Tutorials on Tuesday 26 September 2017 at ISGT Europe 2017 include:



Tutorial T1: Increasing the hosting capacity of the grid

Tuesday 26 September 2017 – full day

Organiser: Prof. Math Bollen, Luleå University of Technology, Skellefteå, Sweden

Speakers: Math Bollen (Luleå University of Technology, Skellefteå, Sweden), Fainan Hassan (Turbo Power Systems, UK)

Abstract: The hosting capacity of the grid is the highest amount of new production or consumption that can be connected without endangering the reliability or voltage quality of other customers. It is the basis of a systematic and transparent method for quantifying and communicating the impact of for example renewable electricity production on the grid. Hosting capacity calculations have been performed for a range of phenomena, from overvoltage and unbalance in low-voltage grids to frequency stability in large transmission grids. The hosting capacity concept is also very suitable for comparing different methods improving the ability of the grid to accept renewable electricity production.

In this tutorial the hosting capacity concept will be introduced and examples of calculations will be given. The limitation of calculations and data collection will be discussed in this context. The majority of the tutorial will treat different methods for increasing the hosting capacity. After a brief presentation of classical methods, three smart-grid methods will be discussed: curtailment of production and consumption; power-electronic solutions with the network user (e.g. voltage and frequency control with distributed generation); and power-electronic solutions in the grid (e.g. soft open points and dynamic voltage regulators).

Practical case studies where network users have the potential to provide additional ancillary services to the grid operator through the control of the power electronics interfaces for distributed generation and microgrids will be discussed. Additionally, the opportunities and challenges for providing cost effective solutions using power electronics based equipment in the medium and low voltage grids will be explored in the light of trial results of practical deployments in the UK where three different methods have been installed and compared for increased hosting capacity of congested networks.

Structure: full-day tutorial

Block 1 – Hosting capacity calculations: overvoltage; overcurrent; unbalance; harmonics; the impact of calculation method and data availability on the hosting capacity estimation. (Math Bollen)

Block 2 – Classical solutions and curtailment for increasing the hosting capacity; the role of communication (Math Bollen)

Block 3 – Power electronic solutions with the network users (Fainan Hassan)

Block 4 – Power electronic solutions in the grid (Fainan Hassan)

Tutorial T2: Energy Internet: Concepts and Key Technologies

Tuesday 26 September 2017 – morning

Organiser: Prof. Yan Zhang, Department of Informatics, University of Oslo, Norway

Speaker: Prof. Yan Zhang, Department of Informatics, University of Oslo, Norway

Abstract: Energy Internet (or Internet of Energy, Energy Informatics) is a vision of future power systems, which will achieve highly efficient interconnection among various types of energy resources, storages, and loads and enable ubiquitous energy sharing on a large scale. The realization of Energy Internet requires deep integration of Internet of Things into smart energy systems.
In this tutorial, we will explain Energy Internet basic concepts, key enabling technologies, and our recent research results. We will first introduce the concepts and main architectures related to Energy Internet. Then, we will discuss on how state-of-the-art information technologies (e.g., cloud cmputing, software defined networking) can be exploited in smart energy networks. Further, we will focus on energy trading and present on how game theory can be used to model and analyze energy trading problems. Demand response management, as a key enabling technology, will be explained to balance and shape the electricity demand and supply. Finally, we will present Internet of Things for electric vehicles and Vehicle-to-Grid, e.g., smart charging schemes and new energy sharing scenarios.

Structure: half-day tutorial

Block 1:

  • Internet of Energy overview
    • Basic concepts
    • Architectures
    • Key enabling technologies: overview
  • Energy Information Networks
    • Basic concepts
    • Architectures
    • State-of-the-art information technologies for smart energy systems (e.g., cloud/edge computing, software defined networks)
    • Cyber Security with typical attacks and defence (e.g., false data injection attack)

Block 2:

  • Demand Response Management
    • Definition and the main concepts
    • Different pricing schemes
    • Modelling and analysis of demand response (e.g., game theory, optimization)
    • Energy scheduling for different objectives
  • Internet of Things for Vehicle-to-Grid (V2G)
    • Electric Vehicles and V2G: concepts and architecture
    • V2G Mobile Energy Networks
    • New applications and models for electric vehicles in energy trading
    • New applications and models for electric vehicles in energy sharing
    • Cyber Security with typical attacks and defence (e.g., battery-status privacy issues)

Tutorial T3: HVDC Transmission Systems

Tuesday 26 September 2017 – afternoon

This Tutorial has been organised in collaboration with the IEEE Italy Section PES Chapter PE31

Organiser: Prof. Neville Watson, Department of Electrical & Computer Engineering, University of Canterbury, New Zealand

Speaker: Prof. Neville Watson, Department of Electrical & Computer Engineering, University of Canterbury, New Zealand

Abstract: Worldwide use of HVDC technology is rapidly increasing due to its many advantages. Advances in solid-state devices have opened the door to new opportunities for HVDC technology. The purpose of this workshop is to give attendees an in-depth understanding of various types of HVDC technologies, their characteristics and applications. From this it will be seen how HVDC transmission complements conventional AC transmission, and this knowledge will allow the identification of the most appropriate technology for a given development. Moreover, the influence of the HVDC system on the AC network will be discussed.  Finally, current issues and emerging trends in HVDC transmission will be covered.

Structure: half-day tutorial

Block 1:

  • Introduction: Fundamentals of HVDC transmission systems
    • Drivers of HVDC Transmission
    • Technological developments
    • Types of HVDC Transmission systems
      • Current Source Converters (CSC) [i.e. HVDC LCC]
      • Voltage Source Converters (VSC) [Self-commutated HVDC]
    • Comparison of Line-Commutated and Self-Commutated HVDC
  •  HVDC Line-Commutated Conversion (LCC) transmission systems
    • Characteristics
    • Advantages and disadvantages
    • Harmonics and reactive power
    • The New Zealand experience
      • Commissioning and testing
      • Line and cable faults

Block 2:

  • Self-commutated HVDC transmission systems
    • Characteristics
    • Multi-Bridge Conversion
    • Sinusoidal (carrier based) PWM
    • Multi-Level Converters
      • Diode (or neutral) – clamped circuit
      • Flying Capacitor
      • Cascaded H-bridge
      • Modular Multi-Level Converter (M2C)
  • Advances in HVDC transmission systems
    • Line-Commutated Conversion (LCC)
      • Capacitor-Commutated Converter
      • Continually tuned AC filters
      • Active DC side filters
      • Reinjection concept
    • Self-Commutated conversion (VSC)
      • Multi-terminal
      • Controls
      • DC Circuit Breakers
      • Power-flow Controllers

Tutorial T4: Introduction to Detection of Non-Technical Losses using Data Analytics

Tuesday 26 September 2017 – afternoon

Organiser: Patrick Glauner, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg

Speakers: Patrick Glauner, Jorge Augusto Meira, Radu State (Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg), Rui Mano (Choice Technologies Holding, Luxembourg)

Abstract: Electricity losses are a frequently appearing problem in power grids. Non-technical losses (NTL) appear during distribution and include, but are not limited to, the following causes: Meter tampering in order to record lower consumptions, bypassing meters by rigging lines from the power source, arranged false meter readings by bribing meter readers, faulty or broken meters, un-metered supply, technical and human errors in meter readings, data processing and billing. NTLs are also reported to range up to 40% of the total electricity distributed in countries such as Brazil, India, Malaysia or Lebanon. This is an introductory level course to discuss how to predict if a customer causes a NTL. In the last years, employing data analytics methods such as data mining and machine learning have evolved as the primary direction to solve this problem. This course will compare and contrast dierent approaches reported in the literature. Practical case studies on real data sets will be included. Therefore, attendees will not only understand, but rather experience the challenges of NTL detection and learn how these challenges could be solved in the coming years.

Special requirements: Attendees should bring a laptop.

Structure: half-day tutorial

Block 1:

  • Introduction to NTL
    • Definition of NTL
    • Impact on economies and grids
  • Introduction to relevant data analytics methods:
    • Overview about data mining and machine learning
    • Comparison of popular models used such as decision trees, neural networks, logistic regression, etc.
  • Overview about the state-of-the-art:
    • Comprehensive comparison of research works presented in the literature based on data analytics
    • Contrast to other approaches such as expert systems, energy balance, etc.
    • Comparison of data sets and evaluation metrics used in NTL research

Block 2:

  • Practical case studies on real data sets used in our research:
    • Overview about our published research results (see references at the end of this document)
    • Presentation of novel work and insights from the real data set
  • Challenges to solve in order to advance NTL detection: class imbalance and evaluation metric, feature description, incorrect inspection results, covariate shift, scalability, comparison of different methods