserve Energy Market Meter Data Management Energy Energy Service Retailer Provider Business Layer Substation Automation Plant Automation MV Distribution, Protection & IEDs Substation Automation MV Distribution, Protection& IEDs Substation Automation Distribution Management System DER Controller LMV Distribution & Protection Smart Meter Virtual Power Plant Control System SBinSG Gateway LV Distribution Protection Building Automation Building Management System Energy Management & Control Page 24 Corporate Technology Copyright Siemens AG All rights reserved.

21 Smart Building in Smart Grid on SGAM: Function Layer: Example Grid congestion management Function Layer Plant Automation Substation Automation Plant Control System MV Distribution, Protection & IEDs Substation Automation Energy Wholesale Market MV Distribution, Protection& IEDs Reserve Energy Market DER Controller LMV Distribution & Protection Energy Service Provider Energy Grid Management supervision Meter Data System Management Grid Substation Automation Distribution Management System emergency support Virtual Power Plant Control System Load shedding SBinSG Gateway Visualization Building Automation Building Management System Energy Management & Control Smart Meter LV Distribution Protection Page 25 Corporate Technology Copyright Siemens AG All rights reserved.

22 Smart Building in Smart Grid on SGAM Information Layer IEC x Plant Control System Energy Wholesale Market ICCP Energy Management System Reserve Energy Market Meter Data Management Energy Service Provider Information Layer Plant Automation Substation Automation MV Distribution, Protection & IEDs Substation Automation MV Distribution, Protection& IEDs Substation Automation Distribution Management System DER Controller LMV Distribution & Protection Smart Meter Virtual Power Plant Control System SBinSG Gateway LMV Distribution Building Automation Building Management System Energy Management & Control Page Page 26 Corporate Technology Copyright Siemens AG All rights reserved.

Smart Building in Smart Grid on SGAM: Communication Layer IEC 60870-5-10x Plant Control System Energy Wholesale Market ICCP Energy Management System Reserve Energy Market

Distribution, Protection& IEDs Substation Automation Distribution Management System DER Controller LMV Distribution & Protection Smart Meter Virtual Power Plant Control

23 Smart Building in Smart Grid on SGAM: Communication Layer IEC x Plant Control System Energy Wholesale Market ICCP Energy Management System Reserve Energy Market Meter Data Management Energy Service Provider Communication Layer Plant Automation Substation Automation MV Distribution, Protection & IEDs Substation Automation MV Distribution, Protection& IEDs Substation Automation Distribution Management System DER Controller LMV Distribution & Protection Smart Meter Virtual Power Plant Control System SBinSG Gateway LMV Distribution Building Automation Building Management System Energy Management & Control Page 27 Corporate Technology Copyright Siemens AG All rights reserved.

24 Smart Building in Smart Grid on SGAM: Component Layer Plant Control System Energy Wholesale Market Energy Management System Reserve Energy Market Meter Data Management Energy Service Provider Component Layer Plant Automation Substation Automation MV Distribution, Protection & IEDs Substation Automation MV Distribution, Protection& IEDs Distribution Automation Distribution Management System DER Controller LMV Distribution & Protection Smart Meter Virtual Power Plant Control System Smart Building Gateway LV Distribution Protection Building Automation Building Management System Energy Management & Control Page 28 Corporate Technology Copyright Siemens AG All rights reserved.

25 Innovative Smart Grid Applications 1. Use Cases & Challenges 2. The Role of Information and Communication Technology 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5. Outlook Multi-modal Energy Systems Page 29 Corporate Technology Copyright Siemens AG All rights reserved.

26 New actuator in the distribution grid: for mastering the complexity local intelligence is needed Controllable substation transformer CHP Unit + Warm water tank Charging station for E-Cars Renewable generation Battery systems Page 30 Corporate Technology Copyright Siemens AG All rights reserved.

Smart Components are a key element for Smart Grids and enable scalability The challenge is to supply Personal Energy Agent (PEA) functionality with the

Brown field approach: separate HW platform to with flexible interfaces to run smart energy agents on.

27 Smart Components are a key element for Smart Grids and enable scalability The challenge is to supply Personal Energy Agent (PEA) functionality with the right cost position. Green field approach: PEA functionality has to be embedded into communication gateways, sensors or actuator components. Brown field approach: separate HW platform to with flexible interfaces to run smart energy agents on. Different domain specific communication protocols have to be supported (IEC 104 BACNet IP, IEC 61850, ModBus, KNX, Wireless M-Bus,...) Page 31 Corporate Technology Copyright Siemens AG All rights reserved.

Self-Organizing Energy Automation Systems (SO EASY) coordinating smart components within the grid SO EASY ARCHITECTURE FOR POWER GRIDS (SCHEMATIC) *) Customer Benefit: *) *) Significant reduction of

28 Self-Organizing Energy Automation Systems (SO EASY) coordinating smart components within the grid SO EASY ARCHITECTURE FOR POWER GRIDS (SCHEMATIC) *) Customer Benefit: *) *) Significant reduction of CAPEX. *) Network Transport Agent Design Principles: Smart components. Use plug and play for engineering. *) Personal Energy Agent Balance Master *) Coordination of local algorithms whenever necessary Area Administrator *) *) Electricity Police *) Personal Energy Agent Page 32 Corporate Technology CT RTC Copyright Siemens AG All rights reserved.

29 The modular structure for energy balancing and grid-operation enables data privacy and split of ownership NTA load flow estimation, diagnosis system model & state, aggregated measurements grid load indicator Area Admin short term balancer voltage control Measurements U,I,P,Q Grid Balance Master long term energy balancer price negotiation or signal or direct control PEAs fast local local fast fast control control power frequency, power quality quality, forecast Power Generation / Consumption Modules fit to regulatory unbundling; decentralized intelligence enables reduced configuration effort and reduced requirements on communication, stable system operation in case of communication system failure Page 33 Corporate Technology Copyright Siemens AG All rights reserved.

Balance Master coordinates the PEA via a virtual market (Local Electricity Exchange

31 Innovative Smart Grid Applications 1. Use Cases & Challenges 2. The Role of Information and Communication Technology 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5. Outlook Multi-modal Energy Systems Page 35 Corporate Technology Copyright Siemens AG All rights reserved.

A detailed Look at the Use Case Renewables Integration Unbalanced phases Voltage rise due to PV in-feed U N The main problems addressed by the DNOs 1) : lack of knowledge about actual grid state

32 A detailed Look at the Use Case Renewables Integration Unbalanced phases Voltage rise due to PV in-feed U N The main problems addressed by the DNOs 1) : lack of knowledge about actual grid state violation of operation limits for transformer / line current bus / node voltage asymmetric power flow harmonic disturbances Many DNOs 1) report about bottlenecks related to PV in-feed, e.g. Allgäuer Überlandwerk GmbH Infra Fürth GmbH N-ERGIE Aktiengesellschaft, Nürnberg E.ON Bayern AG Murauer Stadtwerke GmbH Salzburg Netz GmbH 1) DNO = Distribution Network Operator Page 36 Corporate Technology Smart Grid Copyright Siemens AG All rights reserved.

Knotenspannung U/U N [%] 112 110 108 106 104 102 100 SS-OS SS-US HAS-1 HAS-2 HAS-3

33 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 37 Siemens Corporate Technology, TU München Knoten / Hausanschluss

34 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 38 Siemens Corporate Technology, TU München Knoten / Hausanschluss

35 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 39 Siemens Corporate Technology, TU München Knoten / Hausanschluss

36 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 40 Siemens Corporate Technology, TU München Knoten / Hausanschluss

37 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 41 Siemens Corporate Technology, TU München Knoten / Hausanschluss

38 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 42 Siemens Corporate Technology, TU München Knoten / Hausanschluss

39 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 43 Siemens Corporate Technology, TU München Knoten / Hausanschluss

40 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 44 Siemens Corporate Technology, TU München Knoten / Hausanschluss

Renewable Energy and Electromobility www.

41 Siemens is part of the test field trial in the project IRENE in Allgäu Integration of Renewable Energy and Electromobility Slide 45 Andrei Szabo, CT RTC AUC COS-DE

power network Project Setup: The Project IRENE Engineering and commissioning of SO EASY as a sensor and control system

(e-mobility) Integrate new controllable transformer Goals: Integration of Renewables and E-mobility Reduce Investments in

43 Challenges in 2020 Distributed Renewable Generation Increasing number of electric vehicles Cost efficient extension of power network Project Setup: The Project IRENE Engineering and commissioning of SO EASY as a sensor and control system Coordinating renewable generation and E-mobility Analyze potential of battery storage, both stationary and mobile (e-mobility) Integrate new controllable transformer Goals: Integration of Renewables and E-mobility Reduce Investments in grid extension Avoid unnecessary balancing capacities Minimizes Installation and maintenance efforts Anticipate future market structures Increase added value for regional market Page 47 Corporate Technology Copyright Siemens AG All rights reserved.

The Pilot Project IRENE (Distribution Network near Munich) http://www.projekt-irene.

Measurements at selected house connections/ generators / e- Cars 87 Substations 32

Wind, 1MW Biogas Asymmetry at the transformer an the end of the feeder Voltage

44 The Pilot Project IRENE (Distribution Network near Munich) Medium voltage grid 20 km Test area Measurements at each substation Measurements at selected house connections/ generators / e- Cars 87 Substations 32 Strompioniere with electric vehicles 3MW peak and 0,6MW minimum load 6MW PV, 2MW Wind, 1MW Biogas Asymmetry at the transformer an the end of the feeder Voltage control via PV inverter Page 48 Corporate Technology Copyright Siemens AG All rights reserved.

Communication and System Architecture for the IRENE project Central

harmonics (3 phases) 12, 13 difference in U phases at ~200 positions

in-feed of PV inverter (~30 plants) Local Control System Tap position

Sensor On-load Tap Changer PV Battery Bio- Mass PV Wind 20kV/400V Load

46 Communication and System Architecture for the IRENE project Central Control Server Offline Data Server Measurement System: U k, I k, k, harmonics (3 phases) 12, 13 difference in U phases at ~200 positions Cellular phone network Internet Manipulated Variables: reactive power in-feed of PV inverter (~30 plants) Local Control System Tap position of transformer active power of battery (2 stationary, 40 E-Vehicles) Sensor On-load Tap Changer PV Battery Bio- Mass PV Wind 20kV/400V Load Load Page 50 Corporate Technology Copyright Siemens AG All rights reserved.

power power Area Administrator (AA): Algorithm for Coordinated Voltage/Current Control in Distribution Grids voltage increase

i PEA U jq UI i i U n 1 2 i max U,, U, I I for k TRAFO k, 1 k, m k U min i U from BM i U damping max max i, Ii, k Ii max min

oscillations State equation: Kirchhoff-law.

47 power power Area Administrator (AA): Algorithm for Coordinated Voltage/Current Control in Distribution Grids voltage increase zoom in voltage decrease time [h] time [h] P min,, U U min i i from NTA k i i i PEA TRAFO YU I, PEA of OLTC P P P i i d i 2 P i PEA U jq UI i i U n 1 2 i max U,, U, I I for k TRAFO k, 1 k, m k U min i U from BM i U damping max max i, Ii, k Ii max min max i( Ui; i) Pi, Qi Qi ( Ui; i) Qi to PEA of inverter Optimization: Cost function: Reach desired set points, damp voltage oscillations State equation: Kirchhoff-law. Control value: Active/reactive power of inverters (PV, battery), position of tap-changers Boundary conditions: Voltage & current limits Page 51 Corporate Technology Copyright Siemens AG All rights reserved.

Commissioning of a new Controllable Transformer

49 Knotenspannung U/U N [%] SS-OS SS-US HAS-1 HAS-2 HAS-3 HAS-4 HAS-5 HAS-6 Page 53 Siemens Corporate Technology, TU München Knoten / Hausanschluss

50 Effect of the RONT with remote measurements Without RONT operation With RONT operation 245V 215V Voltage peaks are eliminated, voltage band is better satisfied Currently OLTC running and further tests and optimization is performed Page 54 Corporate Technology Copyright Siemens AG All rights reserved.

51 Comparision of different control strategies for the controllable transformer No Control the RONT is situated always on the nominal tap, no tap changing. Default Control Local controller based only on the voltage with default parameterization (Utarget = 230V) Optimized Local Control, only U Local controller based only on the voltage with optimized parameterization by AA Optimized Local Control, with P&U Local controller based on measurements of voltage and power, estimation of the voltage in grid Control with remote measurements The tap of the transformer is changed based on remote measurements Page 55 Corporate Technology Copyright Siemens AG All rights reserved.

Comparison of PV-generation using different control mechanisms (Emulation) 1) without control: voltage exceeds limit, full generation 2) With Area Administrator: voltage stays within limits, reduced

52 Comparison of PV-generation using different control mechanisms (Emulation) 1) without control: voltage exceeds limit, full generation 2) With Area Administrator: voltage stays within limits, reduced generation 3) With Area Administrator voltage stays within limits, full generation and tap changer: more PV installation possible 1) 2) 3) Page 56 Corporate Technology Copyright Siemens AG All rights reserved.

Energy Agents managing Components with Heat and Electricity Storage Complete System:

Operation Manager EP PEA PEA SO EASY approach: Fuel and CO2 emission allowances

intelligence Responsibility of operation strategy & controller parameterization stays

53 Energy Agents managing Components with Heat and Electricity Storage Complete System: Subsystem under examination: Comp. Operation Manager EP PEA PEA SO EASY approach: Fuel and CO2 emission allowances Reduction of effort & complexity at centralized control instances through distributed intelligence Responsibility of operation strategy & controller parameterization stays Start penalty Operation and Maintenance with component owner. AA PEA LEX PEA Page 57 Corporate Technology Copyright Siemens AG All rights reserved.

Battery Storage System can play a key role for cost

continuous 240 kw Power Pulse ( 60s) 320 kw Capacity

ads-tec (Leclanche cell) Use of SINAMICS Coupling to LV

54 Battery Storage System can play a key role for cost efficient integration of distributed generation Power continuous 240 kw Power Pulse ( 60s) 320 kw Capacity nominal 162 kwh Capacity usable 138 kwh Battery Supplier: ads-tec (Leclanche cell) Use of SINAMICS Coupling to LV Grid Page 58 Corporate Technology Copyright Siemens AG All rights reserved.

55 voltage [%] Energy System Design for optimized storage usage Storage at selected, critical positions is twice effective that uniform distribution. Use of reactive power gives additional improvements! w/o storage storage at every PV plant 30% Pmax selective storage, cosphi = 1.0 selective storage, cosphi = node number in LV grid Page 59 Corporate Technology Copyright Siemens AG All rights reserved.

56 Innovative Smart Grid Applications 1. Use Cases & Challenges 2. The Role of Information and Communication Technology 3. Self-Organizing Energy Automation System and Smart Energy Agents 4. Use Case Renewables Integration 5. Outlook Multi-modal Energy Systems Page 60 Corporate Technology Copyright Siemens AG All rights reserved.

Optimized schedule Objective function: Fuel and CO2 emission allowances Revenue C OM C CHP,START C BH,START C CHP,GAS&CO 2 C BH,GAS&CO

57 Combined Heat & Power (CHP) Operation Functionality Inputs, outputs, objective function Electricity prices Thermal loads Component Models (Gas engine + Thermal Energy Storage) User Specs (weighting factors + constraints) Optimal Control (Mixed Integer Programming) Optimized schedule Objective function: Fuel and CO2 emission allowances Revenue C OM C CHP,START C BH,START C CHP,GAS&CO 2 C BH,GAS&CO 2 C CHP,o&m C BH,o& m R Start penalty Operation and Maintenance Page 61 Corporate Technology Copyright Siemens AG All rights reserved.

58 CHP Operation Manager Comparison of Operation Strategies for CHP units Name Description Start/stop condition Remark Power-led Start/stop decisions depend on electricity prices Start: p W 35 ct/kwh OR SOC* w 0.2 Stop: p q 35 ct / kwh OR SOC Lower fuel utilization Increased number of starts Heat-led Start/stop decisions depend on thermal load Start: SOC w 0.55 Stop: SOC Optimal fuel utilization No backup power capability Operation Manager Start/stop decisions depend on prices and thermal loads Model based cost minimization Economically efficient Backup power capability *State Of Charge Page 62 Corporate Technology Copyright Siemens AG All rights reserved.

59 State of Charge [-] Electric Power [kw] Thermal Load [kw] Electicity Price [ ] CHP Operation Manager Results for a Show Case I prognosis heat-led operation power-led operation operation manager time [h] Page 63 Corporate Technology Copyright Siemens AG All rights reserved.

60 State of Charge [-] Electric Power [kw] Thermal Load [kw] Electicity Price [ ] CHP Operation Manager Results for a Show Case II 1.5 prognosis heat-led operation power-led operation operation manager Name Aggregated Cost after 4 hours [ ] Power-led -0.5 Heat-led Operation Manager time [h] -4.8 Page 64 Corporate Technology Copyright Siemens AG All rights reserved.

From Smart Power Grids to Smart Energy Systems A

likes to realize a smart multi-modal energy

power grid, district heating/cooling, water and

SMART MULTI-MODAL ENERGY SYSTEMS (SCHEMATIC) Water

Nano Grids Combined Heat Coolant- Storage Smart

Agents Gateway Electricity Water Gas Heat Battery

between the Smart Factory & Buildings stakeholder

62 From Smart Power Grids to Smart Energy Systems A modular approach gets even more important if one likes to realize a smart multi-modal energy system, that optimally uses the coupling between power grid, district heating/cooling, water and gas networks. SMART MULTI-MODAL ENERGY SYSTEMS (SCHEMATIC) Water Treat. Nano Grids Combined Heat Coolant- Storage Smart Infrastructure Wind Heat-Pump Personal Energy Agents Gateway Electricity Water Gas Heat Battery Smart Meter Data flow Energy flow Data exchange between the Smart Factory & Buildings stakeholder based on small and standardized interfaces. Smart Power Grid PV Generation Page 66 Corporate Technology Copyright Siemens AG All rights reserved.

63 Conclusion A bottleneck for realization of smart grids are the engineering efforts and the complexity of algorithms and data models. Data privacy and split of ownership can be optimally addressed via a decentralized approach with small and standardized interfaces. One should keep the intelligence (algorithms) for system operation as local as possible to enable scalability. The challenge is to supply smart energy agents with the right cost position. This can be achieved through embedded PEA functionality. A modular approach via smart energy agents gets even more important if one likes to realize a smart multi-modal energy system. We have demonstrated the potential of self-organizing energy automation systems in the IRENE project. Page 67 Corporate Technology Copyright Siemens AG All rights reserved.

Commercialized storage solutions for enhanced grid operation

Commercialized storage solutions for enhanced grid operation Dr. Andreas Luxa Principal Expert, Low and Medium Voltage Division, Amsterdam The power grid is in transformation: Increasingly complex source/generation