Trends in Distribution Automation for Active Distribution Grids. Univ.-Prof. Antonello Monti, Ph.D.

Transcription

1 Trends in Distribution Automation for Active Distribution Grids Univ.-Prof. Antonello Monti, Ph.D.

2 Future Energy Systems 2

3 Key Ingredients 3

4 An evolving view of the customer role Customer 1.0 No connectivity Customer 2.0 Limited Connectivity (Billing use case) Customer 3.0 High Quality Connectivity Active player Service provider/user 4

5 Getting the customer into the System Concerns: Utilities will have to deal with components not owned by the utility Customer hardware as main gateway of system threats NORM beyond the meter.. Next Generation Open Real Time Smart Meter A service oriented secured gateway for customer participation 5

6 Low Cost PMU 6

7 A short view on sensors Reduced number to limit investments Dual use (protections and monitoring) Smart processing for extracting more info from few sources: classical observability is not needed Getting digital as soon as possible 7

8 Substation Automation Unit: Distributed Intelligence solution for Active Distribution Networks Reports Data Acquisition MMS Interfaces Database Applications State Estimation Forecast Arbeitsstation Server DMS and Control Center Substation s DLMS/ COSEM Power control FLISR Management model CIM Bridge model Smart Meters IEC A complete set of services for Substation Automation in a single low cost machine: Substation Automation Unit () Structured with a distributed intelligence approach to support scalability in Distribution Networks 8

9 From traditional centralized SCADA to distributed solutions Centralized Architecture HV grid TSO Distributed Architecture based on Primary Substation Primary Substation HV grid TSO Primary Substation Primary Substation DSO MV grid DMS or SCADA DSO Secondary Substation Microgrid MV MGCC Customer MV SM Secondary Substation Microgrid MV MV grid MGCC Customer MV SM DMS or SCADA Secondary Substation Secondary Substation LV grid LV grid MGCC Microgrid LV Customer LV SM LV grid LV grid MGCC Microgrid LV Customer LV SM 9

10 based automation architecture to and DMS to communication is enabled, but is not necessary HV grid TSO Primary Substation Secondary Substation Microgrid MV MV grid MGCC Primary Substation Customer MV SM Secondary Substation DSO DMS or SCADA Improved Robustness Optimized Communication traffic Estimation and control algorithm are less computationally intensive Less data to be stored DMS and SCADA can still allow full operation over the grid LV grid LV grid MGCC Microgrid LV Customer LV SM 10

11 The real life experience 11 Unareti s MV and LV field demonstrator

12 Examples of implementations in RWTH Demo RWTH Lab PMUs RTDS MV and LV grid Simulated in real time 12

13 Examples of implementations in IDE4L Demo Unareti, Brescia, Italy Secondary Substation in Secondary Substation Primary Substation in Primary Substation 13

14 Going virtual Intelligent Electronic Devices () can be fully virtualized by defining proper interfaces with the field Substation intelligent hardware becomes simply dedicated to digitalization and data transfer This architecture fits very well with the new standards for sensor interfaces and the concept of Data Aggregator in a substation 14

15 5G enables fast and secure virtualization Courtesy of ERICSSON Eurolab 15

16 Active Distribution Management System and PMU Integration of LOCO PMU in cloud architecture Provides phasors of voltage and current In state estimation help improvimng the accuracy In multi area state estimation and control permits to synchronize the phase angles of different areas Frequency and ROCOF can be used to calculate PQ indexes Frequency, ROCOF and synchrophasors can be forwarded to TSOs for dynamic analyisis of the grid 16

17 Phasor Data Concentrator and cloud applications Data are stored in Time series DB or Relational DB like Postgres Open source software for data visualization Example: testing of transducers in Eon ERC laboratories brought some quick voltage drops Example: histogram of frequency reading in the last 3 hours 17

18 Advanced analytics and visualization in the cloud: GRIDHOUND Advanced Analytics based on Artificial Intelligence remove the concept of grid observability: reduced sensor investment Fast computational performance for high dynamic requirements: scalability to cover LV grid Cloud (also private) for HW scalability Option of going in the direction Automation as as Service 18

19 Conclusions Distribution Automation is rapidly changing Distributed intelligence supports scalability and observability down to LV Cloud architectures open the way for HW/SW