Climate resilience Power Systems in Transition Analysis

Another pioneer definition comes from the Multidisciplinary and National Center for Earthquake Engineering Research (MCEER), USA, where a generic organizational resilience framework has been developed that can be applied to any critical infrastructure, including power systems. The term resilience has been used in very different fields of knowledge for many decades, and it has been more recently applied in the power system sector https://24thainews.com/housing-and-utilities-fund-to-switch-to.html due to the increasing number of extreme events which negatively affect power systems . The management of the system in case of extreme events can benefit from the introduction of the property of “resilience”.

Through a comprehensive analysis of past studies, this section underpins the theoretical foundation of our proposed approach, highlighting its novelty and relevance in addressing the gaps identified in current methodologies. The robustness provided by DRO ensures that the strategies developed are not only optimal under normal conditions but also resilient under various potential disruptions11. For each lesson, the report provides a snapshot of how Ukraine has addressed these challenges alongside practical recommendations that policymakers and regulators can tailor to their national risk profiles and priorities. His research interests concern probabilistic risk-based approaches to power system resilience assessment and enhancement, cascading outage analysis, security assessment techniques. He has been active in EU and national research projects on power system resilience, security, adequacy, risk, HVDC, flexibility, ancillary services, also supporting the Italian Authority for energy, the Ministry for Environment and Energy Security, and the TSO.

Term “degradation” refers to both the power supply and the grid infrastructure, thus the property definition can be applied to both infrastructural and operational resilience. “Severity” in the present definition refers to the “severity of the event consequences”, which must be kept separate from the “severity of the event” which in general does not imply any system degradations. The ability to limit the extent, severity and duration of system degradation following an extreme event.

Power system resilience: current definitions and genesis of the new approach

This section presents the current definitions of resilience in the electricity sector and the motivations underlying the proposed resilience definition. These include significantly deteriorated operational capabilities, possibly leading to widespread cascading impacts that could also affect interdependent critical infrastructures with catastrophic consequences. The application of resilience concepts can assist utilities and regulators to encourage prudent investments to enhance system performances in case of extreme events characterised by low frequency of occurrence but significant consequences , . There is thus a gap coming from both the definitions and the way they are applied, such that extreme events are not part of the traditional reliability analyses. Nevertheless, the traditional criterion for the application of these properties may not assure satisfactory performances of the system in case of extreme events. Several definitions of reliability, adequacy and security referenced in Table 1 do not limit the general properties to specific disturbances or contingencies.

2. Key measures to achieve power system resilience

• If the reliability concept did not include resilience, as in the classical approach, then a power system could not be defined reliable in case of extreme events.
• Reliability, adequacy, and security concepts include elements of planning and operation and can be applied to the power system in steady-state, dynamic, and transient conditions, encompassing all elements of the generation, transmission and distribution systems, and loads .
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• The application of resilience concepts can assist utilities and regulators to encourage prudent investments to enhance system performances in case of extreme events characterised by low frequency of occurrence but significant consequences , .
• Power systems designed for resilience return to normal operation far faster during extreme events and can avoid catastrophic societal impacts and costs.
• In this context, following also NERC’s and FERC’s indications, the concept of “resilience” is applied to all those measures which assure the limitation of system degradation in case of extreme events.

This section presents and discusses the definition for power system resilience discussed in CIGRE WG C4.47 . All these definitions underline that the assessment of power system resilience, unlike security, calls for the evaluation of the restoration process. Resiliency includes a diverse range of topics, such as flexibility, hardening, security and recovery

With this specification the new definition links the definition of resilience property with the application criteria (i.e. application to extreme events). For example, some system situations might be deemed as severe by the TSO yet without any grid degradations (e.g. a degraded operating point without security margins and ready to collapse if a single failure happens on a specific grid component). The replacement of the ambiguous term “magnitude” in definition #8 with the two terms “extent and severity” provides further details about the action of the disruptive event and assures a more focused characterization of the dimensions of system degradation, still keeping the definition concise and effective.

The resilience measures developed in Ukraine to bolster a system under extreme stress offer universal insights that transcend the specific context of armed conflict. They can encourage utilities to include climate resilience in their construction plans and operational regimes by mainstreaming climate resilience as a core element in their own long-term energy and climate policies. After establishing a common assessment framework, policymakers need to send appropriate https://telezonepk.com/tag/ufone-utilities/ signals to essential service providers. Mainstreaming climate resilience in energy and climate policies can send a strong signal, encouraging the private sector to consider climate resilience of electricity systems and address potential market failures.

What is Electric Power Resilience?

• The application of artificial intelligence technology, which has less dependence on mathematical models of physical systems and possesses the ability to self-learn from massive data, enables better perception and prediction of extreme events.
• Firstly, from subsection 4.2 it’s worth noticing that reliability can be interpreted as a fundamental property which does not refer to any specific application criterion (no reference to credible or extreme events).
• The proposed model employs DRO to systematically handle these uncertainties by constructing well-defined ambiguity sets and preparing for worst-case scenarios.
• The introduction of the resilience concept integrates the defense and restoration plans into a wider framework for resilience enhancement which is based on the key actionable measures deployed on different time frames (from planning to operation), including organizational aspects of TSOs.

By processing node and edge data, GNNs can predict potential fault lines, optimize load distributions, and suggest optimal restoration paths, thereby minimizing restoration times and enhancing system stability7,8. This research introduces a sophisticated framework that harnesses the capabilities of Graph Neural Networks (GNNs) and Distributionally Robust Optimization (DRO) to enhance the robustness and efficiency of power system restoration processes. Traditional restoration strategies often struggle with the dynamic and complex nature of modern power networks, where the integration of distributed energy resources (DERs) and renewable energy sources has further complicated the landscape2,3.

The scope of power outages is reduced by adjusting operation methods, ensuring the continuous power supply of critical loads. The potential value of various resources should be fully utilized before disasters for improving resilience. Then, a power supply restoration subproblem (the second stage problem) can be established considering DG scheduling and network reconstruction. The operation of repairing faulty components can be modeled as a maintenance personnel scheduling subproblem (the first stage problem). After extreme events occur, resilience improvement strategies can be divided into pre-disaster prevention strategies, disaster response strategies, and post-disaster recovery strategies. It should be pointed out that static evaluation mainly evaluates the system’s resilience from a specific aspect, which is difficult to effectively reflect the performance of grids during the fault recovery stage.

Leveraging GNN for real-time network analysis and fault prediction

Moreover, a system which is reliable with respect to credible events (i.e. adequate and secure, according to the conventional definition of reliability) may not be resilient to extreme events. In fact, security, which requires no loss of conventional loads, is not assured for extreme events but only for credible contingencies, due to the resulting excessive costs. However, assuring the security of the system in case of multiple outages such as the ones produced by extreme events leads to excessive costs in terms of design, planning, operation, and maintenance. Unlike well-established properties like reliability, resilience is a dynamic multifaceted concept which focuses on extreme (also HILP) events, on the evolution of threats over the time and the interdependence among different critical infrastructures.

Virtual power plants (VPPs) are currently one of the main means of resource aggregation in distribution networks. It achieves reliable distribution of multi-source heterogeneous data which provides a platform for artificial intelligence technology. The application of artificial intelligence technology, which has less dependence on mathematical models of physical systems and possesses the ability to self-learn from massive data, enables better perception and prediction of extreme events. These flexible resources can also help enhance the system resilience under extreme events. This will provide an effective foundation for the new power system in the planning stage.