Architecture and Infrastructure Issues in Automatic Meter Reading (AMR) Networks

1. Architecture and Infrastructure Issues in Automatic Meter Reading (AMR) Networks DRTS group meeting 2010-09-07

2. What is AMR? • Automatic Meter Reading • Correctinvoiceeverymonth • New infrastructure for a singleapplication • Expensive (14 BSEK, Sweden) • Little value • Raiseenergycustomerawareness • ”New” requirement on the AMR infrastructure • Otherapplications to come?

3. Technologies • PLC • Cheap, slow, old • The principleused for manyyears • Hybrids • Actualuse is a cost-balancechoise • GPRS/GSM/cell data • Mostuseful and expensive, goodcoverage

4. Cost-effective and scalable • Tidaholm Energi • Advanced meters, homogenous (Aidon meter) • 8-10 parameters, all collectedbatch-wise (1h) • 7000 subscribers • Vattenfall • Needinexpensive AMI, heterogenous • Filteringout 1 parameter / month (1m) • 1 100 000 subscribers (Sweden), 7.8 M overall

5. Challenges • Real-timepropagation of updates • Propagated as raw, or aggregated • Central and distributedusage of updates • 9 hoursdelay of 24h readings No RT applications • Is GPRS the solution? • Cost-effective? Cheapercircuits and data plans. • Telecom systems are built to scale. Infrastructure exists. • Is IP the solution? • IP everywhere: ”Internet of things” • IP is scalable. Infrastructure exists. Largefoot-print?

6. Envolve projectthemes

7. Pre-studies • Data modeling(Aug ’10 – Mar ’11)Understand energy consumption patterns • Build user behavioral models from data • Is current data enough to get new knowledge? • Which sources to fuse? • Data collection(Aug ’10 – Mar ’11) Understand architectural needs • Fusion, where and how? • Scalability and hierarchy • Which users of new knowledge?

8. Data collectionpre-study • How to • Ensureconnectivity • Enablereal-time performance • Make use of distributed fusion • Support large-scale, short-intervaldata collection and processing • Support futurecommunicationneeds

9. Project proposals

10. Project: Max Peak Shifting • Demand-Response decision support • How far can behavioral adaptation go? • How far can decision support go? • Active controller for peak shifting • Spot price vs. load in home • Appliances and smart plugs • Simple user control for decision support, schedule and policy-based actuation • Prioritized load groups in home • Scale up a demonstration model home • Simulation-based study for large-scale effect www.his.se/infofusion/envolve

11. Project: Trends and Alarms • Use aggregated behavior • Anomalies in time series • For individuals or groups • “Virtual sensors”, “indicators” • Interoperability and architectural needs? • Users of the knowledge? • From sector indicators to overall indicator • Fuse with other data sources: Retail, Telephone, SOS Alarm,SMHI, season, .. www.his.se/infofusion/envolve

12. Project: Measure for applications • Next useful applications (“ next killer apps”) • What is the very best possible? • Applications decide what to measure • Applications drive development by needs • Data is central • Data owner, beneficiary, infrastructure • Derived-knowledge owner • Flexible and open data infra-structure is needed Measurements, Raw data Applications www.his.se/infofusion/envolve

13. Project: Magic Advisor • Capitalize on model of behavioral profiles • Give advise to move between profiles • Compare trends with current profile • Improved model • Fuse with additional data sources • Improved real-time and alarms • Innovative user interface • Leverage the involvement, multi-modal? • Added services • Sub-meter installations for refined profiling • Better CR for Utility companies www.his.se/infofusion/envolve

14. Where is research today?

15. SAMPLEPAPERS • "Fusion" - A Comparative Study of Data Storage and Processing Architectures for the Smart Grid • Demand and Response - Demand Side Load Management using a Three Step Optimization Methodology - Power Demand Shifting with Smart Consumers - Control Mechanisms for Residential Electricity Demand in SmartGrids- Dynamic Load Modeling of an HVAC Chiller for Demand Response Applications • Architectures - Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain - Demand Response Architecture- Integration into the Distribution Management System - Incentive Design for Lowest Cost Aggregate Energy Demand Reduction - A Unified Solution for Advanced Metering Infrastructure Integration with a Distribution Management System - Seamless Data Communication and Management over all Levels of the Power System • Communication- High Assurance Smart Grid: Smart Grid Control Systems Communications Architecture - Facilitating a Generic Communication Interface to Distributed Energy Resources Mapping IEC 61850 to RESTful Services - WattDepot: An Open Source Software Ecosystem for Enterprise-scale Energy Data Collection, Storage, Analysis, and Visualization - Scalability of Smart Grid Protocols - Protocols And Their Simulative Evaluation For Massively Distributed DERs - SmartGridLab: A Laboratory-Based Smart Grid Testbed • Privacy - Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures - Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures - Smart Grid Privacy via Anonymization of Smart Metering Data - Survey of Smart Grid Standardization Studies and Recommendations