T1: Smart charging technology and grid services: insights and solutions from Denmark
Organizers: K. Sevdari, T. Unterluggauer, M. Martinelli, Denmark Technical University
The rise of electric vehicles (EVs) will challenge operation and security of power grids. The dilemma that utilities are globally facing is whether to invest in costly grid rein- forcements, apply economic disincentives to prevent charging at specific times of the day or actively procure flexibility services via smart charging approaches. We are currently beginning to tap from the potential of smart charging and are gaining practical experience from public demonstrations. Therefore, this tutorial aims to explain the smart charging process from technology deployment, charging clusters, grid services and impacts, user acceptance and battery degradation issues. The tutorial provides a bird’s eye view of European charging technology and grid services, focusing mostly on the landscape in the Nordic countries. We present the results of a novel city-wide simulation model. We present the modeling approach and implications of price- synchronized smart charging on the low and medium-voltage network of Frederiksberg. Finally, the tutorial provides insights from the Danish commercial bidirectional smart charging case.
Kristian Sevdari (Smart charging from theory to practice):
- Introduction to smart chargers and their current state of art.
- Definition of smart chargers and latest features.
- Linking smart charging with grid services and grid location.
- EVs' flexibility supply chain.
Tim Unterluggauer (Grid impact of Smart Charging on Urban Power Distribution Networks):
- Introduction to cost-based smart charging and the case study of Frederiksberg (Danish FUSE project).
- Modeling of medium-voltage and low-voltage distribution network.
- Modeling of EV demand and smart charging strategies.
- Implications of avalanche effects due to price synchronization.
Mattia Marinelli (Learnings from Danish ACES Project):
- Insights from the Danish ACES project.
- Frequency containment reserve (FCR-N) provision with V2G chargers.
- Nuvve as the aggregator delivering FCR-N to Energinet (Danish TSO).
- Battery degradation based on 5+ years real life measurements.
T2: Machine Learning for Solving Optimal Power Flow Problems
Organizers: M. Chen (City Uni of Hong Kong), S. Low (Caltech, USA)
The optimal power flow (OPF) problem is to determine the least-cost generator dispatch to meet the load in a power network, subject to physical and operational constraints. It is central to power grid operations and underpins various applications, including real-time market clearing, unit commitment, demand response, reliability assessment, and grid modernization endeavors for pursuing carbon neutrality and mitigating climate change. It concerns billions of US dollars each year globally and a 5% saving amounts to 36 billion US dollars.
With increasing uncertainty from intermittent renewable, distributed generation, and flexible loads, the optimal operating point of the electrical power system may change rapidly during real-time operations. As such, grid operators now need to solve OPF problems more frequently to track the optimal operating points. Increasing uncertainty will also demand more frequent reliability and risk assessments. However, the OPF problem with a full AC power flow formulation is non-convex and NP- hard, making it difficult to solve efficiently by iterative solvers, especially for large-scale instances.
Recently, there have been increasing efforts in employing machine learning to solve OPF problems in a fraction of the time used by iterative solvers. To date, various studies have applied learning techniques to generate quality solutions for popular OPF formulations with a few orders of magnitude speedup as compared to iterative solvers, over power networks with realistic topology and load profiles. This tutorial will provide an overview of the ongoing research in developing neural networks and general machine learning schemes to identify active and inactive constraints in OPF problems, to provide warm start points for iterative OPF solvers, to learn iterative strategies for solving OPF problems, to learn the OPF load-solution mapping and then use the mapping to obtain quality OPF solution directly, etc. We will cover both background and the fundamental, state-of-the-art results, open issues, and potential future directions. We will also discuss the applications of the machine learning approaches in solving general constrained optimization puzzles in other problem domains.
- Grid operations and OPF formulations (e.g., standard OPF and stochastic OPF with resource)
- Relevant approaches and recent advances (e.g., robust and chance-constrained optimization)
- Machine learning basics for mapping approximation and constrained optimization
- Hybrid machine learning approaches for solving OPF problems
- End-to-end machine learning for directly solving OPF and constrained optimization problems
- Machine learning for OPF problems over flexible topology and with multi-valued mappings
- Solution feasibility of machine learning for OPF and constrained optimization problems
- Machine learning for solving OPF problems with security and uncertainty considerations
- Unsupervised learning, GNN, and approaches for solving large OPF problems
- Open issues and potential directions
T3: AI and Next Generation Multiple Access (NGMA)-Aided Non-Terrestrial Networks
Organizers: A. Kaushik (Uni of Sussex, UK), W. Shin (Ajou Uni, South Korea)
There is an indispensable need of technical advancements and digital transformation for the next generation communication networks not only supporting terrestrial setups but also the emerging satellite and sky-oriented technologies. In the direction of non-terrestrial networks (NTNs) technology, evolving signal processing methods, edge and cloud computing, deep reinforcement learning techniques for low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) in NTNs, next generation multiple access (NGMA)-assisted deployments for NTNs, etc., have drastically changed the current realization of the space, sky and terrestrial communication networks. This tutorial will present a comprehensive overview of the emerging NTNs based wireless networking including fundamentals, requirements and emerging problem design concepts. The tutorial will cover key enabling technologies for NTNs such as green-artificial intelligence (AI), deep reinforcement learning and edge computing enabled NTNs, robust multiple-input multiple- output (MIMO) beamforming and interference management through NGMA for NTNs which lead to the development of exciting new vertical frameworks.
- Roadmap to 6G and Role of NTN IoTDeep Learning and Edge Computing Assisted NTNs
- Green-AI based Multiple Access and Machine Learning for NTNs
- Multi-Beam Rate-Splitting Multiple Access in NTNs
- High Doppler Effect and Intelligent Metasurfaces-Aided NTNs
- Q and A session
T4: Distributed Energy Resources Cybersecurity: Vulnerabilities, Attacks, Impacts, and Mitigations
Organizers: I. Zografopoulos (KAUST-KSA), C. Konstantinou (KAUST, KSA), S. Lakshminarayana (Warwick, UK)
The tutorial will present a comprehensive overview of the cyberattacks targeting DER assets on both the device and communication levels. We discuss cyberattacks targeting the DER devices them- selves and their autonomous capabilities (e.g., defensive islanding) and ancillary services (e.g., volt- age/frequency regulation, active/reactive power compensation, etc.). DER security should be viewed holistically and is contingent upon the security posture of the inherent DER device architectures (e.g., vendor-specific), the utilized communication protocols, and control schemes (e.g., user, aggregator, or utility -defined). Comprehensive security solutions should encompass an adversary viewpoint capa- ble of not only mitigating previous incidents (as most of the literature does), but proactively defend against “what could happen” scenarios. To bridge this overlooked research gap, the tutorial will first present the motives and resources of adversaries and the crucial components of DER systems, before focusing on DER attacks at the i) communication protocol and ii) device levels. Last, DER protocol- and device- level vulnerabilities, attacks, impacts, and mitigation strategies will be presented.
- Cybersecurity in power systems & threat modeling
- DER protocol-level vulnerabilities, impacts, and mitigations.
- DER device-level vulnerabilities, impacts, and mitigations.
- Case studies
- DER cybersecurity metrics and future challenges
- Concluding remarks
T5: The Role of Satellite in Smart Grid Connectivity
Organizers: G. Cox, Global Invacom, UK
Satellite has traditionally been seen as a niche connectivity solution, and only then for enterprise, with little regard for smart grid/IoT applications. Historically solutions relied upon geostationary satellites but with new satellite constellations being launched and operated in non-geostationary low and medium earth orbits there are now other options available. This tutorial will introduce satellite orbits and the key players, look at link budget and latency and the pros and cons each that each present when used for SCADA/IoT applications.
- Introduction to satellites:
- Introduction to Antennas
- Types and Terminologies
- Link Budgets
- Friis Equation
- Satellite use cases for SCADA
- Pro/Cons inc security considerations
- Worked Examples
- Drawn from Utility use cases