MIGRATING TOWARDS A SMART DISTRIBUTION GRID Authors: Prashanth DUVOOR Ulrike SACHS Satish NATTI Siemens PTI

Frankfurt (Germany), 6-9 June 2011  Past: Only loads have been considered for distribution network planning Information about maximum loads have based on measures in RMU and number of customers Maximum loading of different equipment by diversity factors.  Today Generation has to be considered. Maximum utilization not definitely during peak load conditions Voltage / power quality becomes a critical factor in future Carsten Böse– Germany – Session 5 – Paper 0578 The needs in Network Planning will extremely change (1/2)

Frankfurt (Germany), 6-9 June 2011  Future: Data about consumption for each customer will be available (online/real-time) Actual status of the network can be calculated based on measurements also in distribution networks. Network operation and electronic components have to be taken into consideration Distribution network become more and more active network. New kind of loads are controllable (e.g. heat pumps, eCars, …)  Power System Planning has to consider “copper” and IT Carsten Böse– Germany – Session 5 – Paper 0578 The needs in Network Planning will extremely change (2/2)

Frankfurt (Germany), 6-9 June 2011 Example: 314kWp PV Plant near Erlangen Sunny day in April: 1,9 MWh Cloudy day in April: 1,2 MWh Carsten Böse– Germany – Session 5 – Paper 0578

Frankfurt (Germany), 6-9 June 2011 Carsten Böse– Germany – Session 5 – Paper 0578 Consideration of recorded loads and generation is the basis for future design  Meter Data Management Systems basis for load / generation model  meter data in 15 minutes intervals imported to PSS ® SINCAL  Identification of critical situations by “dynamic” simulations

Frankfurt (Germany), 6-9 June 2011 Carsten Böse– Germany – Session 5 – Paper 0578 DSO IT Distribution network Management  Integration of DMS and OMS systems  Link-up of geo information (GIS) and workforce management (WFM)  Virtual power plant Distribution network automation  Smart feeder automation  Self-configuring substation automation Smart metering  Infrastructure and smart meters  System optimization measures based on meter data  Meter data management Energy Automation Portfolio Renewables Storage Power Transmission Industry Distribution Oil & Gas Power Generation Planning Tool considers DNA and DMS

Frankfurt (Germany), 6-9 June 2011 Example Network  Feeder 1 has nearly 3 MW installed capacity of PV generation, and residential load.  Feeder 2 has a few PHEV charging stations, and residential load.  Feeder 3 has 1.8 MW installed capacity of wind generation, and commercial load. Carsten Böse– Germany – Session 5 – Paper 0578

Frankfurt (Germany), 6-9 June 2011 Advanced Distribution Network Analysis Whether Forecast Load Forecast Controllable Loads Distributed Generation Carsten Böse– Germany – Session 5 – Paper 0578 Volt / var Control Switching Control Power Electronics Demand Response Advanced Distribution Network Analysis including electrical behavior and Distribution Management Systems algorithms for pre- and post analysis

Frankfurt (Germany), 6-9 June 2011 Example: Loads Carsten Böse– Germany – Session 5 – Paper 0578

Frankfurt (Germany), 6-9 June 2011 Eample: Transformer Utilization Carsten Böse– Germany – Session 5 – Paper 0578

Frankfurt (Germany), 6-9 June 2011  Availability of online data import is crucial for precise evaluations of networks  Operational aspects have to be considered in future network planning  Power System analysis will identify new requirements for Distribution Network Automation (DNA) and Management (DMS) Carsten Böse– Germany – Session 5 – Paper 0578 Conclusions