posters of sgem unconference 2013
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DESCRIPTIONAll SGEM Posters presented in the internal seminar "Unconference 2012"
2. Vision and Key Impact Indicators of SGEM Jarmo Partanen, Satu Viljainen, Pertti Jrventausta, Pekka Verho, Sami Repo Lappeenranta University of Technology Tampere University of TechnologySecurity of supply, self-sufficiencyIn Germany 34 GW of photovoltaic cells have been installed,+ 7 GW/a .SGEM unconference 24-25.2013, Vision SGEM 3. The Future Electricity Markets and New Sources of Flexibility Themes: SGEM Vision, Demand Response Koreneff, Gran; Kiviluoma, Juha; Simil, Lassi; Forsstrm, Juha VTT Technical Research Centre of FinlandObjectives 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 flexibilityWith 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 2C scenario (2DS) , the 4C 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 EUs 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.-25.10.2013Source: 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. Jukka Lassila LUT 050 537 3636Taavi Hirvonen Elektrobit 040 3443462Antti Rautiainen TUT 040 849 0916Introduction 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 functionsDescription 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 resourceStefan Forsstrm VES 050 408 5679Matti Lehtonen Aalto 040 581 5726Overall energy storing (V2G) methodology Fast charging - Fast (and also slow) charging power quality measurements - Fast charging service business profitability studiesNext 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 environmentSGEM unconference 24.-25.10.2013, Grid Planning&Solutions, Smart Grid ICT Architectures 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 PihlajamaaModellingStorm Fault AnalysisFault analysisStorm simulationFine tuningOur 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 of the secondary substations operational.Coverage and Redundancy CalculationsFindingsThe 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:email@example.com, firstname.lastname@example.org email@example.com 6. www.cwc.oulu.fiLTE 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: firstname.lastname@example.org.@, email@example.com.@Muokkaa perustyylej osoitt. 10000Total BG traffic1000StreamingAverage 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 sensorLTE 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.FTPVideo Conference100HTTP10SG case 1 (UL)SG case 2 (DL)1DESCRIPTION OF THE WORK Three automatic demand response (ADR) simulation scenariosSG case 2 (UL), case 3 (UL/DL)VoiceSG case 1 (DL)0,1 BG trafficSG (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 WSNMuokkaa tekstin perustyylej osoittamalla toinen tasonetworkTrafc component (peak load)BG trafcLTE only network ADR, AMR and Emergency (UL) SG case 1