Blazhe Gjorgiev


Last Name

Gjorgiev

First Name

Blazhe

ORCID

0000-0001-8759-9324

Organisational unit

09452 - Sansavini, Giovanni / Sansavini, Giovanni

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2017 - 201992020 - 202661
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Publications1 - 10 of 70
  • Meta-heuristic approach for validation and calibration of cascading failure analysis
    Item type: Conference Paper
    Li, Bing; Gjorgiev, Blazhe; Sansavini, Giovanni (2018)
    2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
  • Transmission Expansion Planning via Multi-objective Deep Reinforcement Learning
    Item type: Other Conference Item
    Varbella, Anna; Gjorgiev, Blazhe; Zio, Enrico; et al. (2024)
    Book Of Abstracts for 2024 International Conference on Resilient Systems
  • Coordinated Medium- and Low-voltage Distribution Systems Planning Under Volatile Energy Sources Deployment
    Item type: Other Conference Item
    Oneto, Alfredo Ernesto; Gjorgiev, Blazhe; Sansavini, Giovanni (2024)
    EURO 2024 Conference Handbook & Abstracts: 33rd European Conference on Operational Reseach (EURO XXXIII)
  • PowerGraph: A power grid benchmark dataset for graph neural networks
    Item type: Conference Paper
    Varbella, Anna; Amara, Kenza; Gjorgiev, Blazhe; et al. (2023)
    NeurIPS 2023 Workshop: New Frontiers in Graph Learning
    Public Graph Neural Networks (GNN) benchmark datasets facilitate the use of GNN and enhance GNN applicability to diverse disciplines. The community currently lacks public datasets of electrical power grids for GNN applications. Indeed, GNNs have the potential for capturing complex power grid phenomena over alternative machine learning techniques. Power grids are complex engineered networks that are naturally amenable to graph representations. Therefore, GNN have the potential for capturing the behaviour of power grids over alternative machine learning techniques. To this aim, we develop a graph dataset for cascading failure events, which are the major cause of blackouts in electric power grids. Historical blackout datasets are scarce and incomplete. The assessment of vulnerability and the identification of critical components are usually conducted via computationally expensive offline simulations of cascading failures. Instead, we propose the use of machine learning models for the online detection of cascading failures leveraging the knowledge of the system state at the onset of the cascade. We develop PowerGraph, a graph dataset modelling cascading failures in power grids, designed for two purposes, namely, i) training GNN models for different graph-level tasks including multi-class classification, binary classification, and regression, and ii) explaining GNN models. The dataset generated via physics-based cascading failure model ensures generality of the operating and environmental conditions by spanning diverse failure scenarios. In addition, we foster the use of the dataset to benchmark GNN explainability methods by assigning ground-truth edge-level explanations. PowerGraph helps the development of better GNN models for graph-level tasks and explainability, critical in many domains ranging from chemistry to biology, where the systems and processes can be described as graphs. The dataset is available at https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at https://anonymous.4open.science/r/PowerGraph/.
  • Low-risk-cost design under uncertainties for resilient operations in energy systems
    Item type: Other Conference Item
    Ampellio, Enrico; De Marco, Francesco; Gjorgiev, Blazhe; et al. (2024)
    Book Of Abstracts for 2024 International Conference on Resilient Systems
  • Cascading Failure Analyses for Power System Vulnerability Assessment
    Item type: Other Conference Item
    Gjorgiev, Blazhe; Sansavini, Giovanni (2021)
    Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)
    Power systems as a critical infrastructure are an integral part of human society and are therefore of paramount importance to modern life. Vulnerabilities in the system, that are reviled either by accidental or deliberate events, can cause large losses of power supply with sever social and economic consequences. A tool that identifies the vulnerabilities in a power system can provide the operators the means to support a more reliable power system operation. This paper presents a methodology for power system vulnerability assessment that couples an AC based cascading failure simulation model and a meta-heuristic optimization procedure. The objectives of the assessment is to (1) rank the most important branches in the transmission grid, and (2) identify sets of branches if simultaneously tripped will cause the cascade with highest intensity. The first objective is achieved by ranking the criticality of the branches using two criteria (i) the impact that each branch failure has on the DNS and (ii) the frequency of line overload. The second objective is achieved by hard linking an AC based cascading failure simulation model and a meta-heurist based optimization procedure. The algorithm developed for the purpose of this study is applied to the IEEE 118-bus test system. The results demonstrate the capability of the proposed methodology to identify vulnerabilities in a power system.
  • Changing energy mix and its impact on grid stability
    Item type: Educational Material
    Sansavini, Giovanni; Gabrielli, Paolo; Gjorgiev, Blazhe; et al. (2021)
  • Water-energy nexus: Impact on electrical energy conversion and mitigation by smart water resources management
    Item type: Journal Article
    Gjorgiev, Blazhe; Sansavini, Giovanni (2017)
    Energy Conversion and Management
  • Improving nuclear power plant safety through independent water storage systems
    Item type: Journal Article
    Gjorgiev, Blazhe; Sansavini, Giovanni; Volkanovski, Andrija (2017)
    Nuclear Engineering and Design
  • PowerGraph: A power grid benchmark dataset for graph neural networks
    Item type: Conference Paper
    Varbella, Anna; Amara, Kenza; Gjorgiev, Blazhe; et al. (2024)
    Advances in Neural Information Processing Systems 37
    Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, we must develop grid analysis algorithms to ensure reliable operations. These key tools include power flow analysis and system security analysis, both needed for effective operational and strategic planning. The literature review shows a growing trend of machine learning (ML) models that perform these analyses effectively. In particular, Graph Neural Networks (GNNs) stand out in such applications because of the graph-based structure of power grids. However, there is a lack of publicly available graph datasets for training and benchmarking ML models in electrical power grid applications. First, we present PowerGraph, which comprises GNN-tailored datasets for i) power flows, ii) optimal power flows, and iii) cascading failure analyses of power grids. Second, we provide ground-truth explanations for the cascading failure analysis. Finally, we perform a complete benchmarking of GNN methods for node-level and graph-level tasks and explainability. Overall, PowerGraph is a multifaceted GNN dataset for diverse tasks that includes power flow and fault scenarios with real-world explanations, providing a valuable resource for developing improved GNN models for node-level, graph-level tasks and explainability methods in power system modeling. The dataset is available at https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at https://github.com/PowerGraph-Datasets.
Publications1 - 10 of 70