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Abstract This research article meticulously examines advanced power electronic converters crucial for optimizing electrolyzer perfor- mance in hydrogen production systems. It conducts a thorough review of mature electrolyzer types, detailing their specifications, electric models, manufacturers, and scalability. To meet the high current and stable DC voltage demands of industrial electrolyzers, the study delves into a broad spectrum of AC-DC and DC-DC converter topologies. It explores cutting-edge solutions like 12-pulse and 20-pulse rectifiers, advanced higher multi-pulse rectifiers utilizing conventional and auto-connected transformer units, Multi-Step Auto-connected Transformers (MSAT), polygon autotransformers, and auxiliary filters and circuits such as pulse multiplication circuits and active power filters. These innovations significantly reduce harmonic distortions and enhance power quality, addressing challenges inherent in conventional 6-pulse diode bridge rectifiers. The research also focuses on the efficiency and power factor correction capabilities of Active Front End (AFE) converters and the 3L-DNPC rectifier. Additionally, it investigates various DC-DC converters, including the Continuous Input Current Non-Isolated Bidirec- tional Interleaved Buck-Boost DC-DC Converter, the Interleaved Buck Converter (IBC) with extended duty cycles, the 3-level buck-boost converter with a coupled inductor, Quadratic converters designed for fault tolerance, the three-level interleaved buck converter, among others. Converter designs like the galvanically isolated half-bridge converter with controlled reverse-blocking switches for achieving zero voltage switching (ZVS), the Push–Pull isolated DC-DC converter prioritizing current stability, and the Isolated Full-Bridge Boost Converter (IFBBC) adept at handling fluctuating power outputs from renewable sources are also explored. Moreover, the study scrutinizes a range of converter configurations such as the Two-stage ZVT boost converter with LCL-Type SRC, multiphase interleaved DC-DC stage, and three-port isolated DC/DC converter for efficient integration with multiple energy sources concurrently. Discussing feature trends in power-to-hydrogen systems, the research reviews multi-cell modular rectifiers and multi-stage rectifiers. Overall, the study underscores the critical role of advanced converter topologies in enhancing efficiency, reliability, and power quality in electrolyzer systems, thereby contributing significantly to the progression of sustainable energy technologies.

Abstract Background Virtual power plants (VPPs) represent a pivotal evolution in power system management, offering dynamic solutions to the challenges of renewable energy integration, grid stability, and demand-side management. Originally conceived as a concept to aggregate small-scale distributed energy resources, VPPs have evolved into sophisticated enablers of diverse energy assets, including solar panels, wind turbines, battery storage systems, and demand response units. This review article explores the evolution of VPPs and their pivotal roles as major stakeholders within contemporary power systems. The review opens with a definition of VPPs that clarifies both their fundamental traits and technological foundations. A historical examination of their development highlights major turning points and milestones that illustrate their transforming journey. Main text The methodology used for this article entailed a thorough examination to identify relevant studies, articles, and scholarly works related to virtual power plants. Academic databases were used to gather relevant literature. The literature was organized into categories helping to structure and present information in a logical flow based on the outline created for the review article. The discussions in the article show that the various functions that VPPs perform in power systems are of major interest. VPPs promote the seamless integration of renewable energy sources and provide optimum grid management by aggregating distributed energy resources, which improves sustainability. One of the important components of this evaluation involves taking market and policy considerations. Examining worldwide market patterns and forecasts reveals that VPP usage is rising, and that regulatory frameworks and incentives have a bigger impact on how well they integrate. Conclusion Overcoming obstacles is a necessary step towards realizing full VPP potential. For VPPs to be widely adopted, it is still essential to address technological and operational challenges as they arise. Diverse stakeholders must work together to overcome market obstacles and promote the expansion of the VPP market. This analysis highlights the potential for VPPs to propel the evolution of contemporary power systems toward a more sustainable and effective future by highlighting areas for future research and development.

Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

Optimum operation of power system network becomes of great importance to handle the problem of power shortage. Connecting renewable energy (RE) based distributed generators (DG) into electrical networks (EN) are spreading rapidly to earn more benefits environmentally and economically. This paper aims to optimally operate DGs in order to minimize daily power loss in case of uncertainty of wind and PV system. Three different types of DGs, photovoltaic DG (PVDG), wind DG (WDG) and conventional DG (CDG) are connected to IEEE 33-bus system. Monte Carlo simulation (MCS) is used to generate probable scenarios for wind and photovoltaic power plants based on actual data for wind speed and solar radiation for one day. Fast forward scenario reduction (FFSR) is used to reduce the number of scenarios generated by MCS. Particle swarm optimization (PSO) is used to minimize daily power losses through determining the optimum operating point (operating power and power factor (PF)) for CDG to cope with PVDG and WDG uncertainties.

The Energy Internet (EI) has emerged as a promising field within smart grids, addressing capacity limitations and discrepancies in energy resources. It facilitates a peer-to-peer (P2P) energy trading environment for EI prosumers and consumers, reducing reliance on the main grid. To ensure an efficient P2P trading process, it is essential to route energy through the path with minimal power loss optimally. The Energy Router (ER) plays a pivotal role in governing power flow through EI. This paper presents the development of a novel real-time Zbus-based method for directing P2P energy transactions within the EI considering congestion management. The proposed method depends on bus impedance matrix in forming the routing matrix. Firstly, the ER’s structure and function are outlined in relation to the network, and then to design the EI’s topology using an adjacency matrix representation. Secondly, the proposed method methodology is presented in steps and explained on simple 7-bus microgrid. Additionally, a novel congestion management method is integrated into the routing algorithm, considering transmission line loading and available capacity during route selection. Furthermore, the paper applies a power flow technique to the selected routes to optimize power flow performance. The proposed method demonstrates enhanced directness and accuracy compared to existing approaches. Its efficiency is validated by testing a modified IEEE 14-bus system and the standard IEEE 30-bus system. Importantly, the method successfully identifies the minimum loss path, despite the unnecessity of power loss calculations in the routing algorithm. The key contribution of this research lies in providing a comprehensive and practical solution for P2P energy transactions in the EI, showcasing substantial improvements in accuracy, efficiency, and congestion management compared to previous methods.

The Energy Internet is expected to transform the landscape of electricity generation portfolio, distribution, and consumption through the integration of advanced sensing, communication, and control technologies in daily grid operations. This paper explores the profound impact of various smart grid concepts, such as dynamic pricing, distributed generation, and demand management, on information and communication technologies services within the Energy Internet. The synergy between smart grid principles and the Energy Internet has introduced a new dimension to efforts aimed at enhancing energy efficiency and reducing operational costs in communication networks and data centers. This survey provides a comprehensive overview of the Energy Internet Concept, strategies for achieving energy-efficient communications and data centers, and the dynamic interplay between the Energy Internet and information and communication infrastructures. While previous studies have individually examined the Energy Internet, energy-efficient communications, and green data centers, a critical need exists to systematically categorize and survey research at the intersection of these fields. To bridge this gap, our survey commences by elucidating the energy Internet concept and its architectural framework. Subsequently, an exploration of energy-routing devices and algorithms employed in prior studies is undertaken. Finally, the challenges encountered within the Energy Internet domain are explained. This comprehensive survey aims to offer a panoramic perspective on the Energy Internet, illustrating its conceptual intricacies and challenges, along with an exploration of how previous studies have endeavored to address them.

Abstract This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

With the rapid proliferation of portable and wearable electronics, energy autonomy through efficient energy harvesting has become paramount. Thermoelectric generators (TEGs) stand out as promising candidates due to their silent operation, high reliability, and maintenance-free nature. This paper presents the design, fabrication, and analysis of a micro-scale TEG for powering such devices. A planar configuration was employed for its inherent miniaturization advantages. Finite element analysis using ANSYS reveals that a double-layer device under a 50 K temperature gradient generates an impressive open-circuit voltage of 1417 mV and a power output of 2.4 μW, significantly exceeding its single-layer counterpart (226 mV, 0.12 μW). Validation against the analytical model results yields errors within 2.44% and 2.03% for voltage and power, respectively. Furthermore, a single-layer prototype fabricated using paper shadow masks and sputtering deposition exhibits a voltage of 131 mV for a 50 K temperature difference, thus confirming the feasibility of the proposed design. This work establishes a foundation for developing highly efficient micro-TEGs for powering next-generation portable and wearable electronics.

A centralized secondary control is utilized in a DC islanded microgrid to fine-tune voltage levels following the implementation of droop control. This is done to avoid conflicts between current allocation and voltage adjustments. However, because it introduces a single point of failure, a distributed secondary control is preferred. This paper introduces a consensus-based secondary distributed control approach to restore critical bus voltages to their nominal values and properly distribute current among converters. The critical bus takes the lead in voltage adjustments, with only connected energy resources contributing to regulation. The microgrid is represented as an undirected graph to facilitate consensus building. Two adjustment terms, δv and δi, are generated to assist in returning voltage to its nominal level and correctly allocating current among energy resources. To enhance consistency and improve controller performance compared to those reported in existing literature, all buses are connected to a leader node. In the event of the failure of all converters except one, voltage can still be effectively restored. MATLAB-Simulink simulations are conducted on two medium-voltage DC (MVDC) microgrids to validate the efficiency of the proposed control method. The results confirmed that the proposed control method can effectively maintain voltage stability and enhance the precise distribution of current among agents by 8%.

Wind energy has emerged as a prominent player in the realm of renewable energy sources, both in terms of capacity and technological adaptability. Among the various renewable energy technologies, wind turbine generators stand out as the most widely employed. Recently, gearless permanent magnet synchronous generators have gained traction in the wind energy sector due to their appealing features, such as reduced maintenance costs and the elimination of gearboxes. Nevertheless, challenges remain, particularly concerning the grid-friendly integration of wind turbines, specifically with regard to high voltage ride-through (HVRT) and low voltage ride-through (LVRT) improvements. These challenges pose a threat to grid stability, impede Wind Turbine Generator performance, and may lead to significant damage to wind turbines. To address these concerns, this research proposes an integrated strategy that combines a model predictive control (MPC) superconducting magnetic energy storage (SMES) device with a modified WTG grid-side converter control. By coupling SMES devices to the dc-link of Permanent Magnet Synchronous Generator WTGs, the proposed approach aims to achieve an overvoltage suppression effect during grid disturbances and provide support for grid reactive power. Through various test scenarios, the feasibility and practicality of this suggested technique are demonstrated.

The objective of this paper is to propose a new technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems that utilizes fewer sensors, thereby reducing the hardware cost. The technique aims to achieve efficient MPPT under various environmental conditions by employing a modified SEPIC converter and a model predictive control (MPC)-based MPPT algorithm. To achieve the objective, the proposed technique utilizes only one voltage sensor and one current sensor, significantly reducing the hardware requirements compared to traditional MPPT techniques. The modified SEPIC converter is employed to regulate the voltage and current levels in the PV system. The MPC-based MPPT algorithm is implemented to dynamically adjust the operation of the converter and track the maximum power point. The algorithm incorporates a model predictive control approach, which utilizes a predictive model of the PV system to anticipate and optimize the power output. The algorithm predicts the behavior of the PV system based on the available sensor measurements, allowing for accurate MPPT. The algorithm operates in real-time, providing instantaneous adjustments to maximize power extraction. The study demonstrates that the proposed technique effectively tracks the maximum power point of the PV system using only one voltage sensor and one current sensor, thus reducing the overall hardware cost. The MPC-based MPPT algorithm, in combination with the modified SEPIC converter, achieves efficient power extraction under various operating conditions. The simulation and experimental results indicate that the proposed technique outperforms traditional MPPT techniques in terms of cost-effectiveness and power extraction efficiency.

Model predictive control (MPC) is a well-known control methodology in power electronics systems, due to its ability to deal with the system’s nonlinearities and superior dynamic response. MPC is able to handle multiple variables by adjusting the cost function, but this leads to high computational costs, especially for systems having a big number of switches. This problem increases when not only performance efficiency is required but also minimizing the power losses of these systems. In this paper, a modified MPC algorithm is presented for controlling a three-phase quasi Z-source inverter (qZSI), i.e., providing switching states to be applied for qZSI control, so that, within less computation time, total harmonic distortion (THD) of the output currents is maintained at the minimum level with concurrent minimization of inverter switch power losses. The computational burden is reduced by using calculation loop optimization, reducing the number of switching states in the loop and unrolling the calculation loop. To highlight the effectiveness of the proposed control methodology, a numerical simulation was carried out using MATLAB software. The obtained results have been discussed and compared with those of a recent previous study.

Changes are being implemented in the electrical power grid to accommodate the increased penetration of renewable energy sources interfaced with grid-connected inverters. The grid-forming (GFM) control paradigm of inverters in active power grids has emerged as a technique through which to tackle the effects of the diminishing dominance of synchronous generators (SGs) and is preferred to the grid-following (GFL) control for providing system control and stability in converter-dominated grids. Therefore, the development of the GFM control is important as the grid advances towards 100% inverter-based grids. In this paper, therefore, we aim to review the changing grid scenario; the behaviour of grid-connected inverter control paradigms and major GFM inverter controls, including their modifications to tackle low inertia, reduced power quality, fault-ride through capability, and reduced stability; and the state-of-the-art GFM models that are pushing the universality of GFM inverter control.

The converter efficiency defines the overall efficiency of a renewable energy system. In a full power converter, its components process the whole input power, even if it is not mandatory. Partial power processing (PPP) is an exciting concept for improving overall system efficiency and reducing system cost and size. In PPP, the converter processes the amount of power needed to be processed. This work proposes a PPP based on the high-performance Quasi-Z-Source Series Resonance converter (QZSSRC). The proposed partial power converter has a series-input parallel-output (SIPO) architecture. Previous publications in this area mainly consider converters with step-down voltage regulation, like the phase-shifted full-bridge converter. They underperform in this application due to operating at the maximum power when bucking the voltage the most. This paper studies two other types of dc-dc converters, buck-boost and boost, in SIPO partial power converters. Theoretical operation principles are corroborated with full experimental results given and analyzed. A 300 W prototype of the QZSSRC is used to convert the power of up to 2 kW in a PPP system with overall system efficiency over 99%.

PV systems are penetrating the energy generation market rapidly and their output strongly depends on environmental conditions. To maximize the energy yield, the utilization of power electronic converters with maximum power tracking function is mandatory. Typically, the dc voltage generated by PV is fed into the ac grid using an dc-ac inverter. On the other hand, most modern loads, from small to large ones, are based on dc. Hence, wide adoption of residential dc microgrids (RDC $\mu $ Gs) based on PV energy generation becomes a trend that is following the developments in modern energy-efficient buildings. This paper targets power converters for the integration of PV systems into RDC $\mu $ Gs. It considers two main classes: high step-up converters for PV module-level (parallel optimizer) applications and non-isolated buck-boost converters for the PV string (series optimizer) applications. Due to the importance of the PV topic, a significant number of topologies have been introduced in the literature. However, only selected solutions attracted the interest of the industry. This review paper considers PV converters used in the industry or demonstrated in academic publications at a high technology readiness level with comprehensive experimental justification. This allows us to focus this review on the most valuable solutions and to provide a guide for both industry and academic readers. In the end, future trends in industrial PV systems and associated power converters are discussed.

Due to the low voltage of the PV module and to enable AC module technology, where every PV module has its own DC-AC converter, this paper introduces a new high step-up seven-level two-stage multilevel inverter for a grid-connected Photovoltaic (PV) system. The introduced schematic consists of two stages. The first stage is a switched capacitor boost converter and the second stage is a seven-level seven-switches multilevel inverter. The first stage is designed to extract the maximum power of PV module and step-up its input voltage. The proposed seven-level seven-switches topology generates seven levels with only seven switches. Thus, the number of switches is reduced. The proposed inverter has eight valid switching states. In each switching state only three switches are ON at the same time. The proposed topology has some distinct features such as reduced number of switches and reduced conduction; total harmonic distortion (THD) of the current is 23% in case of pure resistive and with no filtration at the output and less than 3% using a small filter with integration into the grid. Due to the high boosting ability of the switched capacitor boost converter, a wide voltage gain is easily achievable. In this paper the system is tested at different voltage gains and the highest simulated efficiency was around 96%. The developed system is simulated in MATLAB/SIMULINK and in real time using DSPACE1202. Both simulation and real-time results are consistent with the theoretical analysis.

Hierarchical linear control scheme is widely used in ac microgrids. However, its transient response is slow and parameter tuning is time-consuming. Finite Control Set-Model Predictive Control (FCS-MPC) strategy has desired dynamic performance. Nevertheless, it requires an additional sensor to measure the inductor current. This article aims to mitigate these problems by introducing an improved FCS-MPC strategy for paralleled Voltage Source Inverters (VSIs). A capacitor current estimator is employed to reduce the extra current sensor in each VSI. The proposed control scheme consists of two loops: voltage reference generation loop and voltage tracking loop. The voltage reference generation loop achieves accurate load power sharing using virtual impedance-based droop control. Thus, communication is unnecessary among parallel VSIs. The voltage tracking loop utilizes a modified FCS-MPC block with capacitor current estimator to regulate the VSI output voltage. In order to verify the concept of the proposed control strategy, an ac microgrid consisting of two paralleled VSIs is implemented in dSPACE DS1202 hardware-in-the-loop platform. Then a single VSI hardware prototype is implemented and tested experimentally. The proposed method has the merits of good extensibility, low system cost and compact structure. Its steady-state performance is competitive with hierarchical linear control, while the transient response is significantly improved.

Global warming and rising energy demands have increased renewable energy (RE) usage globally. Wind energy has become the most technologically advanced renewable energy source. Wind turbines (WTs) must ride through faults to ensure power system stability. On the flip side, permanent magnet synchronous generators (PMSG)-based wind turbine power plants (WTPPs) are susceptible to grid voltage fluctuations and require extra regulations to maintain regular operations. Due to recent changes in grid code standards, it has become vital to explore alternate fault ride-through (FRT) methods to ensure their capabilities. This research will ensure that FRT solutions available via the Web of Science (WoS) database are vetted and compared in hardware retrofitting, internal software control changes, and hybrid techniques. In addition, a bibliometric analysis is provided, which reveals an ever-increasing volume of works dedicated to the topic. After that, a literature study of FRT techniques for PMSG WTs is carried out, demonstrating the evolution of these techniques over time. This paper concludes that additional research is required to enhance FRT capabilities in PMSG wind turbines and that further attention to topics, such as machine learning tools and the combination of FRT and wind power smoothing approaches, should arise in the following years.

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.

In this article, a new non-inverting buck-boost converter with superior characteristics in both bucking and boosting is presented. The proposed converter has some distinct features, such as high step-up/-down ability and low voltage/current stress on its switching devices. The voltage gain of the proposed converter is double the reported value for the traditional buck-boost converter. Although it has three switches, the three switches operate simultaneously, hence no dead-time is required. Two out of the three switches are under voltage stress equal to half of the output voltage. The overall efficiency of the system is promising because of the ability to select devices with low voltage drops. Converter analysis and steady-state performance in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented in detail. A 1 kW hardware prototype of the converter was implemented in the laboratory; with a step-up ratio of 3.5 and 1 kW power, the measured efficiency is above 95.4%, and with step-up ratio 8, it is around 91.5%.

Maximum Power Point Tracking (MPPT) control is an essential part of every photovoltaic (PV) system, in order to overcome any change in ambient environmental conditions and ensure operation at maximum power.. Recently, micro-inverters have gained a lot of attention due to their ability to track the true MPP for each individual PV module, which is considered a powerful solution to overcome the partial shading and power mismatch problems which exist in series-connected panels. Although the LLC resonant converter has high efficiency and high boosting ability, traditional MPPT techniques based on Pulse Width Modulation (PWM) do not work well with it. In this paper, a fixed frequency predictive MPPT technique is presented for the LLC resonant converter to be used as the first-stage in a PV micro-inverter. Using predictive control enhances the tracking efficiency and reduces the steady state oscillation. Operation with fixed switching frequency for the LLC resonant converter improves the total harmonic distortion profile of the system and ease the selection of circuit magnetic component. To demonstrate the effectiveness of the proposed MPPT technique, the system is simulated using MATLAB/Simulink platform. Furthermore, a 150 W hardware prototype is developed and tested. Both simulation and experimental results are consistent and validate the proper operation of the developed system.