Smart Grid in Practice – The Mainova Smart Ring Unit iNES

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
Curriculum Vitae
Peter Birkner
Study of electrical power engineering and doctoral thesis
at 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)
Numerous publications and lectures on power engineering and economics
Agenda: Distribution System Operation –
Providing electricity at the right place and time
1
Physical consequences of the German „Energiewende“
2
Providing electricity at the right time – smart market
3
Providing electricty at the right place – smart grid
4
Automation of MV and LV in practice – Mainova’s smart grid system iNES
5
Economy of smart grids
6
Future options and prospects
3
1
The German „Energiewende“ is ambitious and
is based on renewables, tough savings and imports
*)
GER
EU
?
Limited import and export capacities
 All European countries are increasing the
installed capacity of renewables
 Renewable energy sources show a
synchonous generation pattern
 Are the electricity savings realistic?

*)
Assuming substantial efficiency increase
and energy savings but also signigicant
electricity imports!
We have to do some homework!
4
1
A rate of 35 % of renewable Energy
means to double the installed generation capacity
Available power plants
(conventional)
Pumped hydro storage
Import / Export
Maximum consumption
Power
Percentage of power generation
5%
18 %
35 %
80 %
122 %
100 %
0%
2050
2020
2010
+
2000
50 %
Installed capacity of renewables
Note: The national energy
concept assumes
substantial efficiency
increase and energy
savings but also
signigicant electricity
imports!
1
Increasing the installed capacity of renewables
without reversible storage results in a saturation
Demand of
energy
(100%)
Installed
renewable
power
Conventional
energies
Generation power /
Power consumption
Renewable generation
curves (today and tomorrow)
Absorption
Storage
35% Renewable
energies
Conventional
load curve
Installed capacity
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 maximum consumption, storage and export
Energy absorption

Additional loads (electrolysis, thermal storage, export)
Storage
Supplement
Time
Energy storage

Reversible storage, shifting loads and generation
(P2G, batteries, pumped hydro storage)

Import / export
Energy supplement

Additional generation (gas turbine, import)
6
1
From a technology point of view the German
„Energiewende“ will be implemented in three steps
by 2020
by 2050
by 2030
Penetration of renewable energy
35 %
- Connection to the network
- Extension and increase of flexibilty
of the network
- Optimization and increase of flexibility
of thermal power plants
45 %

Energy supply
and supplement
- Load shifts (DSM)
- Increase of conventional electricity storage
- New efficient applications for electrical
energy (e.g. heat pumps, electric vehicles)
80 %

Energy
absorption
- Reversible storage of electricity
- New types of power sources
- Alternative use of CO2
- Dynamic stability of the system

New reversible
storages
Mainova has the know-how and the ability to make „Energiewende“ a reality
2
Chemical and thermal energies are indispensable
in order to create enough flexibility
1
Technologies for increasing
flexibility in the electrical system
2
1

CCGT power plants
(Irsching, block 4, η = 60%)

2
Flexible CHPs
(Frankfurt, thermal connection of
steam and gas turbines as well
as boilers decouples electicity
generation from heat production)

Virtual power plants
(Frankfurt)
3

Controlled electrolytic processes
(Frankfurt, 70 MW, production
of Cl2)
4

Controlled cold-storage depots
5
3
4
5
2
Chemical and thermal energies are indispensable
in order to create enough storage capacities
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)
O2

Electricity
Storage
Grid
H2
CH4
Grid
Electricity
H2O
Density of
All numbers mentioned
are corresponding with
an energy volume of
about 40 MJ (ca. 11 kWh)
Power to gas (H2) to gas grid
Power to thermal storage /
to thermal grid
2
A mix from different storage concepts
will be used in the future
Storage concepts and their application
Middle till long time periods
(days, weeks, months)
Short time period
(minutes, hours)
x 100 MW, high voltage
x 1 MW, middle and low voltage
 Import and export
 Import and export
 Pumped hydro storage
 Domestic thermal inertia
 Air pressure storage
 Domestic demand (DSM, DR)
 Power to gas (electrolysis,
sabatier)
 Batteries (immobile, mobile)
Compensation of days without
wind or cloudy days
Compensation of cloud fields or nighttime
 Thermal storages
All storage concepts can contribute to stabilize the grid!
10
3
Electrical grids play a central role in the future and
therefore they have to be developed into „Smart Grids“
Generation
Central
Dispersed
 Solar park
 Solar cells
 Wind park
 μ-CHP
 CCGT
 Biomass
 CHP
…
…
Remote
Close to Load
Grid
Load
Central
Dispersed
 Cities
 Houses
 Airports, skyscrapers
 Farms
 Cold-storage depots
…
…
3
Distribution Grids have to be adjusted
substantially and in a smart way to their new tasks
Load monitoring and load control allow the maximum use of assets
Feed-in
Voltage
Today‘s
grid feed-in
capacity

Voltage

Load
110 % UN
Low load +
high feed-in
Low loard +
basic feed-in
100 % UN
Time
Partial load +
no feed-in
Today‘s
grid take-off
capacity

Voltage

Load
90 % UN
High load +
no feed-in
Length
Take-off
To control means to take grid-related measures (load flow, reactive power) or
to influence loads, generation or decentralized storage (active power)
3
Integration of renewables is supported
by „Smart Grids“ – The pilot project iNES
13
3
Prinziples of grid automation within the project iNES –
Grid interventions first – Customer impacts last
Active
element
(customer)
Operating principle
+
-
The active grid elements (1) are
adressed first and the active elements on the customer side (2) last
2
Sensor
iNES
Sensor
1
Sensor
Active
element
(grid)
Active
element
(grid)
1 - voltage control transformer
2 - reactive power control grid
3 - active power control customer side
The sensor is independent of any
Smart Meter system
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
14
higher amount of renewable energy will be “deleted“ through these interventions
3
Prinziples of grid automation within the project iNES –
Comparison with other „smart“ technologies
Conventional distribution system:
without voltage and current sensors,
without active elements
Voltage controllable MV/LV-transformer
Voltage controllable MV/LVtransformer:
centralized sensors, centralized
active elements (reactive power)
Sensor
voltage controllable MV/LV-transformer
Voltage controllable MV/LV-transformer with wide range control:
decentralized (multi-) sensors,
centralized active elements (reactive
power)
Sensor
iNES
Sensor
Sensor
Active element
(grid)
iNES is based on independent sensors using public
data, however, smart meter could be intregrated
Active element
(grid)
Smart transformer –
Intelligente Ortsnetzstation iNES:
decentralized (multi-) sensors,
decentralized active elements
(active and reactive power)
15
3
Principles of grid automation within the project iNES –
Extension to the medium voltage level
Active element (grid)
iNES
iNES
HV
MV
Sensor
LV
Active element (grid)
iNES
Sensor
LV
Active element (grid)
iNES
MV
Active element (grid)
Sensor
LV
The iNES devices situated in the local transformer stations are used as sensors for the
medium voltage grid. Additional sensors can be installed by the use of voltage transformers
directly in the medium voltage grid. The iNES device in the HV/MV substation works as a
control center. It analyzes the data of the sensors and activates the active elements. E.g., this
can be a medium voltage switch or a tap changer of a HV/MV transformer. Furthermore, the
iNES devices in the local transformer stations can be used as active elements too. They are
able to send control signals into the low voltage network and thus to its iNES components
16
3
iNES – The „Smart Grid“ project of Mainova –
Field tests in Frankfurt
Implementation
Two characteristic test sites in the
Frankfurt area with a high density of
PV have been choosen:

Rural radial LV-grid BergenEnkheim
Relocated farms with large PV
systems,
1 MV/LV transformer station

Urban interconnected LV-grid
Bornheim
Properties from the ABG between
Dortelweiler Straße and Preungesheimer Straße with large PV
systems,
3 MV/LV transformer stations
The smart grid project is carried out in two characteristic areas.
As a consequence the results are meaningful
4
Details of the Maionva fieldtest –
Basic design of the iNES system
– Automation intelligence –
Autonomous monitoring
and control of LV grid
Grid monitoring
Sensor/active element
Sensor
Smart RTU
Identification of network
status and impending
threshold violations
Grid control
Setpoint specifications for
dispersed controllable generators and consumers
Output: active power control (photovoltaic system),
load shedding (heat pump,
electric vehicle)
18
4
Details of the Mainova fieldtest –
Design procedure of the iNES system (example Bornheim)



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
19
4
Details of the Mainova fieldtest –
Monitoring of load flow and implementation of sensors
Today’s and future load
situation on lines
Future minimum and maximum power
Minimum and maximum power today
Line
Synchronous activities of
customers

Higher frequency of
changes

Higher amplitudes

State estimation
methods
Position of sensor
Load situation
Impact of the singularities
Power curve of the line
Load profile with two singularities
Synchronous activities of
customers

Singularities

State estimation
methods
(1)
Line
(2)
Positions of sensors
20
4
Details of the Mainova fieldtest –
iNES central control unit (smart RTU)
Central control unit
(smart RTU)
in a MV/LVtransformer
station
Current and
voltage sensors (with
CTs)
21
4
Details of the Mainova fieldtest –
iNES sensor and iNES sensor / active element
Sensor / active
element
(a-box)
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
Sensor
(m-box) in a
LV outdoor cable
distribution cubicle
Sensor / Measurement Box
(m-box)
22
4
Details of the Mainova fieldtest –
Example for feed-in management
Photovoltaic
system
DC
Converter
AC
1
1
1
1
1
Control P & cos φ
(0…20mA)
Control P
(0..30..60..100%)
1
Energy Meter
Sensor / active
element box
(a-box)
Grid connection
point (MV or LV)
23
4
Details of the Mainova fieldtest –
Communication based on Broadband Power Line
TCP/IP-Data
connection with BPL
over 600 m LV-cabel
BPL Repeater at the
grid connection of the
farm: amplification of
signal
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)
24
4
Details of the Mainova fieldtest –
System architecture of the iNES system
Grid Data
GIS
Web GIS
Data Center
Dispatching
Center
Visualising
Administration
Process parameter
Objects
Control
level
http
Grid Business Objekt Service
Web service
Remote
control nod
IEC 60870-5-104
Router
Internet
IEC 60870-5-104
E.g. GPRS
Modem
Transformer station
equipped with s-box
25
4
Details of the fieldtest –
Voltage changes in the system
local power station
photovoltaic system 86 kW
Time (one day)
Voltage measured at two sensors: Transformerstation and PV
feed-in point
26
5
Static cost comparison –
INES versus grid extension (scenario I)
Scenario I
Photovoltaic system 100kW
400m
Local transformer station
Rated power = 400 kVA
Static cost comparison
iNES versus network expansion
Scenario I: Establishment of 100 kW photovoltaic system
27
5
Static cost comparison –
INES versus grid extension (scenario I)
Scenario I
Photovoltaic system 100kW
400m
iNES*
Amount Total
iNES 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 €
Local power station
Rated power= 400 kVA
Grid extension
Price / Amount Amount
Repowering
transformer station
Transformer 400 kVA
Transformer station
Upgrading of grid
LV-cable NAYY 4x51 SE
Cable laying unattached
Cable laying road coating
Total
Total
12 €/kVA
16,000 €/Piece
0
0
0€
0€
5 €/m
50 €/m
60 €/m
400m
400m
0
2.000 €
20,000 €
0€
22,000 €
* Without customer accessories;
Follow-up project based on the level „iNES mobil“
Economical “iNES entry" despite of initial expenses for the first installation
28
5
Static cost comparison –
INES versus grid extension (scenario II)
Photovoltaic system
80kW
400 m
Scenario II
Establishment
of 2 x 80 kW
photovoltaic
systems
Repowering
of grid required
Local transformer station
Rated power = 400 kVA
200 m
Photovoltaic
system 80kW
29
5
Static cost comparison –
INES versus grid extension (scenario II)
photovoltaic system 80kW
Establishment
of 2 x 80 kW
photovoltaic
systems
400 m
Repowering
of grid required
photovoltaic system 80kW
200 m
iNES*
Local power station
Rated power= 630 kVA
Grid extension
Amount
Total
iNES m-box sensor
1
1500 €
Extension powerline
1
1500 €
Service / engineering
5000 €
Total
8,000 €
* Without customer accessories;
Follow-up project based on the level „iNES mobil“
Price / Amount Amount
Repowering
transformer station
Transformer 630 kVA
CC/-“30%
Transformer station
14 €/kVA
Total
630 kVA 8,820 €
16,000 €/piece
0
0€
LV-cable NAYY 4x51 SE
5 €/ m
600 m
3.000 €
Cable laying unattached
Total
50 €/ m
600 m 30,000 €
41,820 €
Upgrading of grid
5
Business case –
Overall benefit
Grid extension
Automation
Total scenario I
Initial installation
17,300 €
Total scenario I
22,000 €
Total scenario II
Extension
8000 €
Total scenario II
41,820 €
Total
63,820 €
Total
25,300 €
Costs for automation amount to ~40% of
costs of grid extension
31
6
Summary and conclusions –
Network design principles
Fit and forget approach – Cost maximum
Planning
Operation
Planning
Operation
Planning
Operation
 Everything is fixed on a planning level
 Passive
grids for all (accepted) requests
 Restrictions
 No
for requests (conservative planning)
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
32
6
Summary and conclusions –
Power quality and costs for grid extension 1/2
Accumulated
power grid
investments
“As it is”
“Smart Grid 1”
Smart Power Grid 1 –
Grid bound measures

Consumption controlled only in
emergency situations

Operation to the limit

Extensive use or monitoring and
automation of grid
“Smart Grid 2 ”
Smart Power Grid 2 –
Customer bound measures
Time

Controlled or flexible consumption

Reduced grid reinforcement

Effect of reliable flexible consumption
is taken into consideration in the grid
design
Power quality versus investments for grid extension
33
6
Summary and conclusions –
Power quality and costs for grid extension 2/2
Increasing costs
through future
requirements on
the electricity
system
Load management
grid 1+2
Load management
customer and
generation
Time
Smart Markets are operating with price signals and are trying to balance generation
and consumption (market mechanisms). The impact on the system is not instantaneous. 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
34
6
Summary and conclusions –
Control levels of smart grids
Control levels of smart grids (MV and LV)
Market
Supplier
Price
3
Price
Monitoring
Grid
2
1
DSO
Reaction
2
Intervention
of DSO
Maximum
1
Reaction
Load
Customer
Time
Intervention of DSO means:
1.
Absolute priority
2.
Increase of capacity through automated adjustment of grid sectionalizing
3.
Reduction of load and / or generation in a transparent, objective and non-discriminatory manner
35
6
Summary and conclusions –
Organic solar cells have a huge potential for urban use
Installation of organic solar cells at the premises of
Mainova AG, Frankfurt
 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
36
6
Summary and conclusions –
Batteries are opening new options for stabilisation
Energy autonomous households
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)
x 110
37
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
Details of the Mainova fieldtest –
Data flow within the iNES system
39
A
Details of the Mainova fieldtest –
Estimated and measured voltage values
Deviation from estimated voltage
and reference voltage
Bornheim:
Urban interconnected grid with PV systems
Bergen-Enkheim:
Rural radial grid with PV systems
A
Details of the Mainova fieldtest –
Basic concept of the iNES system – Grid model
Grid topology and sensoring
Bornheim:
Urban interconnected grid with PV systems
Bergen-Enkheim:
Rural radial grid with PV systems
41