Digitalization beyond Smart Metering. SCCER-FURIES meeting 2 Nov. 2017/ Lausanne, Switzerland

Transcription

1 FHNW, INSTITUT FÜR AEROSOL- UND SENSORTECHNIK, GRUPPE EL. ENERGIETECHNIK (PROF. SCHULZ) USI, INSTITUTE OF COMPUTATIONAL SCIENCE (PROF. KRAUSE) USI, ADVANCED LEARNING AND RESEARCH INSTITUTE (PROF. MALEK) Dr. Ingo Herbst, Siemens Energy Systems WP1: Arbon Demonstrator Digitalization beyond Smart Metering SCCER-FURIES meeting 2 Nov. 2017/ Lausanne, Switzerland

2 Content a) Targets for industrial partners b) Status of the Jan Jul deliverables c) Collaboration within SCCER, with other SCCERs and industrial partners d) Meetings, knowledge and technology transfer activities with the industry

3 Targets for industrial partners Show the impact of digitalization: How can smart meter contribute to an increased information concerning the existing LV infrastructure (only SW, no HW investments) Use of standard load data, 15min values Use of special synchronized meter reading (PSS, Siemens feature) Integration of smart meter and power quality devices (PQ) under one analysis platform Integration of simulation of power network behavior Comparison of simulated data with real meter data to estimate simulation errors that may appear when estimating the impact of new prosumers: PV, E-car-charging Analyze LV network behavior in formerly unstudied situations Optimize operation at Arbon Energie where possible Scientific Progress Jan 2017 Jul 2018 Deliverables

4 Status of the Jan Jul deliverables

5 Status of the Jan Jul deliverables: on schedule Scientific Progress Jan 2017 Jul 2018 Deliverables

6 Status of deliverables: HW / SW concept in Arbon: done SW/FW at Arbon for PQ and smart metering has been upgraded to accommodate project needs Scientific Progress Jan 2017 Jul 2018 Deliverables

7 Status of deliverables: HW / SW concept in Arbon: done Scientific Progress Jan 2017 Jul 2018 Deliverables

8 Status of deliverables: anonymization: done Anonymization concept done SW coded Paper written by USI Group meter locations of neighbors Testing to be done in Arbon Scientific Progress Jan 2017 Jul 2018 Deliverables

9 Anonymization - Cluster Creation Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y Start from a geographical clients/smart meter distribution 71 nodes X

10 Cluster Creation (1) Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y Identify and list all nodes (smart meters) connected to the same feeder 71 nodes X

11 Cluster Creation (2) Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y Divide the two halves in two subsequent halves, each containing the same number of nodes by moving horizontal (or vertical) axis from left to right (or bottom-up) nodes nodes X

12 Cluster Creation (3) Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y nodes nodes nodes nodes nodes X

13 Cluster Creation (4) Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y Repeat the algorithm until the number of meters in each cluster becomes lower or equal to the given maximum cluster size 49 nodes nodes nodes nodes 97 nodes 38 nodes nodes nodes X

14 Cluster Creation (5) Goal Access, store, analyze and visualize privacy sensitive data Our approach Cluster clients and assign an id and location to each cluster How to cluster Solution: Sliding Ruler Algorithm for clustering closely located smart meters belonging to the same feeder Basic idea Sliding along X and Y axes until partitioning in half +/- 1 is reached Challenges Complexity and performance of the algorithm Y Calculate normalized location (i.e. center of mass ) for each cluster of nodes 9 nodes 9 nodes nodes 9 nodes 8 nodes 9 nodes 9 nodes 9 nodes X

15 Status of deliverables: Use cases formulation: done Analyze PV prosumers: started Analyze losses in the grid: 1 st results based on load data and PQ Analyze losses in the grid: 1 st measurement based on sync. PSS to come Analyze voltage quality Detection of reactive infeed Identify cause of flicker Current asymmetry and load at transformer Current asymmetry and load at smart meter Analyze voltage harmonics Analyze the flexibility potential of boilers Load shifting potential based on real data after optimization by static load management that has been already done in Arbon Scientific Progress Jan 2017 Jul 2018 Deliverables

16 Status of deliverables: Google map: under construction GIS file parsed: smart meter & power lines The data that is drawn on top of the map does NOT use the GoogleMaps-API, it does NOT send ANY data to Google The data is therefore completely inaccessible from the outside Furthermore, it allows us to draw whatever is necessary on the map We are not restricted to the few options for markers provided by the GoogleMaps-API GIS database needs corrections by Arbon Energie All existing meters can be displayed Full visualization functionality to be developed step by step Use case dependent Geographical display Topology Scientific Progress Jan 2017 Jul 2018 Deliverables

17 First results last week: Google map & topology (1) Meter can be displayed on a Google map including power lines (2) Some associations between meter location and power line need optimization (1) green center: transformer station 1, green dots associated. Red dots not yet associated (3) Power flow graph shows integrated power flow at a selected position (4) Topology is established from Arbon s GIS data (4) (1) (2) (3) Scientific Progress Jan 2017 Jul 2018 Deliverables

18 Milestones & Deliverables in SCCER framework: no change M1.1.1a Eye on the grid established D1.1.1a Chapter(s) on the Eye on the grid established, 3.3. USI-ICS, Prof. Krause [Dec 2017] D.1.1.1b Data Analysis on Arbon LV-network-data performed, several use cases, Prof. Malek [2018] M1.2.1c The data selection and aggregation for DSM is established D1.2.1c Chapter(s) on the data selection and aggregation for DSM is established, 3.2 USI-ALaRI, Prof. Malek [Dec 2019] M1.2.5i Simulations on central DSM are operational, battery storage included D1.2.5i Chapter(s) on the Central DSM is operational, 7.1 FHNW, Prof. Malek & Schulz [Dec 2019]. This includes an analysis on flexibilities using a battery and a new boiler control (based on AMIS meter data and installed load switches) Scientific Progress Jan 2017 Jul 2018 Deliverables

19 Collaboration within SCCER, with other SCCERs and with industrial partners

20 Collaboration within SCCER and with other SCCERs Dr. I. Herbst Dr. I. Herbst Dr. J. Zapata Collaborations

21 Collaboration within SCCER FURIES and industrial partners Common Meetings at different sites with all partners done Working meetings of USI/Lukovic / FHNW/Witzig / USI Schaerfig / ZHAW/West getting more and more efficient. Team spirit starts to help solving open issues in collaboration Monthly TelCo with all team members Common work package and delivery documentation established Research partner gain speed Language is a topic in this project, MoM need to be written in 2 languages Industrial partners trust in the project increases as interesting results appear. Collaborations Within SCCER

22 Collaboration with SCCER CREST SCCER CREST (ZHAW) is a partner in the work package Business Cases starting 4/2017 Input to a prosumer storage battery business case comes from the analysis of the existing prosumer load data to quantify production and consumption of energy and estimate relevant proposals for battery usage towards Arbon s prosumers Clarify the legal situation at the moment A positive business case may have impact on the project: Battery installation Lesson Learned: Communication passes through project management to improve efficiency and gain speed: get things done Collaborations With other SCCERs

23 Added value

24 Added value 1) As an add on to the project: a) PhD dissertation completed at USI: Online Disturbance Prediction for Enhanced Availability in Smart Grids (defended on October 25th, 2017) b) A master thesis was started at FHNW: Optimization of power losses of paralleled transformers in the LV power network. Use the information of PQ-meters at the transformer station to decide on the operation mode: Single operation at low load (avoid hysteresis losses) Parallel operation at high load Presentation to industrial partners 12/2017 2) Depending on the speed of the project we can answer actual questions concerning E-Car-Charger and PV integration impact within this project. General Added value

25 Thank you for your attention