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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.

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.

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 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.

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.

Cascading failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on cascading failure reviews and synthesizes how benchmarking and validation can be done for cascading failure analysis, summarizes and reviews the cascading test cases that are available to the international community, and makes recommendations for improving the state of the art.

Nowadays the management of interconnected transmission systems requires security assessment methods able to consider uncertainties due to the increasing presence of renewable generation. Furthermore, also the electrical demand is characterized by a certain level of variability which affects the accuracy of the expected consumption profiles. Thus, probabilistic approaches are an interesting research field to improve reliability of operational planning on future power systems. This work proposes a probabilistic methodology for the evaluation of the Net Transfer Capacity (NTC) between interconnected power grids. The method considers the forecast uncertainties on renewable generation and load consumption, by exploiting the Point Estimate Method (PEM) coupled with Third-order Polynomial Normal Transformation (TPNT). The proposed procedure is applied on a benchmark IEEE test system and validated through comparison with a conventional technique.

The increasing penetration of renewables and the constraints posed by pan-European market make more and more crucial the need to evaluate the dynamic behaviour of the whole grid and to cope with forecast uncertainties from operational planning to online environment. The FP7 EU project iTesla addresses these needs and encompasses several major objectives, including the definition of a platform architecture, a dynamic data structure, and dynamic model validation. The on line security assessment is characterised by a multi-stage filtering process: this includes a “Monte Carlo like approach” which applies the security rules derived from extensive security analyses performed offline to a set of “new base cases” sampled around the power system (PS) forecast state with the aim to discard as many stable contingencies as possible. The paper will focus on the management of historical data — related to stochastic renewable and load snapshots and forecasts-in order to solve some intrinsic criticalities of raw data and to derive a reliable model of the multivariate distributions of renewables and loads conditioned to the specific forecast state of the grid, with the final aim to generate the “uncertainty region” of states around the forecast state.

The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

The purpose of this paper is to present a review of some fundamental concepts and practical applications in the area of common-mode and dependent (CMD) outage events in power systems. The paper is a result of ongoing activity carried out by the Probability Applications for Common and dependent Mode Events (PACME) Working Group (WG) of the Reliability, Risk and Probability Applications Subcommittee. The PACME WG was formed in 2010 to review, advance, and present the research and practical applications in the area of CMD outage events. This paper presents state-of-the-art in research, modeling, and applications of CMD outage events in power system planning and operation. Issues considered include: data monitoring and collection, and probabilistic modeling and evaluation in the planning, and operation of power generation and transmission systems. Additionally, some results obtained from outage data statistics corresponding to CMD outage events in systems such as Generating Availability Data System, Transmission Availability Data System, and Canadian Electrical Association are presented. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The need to analyze high-impact low-probability events on power systems, due to natural and man-related threats calls for comprehensive approaches to security assessment, which exploit the concept of risk. The paper describes a probabilistic approach and a tool, developed in the EU research project AFTER, aimed at selecting the most critical contingencies to be analyzed in depth, starting from short-term probabilistic models of incumbent natural or man-related threats and from component vulnerability curves in an integrated power and ICT system. A fast estimate of the contingency impact is obtained by using topological metrics. Results on a realistic power system include the sensitivity analyses of different contingency selection options, and the verification of the performances of the estimated impact metrics for screening purposes.

An approach for the design of a Special Protection Scheme (SPS) is presented, based on Artificial Intelligence and probabilistic techniques. In detail, the aim of the SPS is to maintain the synchronism of a power plant after grid outages. To improve the SPS design, the impact of communication delays is accounted for in a probabilistic way. The SPS control logic, deciding how much power to curtail in order to keep system stability, is implemented with Decision Trees and, alternatively, with Support Vector Machines. As a test case, a portion of the IEEE 39-bus grid with a Wide Area Measurement System, based on Phasor Measurement Units, is studied. The effectiveness of the designed logic is evaluated in terms of the probability of system instability, which is derived from the Probability Density Functions of the communication delay and of the Critical Delay Time, i.e. the maximum acceptable delay of the SPS action to keep the system stable. The results yield guidelines to design communication systems which, although including further delays due to packets cryptography, minimize the probability of failure of the SPS action.

Security assessment issues on modern power systems become more and more important also because of the growing penetration of DG (Distributed Generation) sources on the electric power systems. Today their penetration is still marginal with respect to the overall system load, but in the future they can cover a significant part of the consumptions, which means that research on power system security assessment has to investigate the possible impacts of such a phenomenon on security issues. Moreover, these models are essential for Microgrid studies and evaluation of their performances. In order to assess the actual impacts of DG sources on operational security it is necessary to have adequate dynamic models of the sources themselves. This paper presents the dynamic models of several renewable and fossil fuel based Distributed Energy Resources (DER) implemented in a well-known time domain simulator for power systems. Some simulation results are discussed.

The paper discusses a set of particularly interesting applications of battery energy storage systems (BESSs) for the provision of dispatching services and presents simulation results of their operation in realistic scenarios, in order to carry out cost/benefit analyses able to assess the profitability of investments or, more often, their distance from the profitability. In fact, in most of the considered case studies, that moreover are referred to possible future regulatory arrangements, the high investment costs of storage systems are not repaid in a reasonable time by the achieved economic benefits. The only two applications resulting potentially profitable concern the primary regulation service with the storage system respectively coupled with a coal fired power plant and with a wind power plant. A significant cost reduction of BESSs is therefore needed, but it is also expected, following improvements of technologies and economies of scale: in such a case, they will have a bright future both in power system applications and in electric mobility.

Power systems are vulnerable to both natural and man-related threats. The resulting contingencies - either single or multiple - may lead to wide-spread blackouts. However, the probabilities of the different events span over a wide range, hence the associated risk may be quite diverse. To address these issues a probabilistic risk assessment methodology and tool were developed within the Seventh Framework Program project AFTER. The aim of this paper is to describe the tool and its novelties, with special emphasis on the possibility to select and rank the most dangerous contingencies on the basis of probabilistic models of different threats affecting power system state, and to evaluate the effects of several factors (renewable penetration, protections and operators' delay) on the risk of operating a power system. Application examples are presented on a model of a realistic power system.

In the context of transmission system planning, research proposes methods to assess the effect of uncertainties of power system operating condition due to forecasting errors of intermittent generation and loads. In particular probabilistic power flow methods are illustrated to calculate the probability distributions of the voltages and the branch currents, starting from the distributions of power injections/absorptions. These uncertainties play a key role in the operational planning of power systems, as certain configurations of load and intermittent generation can cause security problems. This paper aims to propose a probabilistic methodology to assess Net Transfer Capacity (NTC) among network areas, which quantifies forecast error uncertainties by applying the Point Estimate Method (PEM) combined with Third Order Polynomial Normal (TPN) Transformation. This approach is compared with a conventional NTC assessment technique and has been validated against Monte-Carlo benchmark on an IEEE test system.

Power system security assessment techniques can greatly benefit from the adoption of probabilistic approaches, as these can thoroughly describe the effect of uncertainties on the operating condition. The paper proposes a methodology to evaluate the probability of exceeding operational constraints under normal and contingency situations, accounting for uncertain, correlated power injections. In the proposed methodology, an analytical technique for probabilistic power flow is combined with copulas, the latter being introduced to deal with correlations. Copulas allow representing in an effective way the correlation information among variables, i.e. the multi-variate distributions of stochastic variables. A test system based on a realistic transmission network is used to exemplify the method.

Probabilistic techniques can provide a deeper insight into security analysis within operational planning and operation contexts, by quantitatively considering power system uncertainties and contingency impact. This paper proposes a methodology aimed to evaluate how uncertainties on a future power system operating condition affect operational risk indices. Major uncertainties in operational planning are due to errors in the forecasts of renewable generation and load demand, performed some hours ahead of actual PS operation. A probabilistic power flow technique based on Point Estimate Method is used to account for the uncertainties in the initial power system conditions, moreover a third-order polynomial normal transformation is used to account for dependence among random inputs. This technique is integrated inside a risk assessment method and tool to quantify the effect of uncertainties on the operational risk. An application example on the Sicilian power system is described and discussed.

Cascading stems from the interaction between control, protection, and defense systems, and it is affected by uncertainties in system models and external factors. An evaluation of cascading thus requires the collection of exhaustive and reliable data. Uncertainties could be accounted for by applying Monte Carlo techniques to time domain simulation, but a large computational effort would be implied. Static cascading simulation approaches are more suitable to carry out probabilistic cascading evaluation. However, the issue of consistency between static and dynamic simulation outcome arises. The present paper reports results from benchmarking quasi-static, cascading simulation used in the operational risk assessment tool PRACTICE against a time domain simulator. The comparison, performed on the model of a realistic power system, highlights the consistency of the quasi-static approach with time domain simulations at least in the early stages of cascading.

The increasing vulnerability of modern power systems to both natural and man-related threats, as well as their increasing dependency on ICT systems makes it convenient to adopt integrated approaches for security assessment and control. Moreover, the need to analyse extreme, low-probability events (from which many blackouts originated in the past) calls for the adoption of the risk concept to perform more in-depth security analyses. The present paper describes a probabilistic risk-based methodology, developed within the EU research project called AFTER, aimed to perform risk assessment (including hazard, vulnerability and impact analysis) of the integrated electrical power and ICT systems. The paper focuses on the steps to build the assessment framework (threats/vulnerabilities identification and classification, contingency modeling, power system response modeling and computation of risk indicators for operation and planning purposes).

The exploitation of Renewable Energy Sources (RES) allows to reduce the electricity energy supplied by fossil fuelled power plants but introduces significant issues in power system planning and operation. Especially for small islanded systems, a larger share of load covered by RES, mainly wind and photovoltaic (PV) plants, implies lower inertia and reduced regulation resources to address frequency stability issues. The paper focuses on frequency stability criticalities of the power system of Sicily operated as an electric island. Starting from the reconstruction of a real incident occurred in 2011, several scenarios are analyzed, characterized by different countermeasures (e.g. PV retrofitting, advanced controls on wind farms). The aim is to evaluate the benefit on security brought about by the introduction of these solutions. The study relies on the dynamic simulation of the control/defense/protection systems relevant for frequency stability.

The deepening penetration of renewable resources, such as wind and photovoltaic solar, has introduced additional uncertainty into power system operation and control. This added uncertainty, together with the conventional sources of uncertainty, the loads and the availability of resources and transmission assets, makes clear the limitations of the conventional deterministic power flow in power system analysis and security assessment applications. Therefore, the explicit consideration of uncertainty requires the deployment of probabilistic approaches so as to provide the ability to manage the wide spectrum of all possible values of the input and state variables. In this paper, we make use of cumulant-based probabilistic power flow methodology to account for correlations among the input random variables. Extensive testing indicates good performance of probabilistic power flow. We illustrate application of the probabilistic power flow on the 14-bus IEEE test system and present a comparison with the result obtained by the computationally more demanding Monte Carlo approach. The probabilistic power flow results provide valuable information for power system analysis and security assessment and, in particular, provide insights into issues associated with line overloading, over-/under-voltage, and the critical ramping requirements from conventional generators in system with deep penetration of highly variable resources, such as wind farms.

The vulnerability of power systems to natural and man-related threats, as well as their increasing dependency on ICT systems and uncertainties of influent factors, urge to adopt integrated approaches for security assessment and control. Moreover, the need to analyze high impact events (causes of past blackouts) calls for the adoption of the risk concept to perform more in-depth security analyses. The present paper describes a probabilistic risk-based methodology, developed within the EU research project AFTER, aiming to perform risk assessment (including hazard, vulnerability, and impact analysis) of the integrated power and ICT systems. The paper focuses on the steps to build the assessment framework based on: threat/vulnerability identification and classification, contingency modeling, power system response modeling and computation of risk indicators for operation and operational planning purposes.

Interconnected power grids throughout the world are very reliable but occasionally suffer massive blackouts with multibillion dollar costs to society. Cascading failures present severe threats to power grid reliability, and thus reducing their likelihood, mitigation and prevention is of significant importance. This paper is one in a series presented by Cascading Failures Task Force, under the IEEE PES Computer Analytical Methods Subcommittee (CAMS) with primary focus on mitigation and prevention of cascading outages. The paper presents the basic methodologies for mitigation, summarizes currently deployed special protection schemes, and lists cases of successful and unsuccessful mitigation of cascading outages and lessons learned. Future developments and challenges in the area of mitigating cascading outages are also discussed.

Power system security assessment for off-line operational planning and on line operation applications requires various analysis methods aimed to highlight different phenomena (steady-state violations, angle stability, voltage stability, etc.), overall providing an exhaustive vision of the problems. However, carrying out the different analyses in an integrated way is not an easy task. Different types of results come from both conventional deterministic and more advanced risk-based probabilistic assessment tools. Moreover, operators are more and more interested in practical information like the time at their disposal before instability occurs, to deploy adequate control actions. To provide a comprehensive security assessment framework, this paper proposes a methodology and a platform, which integrates the outcomes from probabilistic and deterministic security assessment tools within a unified approach to system security.

In the context of power system operation, probabilistic techniques not only can provide a deeper insight into security aspects, by quantitatively considering power system uncertainties and contingency impact, but they can also be helpful to identify and suggest operators the most adequate control actions to reduce operational risk. This paper proposes an innovative risk-based strategy to control the operational risk of High-Voltage Alternating Current (HVAC) power systems connected to Multi-Terminal High-Voltage Direct Current (MT HVDC) networks like those envisaged for the integration of future, large off-shore wind farms. The proposed method minimizes the operational risk by minimizing the overall costs to re-dispatch both conventional generation and power injections from MT HVDC network, also taking into account the operating limits of the HVDC network (in particular, cable current limits). Results of the methodology applied to an IEEE test system and on a realistic power system are presented and discussed, and some conclusions are drawn.

Probabilistic Risk Assessment (PRA) techniques are raising more and more interest in the context of the operation of high voltage transmission networks because they represent a new approach to the security assessment in power system operation. The classical deterministic methods, which compare the power system performance to a predefined set of requirements (e.g. no current and voltage violations, no stability problems), do not take into account the probability of occurrence and the magnitude of events and do not perform a quantitative characterization of the impact of the contingencies. The fulfillment of the security requirements also for the most critical contingencies determines large security margins, thus higher operational and planning costs. On the other hand the risk of other contingencies may be underestimated. The paper starts by illustrating a methodology to assess the risk of loss of load caused by conventional (N-1) or multiple dependent contingencies resulting into a cascading process. A control strategy is then introduced, aimed to reduce operational risk by optimal preventive redispatching of conventional generators. Simulation results of the application of the approach to an IEEE test system and to a model of the Italian EHV transmission grid are illustrated and discussed.

The great attention of research initiatives to sustainable energy systems calls for contributions in modeling new small size generation plants exploiting innovative technologies. In such a context, the paper presents detailed models of the components and controls forming the thermo-mechanical and electric subsystems of a microturbine power plantP. The modeled thermo-mechanical subsystem includes different control loops: a speed controller for primary frequency control (droop control), an acceleration control loop, which limits the rotor acceleration in case of sudden loss of load or in case of start-up, and a controller to limit the temperature of the exhaust gases below the maximum admissible temperature. The modeling of control schemes in the electric subsystem is another key issue of the paper in view of providing efficient energy production from distributed generation: an active power-voltage (PV) control is adopted for the inverter in case of the operation of a microturbine connected to the grid. This control scheme provides an innovative contribution with respect to the usually adopted active power-reactive power (PQ) control scheme. The adoption of the PV control scheme allows to evaluate the contribution of micro-turbines to voltage support in electric distribution grids. In case of isolated operation of the generation source a voltage-frequency (VF) control scheme is proposed. A test grid is set up for model validation and the simulation results are described and discussed.

The increase of DG penetration in the distribution networks makes more and more interesting the investigation of security issues which are typical of the large transmission grids (transient stability, voltage stability and small signal stability) as well as the study of the DG responses to load changes in the grid. This paper presents the model of a Solid Oxide Fuel Cell (SOFC) dedicated to perform dynamic simulations on MV distribution networks. In particular in the present paper transient stability and load following issues are tackled. The first part of the paper is aimed at describing the model of the SOFC which is then used in the test system. As also a gas turbine is inserted into the test system, a brief look at the gas turbine model is mandatory. The aforementioned models include both the slow dynamics (GT prime mover, electrochemical system time constants for SOFC) necessary to study the DG response to load variations and the fast dynamics (e.g the power converter control and protection systems for SOFCs) necessary to study the transient stability related issues. The results of the analysis (carried out on a test MV distribution network) are reported and some conclusions are drawn.

In operational practice power system security analyses are usually conducted with respect to "credible" contingencies, such as almost all N-l contingencies and some N-2 contingencies suggested by operational experience. On the contrary, extreme contingencies are generally not evaluated, although they can be responsible for major system disturbances and seriously jeopardize system security; within an in-depth analysis they should be analyzed. The paper presents an extensive analysis performed on a realistic network representative of the Italian High Voltage (HV) grid by means of a Dynamic Security Assessment (DSA) application tool integrated in a power system simulator. Besides large sets of N-l and N-2 contingencies, the aforementioned analysis includes also severe and extreme contingencies (in particular N-k contingencies at HV system nodes) and it also evaluates the effects of protection malfunctioning on system security. The contingency list has been created by an ad hoc automatic external procedure linked to the power system simulator. Results highlight the importance of the above extensive analyses also in view of risk- based approaches to security evaluation.

The need to realize LFC control schemes which consider the conditions imposed by the electricity market and which allow the preventive and corrective resolution of power flow overloads leads to the analysis of new solutions which increase the effectiveness and the flexibility of the relevant control functions. The classical centralized LFC controller regulates the power exchanges through the border and the frequency at predefined values, but it does not allow to solve possible overflows on single lines or on defined sections. This paper is aimed at describing an innovative LFC control scheme based on virtual control areas. Its goal is to limit the power flows on adequate sections below a predefined value. As an application example, the situation on the Italian border is presented and commented