Giovanni Sansavini


Last Name

Sansavini

First Name

Giovanni

ORCID

0000-0002-8801-9667

Organisational unit

09452 - Sansavini, Giovanni / Sansavini, Giovanni

Search Results

Publications1 - 10 of 276
  • 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
  • Stochastic unit commitment and reserve scheduling under gas-supply disrupted scenarios
    Item type: Conference Paper
    Antenucci, Andrea; Sansavini, Giovanni (2018)
    2018 IEEE International Energy Conference (ENERGYCON)
  • Environmental impact of pioneering carbon capture, transport and storage chains
    Item type: Conference Paper
    Burger, Johannes; Nöhl, Julian; Seiler, Jan; et al. (2022)
    SSRN ~ Proceedings of the 16th Greenhouse Gas Control Technologies Conference (GHGT-16)
    The transition to net-zero emissions requires large amounts of carbon dioxide to be captured and stored permanently in geological storage. To initiate the large-scale deployment of CO2 capture, transport, and storage (CCTS) value chains, immediate deployment of infrastructure is required. The environmental impacts of a value chain relying on ready-to-use technologies instead of optimal ones with long lead times, such as pipelines, is so far unclear. We assess the environmental impacts through the life cycle assessment of an exemplary CCTS value chain from Switzerland to Norway that only uses immediately available technologies. Even though the system relies on suboptimal technologies and is not optimized for a low climate impact, it shows the capability to effectively sequester CO2 without emitting more during its life cycle than is stored. Contrary to previous studies, the ready-to-use transport modes cause a significant share (56.3 %) of the total global warming impact (GWI) of the value chain. More than 73 % of the total GWI stems from the use of fossil fuels during the operation phase of the value chain.
  • Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities
    Item type: Journal Article
    Sun, Li; Stojadinovic, Bozidar; Sansavini, Giovanni (2019)
    Risk Analysis
    In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.
  • 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)
  • Driving the Residential Heating Transition - Policy Assessment Considering Parametric Uncertainty and Near-Optimal Solutions
    Item type: Other Conference Item
    Brodnicke, Linda; Gabrielli, Paolo; Sérès, Albane; et al. (2023)
  • Energy systems modelling for just transitions
    Item type: Journal Article
    Lonergan, Katherine; Suter, Nicolas; Sansavini, Giovanni (2023)
    Energy Policy
    Policymaking increasingly targets an energy transition that is not only low cost and low carbon, but also just. While energy system models have been useful policymaking tools towards achieving the first two objectives, it is yet unclear to what extent they can also support a just transition. Here, we review 73 recent energy systems modelling studies using an analytical coding frame and observe a diversity of approaches to account for energy justice. While models do show promise in being able to support a just transition, especially in terms of assessing distributional outcomes, many of the approaches in the literature are poorly connected to current energy justice goals and discourses, decreasing the studies’ policy relevance and leaving policymakers with suboptimal planning support. Based on our results, we suggest eight actions for modellers to increase the policy relevance of their studies, which include more direct engagement with policy and research discourses, developing location-specific case studies, leveraging public participation in the modelling process, and considering asset decommissioning.
  • The potential of vehicle-to-grid to support the energy transition: A case study on Switzerland
    Item type: Journal Article
    Di Natale, Loris; Funk, Luca; Rüdisüli, Martin; et al. (2021)
    Energies
    Energy systems are undergoing a profound transition worldwide, substituting nuclear and thermal power with intermittent renewable energy sources (RES), creating discrepancies between the production and consumption of electricity and increasing their dependence on greenhouse gas (GHG) intensive imports from neighboring energy systems. In this study, we analyze the concurrent electrification of the mobility sector and investigate the impact of electric vehicles (EVs) on energy systems with a large share of renewable energy sources. In particular, we build an optimization framework to assess how Evs could compete and interplay with other energy storage technologies to minimize GHG-intensive electricity imports, leveraging the installed Swiss reservoir and pumped hydropower plants (PHS) as examples. Controlling bidirectional EVs or reservoirs shows potential to decrease imported emissions by 33–40%, and 60% can be reached if they are controlled simultaneously and with the support of PHS facilities when solar PV panels produce a large share of electricity. However, even if vehicle-to-grid (V2G) can support the energy transition, we find that its benefits will reach their full potential well before EVs penetrate the mobility sector to a large extent and that EVs only contribute marginally to long-term energy storage. Hence, even with a widespread adoption of EVs, we cannot expect V2G to single-handedly solve the growing mismatch problem between the production and consumption of electricity.
  • 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/.
Publications1 - 10 of 276