Posters of SGEM Unconference 2013

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.

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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: [email protected], [email protected]

[email protected]

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.

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•� 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: [email protected].�, [email protected].�

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


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

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Cabling rate in

60 %

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MV

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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

��

�

��

Results from task 2.3:Fault current as a function of f lt l ti (f 1 f 5 d f 9)

��

��

� ��

��

��

fault location (fp1, fp5 and fp9) with different compensation methods.

�

��

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 ( [email protected] )Göran Koreneff, VTT ( [email protected] )Harri Niska, UEF ( [email protected] )Antti Mutanen, TUT ( [email protected] )

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

��

Pekka A PietiläEmpower IM Oy

Mikko RasiOulun Energia Oy

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Theme: Demand response

Contact person: Samuli Honkapuro, LUT ([email protected])

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.


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


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.


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 ([email protected])Joel Seppälä, Helen Electricity Network ([email protected])Pekka Koponen, VTT ([email protected])

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


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.


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


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

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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

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er lo

ss [W

]

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7.5kW amor

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84

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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

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1

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Po

wer

lo

sses

, p

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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


�

��

� 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


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.


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.


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

��

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??


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