Posters of SGEM Unconference 2013

Vision and Key Impact Indicators of SGEM
Jarmo Partanen, Satu Viljainen, Pertti Järventausta, Pekka Verho, Sami Repo
Lappeenranta University of Technology Tampere University of Technology

Security of supply, self-sufficiency

In Germany 34 GW of photovoltaic cells have been
installed,+ 7 GW/a .

SGEM unconference 24-25.2013, Vision SGEM

The Future Electricity Markets and
New Sources of Flexibility
Themes: SGEM Vision, Demand Response
Koreneff, Göran; Kiviluoma, Juha; Similä, Lassi; Forsström, Juha
VTT Technical Research Centre of Finland

Objectives
We study the European electricity market
development to 2020 and 2035 and how
active resources and increasing variable
power production fit in.

The value of DR indicates future
business potential for flexibility

With only a small amount of DR*, its
value is considerable, but it decreases
rapidly with increasing penetration.

Price scenarios for the future
electricity markets

*) The Demand Response analysed here had relatively high marginal cost (80-150 €/MWh) and was not able to
shift demand in time. The results from a study are based on a unit commitment and dispatch model WILMAR.

Capacity mechanisms needed for
flexibility and resource adequacy?

The IEA demands in the 2°C scenario (2DS) , the 4°C scenario (4DS) , and the carbon neutral scenario (CNS)
are from IEA Nordic Energy Technology Perspectives 2013. The SGEM VTT demand scenario is based on
NREAP:s and on the most recent Finnish energy strategy update material in 2013.

We have assessed power market price
reactions to the EU’s energy market
integration, climate change mitigation,
energy efficiency and RES deployment
policies to 2020 and beyond.
The shale gas revolution has deeply
affected also EU electricity market: fossil
fuel prices are lower and coal is back in
business. Will this last?

Next steps in SGEM WT 7.2
Analysis of integrated European power
markets, variable generation, flexibility
and the value of DER.
We need input from the themes SGEM
Vision, DR, and on development of
distributed generation capacity.

SGEM unconference 24.-25.10.2013

Source: De Vries (2004)

The future demand affects the market
price as well as the, especially nuclear
and RES-E, capacity development.

An
intense
debate
on
capacity
mechanisms in the EU in general and
especially in DE, FR, and GB is ongoing.
We have reviewed
different capacity
mechanisms and
their characteristics
from a SGEM
perspective.

Jukka Lassila
LUT
050 537 3636

Taavi Hirvonen
Elektrobit
040 3443462

Antti Rautiainen
TUT
040 849 0916

Introduction to the task
Key research questions are
- Effects of charging methods to network
- Principles of real time data transfer to
driver related to charging status and routing
to appropriate charging point
- Techniques for voltage quality management
- EVs as energy storages to network (V2G)
- Intelligent interface of plug-in vehicles
- Electricity market impacts and functions

Description of the work
Wireless communication between the
vehicle and charging point: customer
view and needs
- Billing, bonuses, agreements
- Payment in charging point
- Charging the batteries
- Customer information
Charging protocol between EV and EVSE
- Based on ISO/IEC 15118-2 RC version
(July 2013)
- Selected OCPP messages exchange
integrated into SECC state machine
- Basic use case: parking hall with tens of
charging poles and where communication
is done using centralized SECC server
PHEV charging analysis
- Load curves with freely selectable
parameters and assumptions
- Possibilities of different types of PHEVs to
replace liquid fuel with different types of
charging infrastructures
- PHEVs as a demand response resource

Stefan Forsström
VES
050 408 5679

Matti Lehtonen
Aalto
040 581 5726

Overall energy storing (V2G) methodology
Fast charging
- Fast (and also slow) charging power quality
measurements
- Fast charging service business profitability
studies

Next steps
- Developing methodology to define EVs as a
part of electricity distribution (G2V + V2G),
verifying results with actual network data
- Network effects with different scenarios
- EVs and power based transfer tariffs
- Charging control demonstration with a real EV
- Effect of charging infra on EV energy use
- Finalize and optimize charging protocol
implementation for embedded environment

SGEM unconference 24.-25.10.2013, Grid Planning&Solutions, Smart Grid ICT Architectures

Assessment of Interdependencies between
Mobile Communication and Electricity
Distribution Networks
Interdependency of mobile communication and electricity distribution networks has
increased due to automation and digitalization. On-going modernization of
grids has motivated energy companies to seek new cost-effective and
reliable wireless technologies to enable real-time remote control
and monitoring of electricity grids covering vast areas.
Our study focuses on the following questions:
• Are commercial communication networks sufficient for smart grid
communication in sparsely populated areas?
• How vulnerable are the communication networks to different sized failures?
• How should smart grid and mobile communication networks be enhanced in
order to make them more resilient and robust?

© Olli Pihlajamaa

Modelling

Storm Fault Analysis

Fault analysis

Storm simulation

Fine tuning

Our case study concentrated on storm Patrick, which swept
over the Scandinavian peninsula towards the Baltic Sea in
26.12.2011. It was the worst storm in 30 years and caused 60
M€ damages to energy companies in Finland.
The storm Patrick was simulated using outage reports from the
medium-voltage distribution networks. The result graphs below
show the percentage of operational secondary substations,
operational masts and the percentage of no-coverage areas
during the storm without and with battery backup. Red symbols
indicate the failure phases and green ones the recovery phases.
The graph shows that just after the storm, there were only ¼ of
the secondary substations operational.

Coverage and Redundancy Calculations

Findings

The challenge was to build a realistic simulation model to study
interdependencies between electricity distribution and mobile
communication. We implemented a simulation tool, which
enables detailed modelling of electricity distribution networks,
mobile communication networks (e.g., GSM-900, UMTS-900,
and LTE), and 3D propagation environment. To affirm the
reliability, the models and calculation parameters can be finetuned using field measurements in order to make realistic
coverage and redundancy (numbers of base stations available
at the given location) calculations as well as storm fault
analysis.

The redundancy calculations indicated that networks, which are
primarily dedicated to provide coverage, like GSM-900, offer
higher redundancy level in rural areas than the networks, which
provide additional capacity.
The simulations emphasized the importance of ensuring the
power supply of the critical base stations. This improves the
resiliency of telecommunication networks, which in turn has a
significant effect on clearance and repair work and wireless
remote control of electricity distribution entities. The key factors
of telecommunication networks’ resiliency are: the cell size,
coverage redundancy, speed of the clearance work, and the
duration of battery backups.

Contacts:

seppo.horsmanheimo@vtt.fi, jyrki.penttonen@violasystems.com
antti.kostiainen@fi.abb.com

www.cwc.oulu.fi

LTE and Hybrid Sensor-LTE Network performances
in Smart Grid Demand Response Scenarios
Juho Markkula and Jussi Haapola
University of Oulu, Centre for Wireless Communications, P.O.Box 4500, 90014-Oulu, Finland
E-mail: juho.markkula@ee.oulu.@, jussi.haapola@ee.oulu.@

Muokkaa
perustyylejä osoitt.
10000

Total BG traffic

1000

Streaming

Average load [kB/s]

INTRODUCTION
Evaluation of traffic volumes, delivery ratios, and delays under various
demand response (DR) setups for smart grid (SG) communications.
1. Public long term evolution (LTE) network
2. Cluster-based hybrid sensor–LTE network where wireless sensor
network (WSN) clusterheads (CLH) are also equipped with LTE remote
terminal units.
In DR scenarios, varying percentages of end users take part in automated
DR-based load balancing while the rest of the users resort to advanced
metering infrastructure based energy monitoring.

FTP

Video Conference

100

HTTP

10

SG case 1 (UL)

SG case 2 (DL)

1

DESCRIPTION OF THE WORK
Three automatic demand response (ADR) simulation scenarios

SG case 2 (UL), case 3 (UL/DL)

Voice

SG case 1 (DL)

0,1
BG traffic

SG (ADR 20 %)

SG (ADR 60 %)

SG (ADR 100 %)

• Spot pricing and direct load balancing (SG Case 1)
and BG traffic
and BG traffic
and BG traffic
ADR traffic volume
• ADR generation interval: 4 s uplink (UL), 5 min downlink (DL)
Fig. 2. Average LTE loads of SG and BG traffic components.
• Load balancing with local energy generation (SG Case 2)
LTE network: The SG trafNc UL delay is 36 – 722 ms; DL delay is extremely low, 2 ms
• ADR generation interval: 1 s (UL), 30 s (DL)
Packet delivery ratio (PDR) above quality of service QoS requirement for SG traffic (>99%)
• High-intensity load balancing (SG Case 3)
Notable increase in delay and decrease in the PDRs of the BG traf@c
• ADR generation interval: 1 s (UL), 1 s (DL)
components (SG Case 2 and 3)
20, 60, or 100 % of RTUs participate in ADR
Hybrid sensor-LTE network: The SG trafNc delay is 7 – 24 ms, approximately 20 ms
All remote terminal units (RTUs) participate also in automatic meter reading
for UL and 10 ms for DL
(AMR). Public LTE carries typical busy hour traffic as background (BG)
PDR above QoS requirement for SG traffic (>99%) (SG Case 1 and 2)
traffic.
PDR of most SF traffic components below QoS requirement (>99%) (SG Case 3)
Connectivity via cellular LTE
P EAK LOADS , ( PACKET DELIVERY RATIOS IN PERCENTAGES ) AND AVERAGE VALUES OF THE NETWORK DELAYS IN SECONDS
Schematic cellular LTE
Connectivity via WSN

Muokkaa tekstin perustyylejä
osoittamalla
– toinen taso

network

Traffic component (peak load)

BG traffic

LTE only network
ADR, AMR and Emergency (UL)
SG case 1 ( 80.08 kB/s, 88,57 kB/s, 96.34kB/s)
SG case 2 (90.75 kB/s, 120.75 kB/s, 151 kB/s)
SG case 3 (90.75 kB/s, 120.75 kB/s, 151 kB/s)
ADR control and AMR (DL)
SG case 1 ( 0.75 kB/s, 1.05 kB/s, 1.25 kB/s)
Voice (51.84 kB/s)

-

Video conference ( 1,66 MB/s)

(90.6)
0.086

Streaming (0.53 MB/s)

(100)
0.002

• kolmas taso
CLH

-

– neljäs taso
» viides taso
Hybrid sensor-LTE
Network

(99.8)
0.073

HTTP (0.22 MB/s)

(99.2)
0.496

FTP ( 10.68 MB/s)

(94.8)
47.34

Fig. 1. Visualisation of LTE only and hybrid sensor-LTE networks within a single LTE cell.

Simulation topology is generalisation of a suburban environment (790 * 950 m)
• In total: 750 houses (RTUs); 930 user equipment (UE); 1 base station (eNB); 30
custers/CLH (hybrid network); 16 WSN channels (hybrid network)
• UE and RTUs are randomly placed inside 150 *150 m clusters; CLHs and eNB
are centred
LTE network without WSN clusters: RTUs are LTE nodes; No CLHs
Hybrid sensor-LTE network: RTUs are WSN nodes; CLH is LTE and WSN
equipped relay
LTE network includes only LTE channels (modified COST231 Hata urban)
Hybrid sensor-LTE network applied: LTE channels between CLH and LTE eNB;
IEEE 802.15.4 channels (Erceg and free-space) between CLH and RTUs
Building entry loss: approximately 6 dB/wall ([0,2] random number of walls)
The work undertaken here has been funded by TEKES (the Finnish Funding Agency for
Technology and Innovation) project SGEM (Smart Grids and Energy Markets, Dnro
2441/31/2009).

www.cwc.oulu.fi

SG (ADR 20 %) and
BG traffic
SG case 1, case 2, case 3
HYB: (99.5), (99.4), (99.9)
LTE: (100), (100), (100)
HYB: 0.019, 0.019, 0.02
LTE: 0.108, 0.068, 0.036
HYB: (100), (99.9), (98.6)
LTE: (100), (100), (100)
HYB: 0.010, 0.009, 0.007
LTE: 0.002, 0.002, 0.002
HYB:(99.9),(99.5),(99.7)
LTE: (99.8), (99.8), (99.3)
HYB: 0.074, 0.077, 0.075
LTE: 0.073, 0.074, 0.075
HYB: (91.3), (91.1), (91.2)
LTE: (90.8), (90.2), (89.7)
HYB: 0.083, 0.082, 0.077
LTE: 0.077, 0.084, 0.091
HYB: (100), (100), (100)
LTE: (100), (100), (100)
HYB: 0.002, 0.002, 0.002
LTE: 0.002, 0.002, 0.002
HYB: (99.3), (99.2), (99.2)
LTE: (99.2), (99), (98.9)
HYB: 0.503, 0.503, 0.534
LTE: 0.514, 0.575, 0.618
HYB: (94.3), (93.6), (93.7)
E:
), (
), (91.6)
LTE: (94.3), (92.3), (91.6)
HYB: 4
47.43
B: 46.97, 48.7, 47.43
4
LTE: 50.62, 52.60, 60.68
E: 50
:
.68

SG (ADR 60 %) and
BG traffic
HYB: (99.8), (99.6), (99.1)
LTE: (99.9), (99.9), (99.9)
HYB: 0.019, 0.020, 0.021
LTE: 0.097, 0.21, 0.208
HYB: (100), (99.8), (98.4)
LTE: (100), (100), (100)
HYB: 0.009, 0.01, 0.009
LTE: 0.002, 0.002, 0.002
HYB: (99.8), (99.8), (99.6)
LTE: (99.9), (99.8), (99.8)
HYB: 0.075, 0.076, 0.075
LTE: 0.074, 0.075, 0.076
HYB:(90.9), (91.4), (91.1)
LTE: (90.4), (89), (88.5)
HYB: 0.081, 0.078, 0.08
LTE: 0.08, 0.095, 0.115
HYB:(100), (100), (100)
LTE: (100), (100), (100)
HYB: 0.002, 0.002, 0.002
LTE: 0.002, 0.002, 0.002
HYB:(99.3), (99), (99.3)
LTE: (99), (98.4), (97.9)
HYB: 0.539, 0.551, 0.521
LTE: 0.628, 0.766, 0.89
HYB:(93.7), (92.7), (92.8)
E:
), (
), (84.8)
LTE: (92.2), (88.1), (84.8)
HYB: 48.28, 51.43, 50.94
B: 4
50.94
9
LTE: 60.84, 72.97, 80.84
L E 60.8
LTE: 60 84, 72 97 80 84
84

SG (ADR 100 %) and
BG traffic
HYB: (99.7), (99.3), (94.8)
LTE: (99.9), (99.5), (99)
HYB: 0.020, 0.023, 0.024
LTE: 0.111, 0.485, 0.722
HYB: (99.9), (99.4), (96.6)
LTE: (100), (100), (100)
HYB: 0.008, 0.011, 0.014
LTE: 0.002, 0.002, 0.002
HYB: (99.9), (99.4), (99.9)
LTE: (99.8), (99.7), (99.8)
HYB: 0.074, 0.075, 0.074
LTE: 0.076, 0.076, 0.075
HYB: (91.1), (90.7), (91.1)
LTE: (90.1), (88.4), (87.9)
HYB: 0.082, 0.082, 0.084
LTE: 0.094, 0.106, 0.137
HYB: (100), (100), (100)
LTE: (100), (100), (100)
HYB: 0.002, 0.002, 0.002
LTE: 0.002, 0.002, 0.002
HYB: (99.2), (98.9), (99)
LTE: (99), (97.6), (97.1)
HYB: 0.519, 0.566, 0.588
LTE: 0.59, 0.941, 1.137
HYB: (93.4), (91.9), (91.7)
E: 1.6), (81), (77.7)
(81)
7)
LTE: (91.6), (81), (77.7)
HYB: 49.79, 52.86, 55.88
55.88
YB:
YB
8
LTE: 54 15 86.17 105.91
LTE 54.15, 86.17, 105.91
E:
9

SG traffic delivered in hybrid network causes less harm to BG traffic components
than LTE only network.

NEXT STEPS
Similar studies conducted using WSN only (IEEE Std 802.15.4k) lowenergy critical infrastructure monitoring networks
• Preliminary results indicate feasibility of SG Case 1 if network
coordinator supports multiple narrowband (37.5 kHz) channels.
• 99% QoS requirement challenging.
Research and development on robustness of hybrid sensor-LTE
network in ADR cases when eNB is susceptible to temporary failure.
• Relaying in the WSN domain through multiple personal area
networks (PANs) using different frequency channels to the closest
functional eNB.
During SGEM funding period 5, research on ad hoc LTE relaying when
eNBs are susceptible to failure.

Enabling Grid Technologies
Theme: Active Network and System Management
Janne Starck and Jani Valtari (ABB), Heikki Paananen (Elenia), Tapio Lehtonen (MIKES),
Pertti Pakonen and Bashir Siddiqui (TUT), Lauri Helenius (Viola), Henry Rimminen (VTT)

Objectives
What are the technologies and infrastructures for enabling the active distribution network management?
Bring new improved solutions for acquiring measurements, handling the communication of the
measurements, and processing the data in distributed environment in the substation.

Main achievements
Goose over LTE
• Utilizing IEC 61850-8-1 Goose communication
in transfer trip applications
• Tests in laboratory LTE network: 20-40ms
delay when communicating from fixed network
to device in LTE network.
• Results so far in public LTE network: 50ms
point-to-point delays

Centralized Protection
• Utilizing IEC 61850-9-2 process bus
• Tested in RTDS laboratory of TUT
• High Impedance faults of 100kOhm were
detectable

Next steps

Low-cost Fault Pass Indicator
• Sum current of three phases is measured
• Field tested in 4/2012
• Minimum tripping threshold was 5 A
• Earth faults up to 330 Ohms were
detectable

Secondary substation monitoring device
• Capable of detecting Partial Discharges
• PD signals up to 2 MHz can be
successfully captured

New national power and energy standard and PQ analyzer
• Metrology-grade digitozer for LV and MV
• Samples at 250kSPS @ 18-bit resolution
• IEEE 1459 and IEC 40110 power standards
• Extendable to PMU measurements

Fault Pass Indicator: Field tests for next HW generation.
Centralized Protection: New fault type cross-country fault
PQ Analyzer: System integration and analysis software development
Goose over LTE: Field tests with an application
Secondary subsation monitoring device: Finalizing the device and performing field tests


VTT TECHNICAL RESEARCH CENTRE OF FINLAND
www.vtt.fi

Demonstration of a low-cost fault detector for
sum current measurement of overhead MV lines
Henry Rimminen, Research Scientist, VTT • Antti Kostiainen, Solution Development Manager, ABB •
Heikki Seppä, Research Professor, VTT

Introduction
We present field test performance of low-cost wireless current
sensors, which harvest power from the lines. Handmade unit
price was $75 excluding the enclosures. Three sensors measure
current of each phase in a 20 kV power line. They are
synchronized by radio and then locked in to 50 Hz, which enables
sum current calculation. Current is measured with induction coils.
In unearthed and in compensated networks, detection of faults
using sum current is useful, since the earth fault current is often
smaller than the load current. Typical fault detectors rely on
sensing dynamic phenomena on earth faults. With sum current
measurement, one can set a fixed threshold instead of a dynamic
one. See concept in Figure 1.

Figure 3. Measured and reference waveforms during faults.

Minimum tripping threshold was found to be 5 A based on the
healthy state variation of the sum current. See Figure 4. The
recorded earth faults with resistances of 0…330 ȍ were above
this threshold.
The detectors harvest energy from the line with current
transformers. We observed charging of the batteries when the
detectors were set in a low power mode, but the consumption in
measurement mode exceeded the harvested power.

Figure 1. Concept of the system.

Field test performance
The detectors were field tested in Masala, Kirkkonummi,
Finland in April 2012. The field test was arranged by ABB and
Fortum. Figure 2 shows the three detectors at the test site.
Figure 3 shows the measured waveforms (DUT) and the
substation waveforms (Ref.) during four induced earth faults.
The fault resistances were 0, 150, 330 and 5000 ȍ, and the
faults lasted for 400 ms. The waveforms match closely.
Figure 4. Variation of measured sum current in a healthy state.

Conclusions
ƒ We used wireless summation of three-phase current
for earth fault detection

ƒ Earth faults up to 330 ȍ were detectable
ƒ Lowest tripping threshold was 5 A
ƒ Energy harvesting was not yet adequate, but will be
improved in the next generation devices

This work was funded by CLEEN/SGEM program of TEKES –the
Finnish funding Agency for Technology and Innovation.
Figure 2. Detectors installed.

Self Healing City Networks
Osmo Siirto
Helen Electricity Network Ltd.

Self Healing City Networks
The urban society is increasingly more
dependent for uninterrupted electricity. In
this task the means to improve reliability
in Urban Network by Self Healing
technics are studied under Theme Active
Network Management.

Matti Lehtonen
Aalto University

Jukka Kuru
Tekla Oy

CITY – FLIR: Automatic fault location,
fault isolation and supply restoration for
urban power distribution networks
Fault Management logic (FM) ready

Self Healing technics
Reducing the number of interruptions
•
Network operation with sustained
earth fault, compensated neutral
•
Online monitoring, condition
monitoring
Reducing the interruption time
•
Distribution automation
•
Smart Network Management

Main results

k= n

Optimated Distribution Automation
strategy for urban networks
100 % automation
RTU

RTU

RTU

RTU

RTU

RTU

RTU

RTU

RTU

NO

Next steps

k= 2

…

Implementation of Fault Management
logic into CITY-FLIR, proof of concept

RTU

RTU

RTU

RTU

RTU

RTU

Select the
optimum
number of k
for Feederj

Low level fault indications
Finalisation of Self Healing City Networks
Study

k= 1

RTU

RTU

SGEM unconference 24.-25.10.2013, Theme Active Network Management

Large Scale Cabling
Theme: Grid Planning & Solutions
Juha Haakana
LUT

Tommi Lähdeaho
Tomi Hakala
Elenia

Kimmo Kauhaniemi
UVA

Pertti Pakonen
Bashir Siddiqui
TUT

Objectives

Cable construction process

The aims of task 2.3 include the
development of the cable network
p
construction, quality control and condition
assessment processes as well as a costefficient cabling concept.

• P
Proposal for a re-engineered cabling
lf
i
d bli
process

Main achievements
Method for cost-efficient underground
cabling in rural area networks
Background:
• New Electricity Market Act (588/2013) came
into effect in beginning of September in 2013
• 36 h maximum allowed interruption duration in
rural areas and 6 h in urban areas
• Æ Major-disturbance-proofness has to be
improved

– Insulation resistance (IR) measurement
– Sheath integrity (SI) measurement
– Partial discharge (PD) measurement (on-line
or off-line) depending on cable prioritization
ff li ) d
di
bl
i iti ti

100 %
2008

2008

90 %
2008

2008

0007

0007

80 %
0010

0036

0014
0029

Cabling
rate in
LV network

0036

0018

Cabling
rate in
MV network

2002

0101
0028

0024

0041
0733

0040

0014
0029

0018

2002

0101
0028

0024

0041
0733

0040

0042

0043

0042

0043

0045
2001

0045
2001

0795

0058

0795

0058
0799

0799

2007

0798

0077

0097
2007
2007
0079
0098
0080

0797
2007
0102
0106

0107

0777
0092

0135

0734
0195

2006

0212 0774
0212

0657

0314

0212 0774
0212

ϭϬϬͲϵϬй

0657

0786
0661
2003

2000

ϴϵͲϲϬй

0733

0538

ϮϵͲϭϲй

10 %

0796
0784

0399 0733
0433 0785
0791
0659

0471

2005

ϱϵͲϯϬй

Rural area
distribution
companies

0427

0330

0790

30 %

0778

0776 0776

0374
0479
0477

0471

40 %

0307

0759

0272

0317

0796

2005
0538

0290

0229
0233 0733

0231
0320

0778

0776 0776
0427
0399 0733
0433 0785
0791
0659

50 %

0244
0208

0789

0307

0786
0661
2003

0374

0733

0206

60 %

20 %

0657
0783

0212

0759

0272
2000

0784

0794

Urban area
distribution
companies

70 %

2005

0290
0733

0317

0195
0152

0657

0229
0233 0733

0330

0178

0140

0783

0231
0320

0793
0734

0141

0244
0208

0789

0314

0106

0733

2005

0212

0479

0102

0134

2006

0152
0140

0797

0135

0794

0141

0477

0107
0092

0178

0734

0733

0206

0097
2007
2007
0079
0098
0080

0777
0793

0134

0734

0733

2007

0798

0077

2007

Ca
abling rate in MV network
M

0010

ϭϱͲϲй

0790

ϱͲϬй

0%
0%

10 %

20 %

30 %

40 %

50 %

• Proposal for implementation of
commissioning tests

60 %

70 %

80 %

90 % 100 %

Cabling rate in LV network

Conclusions:
C
l i
• Use of cheap ploughing techniques is
pp g g
q
necessary
• LV cabling is more economical compared with
MV cabling
– 1000 V technique to replace low-loaded
MV lines
• Supply security requirements can be met
without full scale cabling => focus the
investments on the most cost-efficient targets
– S
Some t diti
traditional overhead li
l
h d lines can b
be
withstood in the network
– Most suitable sections can be selected for
underground cabling

• Proposal for
p
documentation of
commissioning tests,
g
,
minimum requirements
– Measuring system
– Test voltages and insulation resistances or
PD magnitudes and background noise levels
– PRPD patterns and PD locations, for off-line
measurements also PD inception and
t l
i
ti
d
extinction voltages

Next steps
Proposals and demonstrations for
commissioning and condition monitoring
together with related data management.
management
To find out the best prioritization criterion for
reinvestments of low loaded rural MV
network and effect of electric cars to the
network structure.
Study of effects of New Electricity Market Act
on required cabling rates.

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Smart Grid Protection
Theme: Active Network a System Management
and
Kimmo Kauhaniemi
Sampo Voima
UVA

Hannu Laaksone Ari Wahlroos
en,
Jani Valtari Erkka Kettunen
Valtari,
ABB

Ari Nikander
Ontrei Raipala
TUT

Objectives

Adaptive protection concept

New Smart Grid protection concepts and
methods are developed in tasks 6.5 and
p
2.3 for taking care of changing states of
active network improving fault detection
network,
sensitivity and managing earth faults in
cabled networks
networks.

Protection
P t ti system must adapt to the
t
t d t t th
g
g
changes in network configuration and
state of distributed generators by
• changing relay settings
• enabling or disabling specific protection
functions.
Overcurrent

Main achievements

G

IfDG

Directional OC

Ifsupporting network

Demonstration and evaluation of the
indication of high-resistance earth faults
including faulty phase selection
• Testing the indication method implemented
in the centralized protection system (CPS)
with different types of compensation
practices of earth f lt current (D6 5 16)
ti
f
th fault
t (D6.5.16)

Ifsupporting network

G IfDG

G IfDG

Distance

Practical Demonstration of Adaptive
p
Protection and Microgrid Control in
Hailuoto Pilot

– Faults detectable up to 100kΩ

• Methods for reliable detection of crosscountry faults with CPS
RTDS test environment for CPS

Calculated fault resistances for faulty and healthy feeder
with distributed and centralized compensation (Hedekas).
Real
RF

Calc. RF
PH1

Change in neutral voltage and sum
current, phase angles of Phasors 1 and 3

RF/kΩ
Ω

RF/kΩ
Ω

ΔU0/V

ΔI0/A

PH1/°
°

PH3/°
°

1

1.005
1 005

1782.69
1782 69

5.440
5 440

113.799
113 799

-93.460
93 460

5

5.034

410.61

1.255

113.164

-119.821

10

10.334

195.443

0.614

114.879

-121.798

20

21.115
21 115

91.538
91 538

0.296
0 296

118.187
118 187

-120.124
120 124

30

31.863

59.173

0.194

120.266

-118.582

50

50.191

41.375

0.127

115.948

-123.159

70

75.951

25.217

0.083

116.939

-122.47

100

122.314

12.997

0.0502

121.298

-118.311

Loss of mains protection studies
L
f
i
t ti
t di
• A novel network information system based
LOM risk management concept is
developed (D6 5 19)
(D6.5.19)
• The interactions between LOM protection
and FRT requirements were studied
thoroughly (D5.1.22)

Active management functionalities
ƒ Centralized adaptive protection system
ƒ Protection settings changing based on
microgrid topology i.e. 1) Grid
connected no DG, 2) Grid connected
DG
with DG, 3) SCADA command
(intentional islanding), 4) Black-start
islanding)
Black start
(unintentional islanding), 5) Islanded
operation
ti
ƒ Transition between grid connected and
g
island operation modes

Next steps

• Adaptive protection concept will be further
developed and tested.
Earth faults in large scale cabled rural
networks
• Suitable earth fault protection methods for
Results from task 2.3:
cabled networks will be studied
Fault current as a function of
fault location (fp1, fp5 and fp9)
f lt l
ti (f 1 f 5 d f 9)
• Realization of new implementation of
with different compensation
centralized protection
methods.
• Field tests of earth faults in compensated
network
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Laboratory test environment for wind turbine prototype connected to grid
based on RTDS simulation
Anssi Mäkinen, Jenni Rekola and Heikki Tuusa
Department of Electrical Energy Engineering, Tampere University of Technology
Introduction

Network model in RTDS

Purpose of the study is to create laboratory test setup which takes into account
• The impact of network phenomena to the wind turbine operation
• The impact of the wind turbine operation to the network operation
DC-motor, controlled using thyristor rectifier, is used to emulate the behaviour of wind turbine rotor
The wind turbine consists of permanent magnet synchronous generator, three-level generator side and grid side converters
• Nominal power of both converters are 10 kW and the converters are controlled using dSPACE
Network is modelled in RTDS and simulated point of common coupling (PCC) voltages are realized after scaling to the PCC of the wind turbine
prototype using grid emulator
• Grid emulator is controlled using dSPACE
• Active grid side converter enable bidirectional power flow
Wind turbine PCC currents are measured and after scaling fed to RTDS
• Wind turbine prototype is scaled to have nominal power of 500 kW when connected to RTDS network
dSPACE controlling
wind turbine

Thyristor rectifier

Wind turbine
frequency converter

generator

DC-link
RTDS

transformer

DC-motor

Grid emulator
dSPACE controlling
grid emulator
PCs controlling dSPACEs
PC controlling RTDS

Performance of grid emulator

Controller tuning

Open loop - resistive load

closed loop V-control - resistive load

-20

-10

-30
-40
180

-180

100
0

10

3

10

2

2

Gain [dB]

Gain [dB]

2

0

-200
1
10

3

10
Frequency [Hz]

100

-100

10

2

3

10
Frequency [Hz]

10

3

-10
-20

-30

-30

-40
180

10

10

0

0

0

-10

-40
180

-20
1
10
closed

closed

-360

phase [deg]

Phase (deg)

-180

-180
-360
-540

-540
10

3

10

2

Frequency (Hz)

10

2

10
Frequency [Hz]

3

Frequency (Hz)

100
0

Conclusion

2

10
Frequency [Hz]

2

10
Frequency [Hz]

3

10

positive sequence
negative sequence
2

10
Frequency [Hz]

3

10

200

positive sequence
negative sequence

100
0
-100
-200
1
10

X: 185.8
Y: -3.05

-10

-20
1
10

3

10

200

-100
-200
1
10

X: 170.5
Y: -3.041

-10

-20
1
10

3

10

200

open

0

open

0

positive sequence
negative sequence

Gain [dB]

-20

10

2

10
Frequency [Hz]

3

10

phase [deg]

-10

Wind turbine connected to
the grid modelled in RTDS
• Wind speed 12 m/s

Gain [dB]

Magnitude (dB)

0

closed loop VC-control - wind turbine connected to RTDS grid

closed loop V-control - wind turbine connected to RTDS grid

Open loop - wind turbine connected to RTDS grid

10

0

100
0
-100
-200
1
10

2

10
Frequency [Hz]

3

10

Future work

• Wind turbine prototype is connected successfully to the artificial network which is controlled using RTDS
• If PCC voltages simulated by RTDS are used as grid emulator voltage references
• Emulator performance is decent in frequency range up to 300-600 Hz depending of the load type
• Emulator does not take the operation point of wind turbine (or other load/source) into account
• PCC voltages in different operation points are determined by the emulator filter components rather than
network parameters
• The operation point of wind turbine can be taken into account by using feedback control for the PCC voltages
• The bandwidth of the feedback control limited by
• Resonances of the passive components
• Saturation of the transformer
•
•

3

10

positive sequence
negative sequence

10

Wind turbine in RTDS grid, CV-control, q-channel
Bode Diagram
Gm = 8.31 dB (at 800 Hz) , Pm = 83.7 deg (at 107 Hz)

Wind turbine in RTDS grid, CV-control, d-channel
Bode Diagram
Gm = 7.25 dB (at 818 Hz) , Pm = 80.8 deg (at 105 Hz)
10

10

-200
1
10

10

2

10
Frequency [Hz]

Frequency (Hz)

Frequency (Hz)

2

positive sequence
negative sequence

0

3

10
Frequency [Hz]

-10

-20
1
10

3

10

X: 184.1
Y: -3.005

0

200

100

Gain [dB]

2

2

10
Frequency [Hz]

-100

phase [deg]

10

-20
1
10

3

10

-100
-200
1
10

-360

-10

200

-540

-540

Magnitude (dB)

phase [deg]

closed
open

0
Phase (deg)

Phase (deg)

-360

2

10
Frequency [Hz]

200

closed
open

0
-180

positive sequence
negative sequence

-20
1
10

-20

-40
180

-10

10
X: 164.1
Y: -3.016

phase [deg]

-10

0

closed loop VC-control - resistive load

10

0

phase [deg]

Magnitude (dB)

0

-30

Phase (deg)

Resistive load 2kW

10

0
Magnitude (dB)

10

Gain [dB]

10
Wind turbine in RTDS grid, V-control, q-channel
Bode Diagram
Gm = 8.92 dB (at 800 Hz), Pm = 99 deg (at 107 Hz)

Wind turbine in RTDS grid, V-control, d-channel
Bode Diagram
Gm = 7.66 dB (at 797 Hz) , Pm = 103 deg (at 114 Hz)

• Verification of simulation model of the laboratory environment with measurements in transient
simulations
• Symmetrical fault
• Unsymmetrical fault
• Utilization of grid emulator in other applications
• Solar power grid connection
• Connection and control of renewable energy sources and/or energy storages in microgrid
• LVDC
• Charging / discharging of electric vehicle in different networks
• Etc.

The positive sequence bandwidth using controller with voltage feedback loop is 170 Hz (V-control)
The positive sequence bandwidth using controller with voltage and current feedback loop is 185 Hz (VC-control)

SGEM (Un)Conference 24.-25.10.2013

Methods for load modelling
Summary

Integration of data and models

Accurate load models for different time horizons are developed
in collaboration to enable smart grids and energy markets.

measurements + initial information
= model
= estimation, prediction and optimization

Data from different sources is used for estimating loads. For
example, income taxation statistics can be combined with
share of single family houses to estimate the introduction of
electric vehicles in a network area.

Background
Smart grids are all about distribution side networks and
customers becoming active and smart and thus helping to
manage the expected massive changes in power generation
(more distributed, more renewables, more intermittency, etc.).
The customer side is also experiencing significant changes
such as heat pumps, electric vehicles, micro-CHP, PV, and
dynamic demand response. Thus it is more and more
important and challenging to model and forecast the loads
accurately.
Meanwhile the amount and quality of information available Dynamic load response models
for load modelling improves rapidly. For example, hourly
Load responses to control actions are modelled based on
metered consumption of practically every customer is in
measurements from substations and smart meters, and
Finland available by 2014 due to new technology and
weather and building data.
legislation.
Field tests for response modelling, an example

Putting new meters to good use
We are developing and testing new ways to cluster
customers into new and automatic groups, which has profound
advantages over traditional load profiles (46 customer types)
that hitherto have been in use.

Average measured response of a test group (blue)
vs. a control group (green), difference is dotted
red. One hour long control action. Both groups are
also subject to static Time-of-Use control.

Identified average response per
house to a 1 h long control action
at about -4 C.

14

12

10

0.25

0.2

M-Fri
Saturday
Sunday

9
8

10

8
[kW]

1
2
3
4
5

7
Power [kW]

0.3

6

6
4

5
4

2

3

0.15

0
-20

-15

-10

-5

0

2

5

10

15

20

25

[oC]
4.5

1

0.1

2

4

6

8

10

12
14
Hour

16

18

20

22

What is going on now

4

24

3.5
3

0.05
20

30

40

50

60

70

2.5
[kW]

10

2
1.5
1
0.5

Divide and unite

0
-20

-15

-10

-5

0

5
[oC]

10

15

20

25

There are different purposes and approaches for load
modelling. They can be combined and compared. Short term
forecasting performance is now under scrutiny.
Other main study targets now, essential for all approaches,
are 1) the identification of load types behind a measurement,
and 2) separation of the main sub load(s) from measurement.

Especially household loads are difficult to model and
forecast, because they are the sum of many sub loads, whereof
some are large and distinct, e.g. electric heating. These
essential, distinct, large sub load types will increase in number, More Information
Pekka Koponen, VTT ( pekka.koponen@vtt.fi )
all having different dependencies. An alternative modelling
Göran Koreneff, VTT ( goran.koreneff@vtt.fi )
approach is based on sub-load types instead of customer
Harri Niska, UEF
( harri.niska@uef.fi )
types.
Antti Mutanen, TUT ( antti.mutanen@tut.fi )
CLEEN Summit, 11-12 June 2013

Demand Response Event Flow in a
distributed market environment
Theme: Demand Response
Mikko Rasi
Pekka A Pietilä
Oulun Energia Oy
Empower IM Oy

Objectives

Next steps

Describe
which
electricity
market
information systems are active in DR
actions initiated by active customer or
electricity supplier
Describe selected event flows which start
when supplier or active customer decide
to execute DR operation

Needed
DR
operations
will
be
implemented and integrated between
energy portal and EDM system

Main achievements

Whole information chain and event flow
from energy portal to customers site will
be implemented and tested in Oulun
Energia
active
customer
pilot
environment

Event flow defined and described
including actions for both active customer
and supplier
Special focus has been set on interaction
between supplier’s DR tools (EDM
based) and active customer energy portal
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DZ ƐǇƐƚĞŵ ĞŶǀŝƌŽŶŵĞŶƚ

Theme: Demand response
Contact person: Samuli Honkapuro, LUT (samuli.honkapuro@lut.fi)

Objectives

Main achievements

The objective of the research is to find
out what kinds of business, pricing, and
market models provide the highest
benefits of the smart grid technology for
different stakeholders.

The research concerning business
impacts described here is mainly carried
out in WP 7. However, business impacts
cannot be analyzed without considering
technological development and practical
implications. Thus, the cooperation
between the different themes and WPs
inside SGEM, as well as collaboration
between
research
and
industrial
organizations,
have
been
utmost
important for this research work. For
instance, the impacts and possibilities of
the demand response are being studied
from technological, economical, and
societal perspectives. This is done by
laboratory
demonstrations,
piloting,
analyzing
real-life
data,
and
by
conducting customer surveys and
interviews. This kind of research work
could not be done without SGEM
collaboration.

One of the key elements in these
analyses, which combine the technical
and business research, is the big picture
concerning the holistic impacts of market
player actions. The (simplified) picture
below illustrates these actions and
impacts. Studied issues include:
• The business and pricing models of the
DSO, retailer, and aggregator
• Conflict of interest between the market
players
• Demand response and customer behavior
• Smart metering and energy management
services

TRANSMISSION SYSTEM
OPERATOR (TSO)

DISTRIBUTION SYSTEM
OPERATOR (DSO)

STATE

Monopoly
regulation

TSO tariff

DSO business
model

DSO tariffs

RETAILER /
AGGREGATOR

CUSTOMER

Taxes
Incentives for
customer to optimize
the energy usage

Retail
tariffs
Retailer’s
business model

DSO’s
revenue
demand

Retailer’s
revenue stream

Capital
expenses

Operational
expenses

Investment
needs

Network
losses

Retailer’s revenue
demand

Metering
and billing

Retailer’s
electricity
purchase costs

Total demand
of energy and
power

Peak demand
DSO’s revenue
stream

Accuracy of
load forecast


Electricity
wholesale price

E COSYSTEMS FOR D EMAND R ESPONSE
Petteri Baumgartner

Marko Seppänen

Pertti Järventausta

Joni Markkula

CITER/TUT
+358 40 516 7028

CITER/TUT
+358 40 588 4080

TUT
+358 40 549 2384

TUT
+358 44 544 4448

Objectives
We examine the DR business ecosystem
in the smart grid environment focusing on
the liberalized Nordic electricity markets.
The aim is to afford a blueprint of an
ecosystem to identify the problematic
nodes and provide alternatives how to
overcome possible obstacles in order to
develop a functioning demand response
ecosystem for this field.

Main achievements
Based on earlier work on SGEM, we have
considered that a consumer may not be
treated as the end customer in this
ecosystem. Thus, the value proposition of
DR should be developed by considering a
DSO, TSO, retailer, or even yet non-existing
aggregator as the end customer in this
business ecosystem. Substantial economic,
environmental, and social advantages are
possible through DR utilization in these
cases. For instance, an electricity supplier
can cut its future balancing costs if load
shifting and shedding are at its disposal.

Next steps
We are going to study the business
ecosystems of several different DR
programs and strive for identifying the key
obstacles hindering the development of
thriving DR businesses. We see crucial the
identification of the key elements and their
explicit locations in the ecosystem as well
as detecting the ways to overcome the key
obstacles to bring about the DR businesses
to boom. This work will be supported with
business model examinations.

A value blueprint of DR ecosystem. Herein direct load control
(DLC) program to exploit DR is demonstrated—i.e., one possible
way to do DR business. E.g., some price-based programs pass
the responsibility for load adjustments onto consumers whereby
the blueprint outlines slightly differently.


Demand Response Information Exchange
Jan Segerstam
Empower IM Oy

Objectives
Defining information exchange processes
and information structures to enable the
control of demand response capacity with
different kind of load control equipment in
different electricity network areas.
Main achievements
First version of load control message
structure has been developed in cooperation with SGEM partners.
Next steps
Collecting further requirements for the
message structure as a part of piloting
work with electricity suppliers and DSOs.

6*(0 XQFRQIHUHQFH

Demand Response Pilots
Joni Aalto
Empower IM Oy

Tuomas Åhlman
Vantaan Energia Sähköverkot Oy

Pekka Takki
Helsingin Energia

Objectives
Describing how DR should be
connected to electricity supplier’s
business processes?
Requirements and possibilities of AMR
and HEMS based market-wide DR?
Piloting work in real system environment
with electricity suppliers, DSOs and
HEMS providers.

Main achievements

Next steps

Process descriptions of linking DR
utilization to supplier’s business
processes in different electricity market
levels.
Established partner network for piloting
work.

Starting the piloting work with real
measurement points and loads.
Enabling supplier’s DR actions in
different DSO areas.
Collecting experiences from the
piloting work to further develop a
holistic approach for demand
response.

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Effects of demand response on load profiling
Kaisa Grip, Antti Mutanen and Pertti Järventausta
Tampere University of Technology

Objectives
This study analyses different load alternatives
stemming from combinations of load, demand
response and microgeneration (Figure 1).
Their effects on load profiling accuracy and
development needs are studied. Here, the
combination of load and demand response has
been chosen for more detailed examination.
Figure 3. Behaviour of loads in February 2010–2013

Figure 1. Potential load alternatives

The effect of spot-price based water heater
control can be seen clearly but the effect of
power band control is difficult to see due to the
stochastic variation between years. The effect
of power band control can be seen more clearly
from the load duration curves (Figure 4). Load
was shifted from peak hours to a time of lower
consumption. In load duration curves this can
be seen as a hill under the hysteresis value.

Main achievements
The effect of demand response to customer
level load behaviour was demonstrated with
power band and spot-price based load control.
The energy consumption of a pilot customer
was held under a given threshold value with a
power band based load control (Figure 2) and
the water heater was controlled based on the
spot-price.
Figure 3 shows the combined effect of power
band and spot-price based load control on
February’s load profile.

Figure 2. Load curves

Figure 4. Load duration curves for February’s 2011-2013

Next steps
In terms of load profiling and forecasting, the
new load control functionalities complicate the
modelling and forecasting tasks. To some
extend, the changes can be modelled with new
customer class models. But in order to model
demand response and microgeneration more
accurately we should be able to separate
controllable load and generation from rest of
the load. Then, for example, a solar irradiation
dependent PV model could be used to model
solar panels.

Figure 2. An example of realized control actions when power
band control is used


Task 4.4: Technical l ti
for DR,
T k 4 4 T h i l solutions f DR
customer gateway and ICT systems
t
t
d
t
,
y
,
p ,
,
,
,
Antti Pinomaa, Andrey Lana, Tero Kaipia, Ville Tikka, Pasi Nuutinen, Henri Makkonen, Petri Valtonen
Lappeenranta University of Technology
Marko Pikkarainen, Antti Mäkinen, Pertti Järventausta, Sami Repo,Tampere University of Technology
Markku Kauppinen, Elenia

TUT smart grid laboratory

Introduction
Task 4.4 focuses on
• The technical solutions, applications and ICT
,
pp
architecture in future customer gateway relating to
HEMS and AMR based systems and how they support
the overall aims for demand response and network
management issues

DMS600

Smart grid
functions

SCADA

CIM

Analysis tools

View

DB

IEC61850

Enterprise Service Bus

microSCADA

Primary subst.
automation

Aggregator

IEC61850

Green campus – energy management system

CIM
IEC61850

CIM
IEC61850

OPC UA

OPC DA

HTTP

COSEM

Secondary subst.
automation

IEC61850

Meter reading
DLMS

HTTP

Ethernet
Q
SQL

IEDs

HEMS

Smart
meter

Smart
meter

HEMS

PQ
meter

PMU

Smart
meter

PMU

Other
meas.

Control

Smart
meter

KNX
There

AC
microgrid

RTDS

20 kW

Wind
turbine

PV power plant

EV

( p
(in operation)
)

AC microgrid lab
LAN
L1
L2
L3
N
PE

20 kW
(components
ready to b
d t be
installed)

Aggregator

0 1

0 1

0 1

0 1

0 1

0 1

0 1

0 1

0 1

Z-wave

Measurements
10 V DC

PHEV

0 1

0 1
dSPACE

(6.7 kWh, G2V +
V2G, in operation)

~
~
=

=

~
RTDS

~

PV production

Connection
for loads and
production

=

BEV
(24 kWh, G2V, in
kWh G2V i
operation)

~

(in laboratory
tests)

=

30 kWh

CAN fieldbus
gateway

3-phase supply

Wind turbine
EV charging
SG unit

Fibre optics LAN

N units

Neutral fault management in LV network –
RTDS simulations of AMR meters

Info client

DHCP

Switch
100 Mbit / 1 Gb

Info display

GC server
CAT5

VLAN Green Campus
measurements, etc.

eth0, eth1, eth 2, eth 3
Services: Apache(PHP, etc.),
mySQL, FTP, SFTP
mySQL FTP SFTP, SSH
Samba?
CAT5

DR unit

Info client

Measurement
unit

CAT5

LUT Firewall
Port open:
80

157.24.25.240
255.255.252.0
157.24.24.1

VLAN staff

Info display

SSH admin client,
SSH Admin client
port 22
IP 157.24.25/26.0?

157.24.26.193

LUT LAN

157.24.25.240
Redirection from
www.lut.fi/GC/...

Fig General concept of interactive customer gateway realized in the
Fig.

.

Green Campus environment Schematic of GCSG information network.

Smart Metering Based Dynamic Demand
Response
Summary
Dynamic market based demand response using smart meters
was developed and implemented in large scale. Demand
response reduces costs and risks regarding prices and reliability
of the electricity market and system.

Background and objective
Demand side response enables smart grids, more distributed
generation, full utilization of renewable energy sources, more
electrical vehicles, and better security of the electricity system
and electricity market. Thus it is an essential tool for reducing
emissions and costs.
Dynamic load control via smart metering systems is
developed to replace the traditional static time of use controls
and tariffs. In addition to market price based Demand
Response the solution developed supports many other load
control needs.

In December 2012 dynamic load control started with about
1000 consumers. Observed controlled power was about 17
MW and the total power of the customers was about 20 MW.
(Some non-controllable consumption and lost control
messages.)
Vantaa Energy Electricity Networks completed tests with 1
house and has started new tests. The houses have partial
heating storage.
Fortum is completing a study on how the developed dynamic
demand response model fits to their smart metering system.
SGEM helps E.ON Kainuu in direct load control field tests with
about 7000 partial heating storage houses in time of use
control. Test planning and data analyzing and modeling.

Some field test results, full storage

Old static load control vs. the new
dynamic control

Continuation and collaboration

Results so far (May 2013)
Two smart metering system vendors have implemented the
dynamic demand response operating model developed.
Electricity retailers participating control the loads based on
their needs using the messaging developed.
Helen Electricity Network started field trials in 2010-2011. By
February 2012 about 500 consumers (10 MW) were connected
and in February 2013 about 50 MW. All are full heating storage
houses.

Analyze field test data and develop short term prediction and
optimization models for the loads and dynamic responses.
Study and develop the approach in partial storage heating.
Promote wider adoption. More DSOs, Metering operators,
smart meter vendors, and electricity retailers and aggregators.
Test performance regarding latency and reliability.
Continue collection of data for load and response models.
Promote harmonization of demand response messages.
Report the results.
Promote expansion to new DSOs, retailers and smart
metering systems.

More Information
Pekka Takki, Helen (pekka.takki@helen.fi)
Joel Seppälä, Helen Electricity Network (joel.seppala@helen.fi)
Pekka Koponen, VTT (pekka.koponen@vtt.fi)

SGEM unconference, 24-25 Oct 2013
and CLEEN Summit , 11-12 June 2013

Theme: Grid Planning and Solutions
Matti Lehtonen, Muhammad Humayun, Bruno Sousa
Aalto University

Objectives
• To develop reliability analysis tools for
HV Smart Grid Network.
• Redundant capacity mitigation in HV
Smart Grid using demand response.

Reliability Models
Markov Models in presence of demand response:

DR Capacity in the Network

Results
Three-layer reliability model:

Test Networks

• Redundant capacity of components in
the network proportional to DR capacity
can be mitigated.

• ABC-substations are less reliable
than ABCD-substations.
Next steps
• Investigation of different topologies for
OH and UG HV network.
• Investigation of cost of voltage sags.
• The potential assessment of DR in
mitigating redundant capacity of MV
network.
• Optimal utilization of DR in HV MV
networks for redundancy mitigation.

Spatial Load Analysis
M. Lehtonen, M. Koivisto, V. Rimali, J. Larinkari, H-P Hellman, P. Heine, M. Hyvärinen, S.
Forsström, M. Tella, T. Åhlman, J. Uurasjärvi, J-P Pulkkinen, J. Mörsky, M. Kailu
Aalto University, Helen Sähköverkko, Vantaan Energia Sähköverkot, Elenia, Tekla

Objectives
Supply of electrical energy is vital for the society. To be able to respond appropriately to
the long term future development, the DSOs should anticipate the amount, location and
timing of the power system infrastructure required. Due to numerous uncertainties, a
scenario approach is needed. The present spatial loading and its historical analysis is
the starting point in the planning process. The future plans of the regional and local land
use and the foreseen changes in the use of electricity have to be then assessed. For this
purpose, Spatial Load Analysis and Scenario Tool is essential in Grid Planning.
Spatial load forecast for city districts
a)

d)

Identify changing
consumption
patterns:

b)
c)
Select electricity
consumption
scenario

Identifying spatial, monthly changes in use of electricity

a)
d)

Daily profiles

50

household heated with ground source heat pump
household heated with direct electricity

40
30

b)
MWh

20
10
0
02/12 03/12 04/12 05/12 06/12 07/12 08/12 09/12 10/12 11/12 12/12 01/13 02/13 03/13 04/13 05/13 06/13 07/13 08/13

c)

-10
-20
-30

Results 1FP…4FP

-40

a) Spatial load forecast process outlines
for modelling new housing and office
building development by the year 2030
b) Mathematical and statistical processing
of AMR measurements to generate load
classes and profiles required by load
models
c) Detailed analyses of energy use of
service sector in Helsinki and households
with ground source heat pumps
d) Demonstration of data processing and
visualization of the monthly follow-ups of
spatial electricity consumption

Households

Buildings

Industry

Infrastructure

Construction

Service

Street lighting

Rail traffic

Next steps
Designing scenario models on a specified
form.
Developing spatial data analysis.
Adding background data, e.g. city data
bases, to spatial load analysis.
Modeling and forecasting electricity
consumption using socioeconomic variables
(e.g. GDP).
Demonstrating the scenario tool in NIS.


Statistical Analysis of Large Scale
Wind Power Generation
M. Koivisto, J. Ekström, M. Lehtonen, L. Haarla
Aalto University School of Electrical Engineering

Objectives
As more wind power plants are installed, the effect of wind power on the electric power
system is becoming increasingly important. It is thus important to understand the
contemporaneous behavior of wind power generation in multiple locations. The
estimation of probabilities for very high or low wind speeds in several locations is
required for the long term planning of power systems with a high amount of wind power
capacity. Knowing wind speeds and wind power generation in locations where no wind
speed measurement data yet exist enables creating different power flow scenarios for
long term planning. With the scenarios it is possible to plan grid reinforcements and
reserve capacity.

Main Achievements
•The combined effect of large scale wind
power generation can be analyzed with
statistical models.
•Individual locations are modeled by a
wind speed distribution for each
location.
•The dependence structure of the
multiple locations is analyzed using a
multivariate time series model.
•Each location has its own power curve
to asses the power generation of all the
locations.
•New non-measured locations can be
added to the models.
•Monte Carlo simulations are used to
assess the risk of extreme wind power
generation situations.

The combined production of ten 3.3 MW units when
the units are geographically close to each other.

The combined production of ten 3.3 MW units when
the units are geographically highly spread.

Next steps
Creating different scenarios with high
altitude data
The RXCFs of the data and the transformed VARX Modeling the whole wind power generation
and ARC models (averages of the 100 simulation
structure of Finland
runs) for Vantaa and Pirkkala.

WP 2 / Task 2.5

Development of LVDC Technology
Tero Kaipia, Pasi Peltoniemi, Pasi Nuutinen, Andrey Lana,
Aleksi Mattsson, Jarmo Partanen

Introduction

Jenni Rekola, Heikki Tuusa
Tampere University of Technology

EMI in LVDC system

– Benchmarking common mode (CM) and RF EMI in LVDC
system w.r.t. standard requirements based on
measurements at real-network research platform
– Analysis of safety issues due to disturbance level
dBuA
80
– Disturbance levels
70
originating from the
60
LVDC network are
50
low
Key Results
40
– Converters affect
30
Energy efficiency – Converter losses
mainly to the
20
– Ultimate goal to minimise converter losses
frequency spectrum
10
– Understanding and modelling loss mechanisms based
0
of RF EMI
on measurements
-10
– CM current
-20
– Comparison of measurement techniques (calorimetric/
0.1
1
0.01
magnitude in
MHz
Frequency
electric) and two- and three-level converters
customer-end
Fig. 5
Measured CM current in customer-end
network when CEI is operating (red) and
network does not
turned off (blue).
cause safety issues
CM current

The work aims on improving the technical
performance, energy efficiency and economy of the
LVDC distribution systems by developing converter
technology, control algorithms, analysis methodology
and system design principles. The work is highly
interconnected with the laboratory and field tests.

400 converter losses

iron core

filter losses

350

300

94

300

200
150
100

250
200
150

Adaptive converter control

90
88

– Improvement of CEI control during fault situations Æ
identification of grid faults

100
86

50

50

0

84

0

2.5kW 2.5kW
iron
amor

5kW
iron

5kW
amor

7.5kW 7.5kW
iron
amor

2.5kW 2.5kW
iron
amor

5kW
iron

5kW
amor

2.5 kW 2.5 kW 5 kW
cable

7.5kW 7.5kW
iron
amor

5 kW 7.5 kW 7.5 kW
cable
cable

c)
b)
a)
Comparison of measured losses of a) three-level line converter with iron core or
amorphous core filter inductor, b) three-level customer-end inverters (CEIs) with iron core
or amorphous core filter inductor, and c) total losses of bipolar symmetrically loaded LVDC
system with and without 200 m long 16 mm2 cable

Fig. 1

1

Resonant controller based control structure

200

100

-100

-200
-200

Double DQ based control structure

0.12
T ransformer
IGBT conduction

0.75

0

0.2

0.4

0.6

0.8

i [A]
E

-200

0

200

iD [A]

200

100

0.04

0

-100

-200
-200

-100

0

1

0

0.2

0.4
0.6
Power Output, pu

0.8

100

1

Fig. 6

150

100
80
1500

60
40

1000
20

fsw [kHz]

Fig. 4

500

Fault identification as a part of CEI control
and short-circuit current control methods.

Next Steps

200
Ctot,min [€]

0
iD [A]

0.02

– Modular customer-end inverter (CEI) that utilises
several inverter modules of small nominal power
– Life-cycle cost minimsation as converter design
methodology

Principle of modular converter

-100

Phase based DQ control structure
0.06

Development of modular converter solution

Fig. 3

100

0

-200

b)
a)
a) Measured and modelled two-level CEI efficiency curves with different loads and
respective DC supply voltage drops, and b) respective distribution of power losses.

i.e. 440 VDC

200

100

0.08

Power Output, pu

Fig. 2

100

-100

i [A]
E

CEI#3
CEI#1
LAB
780V
700V
755V
Constant 610V
Worst Unbalance

0.8

0.7

Power losses, pu

Power losses, pu

0.85

0
D

200

IGBT switching
0.9

-100

i [A]

LC filter
0.1

0.95

0

E

250

92

i [A]

Power loss [W]

Power loss [W]

amorphous core

96

converter losses
400

filter losses

Efficiency [%]

350

UDC [V]

Lifetime costs for optimal filters
w.r.t. intermediate DC voltage
and switching frequency

– Converter control methods for reducing DC current
fluctuation and voltage unbalance to minimise the LVDC
system losses
– Design of galvanic isolating DC/DC converter to enabling
optimal power density and to reduce losses and volume
of modular CEI
– Connection and control strategies for interconnecting
electrical energy storages in LVDC system
– New EMI measurements both at laboratory and at realnetwork research platform with different rectifier and
CEI solutions
– Verification of results by comparing laboratory and realnetwork results
– Providing input for standardisation of LVDC systems

SGEM unconference 24.-25.10.2013, Grid Planning and Solutions / Microgrids and DER

200

LUT Suur-Savon Sähkö LVDC Field Test Setup
- T2.4 LVDC Research Pla
atforms and Field Tests Juha Lohjala
Pasi Nuutinen, Andrey Lana, Antti Pinomaa, Pasi
Mika Matikainen, Arto Nieminen
Suur-Savon
Peltoniemi,
Peltoniemi Tero Kaipia Aleksi Mattsson Jarmo Partanen Suur Savon Sähkö Oy
Kaipia,
Mattsson,
Järvi-Suomen E
Jä i S
Energia O
i Oy

Experiences

Introduction
The first implementation of modern LVDC
distribution and CEI based supply in a
continuous use by the DSO since 6/2012
‰ Test setup of utility grid LVDC
distribution with real customers for
ƒ
ƒ

verification of the LVDC technology
related —Grid functionalities

ƒ
ƒ
ƒ

Bidirectional grid-tie rectifying converters
1,7 km of DC cable
Three 16 kVA three-phase CEIs that supply
four customers

‰ The setup is located in Suur-Savon
Sähkö s
Sähkö’s network in Suomenniemi and it
consists of:

‰ The system is reliable in different weather
conditions
ƒ

Back-up supply has been used only once

‰ All special situations have been managed as
i l it ti
h
b
d
planned
‰ The quality of supply has been high
‰ There have been no customer complaints
‰ Control strategies will be studied and developed
to enable more advanced customer-end power
control and other —Grid functionalities
As a result, the first implementation of the utility
s
gr LVDC distribution has been successful
rid

CEI #3

Connected to +DC

CEI #2

±750 VDC

Connected to +DC

CEI #1

Connected to –DC
200 m

Fig. 1

LVDC distribution network field test setup.

Fi 2
ig.

Various measurements in progress.

(a) DC supply voltage of CEI #1.

(c) DC supply voltage of CEI #3.

(b) Phase a voltage of CEI #1.

(d) Phase a voltage of CEI #3.

Fig. 3. Customer-end phase a voltages and DC voltages at CEI #1 (-DC pole) and CEI #3 (+DC pole) during
climatic overvoltage followed by HSAR. The data is recorded automatically and presented in the web portal.


Grid planning and solutions, —Grid and DER
G

T2.4 LVDC Research Pla
atforms and Field Tests
Pasi Nuutinen, Andrey Lana, Antti
Pinomaa, Pasi Peltoniemi, Tero Kaipia,
Aleksi Mattsson, Jarmo Partanen

Juha Lohjala
Tommi Lähdeaho,
Tomi Hakala
Suur-Savo Sähkö Oy
on
Mika Matikaine Arto Nieminen
en,
Elenia Oy
Järvi-Suom Energia Oy
men

‰

Introduction
Task 2.4 focuses on
‰ development and realisation of both
laboratory and field environment
research setups for LVDC technology
The objective of the task is
‰ to provide research environments for
developing, testing and validating
concepts, technology and software for
the LVDC systems
‰ to gather and report valuable practical
experiences from actual distribution
network environment

Description of the work
LUT Suur-Savon Sähkö field setup
(more detailed info in separate poster)
‰ 1.7 km bipolar LVDC network with three
customer-end inverters (CEIs) installed
in Suomenniemi (Fig. 1)
‰ Technical test setup of utility grid LVDC
distribution
‰ Operational since 6/2012

CEI #3

Connected to +DC

Reijo Komsi
ABB Oy Drives

Supervision and development of
system using online measurements
and data logging

Next steps
LUT laboratory
‰ Three-phase modular CEI structure
‰ Galvanic isolation with high-frequency
transformer (isolating DC/DC
converter)
LUT Suur-Savon Sähkö field setup
‰ Initial start-up of grid-tie rectifying
converter capable of bidirectional
power flow
‰ Battery energy storage (BESS)
connection to DC network
‰ Power flow regulation and customerend load control
‰ Possible PV power plant planning and
installation
ABB Elenia
‰ Realisation and start-up of point-topoint LVDC network (Fig. 2)
‰ Gathering experiences from the
LVDC system
‰ Development of concept using online
measurements

CEI #2

±750 VDC

Connected to +DC

CEI #1

Connected to –DC
200 m

Fig. 1. LUT Suur-Savon Sähkö LVDC field setup.


Fig. 2. ABB Elenia point-to-point LVDC network.

Grid planning and solutions, —Grid and DER
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