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All SGEM Posters presented in the internal seminar "Unconference 2012"
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profiling
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
SGEM unconference 24-25.2013, Vision SGEM
In Germany 34 GW of photovoltaic cells have been
installed,+ 7 GW/a .
Security of supply, self-sufficiency
The Future Electricity Markets and
New Sources of FlexibilityThemes: SGEM Vision, Demand Response
Koreneff, Göran; Kiviluoma, Juha; Similä, Lassi; Forsström, Juha
VTT Technical Research Centre of Finland
Objectives
We study the European electricity marketdevelopment to 2020 and 2035 and howactive resources and increasing variablepower production fit in.
Price scenarios for the futureelectricity markets
The value of DR indicates futurebusiness potential for flexibility
With only a small amount of DR*, itsvalue is considerable, but it decreasesrapidly with increasing penetration.*) The Demand Response analysed here had relatively high marginal cost (80-150 €/MWh) and was not able toshift demand in time. The results from a study are based on a unit commitment and dispatch model WILMAR.
Capacity mechanisms needed forflexibility and resource adequacy?
Next steps in SGEM WT 7.2
Analysis of integrated European powermarkets, variable generation, flexibilityand the value of DER.
We need input from the themes SGEMVision, DR, and on development ofdistributed generation capacity.
SGEM unconference 24.-25.10.2013
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 onNREAP:s and on the most recent Finnish energy strategy update material in 2013.
The future demand affects the marketprice as well as the, especially nuclearand RES-E, capacity development.
We have assessed power market pricereactions to the EU’s energy marketintegration, climate change mitigation,energy efficiency and RES deploymentpolicies to 2020 and beyond.
The shale gas revolution has deeplyaffected also EU electricity market: fossilfuel prices are lower and coal is back inbusiness. Will this last?
We have reviewed
different capacity
mechanisms and
their characteristics
from a SGEM
perspective.
An intense debate on capacity
mechanisms in the EU in general and
especially in DE, FR, and GB is ongoing.
So
urc
e:D
eV
ries
(20
04
)
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 workWireless 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
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
Jukka Lassila
LUT
050 537 3636
Antti Rautiainen
TUT
040 849 0916
Stefan Forsström
VES
050 408 5679
Matti Lehtonen
Aalto
040 581 5726
Taavi Hirvonen
Elektrobit
040 3443462
SGEM unconference 24.-25.10.2013, Grid Planning&Solutions, Smart Grid ICT Architectures
© Olli Pihlajamaa
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?
Coverage and Redundancy Calculations 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 fine-
tuned 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.
Contacts: seppo.horsmanheimo@vtt.fi, jyrki.penttonen@violasystems.com
antti.kostiainen@fi.abb.com
Storm Fault Analysis 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.
Findings 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.
Mo
dellin
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Fin
e t
un
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F
au
lt a
naly
sis
Sto
rm s
imu
lati
on
Muokkaa
perustyylejä osoitt.
Muokkaa tekstin perustyylejä
osoittamalla
–� toinen taso
•� kolmas taso
–� neljäs taso
»� viides taso
www.cwc.oulu.fi
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.�
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).
NEXT STEPS
Similar studies conducted using WSN only (IEEE Std 802.15.4k) low-
energy 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.
0,1
1
10
100
1000
10000
BG traffic SG (ADR 20 %)
and BG traffic
SG (ADR 60 %)
and BG traffic
SG (ADR 100 %)
and BG traffic
Aver
age
load [
kB/s]
ADR traffic volume
Total BG traffic
Video Conference
Voice
SG case 2 (DL)
Streaming
SG case 1 (DL)
FTP
HTTPSG case 1 (UL)
SG case 2 (UL), case 3 (UL/DL)
Schematic cellular LTE
network
Hybrid sensor-LTE Network
LTE only network
Connectivity via cellular LTE
Connectivity via WSN
Fig. 2. Average LTE loads of SG and BG traffic components.
LTE network: The SG traf�c UL delay is 36 – 722 ms; DL delay is extremely low, 2 ms
Packet delivery ratio (PDR) above quality of service QoS requirement for SG traffic (>99%)
Notable increase in delay and decrease in the PDRs of the BG traf�c
components (SG Case 2 and 3)
Hybrid sensor-LTE network: The SG traf�c delay is 7 – 24 ms, approximately 20 ms
for UL and 10 ms for DL
PDR above QoS requirement for SG traffic (>99%) (SG Case 1 and 2)
PDR of most SF traffic components below QoS requirement (>99%) (SG Case 3)
PEAK LOADS, (PACKET DELIVERY RATIOS IN PERCENTAGES) AND AVERAGE VALUES OF THE NETWORK DELAYS IN SECONDS
Traffic component (peak load) BG traffic SG (ADR 20 %) andBG trafficSG case 1, case 2, case 3
SG (ADR 60 %) andBG trafficSG case 1, case 2, case 3
SG (ADR 100 %) andBG trafficSG case 1, case 2, case 3
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)
- HYB: (99.5), (99.4), (99.9)LTE: (100), (100), (100)HYB: 0.019, 0.019, 0.02LTE: 0.108, 0.068, 0.036
HYB: (99.8), (99.6), (99.1)LTE: (99.9), (99.9), (99.9)HYB: 0.019, 0.020, 0.021LTE: 0.097, 0.21, 0.208
HYB: (99.7), (99.3), (94.8)LTE: (99.9), (99.5), (99)HYB: 0.020, 0.023, 0.024LTE: 0.111, 0.485, 0.722
ADR control and AMR (DL)SG case 1 ( 0.75 kB/s, 1.05 kB/s, 1.25 kB/s)SG case 2 ( 1.75 kB/s, 3.15 kB/s, 4.85 kB/s)SG case 3 ( 15.25 kB/s, 45.25 kB/s, 75.25 kB/s)
- HYB: (100), (99.9), (98.6)LTE: (100), (100), (100)HYB: 0.010, 0.009, 0.007LTE: 0.002, 0.002, 0.002
HYB: (100), (99.8), (98.4)LTE: (100), (100), (100)HYB: 0.009, 0.01, 0.009LTE: 0.002, 0.002, 0.002
HYB: (99.9), (99.4), (96.6)LTE: (100), (100), (100)HYB: 0.008, 0.011, 0.014LTE: 0.002, 0.002, 0.002
Voice (51.84 kB/s) (99.8)0.073
HYB:(99.9),(99.5),(99.7)LTE: (99.8), (99.8), (99.3)HYB: 0.074, 0.077, 0.075LTE: 0.073, 0.074, 0.075
HYB: (99.8), (99.8), (99.6)LTE: (99.9), (99.8), (99.8)HYB: 0.075, 0.076, 0.075LTE: 0.074, 0.075, 0.076
HYB: (99.9), (99.4), (99.9)LTE: (99.8), (99.7), (99.8)HYB: 0.074, 0.075, 0.074LTE: 0.076, 0.076, 0.075
Video conference ( 1,66 MB/s) (90.6)0.086
HYB: (91.3), (91.1), (91.2)LTE: (90.8), (90.2), (89.7)HYB: 0.083, 0.082, 0.077LTE: 0.077, 0.084, 0.091
HYB:(90.9), (91.4), (91.1)LTE: (90.4), (89), (88.5)HYB: 0.081, 0.078, 0.08LTE: 0.08, 0.095, 0.115
HYB: (91.1), (90.7), (91.1)LTE: (90.1), (88.4), (87.9)HYB: 0.082, 0.082, 0.084LTE: 0.094, 0.106, 0.137
Streaming (0.53 MB/s) (100)0.002
HYB: (100), (100), (100)LTE: (100), (100), (100)HYB: 0.002, 0.002, 0.002LTE: 0.002, 0.002, 0.002
HYB:(100), (100), (100)LTE: (100), (100), (100)HYB: 0.002, 0.002, 0.002LTE: 0.002, 0.002, 0.002
HYB: (100), (100), (100)LTE: (100), (100), (100)HYB: 0.002, 0.002, 0.002LTE: 0.002, 0.002, 0.002
HTTP (0.22 MB/s) (99.2)0.496
HYB: (99.3), (99.2), (99.2)LTE: (99.2), (99), (98.9)HYB: 0.503, 0.503, 0.534LTE: 0.514, 0.575, 0.618
HYB:(99.3), (99), (99.3)LTE: (99), (98.4), (97.9)HYB: 0.539, 0.551, 0.521LTE: 0.628, 0.766, 0.89
HYB: (99.2), (98.9), (99)LTE: (99), (97.6), (97.1)HYB: 0.519, 0.566, 0.588LTE: 0.59, 0.941, 1.137
FTP ( 10.68 MB/s) (94.8)47.34
HYB: (94.3), (93.6), (93.7)LTE: (94.3), (92.3), (91.6)HYB: 46.97, 48.7, 47.43LTE: 50.62, 52.60, 60.68
HYB:(93.7), (92.7), (92.8)LTE: (92.2), (88.1), (84.8)HYB: 48.28, 51.43, 50.94LTE: 60.84, 72.97, 80.84
HYB: (93.4), (91.9), (91.7)LTE: (91.6), (81), (77.7)HYB: 49.79, 52.86, 55.88LTE: 54.15, 86.17, 105.91
�
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.
DESCRIPTION OF THE WORK
Three automatic demand response (ADR) simulation scenarios
•� Spot pricing and direct load balancing (SG Case 1)
•� ADR generation interval: 4 s uplink (UL), 5 min downlink (DL)
•� Load balancing with local energy generation (SG Case 2)
•� ADR generation interval: 1 s (UL), 30 s (DL)
•� High-intensity load balancing (SG Case 3)
•� ADR generation interval: 1 s (UL), 1 s (DL)
20, 60, or 100 % of RTUs participate in ADR
All remote terminal units (RTUs) participate also in automatic meter reading
(AMR). Public LTE carries typical busy hour traffic as background (BG)
traffic.
SG traffic delivered in hybrid network causes less harm to BG traffic components than LTE only network.
E: (94.3), (92.3), ((((91.6)B: 4 333E: 50
), ((( ))), ((B: 46.97, 48.7, 47.4444433333: 50.62, 52.60, 60.68
E: (92.2), (88.1), (((84.8)B: 4 444E
), ((( ))), ((B: 48.28, 51.43, 50.9999944444: 60 84 72 97 80 84
E: (91.6), (81) ((777777.7))YB 888E: 9911111
1.6), (((888111))), (((((7777 7YB: 49.79, 52.86, 55.888888888E: 54 15 86.17, 105.99991111.68 LTEE: 60 8L LTE4, 72.97, 80.84 E: 5454.15, 86.17LTEE: 6060.8L
CLH
Enabling Grid TechnologiesTheme: 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)
ObjectivesWhat are the technologies and infrastructures for enabling the active distribution network management?
Bring new improved solutions for acquiring measurements, handling the communication of themeasurements, and processing the data in distributed environment in the substation.
Main achievements
Next stepsFault 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
SGEM unconference 24.-25.10.2013
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
Centralized Protection
• Utilizing IEC 61850-9-2 process bus
• Tested in RTDS laboratory of TUT
• High Impedance faults of 100kOhm were
detectable
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
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
Secondary substation monitoring device
• Capable of detecting Partial Discharges
• PD signals up to 2 MHz can be
successfully captured
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
Conclusions
� We used wireless summation of three-phase currentfor earth fault detection
� Earth faults up to 330 � were detectable
� Lowest tripping threshold was 5 A
� Energy harvesting was not yet adequate, but will beimproved in the next generation devices
IntroductionWe present field test performance of low-cost wireless currentsensors, which harvest power from the lines. Handmade unitprice was $75 excluding the enclosures. Three sensors measurecurrent of each phase in a 20 kV power line. They aresynchronized by radio and then locked in to 50 Hz, which enablessum current calculation. Current is measured with induction coils.
In unearthed and in compensated networks, detection of faultsusing sum current is useful, since the earth fault current is oftensmaller than the load current. Typical fault detectors rely onsensing dynamic phenomena on earth faults. With sum currentmeasurement, one can set a fixed threshold instead of a dynamicone. See concept in Figure 1.
Minimum tripping threshold was found to be 5 A based on thehealthy state variation of the sum current. See Figure 4. Therecorded earth faults with resistances of 0…330 � were abovethis threshold.
The detectors harvest energy from the line with currenttransformers. We observed charging of the batteries when thedetectors were set in a low power mode, but the consumption inmeasurement mode exceeded the harvested power.
VTT TECHNICAL RESEARCH CENTRE OF FINLANDwww.vtt.fi
Figure 1. Concept of the system.
Field test performanceThe detectors were field tested in Masala, Kirkkonummi,Finland in April 2012. The field test was arranged by ABB andFortum. Figure 2 shows the three detectors at the test site.Figure 3 shows the measured waveforms (DUT) and thesubstation waveforms (Ref.) during four induced earth faults.The fault resistances were 0, 150, 330 and 5000 �, and thefaults lasted for 400 ms. The waveforms match closely.
Figure 2. Detectors installed.
Figure 3. Measured and reference waveforms during faults.
Figure 4. Variation of measured sum current in a healthy state.
This work was funded by CLEEN/SGEM program of TEKES –the Finnish funding Agency for Technology and Innovation.
Self Healing City Networks
Osmo Siirto Matti Lehtonen Jukka Kuru
Helen Electricity Network Ltd. Aalto University Tekla Oy
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.
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
Optimated Distribution Automation strategy for urban networks
CITY – FLIR: Automatic fault location, fault isolation and supply restoration for urban power distribution networks
Fault Management logic (FM) ready
Next steps
Implementation of Fault Management logic into CITY-FLIR, proof of concept
Low level fault indications
Finalisation of Self Healing City Networks Study
SGEM unconference 24.-25.10.2013, Theme Active Network Management
100 % automation
k=
1
Select the optimumnumber of k for Feederj
k=
2k
= n
RTURTURTURTU RTU
NORTU RTU RTU RTU RTURTU RTU
RTURTURTU
RTURTU
…
Large ScaLarge ScaTheme: Grid PlanTheme: Grid Plan
Juha Haakana Tommi LähdeahoLUT Tomi Hakala
Elenia
ObjectivesThe aims of task 2.3 include the development of the cable network pconstruction, quality control and condition assessment processes as well as a cost-assessment processes as well as a cost-efficient cabling concept. Main achievementsMethod for cost-efficient undergroundMethod for cost-efficient undergroundcabling in rural area networksBackground:• New Electricity Market Act (588/2013) came• 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 • � Major-disturbance-proofness has to be
improvedimproved
2008
2008
0007
2008
2008
0007
90 %
100 %
00180036
2002
0101
0014
0010
0028
0029
00180036
2002
0101
0014
0010
0028
0029Cabling rate in
Cabling rate in
60 %
70 %
80 %
MV
net
wo
rk Urban area distribution companies
0799
0733
0042
2007
2007
0098
0058
2007
0107
0134
0102
0106
0080
0092
0024 0028
0040
0041
0043
0045
2001
0795
0798
0077
20070079
0097 0797
07930777
0799
0733
0042
2007
2007
0098
0058
2007
0107
0134
0102
0106
0080
0092
0024 0028
0040
0041
0043
0045
2001
0795
0798
0077
20070079
0097 0797
07930777
LV network MV network
30 %
40 %
50 %
ablin
g r
ate
in M
Rural area distribution companies0734
03070657
0786
0776
0427
0776
0661
0759
02900783
0178
0152
0657
0195
2005
0733
0134
0135
0140
0141
0212
0208
2006
02290233
03172000
021207740212
0244
0272
0374
0399
0791
0733
0733 0206
0789
073302310314
0320
03300784
04710477
0479
0793
0794
2003
0785
0734
0733
0796
0778
04330471
0734
03070657
0786
0776
0427
0776
0661
0759
02900783
0178
0152
0657
0195
2005
0733
0134
0135
0140
0141
0212
0208
2006
02290233
03172000
021207740212
0244
0272
0374
0399
0791
0733
0733 0206
0789
073302310314
0320
03300784
04710477
0479
0793
0794
2003
0785
0734
0733
0796
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04330471
0 %
10 %
20 %
Ca companies
C l i
2005
0538
0659
0790
2005
0538
0659
0790
������� ������ ���� ������ ���� ���
0 %
0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %
Cabling rate in LV network
Conclusions:• Use of cheap ploughing techniques is p p g g q
necessary• LV cabling is more economical compared with• 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 thewithout full scale cabling => focus the investments on the most cost-efficient targets
S t diti l h d li b– Some traditional overhead lines can be withstood in the network
– Most suitable sections can be selected for underground cablingunderground cabling
���������� ��
le Cablingle Cablingning & Solutionsning & Solutions
Kimmo Kauhaniemi Pertti PakonenUVA Bashir Siddiqui
TUT
Cable construction processP l f i d bli• Proposal for a re-engineered cabling process
• Proposal for implementation of commissioning testscommissioning tests– Insulation resistance (IR) measurement
Sheath integrity (SI) measurement– Sheath integrity (SI) measurement– Partial discharge (PD) measurement (on-line
ff li ) d di bl i iti tior off-line) depending on cable prioritization• Proposal for p
documentation of commissioning tests, g ,minimum requirements– Measuring system– Measuring system– Test voltages and insulation resistances or
PD magnitudes and background noise levelsPD magnitudes and background noise levels– PRPD patterns and PD locations, for off-line
t l PD i ti dmeasurements also PD inception and extinction voltages
Next stepsProposals and demonstrations forProposals and demonstrations for commissioning and condition monitoring together with related data managementtogether with related data 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 ActStudy of effects of New Electricity Market Act on required cabling rates.
�����������������
Smart GridSmart Grid Theme: Active Network aTheme: Active Network a
Kimmo KauhaniemiSampo Voima
Hannu LaaksoneJani Valtari ErSampo Voima
UVAJani Valtari, Er
AB
ObjectivesNew Smart Grid protection concepts and methods are developed in tasks 6.5 and p2.3 for taking care of changing states of active network improving fault detectionactive network, improving fault detection sensitivity and managing earth faults in cabled networkscabled networks. Main achievementsMain achievements
Demonstration and evaluation of theDemonstration and evaluation of the indication of high-resistance earth faults including faulty phase selectionincluding faulty phase selection• Testing the indication method implemented
in the centralized protection system (CPS)in the centralized protection system (CPS) with different types of compensation
ti f th f lt t (D6 5 16)practices of earth fault current (D6.5.16)– Faults detectable up to 100kΩ
• Methods for reliable detection of cross-country faults with CPScountry faults with CPS
RTDS test environment for CPS Calculated fault resistances for faulty and healthy feeder with distributed and centralized compensation (Hedekas).
RealRF
Calc. RF
PH1 Change in neutral voltage and sum
current, phase angles of Phasors 1 and 3
RF/kΩΩΩΩ RF/kΩΩΩΩ ΔΔΔΔU0/V ΔΔΔΔI0/A PH1/°°°° PH3/°°°°
1 1 005 1782 69 5 440 113 799 93 4601 1.005 1782.69 5.440 113.799 -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 91 538 0 296 118 187 120 12420 21.115 91.538 0.296 118.187 -120.124
30 31.863 59.173 0.194 120.266 -118.582
50 50.191 41.375 0.127 115.948 -123.159
L f i t ti t di
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 • A novel network information system basedA novel network information system based
LOM risk management concept is developed (D6 5 19)developed (D6.5.19)
• The interactions between LOM protection and FRT requirements were studiedand FRT requirements were studied thoroughly (D5.1.22)
Earth faults in large scale cabled rural networks �� networks
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Results from task 2.3:Fault current as a function of f lt l ti (f 1 f 5 d f 9)
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fault location (fp1, fp5 and fp9) with different compensation methods.
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ProtectionProtectionand System Managementand System Managementen, Ari Wahlrooskka Kettunen
Ari NikanderOntrei Raipalakka Kettunen
BOntrei Raipala
TUT
Adaptive protection conceptP t ti t t d t t thProtection system must adapt to the changes in network configuration and g gstate of distributed generators by• changing relay settings• changing relay settings• enabling or disabling specific protection
functions.G
Overcurrent
IfDG
Directional OC
Ifsupporting networkIfsupporting network
GGDistance
IfDG IfDG
Practical Demonstration of Adaptive pProtection and Microgrid Control in Hailuoto PilotHailuoto Pilot
Active management functionalities� Centralized adaptive protection system � Protection settings changing based on� Protection settings changing based on
microgrid topology i.e. 1) Grid connected no DG 2) Grid connectedconnected no DG, 2) Grid connected with DG, 3) SCADA command (intentional islanding) 4) Black start(intentional islanding), 4) Black-start (unintentional islanding), 5) Islanded
tioperation� Transition between grid connected and g
island operation modes
Next stepsNext steps• Adaptive protection concept will be further
developed and tested.• Suitable earth fault protection methods forSuitable earth fault protection methods for
cabled networks will be studied• Realization of new implementation of• Realization of new implementation of
centralized protection• Field tests of earth faults in compensated
network�����������������
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
SGEM (Un)Conference 24.-25.10.2013
DC-motorThyristor rectifier
DC-linkgenerator Wind turbine
frequency converter
transformer
RTDS
dSPACE controlling
grid emulator
PCs controlling dSPACEs
PC controlling RTDS
Grid emulator
dSPACE controlling
wind turbine
Controller tuning
-40
-30
-20
-10
0
10
Mag
nitu
de
(dB
)
102
103
-540
-360
-180
0
180
Pha
se
(deg
)
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)
Frequency (Hz)
closed
open
-40
-30
-20
-10
0
10
Mag
nitu
de
(dB
)
102
103
-540
-360
-180
0
180
Pha
se
(deg
)
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)
Frequency (Hz)
closed
open
-40
-30
-20
-10
0
10
Ma
gn
itude
(dB
)
102
103
-540
-360
-180
0
180
Ph
as
e(d
eg)
Wind turbine in RTDS grid, V-control, d-channel
Bode Diagram
Gm = 7.66 dB (at 797 Hz) , Pm = 103 deg (at 114 Hz)
Frequency (Hz)
closed
open
-40
-30
-20
-10
0
10
Mag
nitu
de
(dB
)
Wind turbine in RTDS grid, V-control, q-channel
Bode Diagram
Gm = 8.92 dB (at 800 Hz), Pm = 99 deg (at 107 Hz)
Frequency (Hz)
102
103
-540
-360
-180
0
180
Pha
se
(deg
)
closed
open
Performance of grid emulator
101
102
103
-20
-10
0
10
Frequency [Hz]
Gain
[dB
]
Open loop - resistive load
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
101
102
103
-20
-10
0
10
X: 164.1
Y: -3.016
Frequency [Hz]
Gain
[dB
]
closed loop V-control - resistive load
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
101
102
103
-20
-10
0
10
X: 184.1
Y: -3.005
Frequency [Hz]
Gain
[dB
]
closed loop VC-control - resistive load
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
101
102
103
-20
-10
0
10
Frequency [Hz]
Gain
[dB
]
Open loop - wind turbine connected to RTDS grid
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
101
102
103
-20
-10
0
10
X: 170.5
Y: -3.041
Frequency [Hz]
Gain
[dB
]
closed loop V-control - wind turbine connected to RTDS grid
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
101
102
103
-20
-10
0
10
Frequency [Hz]
Gain
[dB
]
closed loop VC-control - wind turbine connected to RTDS grid
X: 185.8
Y: -3.05
101
102
103
-200
-100
0
100
200
Frequency [Hz]
phase
[deg]
positive sequence
negative sequence
Resistive load 2kW
Wind turbine connected to
the grid modelled in RTDS
• Wind speed 12 m/s
Conclusion
• 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
• 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)
Future work
• 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.
Network model in RTDSIntroduction
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
SummaryAccurate load models for different time horizons are developedin collaboration to enable smart grids and energy markets.
BackgroundSmart grids are all about distribution side networks and
customers becoming active and smart and thus helping tomanage the expected massive changes in power generation(more distributed, more renewables, more intermittency, etc.).
The customer side is also experiencing significant changessuch as heat pumps, electric vehicles, micro-CHP, PV, anddynamic demand response. Thus it is more and moreimportant and challenging to model and forecast the loadsaccurately.
Meanwhile the amount and quality of information availablefor load modelling improves rapidly. For example, hourlymetered consumption of practically every customer is inFinland available by 2014 due to new technology andlegislation.
Putting new meters to good useWe are developing and testing new ways to cluster
customers into new and automatic groups, which has profoundadvantages over traditional load profiles (46 customer types)that hitherto have been in use.
Divide and uniteEspecially household loads are difficult to model and
forecast, because they are the sum of many sub loads, whereofsome are large and distinct, e.g. electric heating. Theseessential, distinct, large sub load types will increase in number,all having different dependencies. An alternative modellingapproach is based on sub-load types instead of customertypes.
Integration of data and modelsData from different sources is used for estimating loads. For
example, income taxation statistics can be combined withshare of single family houses to estimate the introduction ofelectric vehicles in a network area.
Dynamic load response modelsLoad responses to control actions are modelled based on
measurements from substations and smart meters, andweather and building data.
What is going on nowThere are different purposes and approaches for load
modelling. They can be combined and compared. Short termforecasting 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.
More InformationPekka Koponen, VTT ( pekka.koponen@vtt.fi )Göran Koreneff, VTT ( goran.koreneff@vtt.fi )Harri Niska, UEF ( harri.niska@uef.fi )Antti Mutanen, TUT ( antti.mutanen@tut.fi )
Methods for load modelling
2 4 6 8 10 12 14 16 18 20 22 24
1
2
3
4
5
6
7
8
9
10
Hour
Po
we
r[k
W]
M-Fri
Saturday
Sunday
10 20 30 40 50 60 70
0.05
0.1
0.15
0.2
0.25
0.3 1
2
3
4
5
-20 -15 -10 -5 0 5 10 15 20 250
2
4
6
8
10
12
14
[oC]
[kW
]
-20 -15 -10 -5 0 5 10 15 20 250
0.5
1
1.5
2
2.5
3
3.5
4
4.5
[oC]
[kW
]
measurements + initial information
=> model
=> estimation, prediction and optimization
Field tests for response modelling, an example
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.
CLEEN Summit, 11-12 June 2013
Demand Response Event Flow in a distributed market environment
Theme: Demand Response
ObjectivesDescribe which electricity marketinformation systems are active in DRactions initiated by active customer orelectricity supplierDescribe selected event flows which startwhen supplier or active customer decideto execute DR operation
Main achievementsEvent flow defined and describedincluding actions for both active customerand supplierSpecial focus has been set on interactionbetween supplier’s DR tools (EDMbased) and active customer energy portal
Next stepsNeeded DR operations will beimplemented and integrated betweenenergy portal and EDM system
Whole information chain and event flowfrom energy portal to customers site willbe implemented and tested in OulunEnergia active customer pilotenvironment
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Pekka A PietiläEmpower IM Oy
Mikko RasiOulun Energia Oy
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Theme: Demand response
Contact person: Samuli Honkapuro, LUT (samuli.honkapuro@lut.fi)
Objectives
The objective of the research is to findout what kinds of business, pricing, andmarket models provide the highestbenefits of the smart grid technology fordifferent stakeholders.
One of the key elements in theseanalyses, which combine the technicaland business research, is the big pictureconcerning the holistic impacts of marketplayer actions. The (simplified) picturebelow illustrates these actions andimpacts. Studied issues include:
• The business and pricing models of theDSO, retailer, and aggregator
• Conflict of interest between the marketplayers
• Demand response and customer behavior
• Smart metering and energy managementservices
Main achievements
The research concerning businessimpacts described here is mainly carriedout in WP 7. However, business impactscannot be analyzed without consideringtechnological development and practicalimplications. Thus, the cooperationbetween the different themes and WPsinside SGEM, as well as collaborationbetween research and industrialorganizations, have been utmostimportant for this research work. Forinstance, the impacts and possibilities ofthe demand response are being studiedfrom technological, economical, andsocietal perspectives. This is done bylaboratory demonstrations, piloting,analyzing real-life data, and byconducting customer surveys andinterviews. This kind of research workcould not be done without SGEMcollaboration.
SGEM unconference 24.-25.10.2013
Incentives for
customer to optimize
the energy usage
Total demand
of energy and
power
Peak demand
Network
losses
Investment
needs
Metering
and billing
DSO’s revenue
stream
Operational
expenses
Capital
expenses
DSO’s
revenue
demand
DSO business
model
Retailer’s
revenue stream
Retail
tariffs
Retailer’s
business model
Retailer’s
electricity
purchase costs
DSO tariffs
Retailer’s revenue
demand
Accuracy of
load forecast
TaxesTSO tariff
Electricity
wholesale price
DISTRIBUTION SYSTEM
OPERATOR (DSO)CUSTOMER
RETAILER /
AGGREGATORSTATE
TRANSMISSION SYSTEM
OPERATOR (TSO)
Monopoly
regulation
ECOSYSTEMS FOR DEMAND RESPONSE
SGEM unconference 24.-25.10.2013
Next steps
We are going to study the businessecosystems of several different DRprograms and strive for identifying the keyobstacles hindering the development ofthriving DR businesses. We see crucial theidentification of the key elements and theirexplicit locations in the ecosystem as wellas detecting the ways to overcome the keyobstacles to bring about the DR businessesto boom. This work will be supported withbusiness model examinations.
Main achievements
Based on earlier work on SGEM, we haveconsidered that a consumer may not betreated as the end customer in thisecosystem. Thus, the value proposition ofDR should be developed by considering aDSO, TSO, retailer, or even yet non-existingaggregator as the end customer in thisbusiness ecosystem. Substantial economic,environmental, and social advantages arepossible through DR utilization in thesecases. For instance, an electricity suppliercan cut its future balancing costs if loadshifting and shedding are at its disposal.
Objectives
We examine the DR business ecosystemin the smart grid environment focusing onthe liberalized Nordic electricity markets.The aim is to afford a blueprint of anecosystem to identify the problematicnodes and provide alternatives how toovercome possible obstacles in order todevelop a functioning demand responseecosystem for this field.
Joni MarkkulaTUT
+358 44 544 4448
Pertti JärventaustaTUT
+358 40 549 2384
Marko SeppänenCITER/TUT
+358 40 588 4080
Petteri BaumgartnerCITER/TUT
+358 40 516 7028
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 ExchangeTheme: Demand Response
Jan Segerstam Empower IM Oy
ObjectivesDefining 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 achievementsFirst version of load control message structure has been developed in co-operation with SGEM partners. Next stepsCollecting further requirements for the message structure as a part of piloting work with electricity suppliers and DSOs.
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Demand Response PilotsTheme: Demand Response
ObjectivesDescribing 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 achievementsProcess descriptions of linking DR utilization to supplier’s business processes in different electricity market levels.Established partner network for piloting work.
Next stepsStarting 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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Joni Aalto Tuomas Åhlman Pekka Takki Empower IM Oy Vantaan Energia Sähköverkot Oy Helsingin Energia
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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 1. Potential load alternatives
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 2. An example of realized control actions when power band control is used
Figure 3. Behaviour of loads in February 2010–2013
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.
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.
SGEM unconference 24.-25.10.2013
Effects of demand response on load profiling Theme: Demand Response
Kaisa Grip, Antti Mutanen and Pertti Järventausta
Tampere University of Technology
T k 4 4 T h i l l ti f DRTask 4.4: Technical solutions for DR,t t d ICT tcustomer gateway and ICT systems
Antti Pinomaa, Andrey Lana, Tero Kaipia, Ville Tikka, Pasi Nuutinen, Henri Makkonen, Petri Valtonen, y , p , , , ,Lappeenranta University of Technology
Marko Pikkarainen, Antti Mäkinen, Pertti Järventausta, Sami Repo,Tampere University of TechnologyMarko Pikkarainen, Antti Mäkinen, Pertti Järventausta, Sami Repo,Tampere University of TechnologyMarkku Kauppinen, Elenia
TUT smart grid laboratoryIntroductionTask 4.4 focuses on• The technical solutions, applications and ICT DMS600 SCADA
Smart grid functions DBAnalysis tools View, pp
architecture in future customer gateway relating toHEMS and AMR based systems and how they support Enterprise Service Bus
IEC61850CIM
the overall aims for demand response and networkmanagement issues
microSCADA Meter readingPrimary subst. Secondary subst.
CIMIEC61850
CIMIEC61850OPC DA OPC UA
Aggregator
COSEM
Green campus – energy management system IEC61850
microSCADA Meter readingautomation automation
DLMSHTTP HTTPIEC61850
SQL
Ethernet
Aggregator
IEDs HEMSSmart meter
Smart meter
Smart meter
Smart meter
ControlHEMS
Q
Other meas.
PQmeter
KNXThere
PMU PMU
20 kW
(in operation)
RTDSAC
microgridPV power plant
Wind turbine
EV
( p )
AC microgrid labLAN
20 kW
(components
d t b
Aggregator
L1L2L3NPE
ready to be
installed)Z-wave0 10 1
Measurements
0 1 0 10 1
30 kWh
PHEV
(6.7 kWh, G2V +
0 10 1 0 10 1
dSPACE
0 1
10 V DCMeasurements
0 1
30 kWh
(in laboratory
tests)
V2G, in operation)
BEV
(24 kWh G2V i
Connection for loads and production
(24 kWh, G2V, in
operation)
~=
~=
~=
RTDS
=~
~ CAN fieldbusgateway
SG unit
Wind turbine
EV charging
PV production 3-phase supply
Switch
Fibre optics LANN units
Info clientDHCP
Neutral fault management in LV network –RTDS simulations of AMR metersSwitch
100 Mbit / 1 Gb
Info displayRTDS simulations of AMR meters
GC servereth0, eth1, eth 2, eth 3
Services: Apache(PHP, etc.), mySQL FTP SFTP SSH
VLAN Green Campus measurements, etc.
LUT FirewallPort open:
80
CAT5 CAT5
mySQL, FTP, SFTP, SSH Samba?
CA
T5
157.24.25.240255.255.252.0157.24.24.1Measurement
unit
DR unit
VLAN staffInfo displayInfo client
SSH admin client, port 22
IP 157.24.25/26.0? 157.24.26.193SSH Admin client
Fig General concept of interactive customer gateway realized in the
LUT LAN 157.24.25.240Redirection fromwww.lut.fi/GC/...
Fig. General concept of interactive customer gateway realized in the
Green Campus environment. Schematic of GCSG information network.
SummaryDynamic market based demand response using smart meterswas developed and implemented in large scale. Demandresponse reduces costs and risks regarding prices and reliabilityof the electricity market and system.
Background and objectiveDemand side response enables smart grids, more distributedgeneration, full utilization of renewable energy sources, moreelectrical vehicles, and better security of the electricity systemand electricity market. Thus it is an essential tool for reducingemissions and costs.
Dynamic load control via smart metering systems isdeveloped to replace the traditional static time of use controlsand tariffs. In addition to market price based DemandResponse the solution developed supports many other loadcontrol needs.
Old static load control vs. the newdynamic control
Results so far (May 2013)Two smart metering system vendors have implemented thedynamic demand response operating model developed.
Electricity retailers participating control the loads based ontheir needs using the messaging developed.
Helen Electricity Network started field trials in 2010-2011. ByFebruary 2012 about 500 consumers (10 MW) were connectedand in February 2013 about 50 MW. All are full heating storagehouses.
In December 2012 dynamic load control started with about1000 consumers. Observed controlled power was about 17MW and the total power of the customers was about 20 MW.(Some non-controllable consumption and lost controlmessages.)
Vantaa Energy Electricity Networks completed tests with 1house and has started new tests. The houses have partialheating storage.
Fortum is completing a study on how the developed dynamicdemand response model fits to their smart metering system.
SGEM helps E.ON Kainuu in direct load control field tests withabout 7000 partial heating storage houses in time of usecontrol. Test planning and data analyzing and modeling.
Some field test results, full storage
Continuation and collaborationAnalyze 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 InformationPekka Takki, Helen (pekka.takki@helen.fi)Joel Seppälä, Helen Electricity Network (joel.seppala@helen.fi)Pekka Koponen, VTT (pekka.koponen@vtt.fi)
Smart Metering Based Dynamic DemandResponse
SGEM unconference, 24-25 Oct 2013and CLEEN Summit , 11-12 June 2013
DR Capacity in the Network
Results
• Redundant capacity of components inthe network proportional to DR capacitycan be mitigated.
• ABC-substations are less reliablethan ABCD-substations.
Next steps
• Investigation of different topologies forOH and UG HV network.
• Investigation of cost of voltage sags.
• The potential assessment of DR inmitigating redundant capacity of MVnetwork.
• Optimal utilization of DR in HV & MVnetworks for redundancy mitigation.
Theme: Grid Planning and Solutions
Matti Lehtonen, Muhammad Humayun, Bruno Sousa
Aalto University
Objectives
• To develop reliability analysis tools forHV Smart Grid Network.
• Redundant capacity mitigation in HVSmart Grid using demand response.
Reliability ModelsMarkov Models in presence of demand response:
Three-layer reliability model:
Test Networks
SGEM unconference 24.-25.10.2013
Spatial Load AnalysisTheme: Grid Planning and Solutions
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.
Next steps
Designing scenario models on a specifiedform.
Developing spatial data analysis.
Adding background data, e.g. city data bases, to spatial load analysis.
Modeling and forecasting electricityconsumption using socioeconomic variables(e.g. GDP).
Demonstrating the scenario tool in NIS.
SGEM unconference 24.-25.10.2013
Results 1FP…4FP
a) Spatial load forecast process outlinesfor modelling new housing and officebuilding development by the year 2030
b) Mathematical and statistical processingof AMR measurements to generate loadclasses and profiles required by loadmodels
c) Detailed analyses of energy use of service sector in Helsinki and householdswith ground source heat pumps
d) Demonstration of data processing and visualization of the monthly follow-ups of spatial electricity consumption
Daily profiles household heated with ground source heat pump
household heated with direct electricity
Spatial load forecast for city districts
Identifying spatial, monthly changes in use of electricity
d)
a)
b)
c)
-40
-30
-20
-10
0
10
20
30
40
50
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
MW
h
Households Buildings Industry Infrastructure Construction Service Street lighting Rail traffic
d)
c)
b)
a)
Identify changing
consumption
patterns:
Select electricity
consumption
scenario
Statistical Analysis of Large Scale
Wind Power GenerationTheme: Grid Planning and Solutions
M. Koivisto, J. Ekström, M. Lehtonen, L. Haarla
Aalto University School of Electrical Engineering
SGEM unconference 24.-25.10.2013
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.
Next steps
Creating different scenarios with high
altitude data
Modeling the whole wind power generation
structure of Finland
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.
The RXCFs of the data and the transformed VARX
and ARC models (averages of the 100 simulation
runs) for Vantaa and Pirkkala.
Tero Kaipia, Pasi Peltoniemi, Pasi Nuutinen, Andrey Lana, Aleksi Mattsson, Jarmo Partanen
Lappeenranta University of Technology
Jenni Rekola, Heikki Tuusa Tampere University of Technology
Introduction 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.
Next Steps – 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 real-network research platform with different rectifier and CEI solutions
– Verification of results by comparing laboratory and real-network results
– Providing input for standardisation of LVDC systems
Key Results Energy efficiency – Converter losses
– Ultimate goal to minimise converter losses – Understanding and modelling loss mechanisms based
on measurements – Comparison of measurement techniques (calorimetric/
electric) and two- and three-level converters
SGEM unconference 24.-25.10.2013, Grid Planning and Solutions / Microgrids and DER
WP 2 / Task 2.5
-20
-10
0
10
20
30
40
50
60
70
dBuA 80
0.01
MHz
10.1
Frequency
CM
curr
ent
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
Fig. 5 Measured CM current in customer-end network when CEI is operating (red) and turned off (blue).
– Disturbance levels originating from the LVDC network are low
– Converters affect mainly to the frequency spectrum of RF EMI
– CM current magnitude in customer-end network does not cause safety issues
i.e. 440 VDCi.e. 440 VDC
500
1000
1500
20
40
60
80
100
150
200
UDC
[V] fsw
[kHz]
Cto
t,m
in [€]
Development of modular converter solution – Modular customer-end inverter (CEI) that utilises
several inverter modules of small nominal power – Life-cycle cost minimsation as converter design
methodology
Fig. 3 Principle of modular converter Fig. 4 Lifetime costs for optimal filters w.r.t. intermediate DC voltage and switching frequency
0 0.2 0.4 0.6 0.8 10
0.02
0.04
0.06
0.08
0.1
0.12
Power Output, pu
Po
wer
loss
es,
pu
LC filter
Transformer
IGBT conduction
IGBT switching
-200 -100 0 100 200
-200
-100
0
100
200
i� [A]
i � [
A]
-200 -100 0 100 200
-200
-100
0
100
200
i� [A]
i � [
A]
-200 -100 0 100 200
-200
-100
0
100
200
i� [A]
i � [
A]
Resonant controller based control structure
Double DQ based control structure
Phase based DQ control structure
Adaptive converter control – Improvement of CEI control during fault situations �
identification of grid faults
Fig. 6 Fault identification as a part of CEI control and short-circuit current control methods.
Fig. 1 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
amorphous core
iron core
0
50
100
150
200
250
300
350
400
2.5kW iron
2.5kW amor
5kW iron
5kW amor
7.5kW iron
7.5kW amor
Pow
er lo
ss [W
]
0
50
100
150
200
250
300
350
400
2.5kW iron
2.5kW amor
5kW iron
5kW amor
7.5kW iron
7.5kW amor
Pow
er lo
ss [W
]
a) b)
84
86
88
90
92
94
96
2.5 kW 2.5 kW cable
5 kW 5 kW cable
7.5 kW 7.5 kW cable
Effic
ienc
y [%
]
c)
converter losses
filter losses
converter losses
filter losses
0 0.2 0.4 0.6 0.8 10.7
0.75
0.8
0.85
0.9
0.95
1
Power Output, pu
Po
wer
lo
sses
, p
u
CEI#3
CEI#1
LAB
780V
700V
755V
Constant 610V
Worst Unbalance
Fig. 2 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.
a) b)
Development of LVDC Technology
LUT & Suur-Savon Sähk- T2.4 LVDC Research Pla
Pasi Nuutinen, Andrey Lana, Antti Pinomaa, Pasi
Peltoniemi Tero Kaipia Aleksi Mattsson Jarmo Partanen
IntroductionThe first implementation of modern LVDC
distribution and CEI based supply in a
continuous use by the DSO since 6/2012
Peltoniemi, Tero Kaipia, Aleksi Mattsson, Jarmo Partanen
Lappeenranta University of Technology
�
��
� Test setup of utility grid LVDC
distribution with real customers for� verification of the LVDC technology
� related �Grid functionalities
� The setup is located in Suur-SavonSähkö’s network in Suomenniemi and it
�
���
Sähkö s network in Suomenniemi and itconsists of:
� Bidirectional grid-tie rectifying converters� 1,7 km of DC cable� Three 16 kVA three-phase CEIs that supply
four customers
As
gr
CEI #2
CEI #3
Connected to +DC
Connected to +DC
±750 VDC
200 m
CEI #1Connected to –DC
Fig. 1 LVDC distribution network field test setup. Fi
(a) DC supply voltage of CEI #1.
SGEM unconference 24.-25.10.2013 G
Fig. 3. Customer-end phase a voltages and DC voltag
climatic overvoltage followed by HSAR. The data is rec
(b) Phase a voltage of CEI #1.
ö LVDC Field Test Setupatforms and Field Tests -
Juha Lohjala
Suur Savon Sähkö OyMika Matikainen, Arto Nieminen
Jä i S E i OSuur-Savon Sähkö Oy Järvi-Suomen Energia Oy
ExperiencesThe system is reliable in different weather
conditions� Back-up supply has been used only once
All i l it ti h b dAll special situations have been managed as
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 functionalitiescontrol and other �Grid functionalities
s a result, the first implementation of the utility
rid LVDC distribution has been successful
ig. 2 Various measurements in progress.
(c) DC supply voltage of CEI #3.
Grid planning and solutions, �Grid and DER
es at CEI #1 (-DC pole) and CEI #3 (+DC pole) during
orded automatically and presented in the web portal.
(d) Phase a voltage of CEI #3.
T2.4 LVDC Research PlaJuha
Suur-Savo
Pasi Nuutinen, Andrey Lana, Antti
Pinomaa, Pasi Peltoniemi, Tero Kaipia,
Mika Matikaine
Järvi-Suom
Aleksi Mattsson, Jarmo Partanen
Lappeenranta University of Technology
Introduction
Task 2.4 focuses on
� development and realisation of both laboratory and field environmentlaboratory 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 systemsthe 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� Technical test setup of utility grid LVDCdistribution
� Operational since 6/2012
CEI #3Connected to +DC
CEI #2
Connected to +DC±750 VDC
SGEM unconference 24.-25.10.2013 G
Fig. 1. LUT & Suur-Savon Sähkö LVDC field setup.
200 m
CEI #1Connected to –DC
atforms and Field TestsTommi Lähdeaho,
Tomi Hakala
Reijo Komsi
ABB Oy Drives
Lohjala
on Sähkö Oy
� Supervision and development of system using online measurements and data logging
Elenia Oyen, Arto Nieminen
men Energia Oy
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 flowpower flow
� Battery energy storage (BESS) connection to DC network
� Power flow regulation and customer-end load control
� Possible PV power plant planning and installationinstallation
ABB & Elenia
� Realisation and start-up of point-to-point LVDC network (Fig. 2)
� Gathering experiences from the LVDC system
� Development of concept using online measurements
Grid planning and solutions, �Grid and DER
Fig. 2. ABB & Elenia point-to-point LVDC network.
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SG Monitoring and Data Utilization Theme
Heikki Paananen, Vesa Hälvä and Turo Ihonen (Elenia), Pekka Verho (TUT),
Erkki Viitala (Emtele), Antti Kostiainen (ABB)
Theme objectives
1. General concept (consisted of systems and functions) of new type of business processes and supporting functions
2. New business potential will be created for device and sensor manufacturers
Next steps
Towards automated proactive data analysis for risk mitigation and cost-efficiency
SGEM unconference 24.-25.10.2013, Theme poster
Automated
Manual
Reactive
Proactive
Fault correction
Maintenance
Work
management
Hot standby
redundancy
Man from
the street
Quality
analysis
Remote
operation Network
awareness
picture
Management
by knowledge
Inspection
Proactive
Pole
inspection
Preparedness
advice
Thermal
imaging
Operation
control
Manual risk
mitigation
Manual
recording
Manual
FLIR
Prioritize
of faults for
correction
Disturbanve
records
Disturbanve
advice
LV-alarm
Protection
FLIR Automated
disturbanve
advice Control of
repeating
reclosures
Automated
Analysis
Maintenance
measurements
Laser
measurements
Automated risk
mitigation
Ordering of
proactive
maintenance
Shared
awareness
picture
Figure: Workshop result, white spot analysis. Red areas are business potential cases.
Figures: Online transformer Remote monitoring: - Live oil quality measurement
- Long period data storing
- Analysis and anomaly alerting - Live installation running in Pirkanmaa area
Achievements
Major development paths discovered in theme workshops. New functionality needs has been defined.
Figure: Workshop result, the greatest challenges of smart secondary substation concepts
Map based view and high/detail-level status of all sites at a glance with remote control of single detectors and sensors.
Task 6.12 SG Proactive Monitoring ResultsVesa Hälvä, Turo Ihonen and Heikki Paananen (Elenia), Erkki Viitala, Ville Sallinen (Emtele), Pekka Verho (TUT)
Task objectives
1.� Proactive monitoring and awareness
2.� Improve operational efficiency
Novel System as a Data Hub
A platform for the novel functionalities (ie.
Automated uploading of disturbance records) and the data produced (ie. Automated analysis) is needed.
The functionalities could be included to excisting systems and/or to a separate dedicated system.
SGEM unconference 24.-25.10.2013, Task poster
Demo Site Built in Pinsiö 110/20kV primary substation
Various technologies utilized for •�Assuring safety and security •�Monitoring critical components •�Preventing unauthorized access
Repetitive Reclosing Analysis Target is to find incipient faults before escalating to a permanent fault.
Simplified version based on number of reclosings in each feeder during certain time period.
Work
Order
Fault
HistoryDataba
se
Fault
Location
Calculation
SCADA DMS
Ope
rator
Work Order
Management
Relay Pick-up
Circuit Breaker State Change
Fault Reactance Disturbance Record
Notifica
tion
Work
Order
Tree
Clearance
Data
Conditi
onData
Feeder
Properties
NIS
Weather
Data
Reclosi
ngAnalysi
s
More sophisticated algorithm including several external data sources.
Meter Data
ManagementSystem
eMeter EnergyIP
Network
InformationSystem
Tekla NIS
SCADA
NetcontrolNetcon3000
Distribution
ManagementSystem
Tekla DMSDa
taHu
b
Electricity Distribution
Process
FieldCom
Actions Work Order
ManagementMicrosoft
Dynamics AX
Theme microgrids and DER
Introduction
The aim is to study operational micro-grid with distributed generation, ener-gy storages and controllable loads.
Research items
• One main driver in designingmicrogrids is to increase reliability
• Integrating DER - both generationand storages - in microgridsincreases the independency
• The conceptual study includesmicrogrids generated by rotatinggenerators and power electronics,on LV and MV levels, on differentpower ranges
• Find and define necessary busi-ness models and market integ-ration model to provide furtherincentives in building microgrids
• End customers’ point of view –households’ awareness regardingsmall scale production, mainmotives and barriers?
Reference Architecture for SmartGrid in Europe
Approach and methods
The focus shall be in developing,designing and building one full scalemicrogrid, which consists of distri-buted generation, energy storagesand island grid generation with thedevices to connect/disconnect withthe fixed grid.
Consumer interest in small scaleproduction and microgrid generationis studied by polling and interviews.What are their main motives andbarriers?
SGEM unconference 24.-25.10.2013, {Microgrids and DER}
Microgrid conceptual figure
Microgrid and DER control
Hannu Laaksonen Omid Palizban Seppo Hänninen Riku Pasonen
ABB University of Vaasa VTT VTT
Introduction
Aim has been to specify the optimal control principles of DG within microgrid as well as testing and development of new passive islanding detection methods.
In addition, new microgrid concept with hybrid AC/DC system and suitable control methods has been developed.
Description of the work
Control principles of microgrids
With respect to the IEC/ISO 62264
standards, hierarchical control and
storage algorithm for microgrids is
developed as shown below:
New islanding detection method
Control development for AC/DC hybrid microgrid operation
Next steps
• Control and design principles of DGs in microgrids are further developed
• Further testing and verification of the new multi-criteria based islanding detection algorithm
• DG integration and islanding studies for AC/DC hybrid
SGEM unconference 24.-25.10.2013 Microgrids and DER
(986/1998)
Energy storages and uGrid technology concepts
Introduction
The aim is to study use of energystorages, storage technology, controlstrategies - specially in microgrids.
Description of the work
Proof of concept on using powerelectronics and batteries for powerbalancing in island grid maintainedwith distributed energy resources
Distribution network case withdifferent storage types for differentapplications
• Domestic level
• Office building level
• District level
Different control strategies
• PV output smoothing
• Economical optimization
• Local voltage control
• Local peak shaving
• Minimal grid power exchange
Next steps
� Focus on grid application approach
• Design principles and control strategiesof energy storages in microgrids
• Different storage technologies
• Forecast methods for RES generationfor storage optimization purposes
• Development and testing of storageand microgrid simulation models
� Storage integration to microgridmanagement
� Proof of concept on using powerelectronics and batteries for powerbalancing
SGEM unconference 24.-25.10.2013, {Theme: Microgrids and DER}
0 100 200 300 400 500 600 700 800 900 1000-6000
-4000
-2000
0
2000
4000
6000
Time [hours]
Pow
er
[W]
Load power
0 100 200 300 400 500 600 700 800 900 1000-6000
-4000
-2000
0
2000
4000
6000
Time [hours]
Pow
er
[W]
Power from Grid
PV gen SOC
Running averagecalculation
Derivativeformulation
Compare difference totrigger limits
Withintriggerlimits?
Maintainfor CDC
OFF-timer
Yes
CDC to ”idle”
Off-timerrunning?
No
Yes
CDC
IncreaseCDC
ON-timer
Issue CDCcontrol
No
Limitexceeded
Filtering
Comparison to powerrate of change limits
Withintriggerlimits?
Yes
Maintain CDC
No
Check withstoragestatus
OK
No
Maxgrid
Difference =generation - average
Exceedingmax gridpower?
CDC to ”charge”
Yes
No
Energy storages in system service applications
(blue boxes) and in energy management
applications (green boxes). A Eurelectric report,
2012: Decentralised storage: impact on future
distribution grids.
Kimmo Kauhaniemi
UVA
+358 44 0244283
Jukka Lassila
LUT
+358 50 5373636
Reijo Komsi
ABB
+358 50 3323224
Kari Mäki
VTT
+358 40 1429785
D 5.1.111: Suitability of PV testing methods for arctic conditions; existing methods and development needs
Atte, Löf Riku, Pasonen Rami, NiemiVTT VTT VTT
IntroductionPV in Nordic conditions and testing.What testing standards are in use and development needs to improve testing and usage of PV in Nordic countries.Progress so far• Literary review of PV testing standards and recommendations• Physics of solar modeling and key parameter differences in Nordic region• Hardware simulator environment built to test measurement algorithm
• Matlab measurement algorithm for PV testing environment
Next steps• Modify hardware simulator for
outdoor PV testing• New PV harvesting concept for
Nordic countries taking account low price of panel and of smoothing grid output
Some ideas for the PV harvesting concept:
���������� ���������������������� �����������
+ =
Bifacial panel90°inc, east-west
Normal panel 45°inc, south
D 5.3.112: AC/DC Hybrid distribution in LV MicrogridRiku, Pasonen
VTT
IntroductionDC distribution integration to LV AC system with joined neutral wire.• One wire less than in separate AC and DC systems• Capacity increase depends on asymmetry level; how much DC neutral can take -> active control needed when AC side is operational• Possibilities for AC or(and) DC microgrid islanded operation Next steps
• Research report on the conceptSimulations on microgrid operation and on selected fault scenarios
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D 5.3.115: Distributed resources and microgrids in community planningHa, Hoang Rinat, Abdurafikov Riku, Pasonen
VTT VTT VTT
Progress so far• Simulation model of DC/DC
converter with galvanic isolation (paper sent for review)
IntroductionCombined planning of Eco efficient housing and DG towards microgrids
Progress so far• Gathering information on example sites
and business models• Talks with city officials for case area• Review on standards and design
practices
Next steps• Get all available information together
and to get understanding on what are the points where different design processes must co-operate
• Still much to be done with the report and case studies
Small Scale Production & Consumers Theme: Microgrids & DER
Merja Pakkanen Maria Tuuri
University of Vaasa University of Vaasa
Objectives
Our main objective is to identify the level of awareness & interest and the main prerequisites, motives and barriers of the household customers regarding their own electricity production.
Main achievements
These results are based on 20 in-depth expert interviews, which helped us to understand the most important issues regarding small scale production.
So far, solar electricity is the most suitable production method option for the households. The most potential groups a r e t y p i c a l l y 5 0 - 6 0 y e a r s o l d , technologically oriented detached house owners. The households would mainly want to produce electricity for their own use, but they would also like to have a possibility to sell their excess electricity.
The main barriers for the households for not to purchase solar panels, are the costs being too high and the repayment period being too long. The acceptable repayment period is less than 10 years which is currently not usually achieved.
Too long repayment period is one of the most significant barriers.
The main motivating factors are possibility to save money in the long run and to decrease the dependency on the electricity company. Environmentalism is a ”nice bonus” but green values are not considered to be the main motivation for the households to produce electricity.
Easiness is key. Purchasing and installing solar panels must be simple and require as litt le bureaucracy as possible. Improved profitability is also ”a must”. Financial supports would obviously also increase the interest but the experts do not consider this being the right solution.
”Possibility to get turnkey installation” is definitely important for the households, because many of them do not have enough time, skills and interest to do everything by themselves.
Next steps The next step is to interview those consumers that already produce their own electricity in order to find out what motivated them to invest in solar panels, how did the process go, have they been satisfied with their decision etc. After that, we aim at doing a questionnaire study for the detached house owners who do not produce electricity: What is their level of awareness and interest, what would be needed in order to activate them etc.? Needed: A good channel for distributing the questionnaire for the detached house owners. Any ideas??
SGEM unconference 24.-25.10.2013
T k 6 6 A ti t k tTask 6.6: Active network management i DER d i idusing DERs and microgrids
Katja Sirviö, Kimmo Kauhaniemi, University of Vaasaj , , y fShengye Lu, Sami Repo, Tampere University of Technology
Erkki Viitala, EmteleErkki Viitala, Emtele
Evolution of LV distribution networksSummaryA t f di t ib t d LV t k t ThA concept for distributed LV network management. The
proposed architecture creates a bridge between fully
centralized automation systems like SCADA and
Intelligent Network of
centralized automation systems like SCADA and
distributed system consisting of secondary substation
automation and smart metering Self-sufficient inMicrogrid Microgrids
automation and smart metering.
Architecture
Self sufficient inSelf sufficient in Electric Energy
Architecture• Integrated automation system (no silos of systems)
T diti l• Hierarchical decentralized system
• Real-time management extended to MV and LV
Traditional
networks
• Autonomous decision making at each hierarchical
Use cases• The network normal operations and the
level
• LV network management is located at secondary
( G S )The network normal operations and the disturbance situations using UML in eachevolution phase
substation automation (INTEGRIS device, IDEV)
Balance
responsible evolution phase• Classification of the actors and class
di i l i hiDMS
MDMS CISNISSCADA NISTSO
Energy
retailer Workforce
management
system diagrams; static relationships• State diagrams of the actors to be done;
Enterprise Service Bus
Substation automation
DSO control centre
Primary substation
Aggregation
systemAMR HUB
system
all the states an actor can have in multipleuse casesSecondary substation
automation
IEDRTU
Secondary substation
automation
PMU
use cases
Smart meter
PQ IEDRTURTUSecondary
substation
Connection point
PMU
Smart meter
Mains
Home energy managementCustomer Cloud based secondary substation automation
Implementation
MeasurementsDERMains
switch
FO B B-PL CWi-Fi
CouplingImplementationSS -IDEV
odem
ode m
odem
Coupling
PC platformswitch
mo
mo
mo
C p at o
F O(ETH )
switchIntegris Communication
Functionalities
RTU Data Collector
RFIDRFID
MV/LV data Octave
User Data Collector
RTU
ZigBee
AnalogIN
Protocol Gateway
modemDB
Collector
Switch Meter Data
Collector
handlerCollector
BB -PLC
modem Smart Meter
ETH
ETH
ch
DER
Smart MeterOption 1
Option 2
HEMS
switc
Power Quality Meter
ETH
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