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Heterogeneous inertia distribution can result in large regional frequency deviations and inter-area oscillations that exceed protection limits configured based on system-wide averaged performance. This paper examines how the spatial distribution of inertia affects frequency heterogeneity. Along with varying inertia distribution, variations in generator turbine-governor control and network topology are studied to make the results more generalisable across wide-ranging operating conditions. An investigation into the effects of different fast frequency response (FFR) schemes on frequency heterogeneity is also presented. The frequency heterogeneity is quantified by calculating cosine similarity between regional frequency trajectories. The key results are obtained using a two-area model and verified using a mixed AC/DC power system. A key finding is that the localness of regional frequency is independent of the inertia of a specific area, nor of the total system inertia. The inertia ratio, described as the ratio of the disturbance area inertia to that of the non-disturbance area, is shown to have a strong correlation with frequency heterogeneity. This correlation is shown to be very robust to changes in generator dynamics and network topology. Providing derivative FFR within the disturbance area always demonstrates benefits regarding frequency heterogeneity inhibition, whereas droop scheme typically introduces deterioration in frequency heterogeneity. • A new measure of inertia distribution to characterise frequency heterogeneity. • Determining the relationship between inertia distribution and frequency heterogeneity. • Analysing of the effects of Fast Frequency Response. • Frequency heterogeneity is independent of the level of regional or system inertia. • Derivative FFR reduces frequency heterogeneity more than droop based FFR. [ABSTRACT FROM AUTHOR]

• An integrated gas and electricity network with heat electrification is developed. • Correlated uncertainties of renewable generation and demand are modelled. • Overload probability of power lines and gas pipelines is evaluated. This paper presents a probabilistic method for assessing overload probability of power lines and gas pipelines in integrated gas and electricity networks with the consideration of heat electrification. The proposed model fully considers the correlated uncertainties associated with intermittent generation and fluctuating loads using suitable probability distribution functions and copulas. The effectiveness of the proposed method is verified with application in two test systems: the integrated 9-bus electricity and 8-node gas network, and the integrated IEEE 39-bus and Belgian 20-node gas network. Numerical results show that heat electrification increases the overload probability of some power lines due to a major shift of heat loads to the electricity network. When the correlations of wind and photovoltaic generation as well as electric, gas and heat loads are considered, the overload probability of these power lines further increases showing the importance of correlation modelling for an accurate estimation of power flows and line overload probability. When an uneven distribution of heat loads (i.e., clustering of low-carbon technologies) in the electricity network is considered, the overload probability of some power lines further increases. Heat electrification decreases the overload probability of most gas pipelines, although the probability of overloading a few gas pipelines increases as a result of supplying gas to gas-fired power generators to meet the electrified heat demand. [ABSTRACT FROM AUTHOR]

Power outages due to cascading failures which are triggered by extreme weather pose an increasing risk to modern societies and draw attention to an emerging need for power network resilience. Machine learning (ML) is used for a real‐time selection process on preventive actions, such as topology reconfiguration and islanding, aiming to reduce the risk of cascading failures. Training data is obtained from Monte Carlo simulations of cascading failures triggered by extreme events. The trained ML‐based decision‐making process uses only predictors that are readily available prior to an extreme event, such as event location and intensity, network topology and load, and requires no further time‐consuming simulations.The proposed decision‐making process is compared to time‐consuming but ideal decision‐making and fast but trivial decision‐making. Demonstrations on the German transmission network show that the proposed ML‐based selection process efficiently prevents the uncontrolled propagation of cascading failures and performs similarly to an ideal decision‐making process whilst being computationally three orders of magnitude faster. [ABSTRACT FROM AUTHOR]

Retaining frequency stability is becoming increasingly challenging as the power system incorporates more non-synchronous generation. Assessing the frequency stability in a system has been predominantly completed by focusing on the quantity of connected synchronous kinetic energy in the system, or inertia. This traditionally was considered a function of generator construction – network factors typically were not considered. The research in this paper investigates how network topology, power flow, droop gain distribution, and inertia distribution all impact frequency stability. A generic four-area model has been created that allows discrete system setups. This research has shown that certain topologies lead to a more severe rate of change of frequency. A key finding is that the frequency drop is further increased when there is greater power flow into the area that experiences the disturbance. The extent to which the rate of change of frequency and frequency drop are influenced differently is emphasized, highlighting the need to procure different services depending on which metric is of primary significance at a specific location. [ABSTRACT FROM AUTHOR]

In recent years, voltage source converter (VSC) HVdc has emerged as an excellent solution for transmitting offshore renewable energy sources and strengthening transmission corridors. It is also reported in the literature that VSC-HVdc is able to enhance specific types of stability. With growing capacity of VSC-HVdc and increasing uncertainty in the current power system, it is essential to perform a rigorous analysis of its impact on the interdependence of power system dynamics. This paper presents a methodology for the thorough assessment of the impact of VSC-HVdc on interdependence among four types of stability: small-disturbance rotor angle, large-disturbance rotor angle, frequency, and voltage. This analysis is performed assuming uncertain conditions for various VSC-HVdc topologies and system loading and generation conditions. The impact of the VSC-HVdc on the interdependence of dynamic performance is assessed using the Spearman correlation coefficient as a quantification tool. The results obtained show that a significant alteration over the interdependence between power system dynamics occurs with the addition of VSC-HVdc. [ABSTRACT FROM AUTHOR]

Abstract The analysis of power systems with a significant share of renewable generation using probabilistic tools is essential to appropriately consider the impact that the variability and intermittency of the renewable generation has on the grid. This paper provides a critical assessment and classification of the available probabilistic computational methods that have been applied to power system stability assessment (including small and large disturbance angular stability, voltage and frequency stability). A probabilistic analysis framework with a state-of-the-art review of the existing literature in the area is presented comprising of a review of (i) input variable modelling, (ii) computational methods and (iii) presentation techniques for the output/results. The most widely used probabilistic methods in power system studies are presented with their specific application areas, advantages, and disadvantages. The purpose of this overview, classification, and assessment of the existing methods is to identify the most appropriate probabilistic methods to be used in their current forms, or suitably modified, for different types of stability studies of power systems containing renewable generation. Highlights • Uncertainty modelling techniques for independent variables. • Uncertainty modelling techniques for correlated variables. • Probabilistic techniques applied to power system stability. • Identification of efficient computational techniques. • Future research needs in this area. [ABSTRACT FROM AUTHOR]

Due to the anticipated proliferation of HVDC links up to and beyond 2020 into transmission systems, it is expected that in some locations, two or more HVDC links will be installed with a short electrical distance between them and will form a multiinfeed (MI)-HVDC scenario for the system. The control and operation of MI-HVDC systems are of particular concern for weak grids such as that likely to be found in the North of Scotland network. It is shown that the ac voltage regulation in the VSC-HVDC link may adversely affect the dynamic performance of the host weak ac system under the MI-HVDC scenario. To overcome this identified limitation associated with the ac voltage controller in the VSC-HVDC link, a new controller modification conforming to the grid code is proposed in this paper. Furthermore, this paper presents a robust control design method for ac voltage control using the fractional order (FO) control design theory. The influences of the proposed FO-based ac voltage controller are assessed alongside various voltage controllers for the representative North Scotland system. It is shown that the FO-based ac voltage controller has favorable effects on the dynamic behavior of weak ac systems with MI-HVDC. [ABSTRACT FROM AUTHOR]

A high level of stochastic dependence (or correlation) exists between different uncertainties (i.e., loads and renewable generation), which is nonlinear and non-Gaussian and it affects power system stability. Accurate modeling of stochastic dependence becomes more important and influential as the penetration of correlated uncertainties (such as renewable generation) increases in the network. The stochastic dependence between uncertainties can be modeled using 1) copula theory and 2) joint probability distributions. These methods have been implemented in this paper and their performances have been compared in assessing the small-disturbance stability of a power system. The value of modeling stochastic dependence with increased renewables has been assessed. Subsequently, the critical uncertainties that most affect the damping of the most critical oscillatory mode have been identified and ranked in terms of their influence using advanced global sensitivity analysis techniques. This has enabled the quantification and identification of the impact of modeling stochastic dependence on the raking of critical uncertainties. The results suggest that multivariate Gaussian copula is the most suitable approach for modeling correlation as it shows consistently low error even at higher levels of renewable energy penetration into the power system. [ABSTRACT FROM PUBLISHER]

This paper evaluates a number of game theoretic (GT) approaches with the aim of assessing their suitability to identify the most influential parameters affecting the small-disturbance stability of a power system. Identification of the most influential parameters affecting system stability will facilitate cost-effective operation and control of a power system in general as it would require limited network monitoring, control and modelling effort by system operators and stakeholders. After identifying the most influential parameters the financial and human resources could be adequately prioritised and deployed to solve or prevent power system stability problems. A priority ranking procedure based on a GT approach has the advantage, compared to other techniques, of considering simultaneously the effect of individual and all possible combinatorial effects of the uncertain parameters (or players in a game context). In this study, the most influential players have been identified through a multi-level approach considering the power flow in the network (by optimal power flow), small disturbance stability aspects (by modal analysis), rational behavior of individual players (by a sensitivity technique) and formation of groups between players (by cooperative game theory). Various GT approaches have been considered namely Shapley Value, Aumann Shapley, Nucleolus, and τ -value approach in order to compare and assess their suitability for power system applications. The results are illustrated using two test networks and considering several approaches of sensitivity analysis and different degrees of variability in the considered uncertainties. [ABSTRACT FROM AUTHOR]

This paper establishes a generic severity function that can be used to produce power system security risk profiles. It is illustrated by analyzing the impacts of system load attributes on the small-disturbance rotor angle stability of a power system. The load attributes contributing to the oscillatory modes can be considered as inherent uncertain variables within power systems and include load power variations, load composition, and load model parameters. Uncertainty in the renewable power generation is also incorporated in the probabilistic modelling and risk assessment to demonstrate the flexibility of the approach. A novel approach is proposed to select the severity functions to logically represent small-disturbance security margin. The risk profile of a power system has been presented considering the probability density functions of power system critical modal damping and a selected set of severity functions. The analysis techniques developed are illustrated with a modified version of the 68-bus NETS-NYPS power system with a high amount of renewable power penetration. The relative importance of the load attributes and the impact of these attributes on stability boundaries have been identified at varying risk levels with respect to their contribution to small-disturbance stability. [ABSTRACT FROM PUBLISHER]

This paper critically evaluates a number of sensitivity analysis (SA) techniques to identify the most influential parameters affecting power system small-disturbance stability. SA of uncertain parameters has attracted increased attention with the adoption of deregulated market structure, intermittent energy resources, and new types of loads. Identification of the most influential parameters affecting system stability using SA techniques will facilitate better operation and control with reduced monitoring (only of the parameters of interest) by system operators and stakeholders. In total, nine SA techniques have been described, implemented, and compared in this paper. These can be categorized into three different types: local, screening, and global SA. This comparative analysis highlights their computational complexity and simulation time. The methods have been illustrated using a two-area power system and 68 bus NETS-NYPS test system. The priority ranking of all uncertain parameters has been evaluated, identifying the most critical parameters with respect to the small-signal stability of the test systems. It is shown that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

• A novel dynamic model of cascading failures in power systems is established. • Increased wind penetration and fast frequency responses are obtained. • Impact of increased wind penetration on cascading risks is quantified. • Behaviour characteristics of fast frequency responses are demonstrated. The ever-growing penetration of renewable based generation is leading to significant increases in the risk of cascading failures in low-inertia, and interconnection-rich, power systems. This paper proposes a framework for quantifying the risk of cascading failures in renewable-rich power systems with fast frequency response (FFR) services. This is achieved by developing a novel time-based dynamic model of cascading failures to capture the transient behaviours of the system response and quantify the risk of blackout. This dynamic cascading failure model is complemented by unit commitment for representative dispatch and includes the provision of FFR. The proposed framework is illustrated using a modified version of the Illinois 200-bus synthetic system. As key outputs, the results emphasize the importance of accurate modelling of ancillary services associated with frequency regulation in cascading failure analysis, and indicate that increased wind penetration can lead to a higher probability of power outages. Adopting FFR services can help to mitigate the cascading risk but may introduce rotor angle instability issues. [ABSTRACT FROM AUTHOR]

The decarbonisation of heat supply will play a critical role in meeting the emissions reduction target. There is, however, great uncertainty associated with the achievable levels of heat decarbonisation and the optimal heat technology mix, which can have serious implications for the future electricity and gas demand. This work employs an integrated gas, electricity and heat supply model to quantify the impacts of heat decarbonisation pathways on the future electricity and gas demand. A case study in the Great Britain is performed considering two heat decarbonisation scenarios in 2050: one is the predominantly electrified heat supply and the other is the predominantly hydrogen-based heat supply. The electricity demand becomes more volatile in the electrified heat scenario as the peak surges to 107.3 GW compared to 51.1 GW in the 2018 reference scenario, while the peak in hydrogen-based heat scenario is 78.4 GW. The peak gas demand declines from 247.6 GW for 2018 to 81.7 GW for electrified heat scenario and to 85.1 GW for hydrogen-based heat scenario, confirming that the seasonality associated with heat demand is shifting away from the gas network and towards electricity network. Moreover, a sensitivity analysis shows that the future electricity demand is highly sensitive to parameters such as relative heat demand, coefficient of performance of air source heat pumps and share of electricity in hydrogen production. Finally, the application of a load shifting strategy demonstrates that demand-side flexibility has the potential to maintain the electricity system balance and minimise the generation and network infrastructure requirements arising from heat electrification. While the case study presented in this paper is based on the Great Britain, the findings regarding the future electricity and gas demand are relevant for the global energy transition. • Integrated operation of gas, electricity and heat supply systems is modelled. • Impacts of heat decarbonisation on GB electricity and gas demand are quantified. • Sensitivity of future electricity demand to input uncertainties is investigated. • Role of flexibility in maintaining the electricity balance is demonstrated. [ABSTRACT FROM AUTHOR]

This paper critically evaluates a number of uncertainty importance measures for use in power system stability studies. Sensitivity analysis of uncertain system parameters is vital as new technologies proliferate and the total level of system uncertainty grows. Accurate assessment of the importance of different uncertainties can guide power system operators towards parameters which will require the greatest levels of mitigation or increased monitoring in order to reduce the uncertainty and its subsequent impact. Local and global sensitivity analysis techniques are described and evaluated within this paper, including nonparametric methods, variance-based approaches, and distribution-based techniques. The techniques are illustrated using a large 295-bus realistic network model of a generic distribution system. Numerical experiments on dynamic models are used in order to assess the impact of uncertainties on the mitigation of system frequency excursions using single-site and distributed energy storage devices. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to efficiently estimate the probability of small-disturbance rotor angle instability of uncertain power systems. Traditional Monte Carlo (MC) approaches are computationally intensive and inefficient, particularly when used to study low probability conditions which result in small disturbance instabilities and develop into serious outage events high impact. The proposed methodology uses importance sampling to focus on conditions which contain the high information content required to make relevant decisions about low probability events. Latin hypercube sampling (LHS) is used to efficiently bound the search space and identify operating conditions leading to a marginally stable or unstable system response. The proposed approach is demonstrated on a model of a multi-area transmission network with a significant capacity of intermittent generation connected through a multi-terminal voltage source converter-based high voltage direct current (VSC-HVDC) grid. It is demonstrated that the methodology yields accurate results with just a small fraction of the sample points required using a conventional numerical MC approach. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for the probabilistic risk assessment of rotor angle instability in power systems using fuzzy inference systems (FISs). The novel two-step approach first models the stochastic uncertainties present within the power system to produced probability density functions (pdfs) for stability indicators. These stability indicators are established for both small and large disturbance rotor angle stability analysis. The pdfs produced are subsequently decomposed into regions based on user-specified threshold values. The outputs from this decomposition are analyzed using fuzzy techniques to complete the risk assessment of instability. The methodology is applied to a multi-area test network into which a VSC-MTDC grid has been embedded to support power transfer from a number of large wind farms. This new combination of probabilistic and fuzzy techniques is shown to provide an effective methodology for quantifying the influence of system uncertainties on the risks of rotor angle stability. [ABSTRACT FROM PUBLISHER]

This paper presents a risk-based probabilistic small-disturbance security analysis (PSSA) methodology for use with power systems with uncertainties. This novel addition to existing dynamic security assessment (DSA) techniques can be used to quantify the small-disturbance stability risks associated with forecasted operating conditions. This approach first establishes the probability density functions (PDFs) for the damping of the critical oscillatory electromechanical modes by modeling the stochastic variation of system uncertainties, such as loading levels, intermittent generation sources, and power flows through voltage-source converter–high-voltage direct current (VSC–HVDC) lines. The produced PDFs are then combined with severity measures (either simple risk matrices or continuous functions) in order to quantify the risk of stability issues for the system associated with the forecasted operating scenario. In addition, the PSSA is used to establish risk-based operational limits and the concept of a probabilistic security margin is introduced to more accurately represent the probabilistic operation of uncertain power systems. The proposed techniques are demonstrated using a multiarea meshed power system incorporating two VSC-HVDC systems, one of which is connected to a large wind farm. [ABSTRACT FROM AUTHOR]

This paper presents comparative analysis of the performance of three efficient estimation methods when applied to the probabilistic assessment of small-disturbance stability of uncertain power systems. The presence of uncertainty in system operating conditions and parameters results in variations in the damping of critical modes and makes probabilistic assessment of system stability necessary. The conventional Monte Carlo (MC) approach, typically applied in such cases, becomes very computationally demanding for very large power systems with numerous uncertain parameters. Three different efficient estimation techniques are therefore compared in this paper—point estimation methods, an analytical cumulant-based approach, and the probabilistic collocation method—to assess their feasibility for use with probabilistic small disturbance stability analysis of large uncertain power systems. All techniques are compared with each other and with a traditional numerical MC approach, and their performance illustrated on a multi-area meshed power system. [ABSTRACT FROM PUBLISHER]

• A probabilistic multi-stability operational boundary assessment based on frequency, large-disturbance rotor angle, small-disturbance rotor angle, and voltage stability for varying renewable energy source penetration levels and loading conditions was investigated and established. • Establishing system's operational boundary from a singular stability type is dangerous as the established operational boundary from each stability type is very different. • The established multi-stability operational boundary is heavily influenced by frequency and large-signal disturbance rotor angle stability. • A significant improvement in the system's operational stability boundaries occur when the VSC-HVDC's ability to independently control active and reactive power via V DC - Q control is fully utilized. This paper presents a framework for defining multi-stability operational boundaries of power systems under varying penetration levels of power electronic interfaced generation and uncertain system's loading. Boundaries are established considering the small-disturbance and large-disturbance rotor angle stability, as well as the frequency and voltage stability of the system. Estimating system's operational boundary from multi-stability perspectives will provide invaluable information on how to best operate the system and how to mitigate certain condition that leads to instability especially in the current deregulated market and high penetration of power electronic interfaced generations. A singular stability assessment and its subsequent operational boundary may be valid only for a specific stability study and those may not be true for other types of stability. This paper illustrates the proposed framework in a modified New England Test System using a probabilistic approach. The impact of these varying loading conditions and renewable energy sources penetration levels are quantified on a per -stability-type basis and then aggregated into the multi-stability perspective. The established multi-stability operational boundary is heavily influenced by the frequency and large-signal disturbance rotor angle stability. A significant improvement in the system's operational stability boundaries, particularly for voltage stability and small disturbance rotor angle stability, occur when the voltage source converter high voltage direct current's ability to independently control active and reactive power via V DC - Q control is fully utilized. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using the Probabilistic Collocation Method (PCM) in power-system studies in the presence of uncertainties. The application of the PCM is illustrated on small disturbance stability studies and voltage stability studies. First, the PCM is used to determine the effects of a supplementary power oscillation damping (POD) controller installed on a VSC-HVDC line in the presences of parameter and operational uncertainties. Second, it is used to calculate the probabilistic distance to voltage collapse in a distribution system based on measurement errors. The PCM is shown to produce accurate statistical results whilst drastically reducing the computational time of the studies in both cases. Additionally, a ranking process based on eigenvalue sensitivity is presented alongside the small disturbance analysis as an appropriate method for reduction of system complexity for extension to large power systems. This technique is validated through the application of the PCM on a large power-system model, yielding accurate probabilistic results. [ABSTRACT FROM PUBLISHER]

• Updates nonlinear bifurcation analysis for voltage stability in modern power system. • Proposes new loadability boundary with the consideration of multiple bifurcations. • Develops new optimization framework to identify the system shortest loading margin. • Improves the system loading margin in control space within the proposed framework. The introduction of large numbers of power electronic converters along with increasing load demand will create voltage stability issues in future power systems. Most present analysis methodologies neglect the non-linearities within these systems. Bifurcation analysis is an increasingly popular method which not only incorporates system non-linearities, but also efficiently provides information about the system trajectory towards multiple instability mechanisms. This paper develops a new concept of combined system loadability with the consideration of saddle-node bifurcations (SNB), limit-induced bifurcations (LIB), and Hopf bifurcations (HB) simultaneously. These concepts have shown great promise in analyzing complex, multi-origin stability phenomena which are starting to appear in power systems and other areas, and are difficult to study using other methods. An optimization approach is applied to identify the system maximum loadability with these different types of bifurcation points which have not previously been considered at the same time. This creates a new system loadability boundary concept consisting of multiple types of bifurcation which is more representative of the actual power system limit boundary. By maximizing the shortest distance to the composite loadability boundary, the system loading margin is increased. Three illustrative systems are used to verify the proposed methodologies. [ABSTRACT FROM AUTHOR]

The paper presents a supplementary VSC-HVDC Power Oscillation Damping (POD) controller based on wide area measurement signals (WAMS). The controller is designed as Multi Input Single Output (MISO) using a Modal Linear Quadratic Gaussian (MLQG) methodology in order to target critical inter-area electromechanical modes. The approach has been tested on a large (16 machine, 68 bus) test network incorporating parallel HVDC/AC transmission and has shown improved damping compared to a traditional Power System Stabilizer (PSS) based controller structure utilizing local signals. The design process has incorporated the effects of wide area signal transmission delays. Variation in these signal delays and the complete loss of signals has been also investigated to establish the robustness of the WAMS based controller and its sensitivity to loss of signals. Extension of the controller to incorporate reactive power modulation has been investigated, as has variation in available active power modulation capacity. The proposed controller performance has been assessed through small and large disturbance analysis. [ABSTRACT FROM PUBLISHER]

The paper presents a robust probabilistic methodology for assessing the performance of power system controllers. In this study, the proposed probabilistic technique is applied to a novel power oscillation damping (POD) controller structure implemented through active power modulation of multiple voltage source converter based high voltage direct current (VSC-HVDC) links within a large heavily meshed network exhibiting multiple inter-area modes. The modal linear quadratic Gaussian (MLQG) controller, as an example of advanced POD, is implemented in both a centralized and a decentralized form for comparison. Following the development and demonstration of the effectiveness of the controller designs, the robust probabilistic performance evaluation is carried out incorporating outage contingencies for generators, lines, and VSC-HVDC lines. The results obtained demonstrate that both controller structures are largely robust to wide ranging operating conditions. For those conditions where controller performance is unsatisfactory, various mitigation techniques are discussed, including the use of classification tools to guide operational limits. A contingency specific controller design is shown to significantly improve performance when robustness cannot be achieved. [ABSTRACT FROM PUBLISHER]

• Methodology developed for the construction of information-rich training data. • A full data-driven machine learning model for small-signal stability assessment. • Bayesian optimisation-based generation rescheduling for preventive control. • Higher performance (52% improvement) compared to engineering-judgement scheduling. Due to the development of communication technologies, a large amount of data, particularly in power systems, can be measured through digital equipment. Efficient analysis of the collected data can bring many benefits to power system operators, particularly in stability assessment. This paper presents an Extreme Gradient Boosting (XGBoost) decision tree (DT) for predicting damping ratios of an inter-area oscillation. This machine learning technique uses system data to predict the damping of critical system oscillations which can be subsequently improved by optimisation-based generation rescheduling. As power systems experience increased modelling nonlinearity introduced by converter-interfaced sources, traditional techniques for eigenvalue computation become difficult to implement in real-time due to the need for an estimated linearised model. An XGBoost DT uses an ensemble technique which provides higher prediction accuracy compared to other DT techniques. The effectiveness of the XGBoost DT is illustrated by using it in conjunction with a Bayesian optimisation to perform active power rescheduling to improve the damping ratio of an inter-area mode. The results show an average relative improvement of 58.10% in the damping ratio of the inter-area oscillation across multiple test cases when the proposed optimisation-based generation rescheduling is implemented in conjunction with the XGBoost-based oscillation damping estimation model. [ABSTRACT FROM AUTHOR]

• A new index indicates the locations where the dynamic HVDC model is required. • The index quantifies mode shape differences between different HVDC modelling. • Mode shape changes indicate errors in damping ratios between different HVDC models. • Higher performance and reliability of the results compared to simple error methods. This paper presents a new measure that can be used to identify the locations where high levels of HVDC modelling can be simplified while maintaining the accuracy of the small-signal stability results. Low-frequency inter-area oscillations have posed a major issue to power system operations and stability, for example by reducing the maximum power transfer on tie lines connected between different regions or causing cascading failure due to ever-growing oscillations. In modern power systems, a significant number of High Voltage Direct Current (HVDC) systems are connected to power networks. An appropriate level of HVDC modelling is required in order to obtain accurate small-signal stability results in multi-infeed HVDC systems. Although accurate results can be obtained by using complex modelling details, this can be prohibitively computationally expensive due to increasing simulation time. In this paper, different levels of modelling fidelity for HVDC systems are investigated in order to establish the impact of the included HVDC system dynamics on small-signal stability. It is shown that some dynamic HVDC modelling, particularly LCC-HVDC and VSC-HVDC with P-Q control, can be replaced by simplified models in order to reduce the simulation time and model complexity while maintaining the accuracy of small-signal stability results. Furthermore, a method to identify which HVDC systems require detailed modelling based on the quantification of changes to critical mode shapes is developed. [ABSTRACT FROM AUTHOR]

• A generalized framework for transient stability assessment. • Practical for power systems with uncertainties. • Useful in development of probabilistic transient stability assessment frameworks. • Easy to implement on existing practical computational network models. This paper introduces a method for the identification of transient stability boundaries in power systems with multidimensional uncertainties. This method uses a parametric space rather than the state-space to identify the critical operating states. First, different transient stability indices are used to map the state-space of system into the index-specific parametric spaces. Then, these index-specific parametric spaces are characterized to identify their practicality and effectiveness to reflect the system's state-space information accurately and to quantify uncertainties. Finally, the transient stability boundaries are identified using the index-specific parametric spaces considering multidimensional uncertainties. The indices studied in this paper are on basis of rotor-angle difference, transient kinetic energy, and transient potential energy. This method is verified using a 16-machine, 68-bus computer model of the New England Test System and the New York Power System in MATLAB and DigSILENT environments. The uncertainty in this paper is represented in up to 4-dimensions and is modeled in form of loading conditions and generation dispatch uncertainties. This method is further applicable to higher dimensions and other forms of uncertainties and has the utility for probabilistic studies and predictive analytics. [ABSTRACT FROM AUTHOR]

• Develops a quantitative comparison methodology between different network models. • Analyzes the impact of VSC inverter and rectifier modes on control-network interactions. • Investigates the locational impact of VSCs on the grid dynamics using a new index. • Performs a comparison of GFL and GFM controls on the degree of hybrid modelling required. The mass integration of power electronics-based devices is changing small-disturbance stability issues in power systems from primarily interactions between synchronous generators to include interactions between power electronic devices and the AC network. However, modelling of AC networks for small-disturbance stability assessment in large power systems generally do not include the transmission network dynamics and, therefore, fails to identify these newly emerging power phenomena. A suggested solution is the hybrid modelling approach which models the vicinity around the power electronics with network dynamics while representing the remainder of the network with a steady-state model. This paper identifies the most appropriate selection of dynamic AC network modelling in accordance with different scenarios including different power electronic control schemes, different power electronic operation modes, and different locations of power electronic devices. It is shown that more of the network must be modelled dynamically when VSCs are located in low SCR regions, operating in inverting mode, and using grid following or grid-forming control with inner current control explicitly modelled. Contrastingly, less dynamic detail is required for high SCR regions, rectifying mode, and when modelling grid forming control without explicit representation of the inner current controllers. Investigations are performed using the IEEE 39-bus test system. [ABSTRACT FROM AUTHOR]

In order to strengthen the onshore transmission network in many parts of the world, voltage-source current–high-voltage direct current (VSC–HVDC) will increasingly be utilized. The effect of the operating point of a VSC-HVDC link and the control strategies employed can substantially affect the electromechanical oscillatory behavior of the ac network as well as the dc-side dynamics. In order for the full, flexible capability of VSC-HVDC to be exploited, further study of the effects of these controllers and their interactions with ac system responses is necessary. This paper addresses this gap. Both modal analysis and transient stability analysis are used to highlight tradeoffs between candidate VSC-HVDC power controllers and to study the electromechanical performance of the integrated ac/dc model. Tests are carried out on both a generic two-area model and a large-scale realistic network with detailed ac generator and HVDC models. [ABSTRACT FROM PUBLISHER]

Modular multilevel converters (MMC) are presently the converter topology of choice for voltage-source converter high-voltage direct-current (VSC-HVDC) transmission schemes due to their very high efficiency. These converters are complex, yet fast and detailed electromagnetic transients simulation models are necessary for the research and development of these transmission schemes. Excellent work has been done in this area, though little objective comparison of the models proposed has yet been undertaken. This paper compares for the first time, the three leading techniques for producing detailed MMC VSC-HVDC models in terms of their accuracy and simulation speed for several typical simulation cases. In addition, an improved model is proposed which further improves the computational efficiency of one method. This paper concludes by presenting evidence-based recommendations for which detailed models are most suitable for which particular studies. [ABSTRACT FROM AUTHOR]

• Special issue on control interactions in power electronic converter dominated power systems. • The Special Issue covered the latest research results on the control interactions. • It covered control interaction mechanism, modeling, analysis and control methods. Guest Editorial. [ABSTRACT FROM AUTHOR]

• Analysis of Frequency Containment Services (FCS) and Power Oscillation Damping (POD) controllers. • Performance of FCS and POD controllers reduces when both controllers are activated. • A FCS controller reduces damping performance following a fault but the impact cannot be seen in the modal analysis. • Additional filters are added to reduce the interaction between both controllers. This paper investigates the interaction between frequency containment services (FCS) and power oscillation damping (POD) ancillary services installed in a Voltage Source Converter High Voltage DC (VSC-HVDC) line. In VSC-HVDC systems, ancillary services can be installed to maintain appropriate levels of power system stability. Both FCS and POD controllers are typically installed in the active power control loop separately. The modulation signals generated by these two ancillary services both affect the active power injected by the converter. When operating a VSC with FCS and POD ancillary services the resultant combination of the modulation signals in the active power loop may result in reduced performance. The results in this study show that performance of controllers reduces when both ancillary services are activated, particularly in low-inertia systems. Modifications to the control structures of both controllers that improve their performance under various disturbances are proposed. [ABSTRACT FROM AUTHOR]

• Brief summary of the Special Issue on Recent Advancements in Electric Power System Development Planning with High-Penetration of Renewable Energy Resources and Dynamic Loads. • Review of the papers included in this special issue. • Viewpoint of the Guest Editorial Board on the present and future areas of research in Power System Planning. [ABSTRACT FROM AUTHOR]