The deployment of voltage source converters (VSC) to facilitate flexible interconnections between the AC grid, renewable energy system (RES) and Multi-terminal DC (MTDC) grid is on the rise. However, significant challenges exist in exploiting coordinated operations for such AC/VSC-MTDC hybrid power systems. One of the most critical issues is how to achieve the optimal operation of such wide-area systems involving several power entities with as minimal communication burden as possible. To address this issue, an enhanced AC/DC optimal power flow (OPF) is specifically proposed. Firstly, a mixed-integer convex AC/DC OPF model is explicitly formulated to describe the optimal operation of such hybrid power systems. Subsequently, a nested distributed optimization method with double iteration loops is developed to offer optimal system-wide decision-making through a more “thorough” distributed communication architecture. In the outer iteration, the original AC/DC OPF problem is decomposed into several slave problems (SPs) associated with systems (including the AC grid and RESs) and one master problem (MP) associated with the integrated VSC-MTDC grid. Generalized Benders decomposition (GBD) serves to solve the master and slave problems iteratively. Techniques such as multi-cut generation and asynchronous updating are utilized to upgrade the GBD performance of computation efficiency and address communication delays. In the inner iteration, the master problem is continuously decomposed into multiple sub-MPs associated with individual VSCs. The alternating direction method of multipliers (ADMM) is employed to solve these sub-MPs iteratively. Proximal terms and heuristic approaches are embedded to enable parallel computation and handling of integer variables. Numerical experiment results finally validate the effectiveness of the proposed enhanced AC/DC OPF. The constructed AC/DC OPF model exhibits acceptable accuracy in terms of power flow calculation, and the developed nested distributed optimization method showcases decent convergence rate and solution optimality performances.

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

DC fault location technology is crucial for estimating the fault location and developing multi-terminal direct current (MTDC) systems. This article presents a novel fault location method using the parameter fitting approach. The propagation of traveling waves (TWs) in the decoupled line-mode fault network is first discussed, resulting in analytical expressions for the backward line-mode current TWs containing fault location information. Then, the adaptive multi-step Levenberg–Marquardt (AMLM) algorithm is applied for parameter fitting owing to its fast processing speed and precision. The exact fault location is estimated using the fitted coefficient. Different testing MTDC systems modeled in PSCAD/EMTDC and a real-time digital simulator (RTDS) validate the proposed fault location method. Based on numerous simulation tests, the AMLM-based parameter fitting and the proposed method are accurate, with errors smaller than 0.5%. Compared to the existing methods, the proposed method has desired performance under close-in faults, can withstand 35 dB noise interference, and obviates the need for an extremely high sampling frequency, estimation of tws velocity, and communication devices.

Hybrid AC/voltage source converter-based multi-terminal DC (VSC-MTDC) power grids play a crucial role in enabling long-distance power transmission and flexible interconnection between AC grids. To fully leverage the functional advantages of such systems, it is essential that they operate in or close to optimal power flow (OPF) conditions. To address this, ACDC-OpFlow is developed as an open-source and cross-language framework for solving AC/DC OPF problems. Its core innovation lies in a unified modeling structure that supports MATLAB, Python, Julia, and C++, with Gurobi used as a consistent solver backend. This framework is beginner-friendly and allows users to work in their preferred programming languages. Both text-based and graph-topology results are provided to help users understand the system-wide power flow distribution and operational status. This work presents the design concept of ACDC-OpFlow, showcases representative example results, and discusses the performance differences observed in multiple programming language implementations.

This paper presents a reinforcement learning controller (RLC) for a single-phase full-bridge rectifier as an interface for a battery energy storage system (BESS). A novel solution is presented that combines the traditional proportional-integral (PI) regulator with an RL-based control strategy using a proximal policy optimization (PPO) agent. In a high-fidelity Simulink-based digital twin setup, the agent learns to perform optimal switching actions for a single-phase full-bridge rectifier to achieve accurate current tracking and improved power quality. Simulation results show stable DC voltage regulation at 200V, tracking response under 0.1s, and harmonic compliance with THD equal to 2.38%. The hybrid control strategy guarantees robust dynamic performance and adaptability in the context of renewable energy and storage systems’ varying source and load conditions. The findings demonstrate the potential of coupling AI-driven control with digital twins to empower the autonomy and resilience of future smart energy systems.

The Modular Multilevel Converter (MMC) has garnered significant interest recently due to its superior harmonic performance and improved efficiency in high-voltage direct current electrical grids. Model Predictive Control (MPC) is widely adopted for the MMC applications, as it provides a straightforward control design, facilitates the inclusion of multiple control objectives through a flexible cost function formulation, and offers excellent control performance. An emerging and promising solution involves integrating MPC with machine learning (ML)-based models, in which neural networks learn MPC behavior and predict the results as the traditional MPC does.In this paper, a multi-layered neural network is designed to approximate the control behavior of MPC correctly, enabling a substantial reduction in computational effort during real-time operation and replacing the complex optimization routines of MPC with lightweight neural network regression models that are both efficient and decoupled from the algorithmic complexity of traditional MPC. The performance of controllers is evaluated under both small and large disturbances in active power and reactive power.

Unscheduled event handling capability and swift recovery from transient events are indispensable study areas to ensure reliability in offshore multiterminal high-voltage dc (MT-HVdc) grids. This article focuses on enhancing the reliability of half-bridge modular multilevel converters (HB-MMCs) in MT-HVdc grids by introducing a predictive dc fault ride-through (DC-FRT) recovery controller and fault separation devices. A novel dc protection-informed zonal DC-FRT scheme for HB-MMCs is proposed, incorporating a model predictive planner for optimized control inputs based on local and interstation measurements and converter constraints. A real-time digital simulator environment simulates the approach, which improves lower level control during fault interruption and suppression by utilizing fault detection and location information. In addition, the study examines two control schemes to assess the impact of communication delays in MT-HVdc grids, a critical factor for system stability and reliability during faults. These schemes include a centralized scheme with delays in input and output signals and a decentralized approach focusing on external signal delays. Both are compared against a baseline centralized control with no delays. These approaches explore alternatives for the placement of the proposed controller, considering potential delays in interstation high-speed communication. The findings underscore the significance of the proposed DC-FRT control in reinforcing MT-HVdc systems against faults, which contributes to efficient recovery and grid stability.

The increased use of High-Voltage Direct Current transmission networks requires appropriate control strategies for the converter stations, which are crucial to ensure uninterrupted energy supply. In this paper, a Polytopic Lyapunov Function-based Hybrid switching control strategy is implemented to combine the merits of Grid Following and Grid Forming control strategies by switching alternatively from one to another at the polytopes’ hyperplanes to ensure good system response even for faulty conditions. The state space equations of the control strategies are used to form the state hyperplanes for the switching rule. Since the hybrid switching control is based on the Polytopic Lyapunov Function, the system is inherently Large Signal Stable. The results obtained by real-time-based simulations using RTDS verify the designed control for various transient phenomena.

The modular multilevel converter (MMC) uses many power electronic components in the high voltage direct current (HVDC) application. One of the major concerns in half-bridge MMC is the fault in the converter submodules. It raises the question of whether the reliability and high-quality performance of the MMC can be increased significantly as the active device controls the power flow between the AC- and DC-sides. During the SM fault within the MMC leg, the unbalance is introduced in-side the MMC converter. The unbalanced voltage within the leg of the MMC will continuously introduce an AC-current component on the DC-side of the converter. Thus, the hybrid proportional-internal (PI) control and proportional-resonant control (PR) is introduced in controlling the power flow within the internal MMC to eliminate the AC-current component and ensure pure DC-current in the internal MMC. This study investigates the internal power flow control of a three-phase rectifier MMC with symmetric and asymmetric SM fault conditions. Compared with conventional control methods, the proposed control can tolerate SM faults and eliminate the AC-current component within the converter, increasing the converter's performance. Simulation results are included and discussed to verify the proposed control.

GigaWatt-scale offshore wind farms are being connected using the Modular Multilevel Converter (MMC), in which submodules act as the main building block. The extensive number of connections has a significant effect on the system's security of supply. Periodic weather conditions and time-dependent labor capacity complicate maintenance, emphasizing the need for more flexibility when replacements are required. Traditionally, submodules are redundant or over-dimensioned to increase reliability, but do not resolve flexibility issues faced by operators. This article presents a novel operation method introducing flexibility in the submodule stress distribution. Specific submodules can be used more or less frequently depending on maintenance achievability. The technical condition of each submodule is determined, and then a selection window with a configurable length determines the submodule-specific insertion frequency, affecting the remaining useful lifetime. The capacitor voltage balance can be guaranteed with traditional sorting methods and is compatible with the lifetime optimization algorithm using a priority factor. The priority can be divided between lifetime optimization and capacitor voltage balancing depending on the operator's needs. This is superior to the traditional methods due to the ability to control the submodule-specific deterioration pace. Analytical evaluation and simulation studies demonstrate the effectiveness of this control approach.

The widespread use of modular multilevel converters (MMCs) in the evolution of complex power grids presents new challenges for grid stability. MMCs have highly nonlinear impedance characteristics due to their complex internal dynamics and intricate control architectures. Due to practical constraints, physics-based models cannot accurately compute these impedances, and the use of closed-box measurement techniques is time-consuming, resulting in a limited amount of data available for impedance characterization. Thus, using current methods to estimate impedances over a wide range of operating points can be unreliable. This paper presents a transfer learning-based framework for MMC impedance characterization using system-level parameters as operating point variables. The proposed approach predicts both AC and DC side impedances simultaneously by extrapolating impedances derived using state-space modeling approaches to real-time electromagnetic transient (EMT) simulations. Finally, the method is evaluated on a practical converter from the CIGRE B4 DC grid test system for various types of controllers and scenarios involving unknown parameters.

The potential of advanced neural networks (NNs) has yet to be explored in the field of HVDC transmission. Implementing such intelligent computational techniques on a real-time digital simulator (RTDS) is challenging due to the need for rapid computation and the risk of overfitting with extensive data generated at tiny time steps. To overcome these limitations, different NN techniques are studied using a supervised and reinforced imitation learning method to mimic the suggested controller with labeled data for real-time applications. Furthermore, the NN component does not necessarily just take a label, and therefore, the authors propose a more advanced approach by incorporating reinforced learning through an error-tracking mechanism into the NN, apart from its loss function. The initial offline processing identifies the best-suited NN technique for online computational feasibility. Both online and offline training methods as well as online adjustments are showcased to provide a robust control solution that is easy to implement. This work deals with developing an intuitive and versatile Toolbox installed on a real-time simulator platform that can integrate complex NN-based control strategies. Extensive simulations on the RTDS platform and experimental investigations of the four terminal HVDC systems validate the interest and viability of the proposed design methodology.

With the domination of modular multilevel converters (MMCs) interfaced power grids, especially for transmission of the wind generated energy, the control of such power electronic interfaced grids is of an utmost important for the proper operation and grid stability. This control is very complex due to multivariable intercoupling and plausible nonlinearity. To enhance the grid stability and reduce the total harmonic distortion (THD) of the converter, the paper proposes development of an optimal voltage level-model predictive control (OVL-MPC) for a fast dynamic response, integrated with classical proportional–integral (PI) outer-loop control for robust steady-state performance. This control eliminates the problems of poor steady-state performance of MPC while achieving faster transient response in comparison to the classical proportional integral (PI) dual-loop control. The work proposes OVL-MPC for lower computational burden in comparison to switching state-based MPC, for the inner loop replacing the classical PI inner loop. With the inherent advantages of lower computational burden and superior transient performance, AC current deadbeat controller is used for the modulation in OVL-MPC. To improve the robustness of the control method, the Moore–Penrose pseudo-inversion is applied to address control parameter mismatches, while the Smith predictor compensates for time delays. The designed control algorithm is tested with two real-time simulation platforms, i.e., OPAL-RT and RTDS for thorough power system validation.

This editorial introduces the Special Issue “Methodologies and Applications of Digital Twin for Renewable-Dominant Power Systems”, which highlights key research addressing the challenges associated with integrating renewable energy sources into power systems. Digital twins provide an advanced framework for modeling, simulation, and optimization of these complex systems, driven by the need to address the impact of power electronics and multi-source energies. The contributions featured in this issue focus on the application of DT technologies in renewable-dominant systems, offering solutions for enhancing system stability, optimization, and operational efficiency.

The performance of existing protection methods for multi-terminal direct current systems depends on the availability and sizes of boundary components. To overcome the limitation, this paper proposes a non-unit DC line protection method based on the normalized backward traveling waves (BTWs) of the 1-mode voltage. Firstly, traveling wave propagation characteristics are analyzed, and a rationalization approach based on vector fitting is proposed. Next, the analytical expressions of normalized BTWs are derived, with the negative correlation between them and fault distance proved. Then, the derivative-free conjugate gradient algorithm is utilized for amplitude fitting and normalization calculation. Finally, a non-unit protection method using the normalized BTWs is developed. The performance is validated for both electromagnetic transient PSCAD/EMTDC and real-time digital RSCAD/RTDS simulation. The results demonstrate that the proposed method can accurately identify faults with various fault resistances and locations without requiring boundary components and high sampling frequencies, and it is robust against noise disturbances.

There is a lack of knowledge on the development of a controller model into a black box dynamically-linked library (DLL) file which can be flexibly tested with a power system model through a real-time co-simulation platform. Developing a test bed to demonstrate the performance and compliance analysis enhances the approval of novel control and protection strategies. This paper presents the development of a controller DLL and a test bench that integrates this DLL with a real-time simulator. The DLL contains a previously developed grid-forming controller for the type‑3 wind turbine and works in co-simulation with the power system model on Real-Time Digital Simulator (RTDS) in real-time.

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper's unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network.The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.

AC/multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint, enabling DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the constructed AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed optimization method.

An economical approach for incorporating Battery Energy Storage Systems (BESS) onto DP-2 vessels is presented in this research. The paper deals with developing a Battery Optimization for Optimal Sizing and Throughput Energy Regulation (BOOSTER) framework for putting research findings into practice by optimizing battery size, technology choice and power generation scheduling while considering battery degradation. Twelve battery sizes are analyzed based on three key performance metrics: return on investment, payback period, and years of profitability. A Mixed Integer Linear Programming (MILP) is developed to operate the energy and power management system of the vessel in a fuel and economically efficient manner. The study considers two load profiles of a DP-2 vessel operating near Taiwan and the North Sea. Our findings emphasize the significance of taking battery ownership costs in the form of energy throughput cost and fuel price into account, resulting in a longer battery lifetime and higher return on investment. The research also proposes a BESS operation matrix that provides vessel operators with valuable information on BESS usage for economic benefits. This matrix translates analytics and decision-making into tangible actions that can be implemented in real-time operations. Based on the findings, energy systems may be optimized for a sustainable future, which benefits vessel operators and industry stakeholders.