Converter-interfaced renewable generation predominates in the development of new power system architectures, particularly in offshore systems. The increase of such multi-converter systems leads to the introduction of a new interaction among the different elements of the system and, therefore, new dynamic phenomena. Such phenomenon is subsynchronous and supersynchronous oscillations (SSO) which result, among other causes, from the interaction of converters with weak networks, such as offshore power systems. Various factors, including the challenge of obtaining analytical models from converter-based generation manufacturers and analysing system measurements during planning and operations, necessitate effective measurement-based methods for the swift and numerically trustworthy identification of various characteristics of SSO. Therefore, this paper analyses the advantages and disadvantages of the Dynamic Mode Decomposition method for identifying SSO. The theoretical background of the technique and the application algorithm are presented. The method is first applied to several synthetic signals exhibiting subsynchronous and supersynchronous modes under various conditions, including noise and different time windows. Then, a converted-based resource connected to an infinite bus is presented, and the method is applied to a group of recorded signals from this system under an external perturbation. This method is proposed as an alternative for analysing SSO in converted-based systems due to its ability to assess non-linear systems and its robustness against noise.

As the Netherlands moves toward climate neutrality by 2050, the national power system will rely heavily on variable renewable energy sources (VRES) such as offshore wind and solar photovoltaics. While previous studies have examined steady-state implications of overplanting and grid reinforcement, less attention has been given to assessing the effectiveness of flexibility resources during longer time periods. This paper presents an operational assessment of the 2050 Dutch transmission system using full-day optimal power flow simulations for typical summer and winter conditions.The synthetic model of the transmission system is developed in DIgSILENT PowerFactory and includes distributed and centralized supply, batteries, electrolyzers, and demand response mechanisms. Using the Mean-Variance Mapping Optimization (MVMO) algorithm with 15-minute resolution, system operation is optimized to minimize active power losses while respecting voltage and thermal limits. The results show that flexibility resources are essential to ensure demand coverage and reduce transmission congestion, especially during periods of high VRES generation. In winter, the centralized nature of offshore wind leads to regional overloads and higher losses, while summer benefits from decentralized PV generation and more balanced load matching. Batteries and hydrogen units show distinct operational patterns, emphasizing the importance of their strategic placement. These findings support the design of control strategies and infrastructure planning for high-VRES transmission systems.

As electrical systems become increasingly complex with the integration of new electronic loads and variable renewable energy sources (VRES), modern tools are essential for their effective management and operation. This paper discusses an initial step toward the complete implementation of a digital twin for the Dutch electrical power grid: the development of a real-time digital model. This model represents the Randstad region’s electrical grid, which has recently been enhanced by substantial offshore wind power installations, including Hollandse Kust Zuid and Hollandse Kust Noord.The Real-Time Electromagnetic Transient (EMT) model described in this study enables the assessment of the impacts of offshore wind integration on network stability and power quality. Network elements have been modeled using RSCAD and implemented within the Real-Time Digital Simulator (RTDS). Detailed simulations are conducted to evaluate the grid’s capacity to handle the active and reactive power influx from the offshore wind farms. This study highlights the critical role of precise modeling in ensuring the reliability and efficiency of wind power integration into the national grid.

The increasing need of cost-effective and large-scale energy storage systems is motivating a strong focus on the deployment of hydrogen based conversion and storage. Hence, the potential of achieving fast dynamic response and high efficiency of electrolyzers makes them attractive to support the mitigation of reliability and stability threats due to the inherent variable power supply from renewables. Suitable dynamic models of new combined solutions, i.e. renewable generation with electrolyzers, are urgently needed to properly characterize and mitigate such threats. Therefore, this paper presents an EMT real-time simulation model of an electrolyzer connected to an offshore 20 MW DC wind power generation system. The envisioned power electronic layout along with the necessary controlling actions are explained in detail. Two modes of electrolyzer operation are proposed the master and slave mode. Based on whether hydrogen production is the priority or grid power injection is priority, the controlling action of the proposed model can be switched. The proposed model is developed as a building block for the study and design of multi-gigawatt scale offshore wind power plants, with stability support from electrolyzers.

This study investigates the dynamic performance of hybrid power systems, with a focus on Multi-Terminal Direct Current (MTDC) interconnected offshore-onshore systems, under various disturbances. Conventional performance metrics such as Rate of Change of Frequency (RoCoF), commonly used for AC systems, are utilized to assess frequency response. Additionally, a modified Rate of Change of Voltage (RoCoV) metric is proposed to capture DC voltage behavior. The effectiveness of these metrics is evaluated through simulations involving various disturbances, including generator outages, line outages, converter outages, and faults. The results demonstrate the ability of the proposed metrics to effectively capture the impact of disturbances on system response, while also identifying limitations in capturing oscillating responses. Furthermore, the parametric sensitivity of control parameters in the converter’s outer control loop is analyzed to assess their influence on system behavior.

High-Voltage Direct Current (HVDC) transmission with Modular Multi-level Converter (MMC) - Bipolar Point-to-Point (BPP) configuration is gaining traction as a solution for integrating renewable energy sources into future power grids. However, one critical challenge associated with MMC-BPP systems is the occurrence and mitigation of oscillations on the DC side. These oscillations can arise due to various factors, including interactions between the AC and DC systems, converter de-blocking after fault events, and the dynamic behavior of connected power sources. The research work presented in this paper addresses a gap by investigating and mitigating oscillations specifically occurring during post-fault converter de-blocking. An enhanced active damping method is proposed that achieves a substantial reduction of these oscillations, ensuring improved system stability during this critical phase. Furthermore, a meticulous parametric sensitivity analysis is conducted on a four-terminal MMC-BPP test system using a real-time simulator to extract valuable insights into the damping method’s effectiveness under various operating conditions.

Cascading failures in power systems are extremely rare occurrences caused by a combination of multiple, low probability events. The looming threat of cyberattacks on power grids, however, may result in unprecedented large-scale cascading failures, leading to a blackout. Therefore, new analysis methods are needed to study such cyber induced phenomena. In this article, we propose a data-driven method for dynamical analysis of power system cascading failures caused by cyberattacks. We provide experimental proof on how attacks may accelerate the cascading failure mechanism, in comparison to historically observed blackouts. Using a dynamic power grid model, consisting of multiple, coordinated protection schemes, we define and analyze the point of no return in a cascading failure sequence by applying the Hilbert–Huang transform for time-frequency analysis. Numerical results indicate, cyberattacks may accelerate cascading failures at least by a factor of 3x. This is due to the excitation and non-damping of multiple frequency modes greater than 1 Hz in a short time span. The proposed method is tested using time domain simulations conducted through a modified IEEE 39-bus test system, which can simulate cascading outages using coordinated protection schemes.

Power electronic interfaced generation (PEIG) has become significantly dominant in the electrical power grid. This development is leading to a decrease in systemic inertia and damping against electrical oscillations. This causes the introduction of new and faster dynamic phenomena. One of these phenomena is sub-synchronous control interaction (SSCI), occurring as sub-synchronous oscillations (SSOs) in the system. Several real-world events reported so far have been related with large wind power plants (WPPs) and the improper tuning of the grid side converter (GSC) of (type-4) fully rated converter (FR C) wind turbines. As other PEl G have similar topologies and control systems, it is a very relevant topic. Weak grid conditions often contribute to the risks of SSO events. This paper proposes supplementary wide-area damping (WAD) to the control system of the GSC, focused on damping excursions of the phase locked loop (PLL). Signals measured by a remote phasor measurement unit (PMU) are communicated to the control system, which uses it for dynamic damping control. The effects of the WAD are tested by comparing the results of linearization-based eigenvalue analysis with and without the addition of WAD. Supplementary analysis conducted by using time-domain simulations and Prony analysis confirm the positive effect of WAD. Numerical tests are performed in DlgSILENT PowerFactory 2023 SP2 on a modified IEEE-39 bus test system.

A large contribution to the total share of electricity generated in European power grid will come from renewable sources of energy soon due to European initiative to become carbon neutral. Renewable Energy Sources (RES) are connected to the grid with Power Electronic (PE) devices, which, if not modelled correctly, provide low confidence in the assessment of the overall system stability. The aim of the paper is twofold; firstly, to evaluate the impacts of Wide Area Monitoring System (WAMS) on grid-forming control with Direct Voltage Control (DVC) and Virtual Synchronous Machine (VSM). Secondly, to perform Modal / Eigenvalue analysis to evaluate the effect of WAMS on selective damping. Modal analysis will be conducted and will serve as a base for calculating matrices B and C. Left and right eigenvectors will enable determination of Controllability and Observability Indices. Damping controller is designed to receive three signals from Phasor Measurement Units (PMUs) and feed the reference signal to the high impact generator. System studied is a modified IEEE 39 New England Bus with two synchronous generators and eight RES. Activation of the damping controllers showed significant improvements in time-domain simulations on overall damping and Eigenvalue confirmed the results by studying the damping ratios of critical modes.

The operation schedule of the power generation units in electrical power systems is determined by the optimisation problem known as unit commitment (UC), aiming at minimising the total cost considering the generation constraints. To obtain a feasible solution from the network perspective, the security-constrained UC (SCUC) problem has been defined to embed the network constraints in the optimisation problem as well. Also, the higher penetration of renewable energy sources (RES) has increased the difficulty of UC problem, mainly due to the uncertainty and the high variability of RES. This paper proposed a SCUC with economic dispatch (SCUCED) optimisation developed in two stages. The first one is the solution of a merit-order based zonal day-ahead market (ZDAM) optimisation to define a preliminary generation schedule. In the second stage, the SCUCED is solved based on AC load flow routines and sensitivity factors to embed the full network representation. The approach is applied to a modified version of the IEEE 39-bus test system.

The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). Energy storage systems (ESS) are used to supply extra power injection to enhance the frequency stability during a disturbance. This paper presents a novel approach for improving the frequency dynamics by incorporating a designed ultracapacitor (UC) with a fully decoupled wind power generation (FDWG) unit. To this aim, a suitable model implementation of UC for real-time simulations is presented. The model constitutes a parallel RC branch, which is appropriate for illustrating the relevant fast UC dynamics that occur within the first milliseconds of the time period of action for fast active-power frequency control services. The frequency performance achieved by the support of the FDWG equipped with UC is compared against the performance achieved by using electrical batteries. The comparison includes the application of droop-derivative frequency control.

Mean-Variance Mapping Optimization (MVMO) belongs to the family of evolutionary algorithms, and has proven to be competitive in solving computationally expensive problems proposed in the Icompetitions CEC2014, CEC2015, and CEC2016. MVMO can tackle such problems by evolving a set of solutions (population based approach) or a single solution (single parent-offspring approach). The evolutionary mechanism of MVMO performs within a normalized search space in the range [0, 1]. The power of MVMO stems from its ability - based on statistical analysis of the evolving solution based on a mapping function - to adaptively shift the search priority from exploration to exploitation. This paper introduces a newly defined mapping function as well as a new rule for using an embedded local search strategy, and presents several tests conducted by using the test bed of the CEC2018 competition. Numerical results indicate significant improvements on the results obtained in CEC2016 competition.

The MVMO algorithm (Mean-Variance Mapping Optimization) has two main features: i) normalized search range for each dimension (associated to each optimization variable); ii) use of a mapping function to generate a new value of a selected optimization variable based on the mean and variance derived from the best solutions achieved so far. The current version of MVMO offers several alternatives. The single parent-offspring version is designed for use in case the evaluation budget is small and the optimization task is not too challenging. The population based MVMO requires more function evaluations, but the results are usually better. Both variants of MVMO can be improved considerably if additionally separate local search algorithms are incorporated. In this case, MVMO is basically responsible for the initial global search. This paper presents the results of a study on the use of the hybrid version of MVMO, called MVMO-PH (population based, hybrid), to solve the IEEE-CEC 2018 test suite for single objective optimization with continuous (real-number) decision variables. Additionally, two new mapping functions representing the unique feature of MVMO are presented.

This paper deals with the process of identifying the parameters of the dynamic equivalent (DE) load model of an active distribution system (ADN) simulated in RTDS using mean-variance mapping optimization (MVMO) algorithm.MVMO is an emerging variant of population-based, evolutionary optimization algorithm whose features include evolution of its solutions through a unique search mechanism within a normalized range of the sample space.Due to the prominent largescale integration of DG in low and medium voltage networks, it is important to develop equivalent models that are suitable for representing the resulting active distribution network in dynamic studies of large power systems.This would significantly reduce the computational demands and simulation time.Moreover, only a defined portion of a system is usually studied, which means that the external system can be substituted with DE thereby allowing the detailed modelling of the focus area.The IEEE 34-Bus distribution system was modified and used as the reference network where measurement data were gathered for identification of the parameters of its developed DE.An optimization-enabled simulation involving MATLAB, which host the MVMO algorithm and RTDS, which simulates the models was established.The reactions of the detailed network and the DE were compared upon subjecting them to different disturbances in the retained system.The effectiveness of the MVMO algorithm in identifying DE parameters based on its unique mapping function is reflected through the results of the response comparison.

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the heuristic optimization field. The main traits of its evolutionary mechanism reside in the adoption of a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables as well as in the use of a special mapping function, which accounts for the actual mean and variance of the normalized optimization variables for mutation operation. MVMO is also open to further extensions and hybridization with other approaches. Along this spirit, this paper surveys the performance of MVMO when executed in its pure algorithmic procedure and when hybridized to include a local search strategy. Numerical experiments are conducted on the IEEE-CEC 2015 optimization test bed on bound constrained single-objective computationally expensive numerical optimization. Remarkably, MVMO proves effective at solving different complex problems within a reduced number of allowed function evaluations.

Mean-variance mapping optimization (MVMO) is an emerging evolutionary algorithm, which adopts a single-solution based approach and performs evolutionary operations within a normalized range of the search for all optimization variables. MVMO uses a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Recently, the MVMO has been extended to a population-based and hybrid variant denoted as MVMO-SH, which includes strategies for local search and multi-parent crossover. This paper provides an study on the performance of MVMO-SH on the IEEE-CEC 2015 competition test suite on learning-based real-parameter single objective optimization. Experimental results evidence the effectiveness of MVMO-SH for successfully solving different optimization problems with different mathematical properties and dimensionality.

Mean-Variance Mapping Optimization (MVMO) constitutes an emerging heuristic optimization algorithm, whose evolutionary mechanism adopts a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables. Besides, MVMO is characterized by an archive of n-best solutions from which the unique mapping function defined by the mean and variance of the optimization variables is derived. The algorithm proceeds by projecting randomly selected variables onto the corresponding mapping function that guides the solution towards the best set achieved so far. Despite the orientation on the best solution the algorithm keeps on searching globally. This paper provides an evaluation of the performance of MVMO when applied for the solution of computationally expensive optimization problems. Experimental tests, conducted on the IEEE-CEC 2014 optimization test bed, highlight the capability of the MVMO to successfully tackle different complex problems within a reduced number of allowed function evaluations.

This paper provides a survey on the performance of the hybrid variant of the Mean-Variance Mapping Optimization (MVMO-SH) when applied for solving the IEEE-CEC 2014 competition test suite on Single Objective RealParameter Numerical Optimization. MVMO-SH adopts a swarm intelligence scheme, where each particle is characterized by its own solution archive and mapping function. Besides, multi-parent crossover is incorporated into the offspring creation stage in order to force the particles with worst fitness to explore other sub-regions of the search space. In addition, MVMO-SH can be customized to perform with an embedded local search strategy. Experimental results demonstrate the search ability of MVMO-SH for effectively tackling a variety of problems with different dimensions and mathematical properties.

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the emerging field of heuristic optimization algorithms, which has been quite successful in solving a variety of power system optimization problems. This paper introduces a hybrid variant of MVMO (MVMO-SH) for solving the IEEECEC 2013 competition test suite. MVMO-SH is based on a swarm scheme of MVMO with embedded local search and multi-parent crossover strategies to increase search diversity and solution quality. Numerical results attest to the promising prospect of MVMO-SH to become a general purpose optimization algorithm.

A methodology for parameter estimation of equivalent wind power plant is proposed in this paper. The parameter algorithm is formulated as an optimization problem which miminize the output between the real system and the mathematical model. The parameters are updated using Newton-Raphson method which depends on the information of trajectory sensitivity functions. A generic equivalent of wind power plant was used as a structure of the model, which is valid for Double-Fed Induction Generator (DFIG) and full Converter Based Wind Turbine. In addition, the procedure to obtain of the trajectory sensitivity functions of the model is presented in this paper. A discussion of the estimated results is also analysed. In general, the convergence happened in a few seconds for most of the cases studied.

Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMOS) is investigated in this paper. Numerical experiments and comparisons with other heuristic optimization methods, which were conducted on several composition test functions, demonstrate the feasibility and effectiveness of MVMOSwhen solving multimodal optimization problems. Sensitivity analysis of the algorithm parameters highlights its robust performance.

Abstract—Vulnerability assessment is one of the main tasks in a Self-Healing Grid structure, since it has the function of detecting the necessity of performing global control actions in real time. Due to the short-time requirements of real time applications, the eligible vulnerability assessment methods have to consider the improvement of calculation time. Although there are several methods capable of performing quick assessment, these techniques are not fast enough to analyze real large power systems in real time. Based on the fact that vulnerability begins to develop in specific regions of the system exhibiting coherent dynamics, large interconnected power systems can be reduced through dynamic equivalence in order to reduce the calculation time. A dynamic equivalent should provide simplicity and accuracy sufficient for system dynamic simulation studies. Since the parameters of the dynamic equivalent cannot be easily derived from the mathematical models of generators and their control systems, numerical identification methods are needed. Such an identification task can be tackled as an optimization problem. This paper introduces a novel heuristic optimization algorithm, namely, the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy of the identified parameters. The identification procedure and the level of accuracy that can be reached are demonstrated using the Ecuadorian-Colombian interconnected system in order to obtain a dynamic equivalent representing the Colombian grid. Keywords-dynamic equivalent; heuristic optimization; Mean-Variance Mapping Optimization; smart grid; vulnerability I.