Dr.-Ing. Peter Birkner, Executive Member of the Board, Mainova AG
Frankfurt am Main, Germany, October 4, 2012
Smart Grid in Practice –The Mainova Smart Ring Unit iNES
IntelliSub Europe 2012, Frankfurt
Study of electrical power engineering and doctoral thesisat Technische Universität München (Dipl.-Ing., Dr.-Ing.)
Positions within RWE Group
Lechwerke AG, Augsburg, GER (11/1987 – 12/2004; Vice President, Business Unit Grid)
Wendelsteinbahn GmbH, Brannenburg, GER (1/2004 – 12/2008; Managing Director)
Vychodoslovenska energetika a.s., Kosice, SK (1/2005 – 8/2008; Member of the Board)
RWE Rhein-Ruhr Netzservice GmbH, Siegen, GER (9/2008 – 6/2011; Managing Director)
Mainova AG, Frankfurt, GER (7/2011 to today; Chief Technical Officer and Member of the Board)
Chairman Networks Committee, Eurelectric, Brussels (6/2008 to today)
Visiting Professor (Electrical Power Engineering) Technicka Universita v Kosiciach, (6/2005 to today)
Lecturer (Electrical Power Engineering) at Universität Bonn (1/2009 to today) and
Universität Wuppertal (6/2010 to today)
Curriculum Vitae Peter Birkner
Numerous publications and lectures on power engineering and economics
Physical consequences of the German „Energiewende“
Providing electricity at the right time – smart market
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2
Agenda: Distribution System Operation –Providing electricity at the right place and time
4 Automation of MV and LV in practice – Mainova’s smart grid system iNES
3 Providing electricty at the right place – smart grid
5 Economy of smart grids
6 Future options and prospects
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The German „Energiewende“ is ambitious and is based on renewables, tough savings and imports
*) Assuming substantial efficiency increase and energy savings but also signigicant electricity imports!
*)
1
Limited import and export capacitiesAll European countries are increasing the
installed capacity of renewables Renewable energy sources show a
synchonous generation patternAre the electricity savings realistic?
We have to do some homework!
EU
GER
?
4
Percentage of power generationM
axim
umco
nsum
ptio
n
Avai
labl
e po
wer
pla
nts
(con
vent
iona
l)
Impo
rt / E
xpor
t
Pum
ped
hydr
o st
orag
e
2010
2020
2050
Installed capacity of renewables
18 % 35 % 80 %
Pow
er
100 %
0 %
50 %
5 %
2000
A rate of 35 % of renewable Energy means to double the installed generation capacity
122 %
Note: The national energy concept assumes substantial efficiencyincrease and energy savings but also signigicant electricityimports!
1
+
Increasing the installed capacity of renewableswithout reversible storage results in a saturation
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Installed renewable power
Renewable energies
Generation power /Power consumption
Time
Storage
Absorption
SupplementStorage
Renewable generation curves (today and tomorrow)
Conventional load curve
In the case that there are more than 35 % of renewables within the total
energy mix, the installed capacity has to be higher than the sum of maxi-
mum consumption, storage and export
Installed capacity
35%
Demand of energy (100%)
Conventional energies
Energy absorption
Additional loads (electrolysis, thermal storage, export)
Energy supplement
Additional generation (gas turbine, import)
Energy storage
Reversible storage, shifting loads and generation (P2G, batteries, pumped hydro storage)
Import / export 6
From a technology point of view the German „Energiewende“ will be implemented in three steps
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- Connection to the network- Extension and increase of flexibilty of the network
- Optimization and increase of flexibilityof thermal power plants
- Load shifts (DSM)- Increase of conventional electricity storage- New efficient applications for electrical energy (e.g. heat pumps, electric vehicles)
- Reversible storage of electricity - New types of power sources- Alternative use of CO2
- Dynamic stability of the system
by 2020 by 2030 by 2050
Energy supply and supplement
Penetration of renewable energy
35 %
80 %
45 %
Energy absorption
New reversible storages
Mainova has the know-how and the ability to make „Energiewende“ a reality
Chemical and thermal energies are indispensablein order to create enough flexibility
2
Technologies for increasing flexibility in the electrical system
CCGT power plants(Irsching, block 4, η = 60%)
Flexible CHPs(Frankfurt, thermal connection of
steam and gas turbines as wellas boilers decouples electicity generation from heat production)
Virtual power plants(Frankfurt)
Controlled electrolytic processes(Frankfurt, 70 MW, production of Cl2)
Controlled cold-storage depots
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3
5
4
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2
3
4
5
2
Chemical and thermal energies are indispensablein order to create enough storage capacities
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Density of
Mechanical energy (1 m³ water, 4 000 m high)
Thermal energy(1 m³ water, 10 K warmer)
Chemical energy(1 m³ gas, 0.8 kg)
Batteries(100 kg Li-Ion batteries)
All numbers mentionedare corresponding with an energy volume of about 40 MJ (ca. 11 kWh)
H2OElec-tricity
H2
O2
Power to gas (H2) to gas grid Power to thermal storage / to thermal grid
Elec-tricity
Sto-rage
Grid
Grid
CH
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Middle till long time periods (days, weeks, months)x 100 MW, high voltage
Import and export Pumped hydro storage Air pressure storage
Power to gas (electrolysis,sabatier)
Compensation of days without wind or cloudy days
Short time period(minutes, hours)x 1 MW, middle and low voltage
Import and export Domestic thermal inertia Domestic demand (DSM, DR) Batteries (immobile, mobile) Thermal storagesCompensation of cloud fields or night-time
All storage concepts can contribute to stabilize the grid!
Storage concepts and their application
A mix from different storage concepts will be used in the future
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Electrical grids play a central role in the future andtherefore they have to be developed into „Smart Grids“
Grid
Generation Central Dispersed Solar park Solar cells Wind park μ-CHP CCGT Biomass CHP … …
Remote Close to Load
Load Central Dispersed Cities Houses
Airports, skyscrapers Farms Cold-storage depots … …
3
Distribution Grids have to be adjustedsubstantially and in a smart way to their new tasks
Time
Feed-in
Take-off
Today‘sgrid take-offcapacity Voltage Load
Today‘sgrid feed-incapacity Voltage Load
Voltage
Length
Low load + high feed-in
Low loard +basic feed-in
100 % UN
90 % UN
110 % UN
Partial load + no feed-in
High load +no feed-in
To control means to take grid-related measures (load flow, reactive power) or to influence loads, generation or decentralized storage (active power)
Load monitoring and load control allow the maximum use of assets
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Integration of renewables is supportedby „Smart Grids“ – The pilot project iNES
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Prinziples of grid automation within the project iNES –Grid interventions first – Customer impacts last
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Operating principle
The active grid elements (1) are adressed first and the active ele-ments on the customer side (2) last
The sensor is independent of any Smart Meter system
iNES Sensor
Active element
(grid)
+ -
Active element
(customer)
2
1
SensorSensor 1 - voltage control transformer2 - reactive power control grid3 - active power control customer side
Quality and network extension
The intervention frequency of the active element on the customer side is registered. This parameter can be used as an indicator for the necessary grid reinforcement or extension
The more interventions on the customer side the DSO is allowed to execute within one year, the smaller and later the network reinforcement or externsion will be. However, a higher amount of renewable energy will be “deleted“ through these interventions
Active element
(grid)
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3Prinziples of grid automation within the project iNES –Comparison with other „smart“ technologies
iNES
Active element (grid)
Sensor
Sensor
Voltage controllable MV/LV-transformer
Sensor
voltage controllable MV/LV-transformer
Conventional distribution system:without voltage and current sensors,without active elements
Voltage controllable MV/LV-transformer:centralized sensors, centralized active elements (reactive power)Voltage controllable MV/LV-trans-former with wide range control:decentralized (multi-) sensors, centralized active elements (reactive power)
Smart transformer –Intelligente Ortsnetzstation iNES:decentralized (multi-) sensors, decentralized active elements (active and reactive power)
Active element (grid)
Sensor
iNES is based on independent sensors using public data, however, smart meter could be intregrated 15
The iNES devices situated in the local transformer stations are used as sensors for themedium voltage grid. Additional sensors can be installed by the use of voltage transformersdirectly in the medium voltage grid. The iNES device in the HV/MV substation works as acontrol center. It analyzes the data of the sensors and activates the active elements. E.g., thiscan be a medium voltage switch or a tap changer of a HV/MV transformer. Furthermore, theiNES devices in the local transformer stations can be used as active elements too. They areable to send control signals into the low voltage network and thus to its iNES components
3Principles of grid automation within the project iNES –Extension to the medium voltage level
iNES
iNES
iNES
iNES
LV
LV
LVMV
MVHV Sensor
Sensor
SensorActive element (grid)
Active element (grid)
Active element (grid)
Active element (grid)
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Two characteristic test sites in the Frankfurt area with a high density of PV have been choosen:
Rural radial LV-grid Bergen-Enkheim Relocated farms with large PV systems,1 MV/LV transformer station
Urban interconnected LV-grid Bornheim Properties from the ABG between Dortelweiler Straße and Preun-gesheimer Straße with large PV systems,3 MV/LV transformer stations
ImplementationTwo characteristic test sites in the Frankfurt area with a high density of PV have been choosen:
Rural radial LV-grid Bergen-Enkheim Relocated farms with large PV systems,1 MV/LV transformer station
Urban interconnected LV-grid Bornheim Properties from the ABG between Dortelweiler Straße and Preun-gesheimer Straße with large PV systems,3 MV/LV transformer stations
Implementation
iNES – The „Smart Grid“ project of Mainova –Field tests in Frankfurt
The smart grid project is carried out in two characteristic areas.As a consequence the results are meaningful
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Details of the Maionva fieldtest –Basic design of the iNES system
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– Automation intelligence –Autonomous monitoring
and control of LV gridGrid monitoring
Identification of network status and impending threshold violations
Grid control
Setpoint specifications for dispersed controllable ge-nerators and consumers
Output: active power con-trol (photovoltaic system),load shedding (heat pump,
electric vehicle)
Sensor/active element
Sensor
Smart RTU
Modeling of load flow calculation in the LV grid
– Taking into account acceptable simplifications in the LV grid
– Choosing the calculation algorithm (analytical or numerical, computation accuracy or speed, iterations and space resp.)
Dealing with information deficits
– Developing estimation algorithms in order to calculate values for unmonitored nodes
– Determining maximum error tolerance in case of threshold violations
Smart selecting and positioning of a minimum number of sensors
– Number of sensors and their positioning is subject to economic aspects
– Combination of smart metering and branch current sensors
– Smart metering is not yet standardized and provides only partially usable measured values
– More substitute values or additional measurement in cable distribution cubicles
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Details of the Mainova fieldtest –Design procedure of the iNES system (example Bornheim)
Line
Minimum and maximum power today
Future minimum and maximum power
Position of sensor
Line
Load profile with two singularities
Power curve of the line
Positions of sensors
Impact of the singularities
(1)
(2)
Load situation
Synchronous activities of customers Higher frequency of
changes Higher amplitudes State estimation
methods
Today’s and future load situation on lines
Synchronous activities of customers Singularities State estimation
methods
Details of the Mainova fieldtest –Monitoring of load flow and implementation of sensors
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Details of the Mainova fieldtest –iNES central control unit (smart RTU)
Central con-trol unit (smart RTU) in a MV/LV-transformer station
Current andvoltage sen-sors (with CTs)
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Sensor / Active element Box (a-box)Smart RTU Small remote
control technology
Powerline Gateway Communication
Direct measurement card
U,I, P. cosφ
Building
Current Transformer (CT)
Voltage tap terminals
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Details of the Mainova fieldtest –iNES sensor and iNES sensor / active element
Sensor / active element(a-box)
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Sensor (m-box) in aLV outdoor cable distribution cubicle
Sensor / Measurement Box (m-box)
DC
AC
Control P & cos φ(0…20mA)Control P (0..30..60..100%)
Energy Meter
1 1 1 1 1 1
Grid connection point (MV or LV)
Photovoltaic system
Converter
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Details of the Mainova fieldtest –Example for feed-in management
Sensor / active element box
(a-box)
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BPL Repeater at the grid connection of the farm: amplification of signal
Details of the Mainova fieldtest –Communication based on Broadband Power Line
BPL Gateway at the photovoltaic system: connecting the PV system to the s-BOX (iNES)
BPL Head end inside the s-BOX for the connection of sensors and active elements in the LV grid (plus connection to the backbone)
TCP/IP-Data connection with BPL over 600 m LV-cabel
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Web GIS
Visualising
Router
E.g. GPRS
Remote control nod
IEC 60870-5-104
Internet
ModemIEC 60870-5-104
Grid Business Objekt Service
Dispatching Center
Process parameterObjects
Web service
http
Grid Data Data Center
AdministrationControl
level
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Details of the Mainova fieldtest –System architecture of the iNES system
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Transformer stationequipped with s-box
GIS
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Details of the fieldtest –Voltage changes in the system
Voltage measured at two sensors: Transformerstation and PV feed-in point
Time (one day)
local power stationphotovoltaic system 86 kW
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Static cost comparison –INES versus grid extension (scenario I)
Local transformer station Rated power = 400 kVA
400mPhotovoltaic system 100kW
Static cost comparisoniNES versus network expansion
Scenario I: Establishment of 100 kW photovoltaic system
Scenario I
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Price / Amount Amount TotalRepowering transformer station Transformer 400 kVA 12 €/kVA 0 0 €Transformer station 16,000 €/Piece 0 0 €
Upgrading of gridLV-cable NAYY 4x51 SE 5 €/m 400m 2.000 €Cable laying unattached 50 €/m 400m 20,000 €Cable laying road coating 60 €/m 0 0 €
Total 22,000 €
Local power station Rated power= 400 kVA
Grid extensioniNES*
400m
Photovoltaic system 100kW
Amount TotaliNES s-box Station 1 4500 €Data Integration 3000 €Algorithm 2000 €iNES m-box sensor 0 0 €iNES a-box active element feeder 1 1800 €
Service / engineering 6000 €Total 17,300 €
Economical “iNES entry" despite of initial expenses for the first installation
* Without customer accessories;Follow-up project based on the level „iNES mobil“
Scenario I
Static cost comparison –INES versus grid extension (scenario I)
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Photovoltaic system 80kW
200 m
400 m
Photovoltaic system 80kW
Establishment of 2 x 80 kWphotovoltaic systems
Repoweringof grid required
Static cost comparison –INES versus grid extension (scenario II)
Scenario II
Local transformer station Rated power = 400 kVA
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photovoltaic system 80kW
200 m
400 m
photovoltaic system 80kW
Price / Amount Amount Total
Repowering transformer stationTransformer 630 kVA CC/-“30% 14 €/kVA 630 kVA 8,820 €
Transformer station 16,000 €/piece 0 0 €Upgrading of grid
LV-cable NAYY 4x51 SE 5 €/ m 600 m 3.000 €Cable laying unattached 50 €/ m 600 m 30,000 €
Total 41,820 €
Amount Total
iNES m-box sensor 1 1500 €
Extension powerline 1 1500 €
Service / engineering 5000 €
Total 8,000 €
Static cost comparison –INES versus grid extension (scenario II)
iNES* Grid extension
* Without customer accessories;Follow-up project based on the level „iNES mobil“
Local power station Rated power= 630 kVA
Establishment of 2 x 80 kWphotovoltaic systems
Repoweringof grid required
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Business case –Overall benefit
Total scenario IInitial installation 17,300 €
Total scenario IIExtension
8000 €
Total 25,300 €
Grid extensionAutomation
Total scenario I 22,000 €
Total scenario II 41,820 €
Total 63,820 €
Costs for automation amount to ~40% of costs of grid extension
Summary and conclusions –Network design principles
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Fit and forget approach – Cost maximum Everything is fixed on a planning level Passive grids for all (accepted) requests Restrictions for requests (conservative planning) No restrictions in operation
Only operation approach – Quality minimum Everything is fixed on an operational level (Hyper-) active grids for all requests No restrictions for requests (no planning) Restrictions in operation
Active management approach – Cost and quality balance Involvement of planning and operational level Optimized grid – active as well as passive aspects Resonable requests Grid becomes a system
Planning Operation
Planning Operation
Planning Operation
Summary and conclusions –Power quality and costs for grid extension 1/2
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Accumulated power grid investments
Time
“As it is”
“Smart Grid 1”
“Smart Grid 2 ”
Smart Power Grid 1 –Grid bound measures Consumption controlled only in
emergency situations Operation to the limit Extensive use or monitoring and
automation of gridSmart Power Grid 2 –Customer bound measures Controlled or flexible consumption Reduced grid reinforcement Effect of reliable flexible consumption
is taken into consideration in the griddesign
Power quality versus investments for grid extension
Increasing coststhrough future requirements on the electricity system
Time
Load managementgrid 1+2
Load managementcustomer and generation
Smart Markets are operating with price signals and are trying to balance generation and consumption (market mechanisms). The impact on the system is not instan-taneous. Interactive smart meters are necessary
Smart Grids are operating with physical signals. They are trying to make maximum use out of the existing grid. They are “simulating” the grid copper plate. The impact on the system is instantaneous. Customers should be concerned only in case of an emergency. Physical sensors and actors are necessary. Congestion management and counter trading methods could be applied
Grid and infrastructure investments can be avoided or postponed
Summary and conclusions –Power quality and costs for grid extension 2/2
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Control levels of smart grids (MV and LV)
Supplier
Grid
Customer
Reaction
DSO
Reaction
3 2 1
2 1
Monitoring
Market
Time
Price
Load
Maximum
Intervention of DSO
Intervention of DSO means: 1. Absolute priority2. Increase of capacity through automated adjustment of grid sectionalizing3. Reduction of load and / or generation in a transparent, objective and non-discriminatory manner
Summary and conclusions –Control levels of smart grids
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Price
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Summary and conclusions –Organic solar cells have a huge potential for urban use
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Mainova is Europe's first energy company with an organic photovoltaic system connected to the public grid
The 70 centimeters wide and two meters long plastic solar cells have been installed within one day
Opposite to conventional solar cells, organic photovoltaic systems do not use any silicon, but they are based on an organic semiconductor consisting of hydrocarbon compounds (polymers)
Organic photovoltaic systems are able to produce power, even in partial shade and in diffuse radiation
Installation of organic solar cells at the premises of Mainova AG, Frankfurt
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Volatility reduction of loadflow
Privat consumption (GER): 4 000 kWh/a, 11 kWh/day
Photovoltaic system: 4 000 kWh/a, (0,1 kW/m², 40 m²)
Battery storage system: 11 kWh/day
Battery capacity: 100 Wh/piece
Number of laptop batteries: 110 pieces (possibly used cells from the automotive industry)
Energy autonomous households
x 110
Summary and conclusions –Batteries are opening new options for stabilisation
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Dr.-Ing. Peter Birkner, Executive Member of the Board, Mainova AG
Frankfurt am Main, October 4, 2012
Analyses – Conclusion – Action
Thank you for your attention!
A
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Details of the Mainova fieldtest –Data flow within the iNES system
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Details of the Mainova fieldtest –Estimated and measured voltage values
Bornheim: Urban interconnected grid with PV systems
Bergen-Enkheim: Rural radial grid with PV systems
Deviation from estimated voltage and reference voltage
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Details of the Mainova fieldtest –Basic concept of the iNES system – Grid model
Bornheim: Urban interconnected grid with PV systems
Bergen-Enkheim: Rural radial grid with PV systems
Grid topology and sensoring
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