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Although the control of modular multi-level converters (MMCs) in high-voltage direct-current (HVDC) networks has become a mature subject these days, the potential for adverse interactions between different converter controls remains an under-researched challenge attracting the attention from both academia and industry. Even for point-to-point HVDC links (i.e., simple HVDC systems), converter control interactions may result in the shifting of system operating voltages, increased power losses, and unintended power imbalances at converter stations. To bridge this research gap, the risk of multiple cross-over of control characteristics of MMCs is assessed in this paper through mathematical analysis, computational simulation, and experimental validation. Specifically, the following point-to-point HVDC link configurations are examined: (1) one MMC station equipped with a current versus voltage droop control and the other station equipped with a constant power control; and (2) one MMC station equipped with a power versus voltage droop control and the other station equipped with a constant current control. Design guidelines for droop coefficients are provided to prevent adverse control interactions. A 60-kW MMC test-rig is used to experimentally verify the impact of multiple crossing of control characteristics of the DC system configurations, with results verified through software simulation in MATLAB/Simulink using an open access toolbox. Results show that in operating conditions of 650 V and 50 A (DC voltage and DC current), drifts of 7.7% in the DC voltage and of 10% in the DC current occur due to adverse control interactions under the current versus voltage droop and power control scheme. Similarly, drifts of 7.7% both in the DC voltage and power occur under the power versus voltage droop and current control scheme. This work was supported by the EU FP7 program, through the project “BEyond State of the art Technologies for re-Powering AC corridors and multi-Terminal HVDC Systems” (BEST-PATHS), grant agreement 612748. The simulation toolbox can be downloaded from the project website at www.bestpaths-project.eu (accessed on 10 December 2021).

This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems.

Extreme weather events are a major concern for Transmission System Operators (TSOs) because of their increasing frequency and disruptive effects on system infrastructure and load supply. The assessment of component vulnerability to natural threats is essential for an accurate evaluation of power system resilience. However this task is very challenging also due to the interactions among different threats: models should be sufficiently complete to take this complexity into account, but also practical enough to be applied to large power systems. A notable example concerns snow and wind, which determine combined actions both directly on the infrastructure of an overhead line (OHL) and indirectly on the environment (such as trees) around the line path. The paper presents an analytical model of the vulnerability of OHL to the combined actions of snow and wind, assuring a good tradeoff between accuracy and tractability. Simulations performed on a line from an area of the Italian transmission grid show the ability of the model to account for the combinations of snow and wind induced actions both on the OHL infrastructure and on the environment interfering with the OHL.

Extreme weather events are a major concern for Transmission System Operators (TSOs) because of their increasing frequency and disruptive effects on system infrastructure and load supply. Modelling the component vulnerabilities to natural threats is a fundamental pillar to face such events in a resilience perspective. In particular, advanced analytical models of component vulnerability to threats have an important advantage over statistical models: being less sensitive to classification errors, they may favour the development of approaches and tools to support operators’ choices in power system management. Among all natural threats, strong wind represents a very important cause of damages to HV and EHV lines, together with wet snow, in the Italian EHV grid. This paper proposes analytical models to simulate the vulnerability of overhead lines to the direct and indirect effects of strong wind, considering three major subcomponents of the line namely phase conductors, shield wires, and tower supports. Simulations performed on a set of HV and EHV lines from a portion of the Italian grid demonstrate the ability of the model to quantify the benefit of specific mitigation measures on the line vulnerability to strong wind.

The large penetration of renewable energy sources, characterized by correlated forecast uncertainty, calls for suitable tools to help power system operators manage security. This paper proposes a probabilistic preventive redispatching algorithm for conventional generators aimed at assuring N-1 security in presence of correlated uncertainties of renewable sources and loads. The algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method and Cornish Fisher expansion to characterize the chance constraints in a linear programming optimization problem. Simulations performed on an IEEE test system demonstrate the effectiveness of the proposed security control method in limiting the probability of violating branch power limits in N and N-1 state.

The EU-FP7 iTesla project developed a toolbox that assesses dynamic security of large electric power systems in the presence of forecast uncertainties. In particular, one module extracts plausible realizations of the stochastic variables (power injections of RES Renewable Energy Sources, load power absorptions). It is built upon historical data series of hourly forecasts and realizations of the stochastic variables at HV (High-Voltage) nodes in the French transmission grid. Data reveal a large diversity of forecast error distributions: characterizing them allows to adapt the module to the data, improving the results. The algorithm here presented is aimed to automatically classify all the forecast error variables and to cluster them into smoother variables. The main steps of the algorithm are filtering of the variables with too many missing data or too low variance, outliers detection by two methods (Chebyshev inequality, quantile method), separation of unimodal variables from multimodal ones by exploiting a peak counting algorithm, Gaussian mixtures, comparison with asymmetrical distributions, multimodality index, clustering of the multimodal variables whose sum is unimodal, comparing two alternative algorithms (the former based on hierarchical clusterization, accounting for correlation and geographical closeness, and the latter on the identification of the same initial characters in the identification codes).

Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. This paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures that are an integral part of the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented, for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members’ experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions.

Nowadays, distribution network operators are urged by regulatory authorities to reduce the load disruptions due to extreme weather events, i.e., to enhance network resilience: in particular, in Italy they are required to present a yearly plan (called “resilience plans”) describing the interventions aimed to improve network resilience. To this purpose, they need new methodologies and tools to assess the network resilience and to quantify the benefits of countermeasures. This paper proposes the application of a risk-based framework and tool to assess the impacts of extreme weather events in T&D grids, which anticipate critical network situations in presence of incumbent weather threats. To do this, the forecasting of weather events is combined with the component vulnerability models in order to predict which components are more prone to fail. Based on this set of components, the set of most risky contingencies is identified and their impacts on the distribution network in terms of unsupplied load are quantified. The major advantage of the applied methodology is its generality: in fact, it is applicable to both distribution and transmission systems as well as integrated transmission and distribution (T&D) systems, considering the peculiarities of each type of grid, in terms of operation, maintenance and component vulnerabilities. In particular, the application refers to a distribution network connected to a portion of high voltage transmission system in a mountainous zone, with focus on two major threats in the area, i.e., wet snow and fall of trees induced by combined wind and snow. The methodology also quantifies the benefits brought to the system resilience by countermeasures such as reconductoring, optimal reconfiguration or new right-of-way maintenance procedures. Simulations demonstrate the ability of the methodology to support T&D operators in an operational planning context in case of different incumbent threats.

Exploiting multi-terminal High Voltage Direct Current (MTDC) grids for offshore wind park integration into continental bulk AC system raises several technical challenges, in particular the verification of the compliance of MTDC grids and offshore wind parks to the performance requirements (in terms of frequency, voltage support and fault ride through) which are being established by the grid codes, and the availability of general and customizable models to simulate their response. This work is aimed to provide a set of “open access” general models, able to fulfil the grid code requirements if suitably tuned, enabling the TSOs to perform their analyses without relying exclusively on vendor-specific models. In order to verify the realistic response of these models, an example of MTDC grid is simulated, connecting it to plausible operating scenarios of a real world AC system (the West Denmark grid) in target year 2020: some indications to properly tune control parameters are provided together with the results of several simulations aimed to check the fulfilment of grid code requirements in case of typical contingencies.

Extreme weather events are getting more and more frequent due to climate changes and often determine large load disruptions on power systems. As a preliminary step to face this issue, an analysis of the component vulnerabilities to natural threats which may cause multiple, dependent contingencies is required. Furthermore, the availability of advanced analytical models describing the vulnerability of components to threats can favour the development of approaches and tools to support operators' choices in power system management. This paper presents an analytical model to simulate the mechanical vulnerability of overhead lines against wet snow events, considering three main subcomponents of the line namely phase conductors, shield wires, and tower supports. The simulations performed on realistic support types and a reasonable range of mechanical loads, due to the combined action of wet snow/ice and wind, can be useful to identify the weakest subcomponents of the lines and to compute the return times of the outage events.

A major concern for Transmission System Operators (TSOs) is to effectively manage power systems during extreme weather events. This need calls for new methods and tools to support operational planning by forecasting critical conditions in the grid, and to quantify the benefits of the measures deployed to improve system performance under these events. For this reasons, the concept of resilience is particularly useful to build a proper assessment framework. This paper presents an in-depth resilience assessment methodology supporting the evaluation of the system response under different assumptions on threat intensity and component vulnerability, including the modeling of countermeasures aimed to improve system resilience. In particular the paper investigates the models for anti-torsional devices and ice-phobic coatings, i.e. two grid hardening measures against wet snow events. The case studies applied to a model of a realistic EHV/HV transmission system demonstrate the potentialities of the tool, which can be useful for a future quantitative techno-economic assessment of the portfolio of resilience boosting measures.

Natural events may affect the power system infrastructure, leading to multiple, dependent contingencies and widespread load disruptions. Given the high costs of these load disruptions, Transmission System Operators (TSOs) are interested in predicting potentially critical scenarios and in applying suitable preventive and/or corrective countermeasures. In order to increase the resilience of the power system against these events, a preliminary step consists of analyzing the vulnerabilities to natural threats. The paper presents a comprehensive methodology and tool for resilience assessment, based on an extended definition of risk and suitable to support operational planning (identifying the most risky contingencies) and to perform engineering studies on what-if scenarios. The selected contingencies are analyzed, by evaluating the system response under different assumptions on threat intensity and component vulnerability. The application of the tool to a realistic study case, referring to an emergency scenario in a portion of the Italian EHV/HV grid, show the added value of the proposed approach in predicting critical conditions in power system operation compared to conventional security analyses.

In-depth security analyses of power systems (PSs) require to consider the vulnerabilities to natural and human-related threats, which may cause multiple dependent contingencies. On the other hand, such events often lead to high impact on the system, so that decision-making aimed to enhance security may become difficult. Introducing the uncertainty, the risk associated to each contingency can be evaluated, thus allowing to perform effective contingency ranking. This paper describes an in-depth security assessment methodology, based on an “extended” definition of risk (including threats, vulnerability, contingency, and impact) aimed to perform the risk assessment of the integrated power and Information and Communication Technology (ICT) systems. The results of the application to test cases and realistic PSs show the added value of the proposed approach with respect to conventional security analyses in dealing with uncertainty of threats, vulnerabilities, and system response.

Increasingly severe weather threats urge system operators to apply a systematic approach to cope with emergencies. In this context, a sound estimation of the time needed to recover electric service and infrastructure plays an important role for recovery management. The paper proposes a quantitative methodology to simulate the service and infrastructure recovery process with its uncertainties, highlighting the dependence of the main recovery characteristics (e.g. duration, energy not supplied) on several technical and environmental aspects (e.g. visibility, road availability). Simulations run on a realistic HV (High Voltage) grid show that results are consistent with operator's experience, and the approach can be used to improve the organization and management of the recovery process at planning and actual emergency stages.

Extreme weather events can determine large load disruptions on power systems; this calls for the analysis of the vulnerabilities to natural threats which may cause multiple, dependent contingencies. Given the high social and economic costs of such disruptions, the Transmission System Operators (TSOs) are interested in predicting these potential critical scenarios and in applying suitable preventive and/or corrective countermeasures to increase system resilience. The paper presents an in-depth resilience assessment methodology suitable, in operational planning mode, to predict the most risky contingencies which will affect the power system and, in the engineering mode, to analyse “what if” scenarios, by evaluating the system response under different assumptions for threat intensity and component vulnerability. The paper also provides a framework for operational and hardening countermeasures which can boost system resilience. In particular, the wet snow storm is the threat under study, and two mitigation measures are accounted for: the ice-phobic coatings of line conductors and the preventive redispatching of conventional generators to assure the minimum anti-icing currents. The sensitivity analyses carried out for different threat intensities on a model of the Italian EHV grid can help identify adequate mitigation measures.

High Voltage Direct Current (HVDC) links help to integrate large amounts of power from Renewable Energy Sources (RES), however they may interact in complex ways with the control, defense, and protection systems of the power grid. The paper analyses some of the stability phenomena and interactions that may affect small systems weakly connected with the bulk power system via HVDC links, and characterized by low inertia, low short circuit power, and high production from RES. To this purpose, detailed time-domain simulations are performed, based on phasor models, to study severe disturbances such as short circuits close to the AC terminals of HVDC link. The stability outcome is significantly dependent on the control and defense system parameters. A sensitivity analysis is performed and the resulting changes in system performance indicators are discussed.

The increasing penetration of renewables in a liberalised market environment makes more and more crucial the need to evaluate the dynamic behaviour of the grid and to cope with forecast uncertainties in operational planning and quasi-online environment. The EU FP7 project iTesla addressed these needs and achieved several major objectives, including the definition of a platform architecture, a dynamic data structure, and dynamic model validation. Specifically, one of the modules of the platform runs a model to compute the multivariate distributions of renewable and load forecast errors, based on historical data series and conditioned to the specific forecast state of the grid, with the aim to sample the “uncertainty region” of states around the forecast state. However, the model performance assessment needed to be completed. Therefore, the paper focuses on the verification process of the model performances and on the potential upgrades to provide a satisfactory trade-off between accuracy and predictive capability.

Security analyses in modern power systems require to analyse the vulnerabilities to natural threats which affect the power system in the future 24–72 hours, potentially causing multiple, dependent contingencies. These events often lead to high impact on the system, so that decision-making aimed to enhance security may become difficult. An effective risk-based contingency analysis and contingency ranking can be performed by considering uncertainty of incumbent threats, system state and response. To this purpose, threat and vulnerability analyses can benefit from the latest developments in big data applications. The paper presents the integration of accurate forecasts, coming from advanced numerical weather prediction systems and related to the specific hazard of wet snows, with a risk-based security assessment tool. Simulation results are compared against public information about outages recorded during a recent extreme wet snow event in the North of Italy, confirming the importance of data-driven hazard analyses integrated in security assessment applications.

Various methodologies exist for assessing the risk of cascading outage in power systems, differing in the cascading mechanisms considered and in the way they are modeled. These methodologies can be classified in three groups: static computation (QSS methodologies), dynamic computation (dynamic methodologies), or a combination of both (hybrid methodologies). The objective of this paper is to benchmark the performance of several widely used QSS cascading outage methodologies. For that purpose, they are applied on a unique system, the RTS-96, and the results are compared. Several metrics and indicators are used for that comparison: expected demand loss, distribution of demand loss, distribution of lines outaged and critical lines. Results show common trends but also discrepancies between methodologies. It implies that there is not yet a standardized way to analyze the risk of cascading outage in power systems, and that the specific tool used by a power system engineer can impact the recommendations.

Natural threats may cause multiple dependent contingencies on a power system (PS), leading to partial or total load disconnection. Given the high social and economic costs of such disruptions, the Transmission System Operators (TSOs) are interested not only in predicting these potential critical scenarios, but also in applying suitable preventive and/or corrective countermeasures to increase the system resilience. In case of wet snow storms one interesting countermeasure consists in increasing the line current to avoid the formation of an ice sleeve on the conductors. This paper proposes a Security-Constrained Redispatching (SC-R) run on hourly basis during the whole forecasted duration of the wet snow event and aimed to assure, in addition to N −1 security, a minimum current on the most exposed lines thus preventing ice sleeve formation. Simulations performed on a realistic wet snow event in the Italian EHV grid confirm the effectiveness of the proposed approach under specific weather conditions.

Extreme weather events are getting more and more frequent due to climate changes and often determine large load disruptions on power systems; this calls for the analysis of the vulnerabilities to natural threats which may cause multiple, dependent contingencies. In this context, exploiting the data coming from forecasting systems in a risk-based security assessment environment can help anticipate the most risky contingencies provoked by the weather event itself. The paper proposes an in-depth risk-based security assessment methodology, based on an extended definition of risk and aimed to predict the most risky contingencies which will affect the power system (contingency forecasting) on the basis of the k-hour ahead forecasts of the weather events. Big data analytics can be useful to get an accurate model for weather-related threats. The relevant software platform integrates the security assessment methodology with weather prediction systems. The application to a realistic wet snow threat scenario in the Italian transmission grid shows the added value of the proposed approach with respect to conventional security analyses, by defining a set of single and multiple contingencies evolving with the weather disturbance, thus complementing the conventional N-l security criterion.

The simulation of uncertainties due to renewable and load forecasts is becoming more and more important in security assessment analyses performed on large scale networks. This paper presents an efficient method to account for forecast uncertainties in probabilistic power flow (PPF) applications, based on the combination of PCA (Principal Component Analysis) and PEM (point Estimate Method), in the context of operational planning studies applied to large scale AC grids. The benchmark against the conventional PEM method applied to large power system models shows that the proposed method assures high speed up ratios, preserving a good accuracy of the marginal distributions of the outputs.

The exploitation of multi-terminal (MT) HVDC (High Voltage Direct Current) grids for offshore wind farm integration into continental bulk AC system raises several technical issues, in particular the verification of the compliance of MTDC grids and offshore wind parks to the performance requirements (in terms of frequency, voltage support and fault ride through) which are being established by the grid codes, and the availability of general and customizable models to simulate their response. The goal of this work is to provide a set of “open” general models, able to fulfill the grid code requirements if suitably tuned, enabling the TSOs to perform their analyses without relying exclusively on vendor-specific models. In order to verify the realistic response of these models, an example of MTDC grid is simulated, connecting it to several plausible operating scenarios of a real world AC system (the West Denmark grid) in target year 2020: after a preliminary N-1 static security assessment study on the integrated AC/DC system, some simulations are performed to check the fulfillment of grid code requirements in case of typical contingencies.

Decarbonisation of the European electricity system can become dauntingly costly due to transmission and distribution network issues arising from the integration of intermittent renewable generation sources. It is expected that wind energy will be the principal renewable source by 2050 and, as such, a number of initiatives in the academia and in the industry are being carried out to propose solutions to best accommodate the wind resource. This paper presents work carried out by DEMO 1 partners within the EU FP7 project BEST PATHS. A MATLAB/Simulink toolbox consisting of the necessary building blocks for the simulation and integration of offshore wind farms using enabling technologies such as multiterminal high-voltage direct-current grids is presented. To illustrate the toolbox capabilities, a number of system topologies is studied. System performance is assessed and measured against a set of key performance indicators. To ensure knowledge dissemination, the toolbox has been made available as open access in the BEST PATHS project website.

Accounting for the increasing uncertainties related to forecast of renewables is becoming an essential requirement while assessing the security of future power system scenarios. The FP7 EU project iTesla tackles these needs and reaches several major objectives, including the development of a security platform architecture. In particular, the platform implements a complex stochastic dependence model to simulate a reasonable cloud of plausible “future” states — due to renewable forecast — around the expected state, and evaluates the security on relevant states sampling the cloud of uncertainty. The paper focuses on the proposed model of the uncertainty and its exploitation in power system security assessment process.

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

When a transmission line close to points of common coupling (PCCs) experiences a short-circuit (SC) fault, the fast reactive power control of voltage-source converter-HVDC (VSC-HVDC) is likely to affect the protective relay operation of transmission lines. To study the performance of distance relays on an ac grid with an offshore wind HVDC network, this paper presents an apparent impedance calculation method, which utilizes the bus impedance matrix (Zbus) to calculate the impedances viewed by distance relays during a three-phase SC fault. The proposed method is used to identify the potential miscoordinated Zone 2 relays in the proposed combined ac/dc system. The analysis is verified by software simulation results. It is shown that the proposed method results in accurate impedances viewed by distance relays. It also identifies the protective device settings on the ac grid that need to be adjusted due to HVDC control of offshore wind generators.