profiling
Vision and Key Impact Indicators of SGEM
Jarmo Partanen, Satu Viljainen, Pertti Järventausta, Pekka Verho, Sami Repo
Lappe...
The Future Electricity Markets and
New Sources of Flexibility
Themes: SGEM Vision, Demand Response
Koreneff, Göran; Kivilu...
Jukka Lassila
LUT
050 537 3636

Taavi Hirvonen
Elektrobit
040 3443462

Antti Rautiainen
TUT
040 849 0916

Introduction to ...
Assessment of Interdependencies between
Mobile Communication and Electricity
Distribution Networks
Interdependency of mobi...
www.cwc.oulu.fi

LTE and Hybrid Sensor-LTE Network performances
in Smart Grid Demand Response Scenarios
Juho Markkula and ...
Enabling Grid Technologies
Theme: Active Network and System Management
Janne Starck and Jani Valtari (ABB), Heikki Paanane...
VTT TECHNICAL RESEARCH CENTRE OF FINLAND
www.vtt.fi

Demonstration of a low-cost fault detector for
sum current measuremen...
Self Healing City Networks
Osmo Siirto
Helen Electricity Network Ltd.

Self Healing City Networks
The urban society is inc...
Large Scale Cabling
Theme: Grid Planning & Solutions
Juha Haakana
LUT

Tommi Lähdeaho
Tomi Hakala
Elenia

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

Hannu Laaksone Ari W...
Laboratory test environment for wind turbine prototype connected to grid
based on RTDS simulation
Anssi Mäkinen, Jenni Rek...
Methods for load modelling
Summary

Integration of data and models

Accurate load models for different time horizons are d...
Demand Response Event Flow in a
distributed market environment
Theme: Demand Response
Mikko Rasi
Pekka A Pietilä
Oulun Ene...
Theme: Demand response
Contact person: Samuli Honkapuro, LUT (samuli.honkapuro@lut.fi)

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Petteri Baumgartner

Marko Seppänen

Pertti Järventausta

Joni Markkula

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Demand Response Information Exchange
Theme: Demand Response
Jan Segerstam
Empower IM Oy

Objectives
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Demand Response Pilots
Theme: Demand Response
Joni Aalto
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Tuomas Åhlman
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Effects of demand response on load profiling
Theme: Demand Response
Kaisa Grip, Antti Mutanen and Pertti Järventausta
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Theme: Grid Planning and Solutions
Matti Lehtonen, Muhammad Humayun, Bruno Sousa
Aalto University

Objectives
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Spatial Load Analysis
Theme: Grid Planning and Solutions
M. Lehtonen, M. Koivisto, V. Rimali, J. Larinkari, H-P Hellman, P...
Statistical Analysis of Large Scale
Wind Power Generation
Theme: Grid Planning and Solutions
M. Koivisto, J. Ekström, M. L...
WP 2 / Task 2.5

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Tero Kaipia, Pasi Peltoniemi, Pasi Nuutinen, Andrey Lana,
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Posters of SGEM Unconference 2013

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All SGEM Posters presented in the internal se​minar "Unconference 2012"

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Posters of SGEM Unconference 2013

  1. 1. profiling
  2. 2. Vision and Key Impact Indicators of SGEM Jarmo Partanen, Satu Viljainen, Pertti Järventausta, Pekka Verho, Sami Repo Lappeenranta University of Technology Tampere University of Technology Security of supply, self-sufficiency In Germany 34 GW of photovoltaic cells have been installed,+ 7 GW/a . SGEM unconference 24-25.2013, Vision SGEM
  3. 3. The Future Electricity Markets and New Sources of Flexibility Themes: SGEM Vision, Demand Response Koreneff, Göran; Kiviluoma, Juha; Similä, Lassi; Forsström, Juha VTT Technical Research Centre of Finland Objectives We study the European electricity market development to 2020 and 2035 and how active resources and increasing variable power production fit in. The value of DR indicates future business potential for flexibility With only a small amount of DR*, its value is considerable, but it decreases rapidly with increasing penetration. Price scenarios for the future electricity markets *) The Demand Response analysed here had relatively high marginal cost (80-150 €/MWh) and was not able to shift demand in time. The results from a study are based on a unit commitment and dispatch model WILMAR. Capacity mechanisms needed for flexibility and resource adequacy? The IEA demands in the 2°C scenario (2DS) , the 4°C scenario (4DS) , and the carbon neutral scenario (CNS) are from IEA Nordic Energy Technology Perspectives 2013. The SGEM VTT demand scenario is based on NREAP:s and on the most recent Finnish energy strategy update material in 2013. We have assessed power market price reactions to the EU’s energy market integration, climate change mitigation, energy efficiency and RES deployment policies to 2020 and beyond. The shale gas revolution has deeply affected also EU electricity market: fossil fuel prices are lower and coal is back in business. Will this last? Next steps in SGEM WT 7.2 Analysis of integrated European power markets, variable generation, flexibility and the value of DER. We need input from the themes SGEM Vision, DR, and on development of distributed generation capacity. SGEM unconference 24.-25.10.2013 Source: De Vries (2004) The future demand affects the market price as well as the, especially nuclear and RES-E, capacity development. An intense debate on capacity mechanisms in the EU in general and especially in DE, FR, and GB is ongoing. We have reviewed different capacity mechanisms and their characteristics from a SGEM perspective.
  4. 4. Jukka Lassila LUT 050 537 3636 Taavi Hirvonen Elektrobit 040 3443462 Antti Rautiainen TUT 040 849 0916 Introduction to the task Key research questions are - Effects of charging methods to network - Principles of real time data transfer to driver related to charging status and routing to appropriate charging point - Techniques for voltage quality management - EVs as energy storages to network (V2G) - Intelligent interface of plug-in vehicles - Electricity market impacts and functions Description of the work Wireless communication between the vehicle and charging point: customer view and needs - Billing, bonuses, agreements - Payment in charging point - Charging the batteries - Customer information Charging protocol between EV and EVSE - Based on ISO/IEC 15118-2 RC version (July 2013) - Selected OCPP messages exchange integrated into SECC state machine - Basic use case: parking hall with tens of charging poles and where communication is done using centralized SECC server PHEV charging analysis - Load curves with freely selectable parameters and assumptions - Possibilities of different types of PHEVs to replace liquid fuel with different types of charging infrastructures - PHEVs as a demand response resource Stefan Forsström VES 050 408 5679 Matti Lehtonen Aalto 040 581 5726 Overall energy storing (V2G) methodology Fast charging - Fast (and also slow) charging power quality measurements - Fast charging service business profitability studies Next steps - Developing methodology to define EVs as a part of electricity distribution (G2V + V2G), verifying results with actual network data - Network effects with different scenarios - EVs and power based transfer tariffs - Charging control demonstration with a real EV - Effect of charging infra on EV energy use - Finalize and optimize charging protocol implementation for embedded environment SGEM unconference 24.-25.10.2013, Grid Planning&Solutions, Smart Grid ICT Architectures
  5. 5. Assessment of Interdependencies between Mobile Communication and Electricity Distribution Networks Interdependency of mobile communication and electricity distribution networks has increased due to automation and digitalization. On-going modernization of grids has motivated energy companies to seek new cost-effective and reliable wireless technologies to enable real-time remote control and monitoring of electricity grids covering vast areas. Our study focuses on the following questions: • Are commercial communication networks sufficient for smart grid communication in sparsely populated areas? • How vulnerable are the communication networks to different sized failures? • How should smart grid and mobile communication networks be enhanced in order to make them more resilient and robust? © Olli Pihlajamaa Modelling Storm Fault Analysis Fault analysis Storm simulation Fine tuning Our case study concentrated on storm Patrick, which swept over the Scandinavian peninsula towards the Baltic Sea in 26.12.2011. It was the worst storm in 30 years and caused 60 M€ damages to energy companies in Finland. The storm Patrick was simulated using outage reports from the medium-voltage distribution networks. The result graphs below show the percentage of operational secondary substations, operational masts and the percentage of no-coverage areas during the storm without and with battery backup. Red symbols indicate the failure phases and green ones the recovery phases. The graph shows that just after the storm, there were only ¼ of the secondary substations operational. Coverage and Redundancy Calculations Findings The challenge was to build a realistic simulation model to study interdependencies between electricity distribution and mobile communication. We implemented a simulation tool, which enables detailed modelling of electricity distribution networks, mobile communication networks (e.g., GSM-900, UMTS-900, and LTE), and 3D propagation environment. To affirm the reliability, the models and calculation parameters can be finetuned using field measurements in order to make realistic coverage and redundancy (numbers of base stations available at the given location) calculations as well as storm fault analysis. The redundancy calculations indicated that networks, which are primarily dedicated to provide coverage, like GSM-900, offer higher redundancy level in rural areas than the networks, which provide additional capacity. The simulations emphasized the importance of ensuring the power supply of the critical base stations. This improves the resiliency of telecommunication networks, which in turn has a significant effect on clearance and repair work and wireless remote control of electricity distribution entities. The key factors of telecommunication networks’ resiliency are: the cell size, coverage redundancy, speed of the clearance work, and the duration of battery backups. Contacts: seppo.horsmanheimo@vtt.fi, jyrki.penttonen@violasystems.com antti.kostiainen@fi.abb.com
  6. 6. www.cwc.oulu.fi LTE and Hybrid Sensor-LTE Network performances in Smart Grid Demand Response Scenarios Juho Markkula and Jussi Haapola University of Oulu, Centre for Wireless Communications, P.O.Box 4500, 90014-Oulu, Finland E-mail: juho.markkula@ee.oulu.@, jussi.haapola@ee.oulu.@ Muokkaa perustyylejä osoitt. 10000 Total BG traffic 1000 Streaming Average load [kB/s] INTRODUCTION Evaluation of traffic volumes, delivery ratios, and delays under various demand response (DR) setups for smart grid (SG) communications. 1. Public long term evolution (LTE) network 2. Cluster-based hybrid sensor–LTE network where wireless sensor network (WSN) clusterheads (CLH) are also equipped with LTE remote terminal units. In DR scenarios, varying percentages of end users take part in automated DR-based load balancing while the rest of the users resort to advanced metering infrastructure based energy monitoring. FTP Video Conference 100 HTTP 10 SG case 1 (UL) SG case 2 (DL) 1 DESCRIPTION OF THE WORK Three automatic demand response (ADR) simulation scenarios SG case 2 (UL), case 3 (UL/DL) Voice SG case 1 (DL) 0,1 BG traffic SG (ADR 20 %) SG (ADR 60 %) SG (ADR 100 %) • Spot pricing and direct load balancing (SG Case 1) and BG traffic and BG traffic and BG traffic ADR traffic volume • ADR generation interval: 4 s uplink (UL), 5 min downlink (DL) Fig. 2. Average LTE loads of SG and BG traffic components. • Load balancing with local energy generation (SG Case 2) LTE network: The SG trafNc UL delay is 36 – 722 ms; DL delay is extremely low, 2 ms • ADR generation interval: 1 s (UL), 30 s (DL) Packet delivery ratio (PDR) above quality of service QoS requirement for SG traffic (>99%) • High-intensity load balancing (SG Case 3) Notable increase in delay and decrease in the PDRs of the BG traf@c • ADR generation interval: 1 s (UL), 1 s (DL) components (SG Case 2 and 3) 20, 60, or 100 % of RTUs participate in ADR Hybrid sensor-LTE network: The SG trafNc delay is 7 – 24 ms, approximately 20 ms All remote terminal units (RTUs) participate also in automatic meter reading for UL and 10 ms for DL (AMR). Public LTE carries typical busy hour traffic as background (BG) PDR above QoS requirement for SG traffic (>99%) (SG Case 1 and 2) traffic. PDR of most SF traffic components below QoS requirement (>99%) (SG Case 3) Connectivity via cellular LTE P EAK LOADS , ( PACKET DELIVERY RATIOS IN PERCENTAGES ) AND AVERAGE VALUES OF THE NETWORK DELAYS IN SECONDS Schematic cellular LTE Connectivity via WSN Muokkaa tekstin perustyylejä osoittamalla – toinen taso network Traffic component (peak load) BG traffic LTE only network ADR, AMR and Emergency (UL) SG case 1 ( 80.08 kB/s, 88,57 kB/s, 96.34kB/s) SG case 2 (90.75 kB/s, 120.75 kB/s, 151 kB/s) SG case 3 (90.75 kB/s, 120.75 kB/s, 151 kB/s) ADR control and AMR (DL) SG case 1 ( 0.75 kB/s, 1.05 kB/s, 1.25 kB/s) 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) Voice (51.84 kB/s) - Video conference ( 1,66 MB/s) (90.6) 0.086 Streaming (0.53 MB/s) (100) 0.002 • kolmas taso CLH - – neljäs taso » viides taso Hybrid sensor-LTE Network (99.8) 0.073 HTTP (0.22 MB/s) (99.2) 0.496 FTP ( 10.68 MB/s) (94.8) 47.34 Fig. 1. Visualisation of LTE only and hybrid sensor-LTE networks within a single LTE cell. Simulation topology is generalisation of a suburban environment (790 * 950 m) • In total: 750 houses (RTUs); 930 user equipment (UE); 1 base station (eNB); 30 custers/CLH (hybrid network); 16 WSN channels (hybrid network) • UE and RTUs are randomly placed inside 150 *150 m clusters; CLHs and eNB are centred LTE network without WSN clusters: RTUs are LTE nodes; No CLHs Hybrid sensor-LTE network: RTUs are WSN nodes; CLH is LTE and WSN equipped relay LTE network includes only LTE channels (modified COST231 Hata urban) Hybrid sensor-LTE network applied: LTE channels between CLH and LTE eNB; IEEE 802.15.4 channels (Erceg and free-space) between CLH and RTUs Building entry loss: approximately 6 dB/wall ([0,2] random number of walls) The work undertaken here has been funded by TEKES (the Finnish Funding Agency for Technology and Innovation) project SGEM (Smart Grids and Energy Markets, Dnro 2441/31/2009). www.cwc.oulu.fi SG (ADR 20 %) and BG traffic SG case 1, case 2, case 3 HYB: (99.5), (99.4), (99.9) LTE: (100), (100), (100) HYB: 0.019, 0.019, 0.02 LTE: 0.108, 0.068, 0.036 HYB: (100), (99.9), (98.6) LTE: (100), (100), (100) HYB: 0.010, 0.009, 0.007 LTE: 0.002, 0.002, 0.002 HYB:(99.9),(99.5),(99.7) LTE: (99.8), (99.8), (99.3) HYB: 0.074, 0.077, 0.075 LTE: 0.073, 0.074, 0.075 HYB: (91.3), (91.1), (91.2) LTE: (90.8), (90.2), (89.7) HYB: 0.083, 0.082, 0.077 LTE: 0.077, 0.084, 0.091 HYB: (100), (100), (100) LTE: (100), (100), (100) HYB: 0.002, 0.002, 0.002 LTE: 0.002, 0.002, 0.002 HYB: (99.3), (99.2), (99.2) LTE: (99.2), (99), (98.9) HYB: 0.503, 0.503, 0.534 LTE: 0.514, 0.575, 0.618 HYB: (94.3), (93.6), (93.7) E: ), ( ), (91.6) LTE: (94.3), (92.3), (91.6) HYB: 4 47.43 B: 46.97, 48.7, 47.43 4 LTE: 50.62, 52.60, 60.68 E: 50 : .68 SG (ADR 60 %) and BG traffic SG case 1, case 2, case 3 HYB: (99.8), (99.6), (99.1) LTE: (99.9), (99.9), (99.9) HYB: 0.019, 0.020, 0.021 LTE: 0.097, 0.21, 0.208 HYB: (100), (99.8), (98.4) LTE: (100), (100), (100) HYB: 0.009, 0.01, 0.009 LTE: 0.002, 0.002, 0.002 HYB: (99.8), (99.8), (99.6) LTE: (99.9), (99.8), (99.8) HYB: 0.075, 0.076, 0.075 LTE: 0.074, 0.075, 0.076 HYB:(90.9), (91.4), (91.1) LTE: (90.4), (89), (88.5) HYB: 0.081, 0.078, 0.08 LTE: 0.08, 0.095, 0.115 HYB:(100), (100), (100) LTE: (100), (100), (100) HYB: 0.002, 0.002, 0.002 LTE: 0.002, 0.002, 0.002 HYB:(99.3), (99), (99.3) LTE: (99), (98.4), (97.9) HYB: 0.539, 0.551, 0.521 LTE: 0.628, 0.766, 0.89 HYB:(93.7), (92.7), (92.8) E: ), ( ), (84.8) LTE: (92.2), (88.1), (84.8) HYB: 48.28, 51.43, 50.94 B: 4 50.94 9 LTE: 60.84, 72.97, 80.84 L E 60.8 LTE: 60 84, 72 97 80 84 84 SG (ADR 100 %) and BG traffic SG case 1, case 2, case 3 HYB: (99.7), (99.3), (94.8) LTE: (99.9), (99.5), (99) HYB: 0.020, 0.023, 0.024 LTE: 0.111, 0.485, 0.722 HYB: (99.9), (99.4), (96.6) LTE: (100), (100), (100) HYB: 0.008, 0.011, 0.014 LTE: 0.002, 0.002, 0.002 HYB: (99.9), (99.4), (99.9) LTE: (99.8), (99.7), (99.8) HYB: 0.074, 0.075, 0.074 LTE: 0.076, 0.076, 0.075 HYB: (91.1), (90.7), (91.1) LTE: (90.1), (88.4), (87.9) HYB: 0.082, 0.082, 0.084 LTE: 0.094, 0.106, 0.137 HYB: (100), (100), (100) LTE: (100), (100), (100) HYB: 0.002, 0.002, 0.002 LTE: 0.002, 0.002, 0.002 HYB: (99.2), (98.9), (99) LTE: (99), (97.6), (97.1) HYB: 0.519, 0.566, 0.588 LTE: 0.59, 0.941, 1.137 HYB: (93.4), (91.9), (91.7) E: 1.6), (81), (77.7) (81) 7) LTE: (91.6), (81), (77.7) HYB: 49.79, 52.86, 55.88 55.88 YB: YB 8 LTE: 54 15 86.17 105.91 LTE 54.15, 86.17, 105.91 E: 9 SG traffic delivered in hybrid network causes less harm to BG traffic components than LTE only network. NEXT STEPS Similar studies conducted using WSN only (IEEE Std 802.15.4k) lowenergy critical infrastructure monitoring networks • Preliminary results indicate feasibility of SG Case 1 if network coordinator supports multiple narrowband (37.5 kHz) channels. • 99% QoS requirement challenging. Research and development on robustness of hybrid sensor-LTE network in ADR cases when eNB is susceptible to temporary failure. • Relaying in the WSN domain through multiple personal area networks (PANs) using different frequency channels to the closest functional eNB. During SGEM funding period 5, research on ad hoc LTE relaying when eNBs are susceptible to failure.
  7. 7. Enabling Grid Technologies Theme: Active Network and System Management Janne Starck and Jani Valtari (ABB), Heikki Paananen (Elenia), Tapio Lehtonen (MIKES), Pertti Pakonen and Bashir Siddiqui (TUT), Lauri Helenius (Viola), Henry Rimminen (VTT) Objectives What are the technologies and infrastructures for enabling the active distribution network management? Bring new improved solutions for acquiring measurements, handling the communication of the measurements, and processing the data in distributed environment in the substation. Main achievements Goose over LTE • Utilizing IEC 61850-8-1 Goose communication in transfer trip applications • Tests in laboratory LTE network: 20-40ms delay when communicating from fixed network to device in LTE network. • Results so far in public LTE network: 50ms point-to-point delays Centralized Protection • Utilizing IEC 61850-9-2 process bus • Tested in RTDS laboratory of TUT • High Impedance faults of 100kOhm were detectable Next steps Low-cost Fault Pass Indicator • Sum current of three phases is measured • Field tested in 4/2012 • Minimum tripping threshold was 5 A • Earth faults up to 330 Ohms were detectable Secondary substation monitoring device • Capable of detecting Partial Discharges • PD signals up to 2 MHz can be successfully captured New national power and energy standard and PQ analyzer • Metrology-grade digitozer for LV and MV • Samples at 250kSPS @ 18-bit resolution • IEEE 1459 and IEC 40110 power standards • Extendable to PMU measurements Fault Pass Indicator: Field tests for next HW generation. Centralized Protection: New fault type cross-country fault PQ Analyzer: System integration and analysis software development Goose over LTE: Field tests with an application Secondary subsation monitoring device: Finalizing the device and performing field tests SGEM unconference 24.-25.10.2013
  8. 8. VTT TECHNICAL RESEARCH CENTRE OF FINLAND www.vtt.fi Demonstration of a low-cost fault detector for sum current measurement of overhead MV lines Henry Rimminen, Research Scientist, VTT • Antti Kostiainen, Solution Development Manager, ABB • Heikki Seppä, Research Professor, VTT Introduction We present field test performance of low-cost wireless current sensors, which harvest power from the lines. Handmade unit price was $75 excluding the enclosures. Three sensors measure current of each phase in a 20 kV power line. They are synchronized by radio and then locked in to 50 Hz, which enables sum current calculation. Current is measured with induction coils. In unearthed and in compensated networks, detection of faults using sum current is useful, since the earth fault current is often smaller than the load current. Typical fault detectors rely on sensing dynamic phenomena on earth faults. With sum current measurement, one can set a fixed threshold instead of a dynamic one. See concept in Figure 1. Figure 3. Measured and reference waveforms during faults. Minimum tripping threshold was found to be 5 A based on the healthy state variation of the sum current. See Figure 4. The recorded earth faults with resistances of 0…330 ȍ were above this threshold. The detectors harvest energy from the line with current transformers. We observed charging of the batteries when the detectors were set in a low power mode, but the consumption in measurement mode exceeded the harvested power. Figure 1. Concept of the system. Field test performance The detectors were field tested in Masala, Kirkkonummi, Finland in April 2012. The field test was arranged by ABB and Fortum. Figure 2 shows the three detectors at the test site. Figure 3 shows the measured waveforms (DUT) and the substation waveforms (Ref.) during four induced earth faults. The fault resistances were 0, 150, 330 and 5000 ȍ, and the faults lasted for 400 ms. The waveforms match closely. Figure 4. Variation of measured sum current in a healthy state. Conclusions ƒ We used wireless summation of three-phase current for earth fault detection ƒ Earth faults up to 330 ȍ were detectable ƒ Lowest tripping threshold was 5 A ƒ Energy harvesting was not yet adequate, but will be improved in the next generation devices This work was funded by CLEEN/SGEM program of TEKES –the Finnish funding Agency for Technology and Innovation. Figure 2. Detectors installed.
  9. 9. Self Healing City Networks Osmo Siirto Helen Electricity Network Ltd. Self Healing City Networks The urban society is increasingly more dependent for uninterrupted electricity. In this task the means to improve reliability in Urban Network by Self Healing technics are studied under Theme Active Network Management. Matti Lehtonen Aalto University Jukka Kuru Tekla Oy CITY – FLIR: Automatic fault location, fault isolation and supply restoration for urban power distribution networks Fault Management logic (FM) ready Self Healing technics Reducing the number of interruptions • Network operation with sustained earth fault, compensated neutral • Online monitoring, condition monitoring Reducing the interruption time • Distribution automation • Smart Network Management Main results k= n Optimated Distribution Automation strategy for urban networks 100 % automation RTU RTU RTU RTU RTU RTU RTU RTU RTU NO Next steps k= 2 … Implementation of Fault Management logic into CITY-FLIR, proof of concept RTU RTU RTU RTU RTU RTU Select the optimum number of k for Feederj Low level fault indications Finalisation of Self Healing City Networks Study k= 1 RTU RTU SGEM unconference 24.-25.10.2013, Theme Active Network Management
  10. 10. Large Scale Cabling Theme: Grid Planning & Solutions Juha Haakana LUT Tommi Lähdeaho Tomi Hakala Elenia Kimmo Kauhaniemi UVA Pertti Pakonen Bashir Siddiqui TUT Objectives Cable construction process The aims of task 2.3 include the development of the cable network p construction, quality control and condition assessment processes as well as a costefficient cabling concept. • P Proposal for a re-engineered cabling lf i d bli process Main achievements Method for cost-efficient underground cabling in rural area networks Background: • New Electricity Market Act (588/2013) came into effect in beginning of September in 2013 • 36 h maximum allowed interruption duration in rural areas and 6 h in urban areas • Æ Major-disturbance-proofness has to be improved – Insulation resistance (IR) measurement – Sheath integrity (SI) measurement – Partial discharge (PD) measurement (on-line or off-line) depending on cable prioritization ff li ) d di bl i iti ti 100 % 2008 2008 90 % 2008 2008 0007 0007 80 % 0010 0036 0014 0029 Cabling rate in LV network 0036 0018 Cabling rate in MV network 2002 0101 0028 0024 0041 0733 0040 0014 0029 0018 2002 0101 0028 0024 0041 0733 0040 0042 0043 0042 0043 0045 2001 0045 2001 0795 0058 0795 0058 0799 0799 2007 0798 0077 0097 2007 2007 0079 0098 0080 0797 2007 0102 0106 0107 0777 0092 0135 0734 0195 2006 0212 0774 0212 0657 0314 0212 0774 0212 ϭϬϬͲϵϬй 0657 0786 0661 2003 2000 ϴϵͲϲϬй 0733 0538 ϮϵͲϭϲй 10 % 0796 0784 0399 0733 0433 0785 0791 0659 0471 2005 ϱϵͲϯϬй Rural area distribution companies 0427 0330 0790 30 % 0778 0776 0776 0374 0479 0477 0471 40 % 0307 0759 0272 0317 0796 2005 0538 0290 0229 0233 0733 0231 0320 0778 0776 0776 0427 0399 0733 0433 0785 0791 0659 50 % 0244 0208 0789 0307 0786 0661 2003 0374 0733 0206 60 % 20 % 0657 0783 0212 0759 0272 2000 0784 0794 Urban area distribution companies 70 % 2005 0290 0733 0317 0195 0152 0657 0229 0233 0733 0330 0178 0140 0783 0231 0320 0793 0734 0141 0244 0208 0789 0314 0106 0733 2005 0212 0479 0102 0134 2006 0152 0140 0797 0135 0794 0141 0477 0107 0092 0178 0734 0733 0206 0097 2007 2007 0079 0098 0080 0777 0793 0134 0734 0733 2007 0798 0077 2007 Ca abling rate in MV network M 0010 ϭϱͲϲй 0790 ϱͲϬй 0% 0% 10 % 20 % 30 % 40 % 50 % • Proposal for implementation of commissioning tests 60 % 70 % 80 % 90 % 100 % Cabling rate in LV network Conclusions: C l i • Use of cheap ploughing techniques is pp g g q necessary • LV cabling is more economical compared with MV cabling – 1000 V technique to replace low-loaded MV lines • Supply security requirements can be met without full scale cabling => focus the investments on the most cost-efficient targets – S Some t diti traditional overhead li l h d lines can b be withstood in the network – Most suitable sections can be selected for underground cabling • Proposal for p documentation of commissioning tests, g , minimum requirements – Measuring system – Test voltages and insulation resistances or PD magnitudes and background noise levels – PRPD patterns and PD locations, for off-line measurements also PD inception and t l i ti d extinction voltages Next steps Proposals and demonstrations for commissioning and condition monitoring together with related data management. management To find out the best prioritization criterion for reinvestments of low loaded rural MV network and effect of electric cars to the network structure. Study of effects of New Electricity Market Act on required cabling rates. 6*(0 XQFRQIHUHQ QFH
  11. 11. Smart Grid Protection Theme: Active Network a System Management and Kimmo Kauhaniemi Sampo Voima UVA Hannu Laaksone Ari Wahlroos en, Jani Valtari Erkka Kettunen Valtari, ABB Ari Nikander Ontrei Raipala TUT Objectives Adaptive protection concept New Smart Grid protection concepts and methods are developed in tasks 6.5 and p 2.3 for taking care of changing states of active network improving fault detection network, sensitivity and managing earth faults in cabled networks networks. Protection P t ti system must adapt to the t t d t t th g g changes in network configuration and state of distributed generators by • changing relay settings • enabling or disabling specific protection functions. Overcurrent Main achievements G IfDG Directional OC Ifsupporting network Demonstration and evaluation of the indication of high-resistance earth faults including faulty phase selection • Testing the indication method implemented in the centralized protection system (CPS) with different types of compensation practices of earth f lt current (D6 5 16) ti f th fault t (D6.5.16) Ifsupporting network G IfDG G IfDG Distance Practical Demonstration of Adaptive p Protection and Microgrid Control in Hailuoto Pilot – Faults detectable up to 100kΩ • Methods for reliable detection of crosscountry faults with CPS RTDS test environment for CPS Calculated fault resistances for faulty and healthy feeder with distributed and centralized compensation (Hedekas). Real RF Calc. RF PH1 Change in neutral voltage and sum current, phase angles of Phasors 1 and 3 RF/kΩ Ω RF/kΩ Ω ΔU0/V ΔI0/A PH1/° ° PH3/° ° 1 1.005 1 005 1782.69 1782 69 5.440 5 440 113.799 113 799 -93.460 93 460 5 5.034 410.61 1.255 113.164 -119.821 10 10.334 195.443 0.614 114.879 -121.798 20 21.115 21 115 91.538 91 538 0.296 0 296 118.187 118 187 -120.124 120 124 30 31.863 59.173 0.194 120.266 -118.582 50 50.191 41.375 0.127 115.948 -123.159 70 75.951 25.217 0.083 116.939 -122.47 100 122.314 12.997 0.0502 121.298 -118.311 Loss of mains protection studies L f i t ti t di • A novel network information system based LOM risk management concept is developed (D6 5 19) (D6.5.19) • The interactions between LOM protection and FRT requirements were studied thoroughly (D5.1.22) Active management functionalities ƒ Centralized adaptive protection system ƒ Protection settings changing based on microgrid topology i.e. 1) Grid connected no DG, 2) Grid connected DG with DG, 3) SCADA command (intentional islanding), 4) Black-start islanding) Black start (unintentional islanding), 5) Islanded operation ti ƒ Transition between grid connected and g island operation modes Next steps • Adaptive protection concept will be further developed and tested. Earth faults in large scale cabled rural networks • Suitable earth fault protection methods for Results from task 2.3: cabled networks will be studied Fault current as a function of fault location (fp1, fp5 and fp9) f lt l ti (f 1 f 5 d f 9) • Realization of new implementation of with different compensation centralized protection methods. • Field tests of earth faults in compensated network 6*(0 XQFRQIHUHQ QFH /ͬ ϲϬ ϱϬ ϰϬ ϯϬ ϮϬ ĨƉϭ ĨƉϱ ĨƉϵ ϭϬ Ϭ
  12. 12. Laboratory test environment for wind turbine prototype connected to grid based on RTDS simulation Anssi Mäkinen, Jenni Rekola and Heikki Tuusa Department of Electrical Energy Engineering, Tampere University of Technology Introduction Network model in RTDS Purpose of the study is to create laboratory test setup which takes into account • The impact of network phenomena to the wind turbine operation • The impact of the wind turbine operation to the network operation DC-motor, controlled using thyristor rectifier, is used to emulate the behaviour of wind turbine rotor The wind turbine consists of permanent magnet synchronous generator, three-level generator side and grid side converters • Nominal power of both converters are 10 kW and the converters are controlled using dSPACE Network is modelled in RTDS and simulated point of common coupling (PCC) voltages are realized after scaling to the PCC of the wind turbine prototype using grid emulator • Grid emulator is controlled using dSPACE • Active grid side converter enable bidirectional power flow Wind turbine PCC currents are measured and after scaling fed to RTDS • Wind turbine prototype is scaled to have nominal power of 500 kW when connected to RTDS network dSPACE controlling wind turbine Thyristor rectifier Wind turbine frequency converter generator DC-link RTDS transformer DC-motor Grid emulator dSPACE controlling grid emulator PCs controlling dSPACEs PC controlling RTDS Performance of grid emulator Controller tuning Open loop - resistive load closed loop V-control - resistive load -20 -10 -30 -40 180 -180 100 0 10 3 10 2 2 Gain [dB] Gain [dB] 2 0 -200 1 10 3 10 Frequency [Hz] 100 -100 10 2 3 10 Frequency [Hz] 10 3 -10 -20 -30 -30 -40 180 10 10 0 0 0 -10 -40 180 -20 1 10 closed closed -360 phase [deg] Phase (deg) -180 -180 -360 -540 -540 10 3 10 2 Frequency (Hz) 10 2 10 Frequency [Hz] 3 Frequency (Hz) 100 0 Conclusion 2 10 Frequency [Hz] 2 10 Frequency [Hz] 3 10 positive sequence negative sequence 2 10 Frequency [Hz] 3 10 200 positive sequence negative sequence 100 0 -100 -200 1 10 X: 185.8 Y: -3.05 -10 -20 1 10 3 10 200 -100 -200 1 10 X: 170.5 Y: -3.041 -10 -20 1 10 3 10 200 open 0 open 0 positive sequence negative sequence Gain [dB] -20 10 2 10 Frequency [Hz] 3 10 phase [deg] -10 Wind turbine connected to the grid modelled in RTDS • Wind speed 12 m/s Gain [dB] Magnitude (dB) 0 closed loop VC-control - wind turbine connected to RTDS grid closed loop V-control - wind turbine connected to RTDS grid Open loop - wind turbine connected to RTDS grid 10 0 100 0 -100 -200 1 10 2 10 Frequency [Hz] 3 10 Future work • Wind turbine prototype is connected successfully to the artificial network which is controlled using RTDS • If PCC voltages simulated by RTDS are used as grid emulator voltage references • Emulator performance is decent in frequency range up to 300-600 Hz depending of the load type • Emulator does not take the operation point of wind turbine (or other load/source) into account • PCC voltages in different operation points are determined by the emulator filter components rather than network parameters • The operation point of wind turbine can be taken into account by using feedback control for the PCC voltages • The bandwidth of the feedback control limited by • Resonances of the passive components • Saturation of the transformer • • 3 10 positive sequence negative sequence 10 Wind turbine in RTDS grid, CV-control, q-channel Bode Diagram Gm = 8.31 dB (at 800 Hz) , Pm = 83.7 deg (at 107 Hz) Wind turbine in RTDS grid, CV-control, d-channel Bode Diagram Gm = 7.25 dB (at 818 Hz) , Pm = 80.8 deg (at 105 Hz) 10 10 -200 1 10 10 2 10 Frequency [Hz] Frequency (Hz) Frequency (Hz) 2 positive sequence negative sequence 0 3 10 Frequency [Hz] -10 -20 1 10 3 10 X: 184.1 Y: -3.005 0 200 100 Gain [dB] 2 2 10 Frequency [Hz] -100 phase [deg] 10 -20 1 10 3 10 -100 -200 1 10 -360 -10 200 -540 -540 Magnitude (dB) phase [deg] closed open 0 Phase (deg) Phase (deg) -360 2 10 Frequency [Hz] 200 closed open 0 -180 positive sequence negative sequence -20 1 10 -20 -40 180 -10 10 X: 164.1 Y: -3.016 phase [deg] -10 0 closed loop VC-control - resistive load 10 0 phase [deg] Magnitude (dB) 0 -30 Phase (deg) Resistive load 2kW 10 0 Magnitude (dB) 10 Gain [dB] 10 Wind turbine in RTDS grid, V-control, q-channel Bode Diagram Gm = 8.92 dB (at 800 Hz), Pm = 99 deg (at 107 Hz) Wind turbine in RTDS grid, V-control, d-channel Bode Diagram Gm = 7.66 dB (at 797 Hz) , Pm = 103 deg (at 114 Hz) • Verification of simulation model of the laboratory environment with measurements in transient simulations • Symmetrical fault • Unsymmetrical fault • Utilization of grid emulator in other applications • Solar power grid connection • Connection and control of renewable energy sources and/or energy storages in microgrid • LVDC • Charging / discharging of electric vehicle in different networks • Etc. The positive sequence bandwidth using controller with voltage feedback loop is 170 Hz (V-control) The positive sequence bandwidth using controller with voltage and current feedback loop is 185 Hz (VC-control) SGEM (Un)Conference 24.-25.10.2013
  13. 13. Methods for load modelling Summary Integration of data and models Accurate load models for different time horizons are developed in collaboration to enable smart grids and energy markets. measurements + initial information = model = estimation, prediction and optimization Data from different sources is used for estimating loads. For example, income taxation statistics can be combined with share of single family houses to estimate the introduction of electric vehicles in a network area. Background Smart grids are all about distribution side networks and customers becoming active and smart and thus helping to manage the expected massive changes in power generation (more distributed, more renewables, more intermittency, etc.). The customer side is also experiencing significant changes such as heat pumps, electric vehicles, micro-CHP, PV, and dynamic demand response. Thus it is more and more important and challenging to model and forecast the loads accurately. Meanwhile the amount and quality of information available Dynamic load response models for load modelling improves rapidly. For example, hourly Load responses to control actions are modelled based on metered consumption of practically every customer is in measurements from substations and smart meters, and Finland available by 2014 due to new technology and weather and building data. legislation. Field tests for response modelling, an example Putting new meters to good use We are developing and testing new ways to cluster customers into new and automatic groups, which has profound advantages over traditional load profiles (46 customer types) that hitherto have been in use. Average measured response of a test group (blue) vs. a control group (green), difference is dotted red. One hour long control action. Both groups are also subject to static Time-of-Use control. Identified average response per house to a 1 h long control action at about -4 C. 14 12 10 0.25 0.2 M-Fri Saturday Sunday 9 8 10 8 [kW] 1 2 3 4 5 7 Power [kW] 0.3 6 6 4 5 4 2 3 0.15 0 -20 -15 -10 -5 0 2 5 10 15 20 25 [oC] 4.5 1 0.1 2 4 6 8 10 12 14 Hour 16 18 20 22 What is going on now 4 24 3.5 3 0.05 20 30 40 50 60 70 2.5 [kW] 10 2 1.5 1 0.5 Divide and unite 0 -20 -15 -10 -5 0 5 [oC] 10 15 20 25 There are different purposes and approaches for load modelling. They can be combined and compared. Short term forecasting performance is now under scrutiny. Other main study targets now, essential for all approaches, are 1) the identification of load types behind a measurement, and 2) separation of the main sub load(s) from measurement. Especially household loads are difficult to model and forecast, because they are the sum of many sub loads, whereof some are large and distinct, e.g. electric heating. These essential, distinct, large sub load types will increase in number, More Information Pekka Koponen, VTT ( pekka.koponen@vtt.fi ) all having different dependencies. An alternative modelling Göran Koreneff, VTT ( goran.koreneff@vtt.fi ) approach is based on sub-load types instead of customer Harri Niska, UEF ( harri.niska@uef.fi ) types. Antti Mutanen, TUT ( antti.mutanen@tut.fi ) CLEEN Summit, 11-12 June 2013
  14. 14. Demand Response Event Flow in a distributed market environment Theme: Demand Response Mikko Rasi Pekka A Pietilä Oulun Energia Oy Empower IM Oy Objectives Next steps Describe which electricity market information systems are active in DR actions initiated by active customer or electricity supplier Describe selected event flows which start when supplier or active customer decide to execute DR operation Needed DR operations will be implemented and integrated between energy portal and EDM system Main achievements Whole information chain and event flow from energy portal to customers site will be implemented and tested in Oulun Energia active customer pilot environment Event flow defined and described including actions for both active customer and supplier Special focus has been set on interaction between supplier’s DR tools (EDM based) and active customer energy portal KƚŚĞƌ ĞůĞĐƚƌŝĐŝƚLJ ƐƵƉƉůŝĞƌƐ ĐƚŝǀĞ ĐƵƐƚŽŵĞƌ ĞŶĞƌŐLJ ƉŽƌƚĂů ĐƚŝǀĞ ĐƵƐƚŽŵĞƌ h/ ůĞĐƚƌŝĐŝƚLJ ŵĂƌŬĞƚ ŝŶĨŽƌŵĂƚŝŽŶ ĞdžĐŚĂŶŐĞ ^ƵƉƉůŝĞƌ͛Ɛ D^ ^ƵƉƉůŝĞƌ͛Ɛ D ^ƵƉƉůŝĞƌ͛Ɛ /^ ^K͛Ɛ D KƚŚĞƌ ^KƐ /ŶƚĞŐƌĂƚŝŽŶ ƉůĂƚĨŽƌŵ ^K͛Ɛ /^ 6*(0 XQFRQIHUHQFH DZ ƐLJƐƚĞŵ ĞŶǀŝƌŽŶŵĞŶƚ
  15. 15. Theme: Demand response Contact person: Samuli Honkapuro, LUT (samuli.honkapuro@lut.fi) Objectives Main achievements The objective of the research is to find out what kinds of business, pricing, and market models provide the highest benefits of the smart grid technology for different stakeholders. The research concerning business impacts described here is mainly carried out in WP 7. However, business impacts cannot be analyzed without considering technological development and practical implications. Thus, the cooperation between the different themes and WPs inside SGEM, as well as collaboration between research and industrial organizations, have been utmost important for this research work. For instance, the impacts and possibilities of the demand response are being studied from technological, economical, and societal perspectives. This is done by laboratory demonstrations, piloting, analyzing real-life data, and by conducting customer surveys and interviews. This kind of research work could not be done without SGEM collaboration. One of the key elements in these analyses, which combine the technical and business research, is the big picture concerning the holistic impacts of market player actions. The (simplified) picture below illustrates these actions and impacts. Studied issues include: • The business and pricing models of the DSO, retailer, and aggregator • Conflict of interest between the market players • Demand response and customer behavior • Smart metering and energy management services TRANSMISSION SYSTEM OPERATOR (TSO) DISTRIBUTION SYSTEM OPERATOR (DSO) STATE Monopoly regulation TSO tariff DSO business model DSO tariffs RETAILER / AGGREGATOR CUSTOMER Taxes Incentives for customer to optimize the energy usage Retail tariffs Retailer’s business model DSO’s revenue demand Retailer’s revenue stream Capital expenses Operational expenses Investment needs Network losses Retailer’s revenue demand Metering and billing Retailer’s electricity purchase costs Total demand of energy and power Peak demand DSO’s revenue stream Accuracy of load forecast SGEM unconference 24.-25.10.2013 Electricity wholesale price
  16. 16. E COSYSTEMS FOR D EMAND R ESPONSE Petteri Baumgartner Marko Seppänen Pertti Järventausta Joni Markkula CITER/TUT +358 40 516 7028 CITER/TUT +358 40 588 4080 TUT +358 40 549 2384 TUT +358 44 544 4448 Objectives We examine the DR business ecosystem in the smart grid environment focusing on the liberalized Nordic electricity markets. The aim is to afford a blueprint of an ecosystem to identify the problematic nodes and provide alternatives how to overcome possible obstacles in order to develop a functioning demand response ecosystem for this field. Main achievements Based on earlier work on SGEM, we have considered that a consumer may not be treated as the end customer in this ecosystem. Thus, the value proposition of DR should be developed by considering a DSO, TSO, retailer, or even yet non-existing aggregator as the end customer in this business ecosystem. Substantial economic, environmental, and social advantages are possible through DR utilization in these cases. For instance, an electricity supplier can cut its future balancing costs if load shifting and shedding are at its disposal. Next steps We are going to study the business ecosystems of several different DR programs and strive for identifying the key obstacles hindering the development of thriving DR businesses. We see crucial the identification of the key elements and their explicit locations in the ecosystem as well as detecting the ways to overcome the key obstacles to bring about the DR businesses to boom. This work will be supported with business model examinations. A value blueprint of DR ecosystem. Herein direct load control (DLC) program to exploit DR is demonstrated—i.e., one possible way to do DR business. E.g., some price-based programs pass the responsibility for load adjustments onto consumers whereby the blueprint outlines slightly differently. SGEM unconference 24.-25.10.2013
  17. 17. Demand Response Information Exchange Theme: Demand Response Jan Segerstam Empower IM Oy Objectives Defining information exchange processes and information structures to enable the control of demand response capacity with different kind of load control equipment in different electricity network areas. Main achievements First version of load control message structure has been developed in cooperation with SGEM partners. Next steps Collecting further requirements for the message structure as a part of piloting work with electricity suppliers and DSOs. 6*(0 XQFRQIHUHQFH
  18. 18. Demand Response Pilots Theme: Demand Response Joni Aalto Empower IM Oy Tuomas Åhlman Vantaan Energia Sähköverkot Oy Pekka Takki Helsingin Energia Objectives Describing how DR should be connected to electricity supplier’s business processes? Requirements and possibilities of AMR and HEMS based market-wide DR? Piloting work in real system environment with electricity suppliers, DSOs and HEMS providers. Main achievements Next steps Process descriptions of linking DR utilization to supplier’s business processes in different electricity market levels. Established partner network for piloting work. Starting the piloting work with real measurement points and loads. Enabling supplier’s DR actions in different DSO areas. Collecting experiences from the piloting work to further develop a holistic approach for demand response. 6XSSOLHU (QHUJ 0DUNHW ,QWHUDFWLRQ VXSSO SODQQLQJ +(06 '5 6HUYLFH ,QIRUPDWLRQ PJPW RSHUDWLRQV %XVLQHVV RSHUDWLRQV 0DUNHW RSHUDWLRQV 'HULYDWLYH WUDGLQJ 'D DKHDG DXFWLRQ (/6327
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  20. 20. Effects of demand response on load profiling Theme: Demand Response Kaisa Grip, Antti Mutanen and Pertti Järventausta Tampere University of Technology Objectives This study analyses different load alternatives stemming from combinations of load, demand response and microgeneration (Figure 1). Their effects on load profiling accuracy and development needs are studied. Here, the combination of load and demand response has been chosen for more detailed examination. Figure 3. Behaviour of loads in February 2010–2013 Figure 1. Potential load alternatives The effect of spot-price based water heater control can be seen clearly but the effect of power band control is difficult to see due to the stochastic variation between years. The effect of power band control can be seen more clearly from the load duration curves (Figure 4). Load was shifted from peak hours to a time of lower consumption. In load duration curves this can be seen as a hill under the hysteresis value. Main achievements The effect of demand response to customer level load behaviour was demonstrated with power band and spot-price based load control. The energy consumption of a pilot customer was held under a given threshold value with a power band based load control (Figure 2) and the water heater was controlled based on the spot-price. Figure 3 shows the combined effect of power band and spot-price based load control on February’s load profile. Figure 2. Load curves Figure 4. Load duration curves for February’s 2011-2013 Next steps In terms of load profiling and forecasting, the new load control functionalities complicate the modelling and forecasting tasks. To some extend, the changes can be modelled with new customer class models. But in order to model demand response and microgeneration more accurately we should be able to separate controllable load and generation from rest of the load. Then, for example, a solar irradiation dependent PV model could be used to model solar panels. Figure 2. An example of realized control actions when power band control is used SGEM unconference 24.-25.10.2013
  21. 21. Task 4.4: Technical l ti for DR, T k 4 4 T h i l solutions f DR customer gateway and ICT systems t t d t , y , p , , , , Antti Pinomaa, Andrey Lana, Tero Kaipia, Ville Tikka, Pasi Nuutinen, Henri Makkonen, Petri Valtonen Lappeenranta University of Technology Marko Pikkarainen, Antti Mäkinen, Pertti Järventausta, Sami Repo,Tampere University of Technology Markku Kauppinen, Elenia TUT smart grid laboratory Introduction Task 4.4 focuses on • The technical solutions, applications and ICT , pp architecture in future customer gateway relating to HEMS and AMR based systems and how they support the overall aims for demand response and network management issues DMS600 Smart grid functions SCADA CIM Analysis tools View DB IEC61850 Enterprise Service Bus microSCADA Primary subst. automation Aggregator IEC61850 Green campus – energy management system CIM IEC61850 CIM IEC61850 OPC UA OPC DA HTTP COSEM Secondary subst. automation IEC61850 Meter reading DLMS HTTP Ethernet Q SQL IEDs HEMS Smart meter Smart meter HEMS PQ meter PMU Smart meter PMU Other meas. Control Smart meter KNX There AC microgrid RTDS 20 kW Wind turbine PV power plant EV ( p (in operation) ) AC microgrid lab LAN L1 L2 L3 N PE 20 kW (components ready to b d t be installed) Aggregator 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 Z-wave Measurements 10 V DC PHEV 0 1 0 1 dSPACE (6.7 kWh, G2V + V2G, in operation) ~ ~ = = ~ RTDS ~ PV production Connection for loads and production = BEV (24 kWh, G2V, in kWh G2V i operation) ~ (in laboratory tests) = 30 kWh CAN fieldbus gateway 3-phase supply Wind turbine EV charging SG unit Fibre optics LAN N units Neutral fault management in LV network – RTDS simulations of AMR meters Info client DHCP Switch 100 Mbit / 1 Gb Info display GC server CAT5 VLAN Green Campus measurements, etc. eth0, eth1, eth 2, eth 3 Services: Apache(PHP, etc.), mySQL, FTP, SFTP mySQL FTP SFTP, SSH Samba? CAT5 DR unit Info client Measurement unit CAT5 LUT Firewall Port open: 80 157.24.25.240 255.255.252.0 157.24.24.1 VLAN staff Info display SSH admin client, SSH Admin client port 22 IP 157.24.25/26.0? 157.24.26.193 LUT LAN 157.24.25.240 Redirection from www.lut.fi/GC/... Fig General concept of interactive customer gateway realized in the Fig. . Green Campus environment Schematic of GCSG information network.
  22. 22. Smart Metering Based Dynamic Demand Response Summary Dynamic market based demand response using smart meters was developed and implemented in large scale. Demand response reduces costs and risks regarding prices and reliability of the electricity market and system. Background and objective Demand side response enables smart grids, more distributed generation, full utilization of renewable energy sources, more electrical vehicles, and better security of the electricity system and electricity market. Thus it is an essential tool for reducing emissions and costs. Dynamic load control via smart metering systems is developed to replace the traditional static time of use controls and tariffs. In addition to market price based Demand Response the solution developed supports many other load control needs. In December 2012 dynamic load control started with about 1000 consumers. Observed controlled power was about 17 MW and the total power of the customers was about 20 MW. (Some non-controllable consumption and lost control messages.) Vantaa Energy Electricity Networks completed tests with 1 house and has started new tests. The houses have partial heating storage. Fortum is completing a study on how the developed dynamic demand response model fits to their smart metering system. SGEM helps E.ON Kainuu in direct load control field tests with about 7000 partial heating storage houses in time of use control. Test planning and data analyzing and modeling. Some field test results, full storage Old static load control vs. the new dynamic control Continuation and collaboration Results so far (May 2013) Two smart metering system vendors have implemented the dynamic demand response operating model developed. Electricity retailers participating control the loads based on their needs using the messaging developed. Helen Electricity Network started field trials in 2010-2011. By February 2012 about 500 consumers (10 MW) were connected and in February 2013 about 50 MW. All are full heating storage houses. Analyze field test data and develop short term prediction and optimization models for the loads and dynamic responses. Study and develop the approach in partial storage heating. Promote wider adoption. More DSOs, Metering operators, smart meter vendors, and electricity retailers and aggregators. Test performance regarding latency and reliability. Continue collection of data for load and response models. Promote harmonization of demand response messages. Report the results. Promote expansion to new DSOs, retailers and smart metering systems. More Information Pekka Takki, Helen (pekka.takki@helen.fi) Joel Seppälä, Helen Electricity Network (joel.seppala@helen.fi) Pekka Koponen, VTT (pekka.koponen@vtt.fi) SGEM unconference, 24-25 Oct 2013 and CLEEN Summit , 11-12 June 2013
  23. 23. Theme: Grid Planning and Solutions Matti Lehtonen, Muhammad Humayun, Bruno Sousa Aalto University Objectives • To develop reliability analysis tools for HV Smart Grid Network. • Redundant capacity mitigation in HV Smart Grid using demand response. Reliability Models Markov Models in presence of demand response: DR Capacity in the Network Results Three-layer reliability model: Test Networks • Redundant capacity of components in the network proportional to DR capacity can be mitigated. • ABC-substations are less reliable than ABCD-substations. Next steps • Investigation of different topologies for OH and UG HV network. • Investigation of cost of voltage sags. • The potential assessment of DR in mitigating redundant capacity of MV network. • Optimal utilization of DR in HV MV networks for redundancy mitigation. SGEM unconference 24.-25.10.2013
  24. 24. Spatial Load Analysis Theme: 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. Spatial load forecast for city districts a) d) Identify changing consumption patterns: b) c) Select electricity consumption scenario Identifying spatial, monthly changes in use of electricity a) d) Daily profiles 50 household heated with ground source heat pump household heated with direct electricity 40 30 b) MWh 20 10 0 02/12 03/12 04/12 05/12 06/12 07/12 08/12 09/12 10/12 11/12 12/12 01/13 02/13 03/13 04/13 05/13 06/13 07/13 08/13 c) -10 -20 -30 Results 1FP…4FP -40 a) Spatial load forecast process outlines for modelling new housing and office building development by the year 2030 b) Mathematical and statistical processing of AMR measurements to generate load classes and profiles required by load models c) Detailed analyses of energy use of service sector in Helsinki and households with ground source heat pumps d) Demonstration of data processing and visualization of the monthly follow-ups of spatial electricity consumption Households Buildings Industry Infrastructure Construction Service Street lighting Rail traffic Next steps Designing scenario models on a specified form. Developing spatial data analysis. Adding background data, e.g. city data bases, to spatial load analysis. Modeling and forecasting electricity consumption using socioeconomic variables (e.g. GDP). Demonstrating the scenario tool in NIS. SGEM unconference 24.-25.10.2013
  25. 25. Statistical Analysis of Large Scale Wind Power Generation Theme: 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. The combined production of ten 3.3 MW units when the units are geographically close to each other. The combined production of ten 3.3 MW units when the units are geographically highly spread. Next steps Creating different scenarios with high altitude data The RXCFs of the data and the transformed VARX Modeling the whole wind power generation and ARC models (averages of the 100 simulation structure of Finland runs) for Vantaa and Pirkkala. SGEM unconference 24.-25.10.2013
  26. 26. WP 2 / Task 2.5 Development of LVDC Technology Tero Kaipia, Pasi Peltoniemi, Pasi Nuutinen, Andrey Lana, Aleksi Mattsson, Jarmo Partanen Lappeenranta University of Technology Introduction Jenni Rekola, Heikki Tuusa Tampere University of Technology EMI in LVDC system – Benchmarking common mode (CM) and RF EMI in LVDC system w.r.t. standard requirements based on measurements at real-network research platform – Analysis of safety issues due to disturbance level dBuA 80 – Disturbance levels 70 originating from the 60 LVDC network are 50 low Key Results 40 – Converters affect 30 Energy efficiency – Converter losses mainly to the 20 – Ultimate goal to minimise converter losses frequency spectrum 10 – Understanding and modelling loss mechanisms based 0 of RF EMI on measurements -10 – CM current -20 – Comparison of measurement techniques (calorimetric/ 0.1 1 0.01 magnitude in MHz Frequency electric) and two- and three-level converters customer-end Fig. 5 Measured CM current in customer-end network when CEI is operating (red) and network does not turned off (blue). cause safety issues CM current The work aims on improving the technical performance, energy efficiency and economy of the LVDC distribution systems by developing converter technology, control algorithms, analysis methodology and system design principles. The work is highly interconnected with the laboratory and field tests. 400 converter losses iron core filter losses 350 300 94 300 200 150 100 250 200 150 Adaptive converter control 90 88 – Improvement of CEI control during fault situations Æ identification of grid faults 100 86 50 50 0 84 0 2.5kW 2.5kW iron amor 5kW iron 5kW amor 7.5kW 7.5kW iron amor 2.5kW 2.5kW iron amor 5kW iron 5kW amor 2.5 kW 2.5 kW 5 kW cable 7.5kW 7.5kW iron amor 5 kW 7.5 kW 7.5 kW cable cable c) b) a) Comparison of measured losses of a) three-level line converter with iron core or amorphous core filter inductor, b) three-level customer-end inverters (CEIs) with iron core or amorphous core filter inductor, and c) total losses of bipolar symmetrically loaded LVDC system with and without 200 m long 16 mm2 cable Fig. 1 1 Resonant controller based control structure 200 100 -100 -200 -200 Double DQ based control structure 0.12 T ransformer IGBT conduction 0.75 0 0.2 0.4 0.6 0.8 i [A] E -200 0 200 iD [A] 200 100 0.04 0 -100 -200 -200 -100 0 1 0 0.2 0.4 0.6 Power Output, pu 0.8 100 1 Fig. 6 150 100 80 1500 60 40 1000 20 fsw [kHz] Fig. 4 500 Fault identification as a part of CEI control and short-circuit current control methods. Next Steps 200 Ctot,min [€] 0 iD [A] 0.02 – Modular customer-end inverter (CEI) that utilises several inverter modules of small nominal power – Life-cycle cost minimsation as converter design methodology Principle of modular converter -100 Phase based DQ control structure 0.06 Development of modular converter solution Fig. 3 100 0 -200 b) a) a) Measured and modelled two-level CEI efficiency curves with different loads and respective DC supply voltage drops, and b) respective distribution of power losses. i.e. 440 VDC 200 100 0.08 Power Output, pu Fig. 2 100 -100 i [A] E CEI#3 CEI#1 LAB 780V 700V 755V Constant 610V Worst Unbalance 0.8 0.7 Power losses, pu Power losses, pu 0.85 0 D 200 IGBT switching 0.9 -100 i [A] LC filter 0.1 0.95 0 E 250 92 i [A] Power loss [W] Power loss [W] amorphous core 96 converter losses 400 filter losses Efficiency [%] 350 UDC [V] Lifetime costs for optimal filters w.r.t. intermediate DC voltage and switching frequency – Converter control methods for reducing DC current fluctuation and voltage unbalance to minimise the LVDC system losses – Design of galvanic isolating DC/DC converter to enabling optimal power density and to reduce losses and volume of modular CEI – Connection and control strategies for interconnecting electrical energy storages in LVDC system – New EMI measurements both at laboratory and at realnetwork research platform with different rectifier and CEI solutions – Verification of results by comparing laboratory and realnetwork results – Providing input for standardisation of LVDC systems SGEM unconference 24.-25.10.2013, Grid Planning and Solutions / Microgrids and DER 200
  27. 27. LUT Suur-Savon Sähkö LVDC Field Test Setup - T2.4 LVDC Research Pla atforms and Field Tests Juha Lohjala Pasi Nuutinen, Andrey Lana, Antti Pinomaa, Pasi Mika Matikainen, Arto Nieminen Suur-Savon Peltoniemi, Peltoniemi Tero Kaipia Aleksi Mattsson Jarmo Partanen Suur Savon Sähkö Oy Kaipia, Mattsson, Järvi-Suomen E Jä i S Energia O i Oy Lappeenranta University of Technology Experiences Introduction The first implementation of modern LVDC distribution and CEI based supply in a continuous use by the DSO since 6/2012 ‰ Test setup of utility grid LVDC distribution with real customers for ƒ ƒ verification of the LVDC technology related —Grid functionalities ƒ ƒ ƒ Bidirectional grid-tie rectifying converters 1,7 km of DC cable Three 16 kVA three-phase CEIs that supply four customers ‰ The setup is located in Suur-Savon Sähkö s Sähkö’s network in Suomenniemi and it consists of: ‰ The system is reliable in different weather conditions ƒ Back-up supply has been used only once ‰ All special situations have been managed as i l it ti h b d planned ‰ The quality of supply has been high ‰ There have been no customer complaints ‰ Control strategies will be studied and developed to enable more advanced customer-end power control and other —Grid functionalities As a result, the first implementation of the utility s gr LVDC distribution has been successful rid CEI #3 Connected to +DC CEI #2 ±750 VDC Connected to +DC CEI #1 Connected to –DC 200 m Fig. 1 LVDC distribution network field test setup. Fi 2 ig. Various measurements in progress. (a) DC supply voltage of CEI #1. (c) DC supply voltage of CEI #3. (b) Phase a voltage of CEI #1. (d) Phase a voltage of CEI #3. Fig. 3. Customer-end phase a voltages and DC voltages at CEI #1 (-DC pole) and CEI #3 (+DC pole) during climatic overvoltage followed by HSAR. The data is recorded automatically and presented in the web portal. SGEM unconference 24.-25.10.2013 Grid planning and solutions, —Grid and DER G
  28. 28. T2.4 LVDC Research Pla atforms and Field Tests Pasi Nuutinen, Andrey Lana, Antti Pinomaa, Pasi Peltoniemi, Tero Kaipia, Aleksi Mattsson, Jarmo Partanen Lappeenranta University of Technology Juha Lohjala Tommi Lähdeaho, Tomi Hakala Suur-Savo Sähkö Oy on Mika Matikaine Arto Nieminen en, Elenia Oy Järvi-Suom Energia Oy men ‰ Introduction Task 2.4 focuses on ‰ development and realisation of both laboratory and field environment research setups for LVDC technology The objective of the task is ‰ to provide research environments for developing, testing and validating concepts, technology and software for the LVDC systems ‰ to gather and report valuable practical experiences from actual distribution network environment Description of the work LUT Suur-Savon Sähkö field setup (more detailed info in separate poster) ‰ 1.7 km bipolar LVDC network with three customer-end inverters (CEIs) installed in Suomenniemi (Fig. 1) ‰ Technical test setup of utility grid LVDC distribution ‰ Operational since 6/2012 CEI #3 Connected to +DC Reijo Komsi ABB Oy Drives Supervision and development of system using online measurements and data logging Next steps LUT laboratory ‰ Three-phase modular CEI structure ‰ Galvanic isolation with high-frequency transformer (isolating DC/DC converter) LUT Suur-Savon Sähkö field setup ‰ Initial start-up of grid-tie rectifying converter capable of bidirectional power flow ‰ Battery energy storage (BESS) connection to DC network ‰ Power flow regulation and customerend load control ‰ Possible PV power plant planning and installation ABB Elenia ‰ Realisation and start-up of point-topoint LVDC network (Fig. 2) ‰ Gathering experiences from the LVDC system ‰ Development of concept using online measurements CEI #2 ±750 VDC Connected to +DC CEI #1 Connected to –DC 200 m Fig. 1. LUT Suur-Savon Sähkö LVDC field setup. SGEM unconference 24.-25.10.2013 Fig. 2. ABB Elenia point-to-point LVDC network. Grid planning and solutions, —Grid and DER G
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  43. 43. 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 Achievements Major development paths discovered in theme workshops. New functionality needs has been defined. Figures: Online transformer Remote monitoring: - Live oil quality measurement - Long period data storing - Analysis and anomaly alerting - Live installation running in Pirkanmaa area Automated FLIR Automated disturbanve advice Next steps Automated risk mitigation Control of repeating reclosures Protection LV-alarm Disturbanve records Disturbanve advice Maintenance measurements Prioritize of faults for correction Shared awareness picture Manual FLIR Hot standby redundancy Laser measurements Manual recording Reactive Work management Ordering of proactive maintenance Automated Analysis Remote operation Network awareness picture Quality analysis Towards automated proactive data analysis for risk mitigation and cost-efficiency Proactive Manual risk mitigation Proactive Operation control Management by knowledge Preparedness advice Thermal imaging Man from the street Pole inspection Maintenance Fault correction Inspection Manual Figure: Workshop result, white spot analysis. Red areas are business potential cases. Figure: Workshop result, the greatest challenges of smart secondary substation concepts SGEM unconference 24.-25.10.2013, Theme poster
  44. 44. Task 6.12 SG Proactive Monitoring Results Vesa 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 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. Demo Site Built in Pinsiö 110/20kV primary substation Various technologies utilized for •Assuring safety and security •Monitoring critical components •Preventing unauthorized access More sophisticated algorithm including several external data sources. Fault History Databa se Relay Pick-up Circuit Breaker State Change Fault Reactance Disturbance Record Fault Locatio n Calcula tion Reclosi ng Analysi s Notifica tion SCADA Work Work Order Order Ope rato r DMS Work Order Manageme nt NIS Conditi on Data Weather Data Tree Cleara nce Data Feeder Propert ies Novel System as a Data Hub Map based view and high/detail-level status of all sites at a glance with remote control of single detectors and sensors. 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. Network Information System Tekla NIS FieldCom Da ta Hu b SCADA Netcontrol Netcon3000 Distribution Management System Tekla DMS Electricity Distribution Process Actions SGEM unconference 24.-25.10.2013, Task poster Work Order Management Microsoft Dynamics AX Meter Data Management System eMeter EnergyIP
  45. 45. Theme microgrids and DER Introduction The aim is to study operational microgrid with distributed generation, energy storages and controllable loads. Microgrid conceptual figure Research items • One main driver in designing microgrids is to increase reliability • Integrating DER - both generation and storages - in microgrids increases the independency • The conceptual study includes microgrids generated by rotating generators and power electronics, on LV and MV levels, on different power ranges • Find and define necessary business models and market integration model to provide further incentives in building microgrids • End customers’ point of view – households’ awareness regarding small scale production, main motives and barriers? Reference Architecture for Smart Grid in Europe Approach and methods The focus shall be in developing, designing and building one full scale microgrid, which consists of distributed generation, energy storages and island grid generation with the devices to connect/disconnect with the fixed grid. Consumer interest in small scale production and microgrid generation is studied by polling and interviews. What are their main motives and barriers? SGEM unconference 24.-25.10.2013, {Microgrids and DER}
  46. 46. Microgrid and DER control Hannu Laaksonen ABB Omid Palizban University of Vaasa 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 Seppo Hänninen VTT Riku Pasonen VTT New islanding detection method (986/1998) Control principles of microgrids With respect to the IEC/ISO 62264 standards, hierarchical control and storage algorithm for microgrids is developed as shown below: 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
  47. 47. Energy storages and uGrid technology concepts Reijo Komsi ABB +358 50 3323224 Kari Mäki VTT +358 40 1429785 Kimmo Kauhaniemi UVA +358 44 0244283 Jukka Lassila LUT +358 50 5373636 Introduction The aim is to study use of energy storages, storage technology, control strategies - specially in microgrids. Description of the work Proof of concept on using power electronics and batteries for power balancing in island grid maintained with distributed energy resources Distribution network case with different storage types for different applications • 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 Max grid Next steps Æ Focus on grid application approach • Design principles and control strategies of energy storages in microgrids • Different storage technologies • Forecast methods for RES generation for storage optimization purposes • Development and testing of storage and microgrid simulation models Æ Storage integration to microgrid management Æ Proof of concept on using power electronics and batteries for power balancing SOC PV gen Exceeding max grid power? 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. Yes No Load power Off-timer running? Yes Power from Grid 6000 6000 4000 4000 2000 2000 No Running average calculation Derivative formulation CDC to ”charge” Increase CDC ON-timer Within trigger limits? Maintain for CDC OFF-timer No Filtering Comparison to power rate of change limits No Within trigger limits? Power [W] Issue CDC control Limit exceeded Power [W] Difference = generation - average Compare difference to trigger limits 0 0 Yes Yes No CDC to ”idle” Check with storage status Maintain CDC -2000 -2000 OK -4000 -4000 CDC -6000 0 100 200 300 400 500 Time [hours] 600 700 800 900 1000 -6000 0 100 200 SGEM unconference 24.-25.10.2013, {Theme: Microgrids and DER} 300 400 500 Time [hours] 600 700 800 900 1000
  48. 48. D 5.1.111: Suitability of PV testing methods for arctic conditions; existing methods and development needs Atte, Löf VTT Riku, Pasonen VTT Rami, Niemi VTT Introduction PV 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 panel 90° inc, eastwest = Normal panel 45° inc, south 6*(0 XQFRQIHUHQFH —*ULGV DQG '(5
  49. 49. D 5.3.112: AC/DC Hybrid distribution in LV Microgrid Riku, Pasonen VTT Introduction DC 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 Progress so far • Simulation model of DC/DC converter with galvanic isolation (paper sent for review) Next steps • Research report on the concept Simulations on microgrid operation and on selected fault scenarios D 5.3.115: Distributed resources and microgrids in community planning Ha, Hoang VTT Introduction Combined planning of Eco efficient housing and DG towards microgrids Rinat, Abdurafikov VTT Riku, Pasonen VTT 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 6*(0 XQFRQIHUHQFH —*ULGV DQG '(5
  50. 50. Small Scale Production Consumers Theme: Microgrids DER Merja Pakkanen University of Vaasa Maria Tuuri 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 are typically 50-60 years old, 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. 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 little 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 Too long repayment period is one of the most significant barriers. 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
  51. 51. Task 6.6: Active t T k 6 6 A ti network management k t using DER and microgrids i DERs d i id j , , y f Katja Sirviö, Kimmo Kauhaniemi, University of Vaasa Shengye Lu, Sami Repo, Tampere University of Technology Erkki Viitala, Emtele Summary A concept for distributed LV network management. The t f di t ib t d t k t Th proposed architecture creates a bridge between fully centralized automation systems like SCADA and distributed system consisting of secondary substation automation and smart metering metering. Evolution of LV distribution networks Intelligent Network of Microgrid Microgrids Self sufficient Self-sufficient in Electric Energy Architecture • Integrated automation system (no silos of systems) • Hierarchical decentralized system • Real-time management extended to MV and LV networks • Autonomous decision making at each hierarchical level • LV network management is located at secondary substation automation ( (INTEGRIS device, IDEV) G S ) Traditional T diti l Balance responsible Energy retailer TSO Aggregation system SCADA DMS NIS CIS Workforce management system AMR HUB Primary substation Substation automation RTU IED PMU Secondary substation automation Secondary substation automation RTU PMU Connection point Customer NIS Enterprise Service Bus DSO control centre Secondary substation MDMS PQ RTU IED Smart meter Cloud based secondary substation automation Home energy management DER Measurements Mains switch FO Wi-Fi B B-PL C Implementation Coupling PC platform C p at o switch Integris Communication Functionalities FO (ETH ) User Data Collector RTU Switch RTU Data Collector RFID MV /LV data handler RFID Octave Meter Data Collector DB modem BB -PLC ETH Smart Meter Option 1 ETH modem Option 2 s witc h c Analog IN Protocol Gateway mo odem odem mo mo odem SS -IDEV ZigBee Use cases • The network normal operations and the disturbance situations using UML in each evolution phase • Classification of the actors and class diagrams; static relationships di i l i hi • State diagrams of the actors to be done; all the states an actor can have in multiple use cases ETH Smart Meter HEMS DER Power Quality Meter

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