Conference Day Two | Wednesday 26th September 2018

08:00

Welcome refreshments

08:50

Welcome back from the chair

Benjamin Kauffmann,  Senior Manager  – Accenture Smart Grid Services

09:00

Combining Data – creating an intuitive framework for combining multiple data streams of varying structure, quality, and time series to deliver more meaningful insights and actions

  • Gathering data from distinct sources and uniformly compiling it to produce more complex and detailed data sets for analysis
  • Overcoming structural differences between data drawn from a variety of systems implemented without consideration of interoperability
  • Enforcing standards for the recording of data to ensure its consistency and completeness and minimise the data preparation required to perform analytics
  • Integrating real time data with historic time series data
  • Incorporating data from every source possible to build a complete picture of your operations and inform a wide suite of operational and commercial use cases

Robin Hagemans, Partner (Former Senior Manager Data & Insights at Alliander) – Infiniot

09:45

Realtime Data – extending your real-time data capacity and capability to allow instantaneous response from operations and maintenance

  • Defining the challenges and opportunities posed by gathering and processing data in high resolution for use in daily network activity
  • Developing data architecture capable of processing vast quantities of data generated in real time
  • Creating accessible dashboards and monitoring systems fed by real-time data for use by asset management and network control teams
  • Measuring the impact of real-time data for better grid performance

Luca Grella, Innovation Workstream Lead – UK Power Networks

10:30

Morning refreshments exhibition and networking

11:00

Technology Innovation Panel during this session 3 big data platform, tool, and cyber-security suppliers will present their technology innovations, reveal how utility market trends are informing their product development activities, and invite suggestions from the audience on how they can better meet future utility needs both on a technical and commercial level. Come armed with your questions and leave with clarity on how future technologies are evolving to better meet end-user needs.

Andy Gay, Programme Manager for Advanced Utility Analytics – GE Power

Jennifer Major, Head of loT- SAS UK & Ireland

Mark Ewen, Vice President of Global Strategy for Utilities and Geospatial – Cyient

12:30

Lunch, exhibition and networking

14:00

Dynamic Sizing of Balancing Reservesexploiting machine learning techniques increase the reliability of the balancing mechanism while reducing the cost of procurement

  • Designing innovative methodologies to cope with the increasing variability of imbalance risk by dynamically dimensioning FRR needs in D-1 timescales
  • Determining the highest impact factors driving imbalance risk and converting these metrics into a selection for optimal features
  • Applying machine learning algorithms to optimise reserve sizing based on system condition forecasts including solar and wind generation or planned outages
  • Selecting a machine learning algorithm to guarantee transparency and intuitiveness, thereby eliciting trust from key users
  • Collaborating with relevant end users throughout proof-of-concept and implementation to ensure the needs of all market participants are considered
  • Guaranteeing the robustness of the dynamic sizing methodology for future system evolutions such as nuclear phase-out, additional RES increase, and new HVDC cables

Guillaume Leclercq, Senior Consultant – N-Side
José Gonzalez Pastor, Economic and Adequacy Analyst – Elia

14:45

Non-Technical Loss Use Case – combining granular grid and consumer data to isolate and efficiently eliminate non-technical losses

  • Optimising revenue protection by locating non-technical losses and moving more quickly to curtail them
  • Using a combination of historical inspection results and smart meter data to train a supervised machine learning model to predict where losses are located
  • Automatically extracting important features related to demand such as the identification of drops in consumption
  • Estimating the amount of energy which will be recovered to guide future inspections
  • Enabling a targeted approach to guide investigations into energy theft, protect revenue, and reduce bills for law-abiding customers

Mario Namtao Shianti Larcher, Data Scientist – Enel

15:30

Afternoon refreshments, exhibition and networking

16:00

Visualisation – utilising innovative tools to provide impactful and accessible reports and dashboards to internal and external stakeholders

  • Empowering data science teams to translate complex data-driven information into powerful insights for an array of business functions
  • Interpreting clients’ requirements and establishing a realistic scope of work for visual output
  • Employing multiple visualization tools to meet the notably different demands of distinct business units
  • Enabling self-service business intelligence for advanced users
  • Combining analyses of multiple data sources to produce complex yet intuitive visualisations such as heat maps and dashboards
  • Meaningfully communicating your data analytics output to enhance the performance of multiple business units and meet the demands of external agents

Ivan Sturlic, Head of IT Department for Power System Planning, Analysis and Market Support – HOPS

16:45

Grid Planning Use Case – identifying the optimal range of data sources and analytics models to support accuracy in long range grid planning and investment decision making

  • Developing situational awareness of the grid and enhancing this by overcoming data quality issues including those in geospatial data
  • Developing a “digital twin” model for the connectivity and topology of a low voltage network
  • Identifying underperforming spots within the grid
  • Using data on consumption and grid capacity to inform efficient future development of the network
  • Producing models for likely network requirements which incorporate a vast number of variables
  • Developing projections for multiple energy scenarios while accounting for the potentially unpredictable uptake of new technologies
  • Increasing the depth of analytics to generate prescriptive insight into your grid planning strategy

Dieter Vonken, Senior Manager, Asset Management Excellence & Data Analytics – Deloitte

Jean-Pierre Hollevoet, Director Network and Asset Management- Fluvius

17:30

Close of conference day two

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