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This paper presents the "Three Gorges Hydrogen Boat No. 1", a novel green hydrogen-powered vessel that has been successfully delivered and is currently sailing. This vessel, integrated with a hydrogen production and bunkering station at its dedicated dock, achieves zero-carbon emissions. It stores 240 kg of 35 MPa gaseous hydrogen and has a fuel cell system rated at 500 kW. We analysed the engineering details of the marine hydrogen system, including hydrogen bunkering, storage, supply, fuel cell, and the hybrid power system with lithium-ion batteries. In the first bunkering trial, the vessel was safely refuelled with 200 kg of gaseous hydrogen in 156 min via a bunkering station 13 m above the water surface. The maximum hydrogen pressure and temperature recorded during bunkering were 35.05 MPa and 39.04 °C, respectively, demonstrating safe and reliable shore-to-ship bunkering. For the sea trial, the marine hydrogen system operated successfully during a 3-h voyage, achieving a maximum speed of 28.15 km/h (15.2 knots) at rated propulsion power. The vessel exhibited minimal noise and vibration, and its dynamic response met load change requirements. To prevent rapid load changes to the fuel cells, 68 s were used to reach 483 kW from startup, and 62 s from 480 kW to zero. The successful bunkering and operation of this hydrogen-powered vessel demonstrates the feasibility of zero-carbon emission maritime transport. However, four lessons were identified concerning bunkering speed, hydrogen cylinder leakage, hydrogen pressure regulator malfunctions, and fuel cell room space. The novelty of this work lies in the practical demonstration of a fully operational, hydrogen-powered maritime vessel achieving zero emissions, encompassing its design, building, operation, and lessons learned. These parameters and findings can be used as a baseline for further engineering research. [Display omitted] • Configuration of a zero-carbon emission vessel using green hydrogen fuel. • Engineering details of a 500 kW marine hydrogen and PEMFC system. • Hydrogen bunkering process, pressure, and temperature management. • Performance of a hydrogen-powered vessel during sailing. • Lessons learned from the design and construction of a marine hydrogen system. [ABSTRACT FROM AUTHOR]

This study investigates the low‐frequency oscillations (LFOs) in MMC‐MVDC systems with high photovoltaic (PV) penetration, considering the whole operating conditions of the grid‐connected MMC, including both the inverter and rectifier modes. First, the multi‐port impedance models of converters and networks are developed, upon which a stability criterion is defined. The mechanism of LFOs is then identified using bus participation analysis and parameter sensitivity analysis. In addition, the stable zones of the dominant parameters are identified, taking into account the whole operating conditions of the grid‐connected MMC. Interestingly, a phase‐locked loop may induce LFOs in two low‐frequency ranges. Finally, the experimental results confirm the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine's working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs. [ABSTRACT FROM AUTHOR]

This article proposes an optimal approach of improving the dynamic stability in power system by using hybrid method based power system-stabiliser. The proposed hybrid approach is an integration of Owl Search algorithm (OSA) and Improved Grey Wolf Optimizer (IGWO); hence, it is known as OSA-IGWO method. The objective of the proposed method is to enhance the power system's dynamic stability. This notion offers a singular approach to design the sturdy Power System Stabiliser (PSS) and enhance the dynamic energy machine balance. The Modified Heffron-Phillip's version can be evolved through generator facet transformer voltage because endless bus voltage and connection are considered to lessen the complexity with computational time. An energy machine stabiliser is referred to as Modified Power System Stabiliser (MPSS) that can be evolved and incorporated with PID controller; the usage of proposed approach in a Single-Machine-Infinity Bus-System uses Modified Heffron-Phillips (MHP) version. The OSA-IGWO approach may be used to select the benefit sets of PID-MPSS. The proposed version complements the dynamic overall performance of machine via the way of means of growing the damping ratio of the energy machine below diverse running conditions. The OSA-IGWO method is done in MATLABplatform, and its performance is compared with existing methods. [ABSTRACT FROM AUTHOR]

Introduction. This paper presents the novel design and analysis of a hybrid renewable energy system that combines a wave energy converter (WEC) with a floating photovoltaic (FPV) system for offshore installation, with a specific focus on Oran as a case study. The purpose of integrating these two technologies is to harness both wave and solar energy, thereby maximizing energy output and enhancing the reliability of renewable energy sources in offshore environments. The goal of this study is to develop a hybrid system that leverages the complementary nature of WEC and FPV technologies to maximize energy output and improve reliability. By integrating these technologies, the system aims to overcome the limitations of standalone energy systems. The methodology includes selecting suitable WEC and FPV technologies, optimizing their configurations, and analyzing their combined performance under various environmental conditions. To assess the energy production potential, structural stability, and economic feasibility of the hybrid system, computational simulations and data analysis are employed. This comprehensive approach ensures rigorous testing and optimization for real-world applications. The results demonstrate substantial improvements in energy yield and system resilience compared to standalone WEC or FPV systems. The hybrid system shows enhanced performance, particularly in consistent energy output and structural robustness. These findings indicate that combining WEC and FPV technologies can lead to more reliable and efficient offshore renewable energy solutions. The practical values are significant, providing insights into efficient and sustainable offshore renewable energy solutions. By focusing on Oran, it offers a localized perspective that can be adapted to similar coastal areas globally, contributing to the advancement of renewable energy technologies. The hybrid system’s enhanced reliability and efficiency support the broader goal of sustainable energy development in marine environments, highlighting its potential for widespread application and impact. [ABSTRACT FROM AUTHOR]

This paper proposes an improved slime mould algorithm (ISMA) to minimize the total electricity generation cost of thermal power plants (TPs) in newly developed hybrid power systems with pumped storage hydropower plants (PSHPs), cascaded hydropower plants (CHPs), photovoltaic power plants (PVPs), conventional hydropower plants, and TPs. ISMA is a better version of the original slime mould algorithm (OSMA) by cancelling OSMA's shortcomings and applying new methods to update new control variables. ISMA replaces two equations of OSMA to update new solutions. In the first equation, ISMA search around each old solution by adding one more increased interval, while OSMA multiplies each old solution by a vector with terms having smaller values than one. In the second equation, OSMA searches around the best solution by adding one more increased interval, but ISMA has a more flexible search by expanding search zones with two additional increased intervals or keeping narrow search zones with one additional increased interval. In addition to OSMA, ISMA is also compared to equilibrium optimizer (EO), jellyfish algorithm (JS), and northern goshawk optimization algorithm (NGO). After solving a test system, ISMA reaches a smaller cost than NGO, JS, EO, and OSMA by 2.43%, 0.68%, 2.83%, and 1.52%, respectively. Then, ISMA is applied for two cases: considering and neglecting the water storage function of PSHPs. Thanks to the water storage function of PSHPs, the total cost of the system is smaller than another case without the water storage function of PSHPs by $196,578.6, corresponding to 8.4%. Clearly, the contribution of the PSHPs to the economic effectiveness of hybrid power systems is huge, and the proposed ISMA is a very suitable optimization tool for finding operation solutions for newly developed hybrid power systems. Hence, the proposed ISMA should be tried for other hybrid power systems with the PSHPs and other power sources, as well as more practical conditions. [ABSTRACT FROM AUTHOR]

Providing sustainable electricity access to remote areas is critical for economic development and environmental preservation. This study investigates the performance of single‐source and hybrid renewable energy systems for the town of Zahedan, Iran, which has significant solar and wind energy potential. Using TRNSYS software, eight configurations were simulated and analyzed, comprising two single‐source (photovoltaic [PV] and wind turbine [WT]) and six hybrid systems incorporating combinations of PV panels, WTs, alkaline fuel cells, and diesel generators. The analysis revealed that hybrid systems, particularly those combining PV and WT, outperformed single‐source configurations. For instance, a hybrid system with 800 kW of PV and a 50 kW WT reduced diesel consumption by 35% and CO2 emissions by 45% compared to a system relying solely on a diesel generator. Conversely, the configuration involving WTs, fuel cells, and diesel generators showed high energy dumping (1,821,776 kWh) and considerable diesel usage, underscoring the challenges of maintaining energy balance without solar integration. Overall, hybrid renewable systems generally provide enhanced reliability and environmental benefits, although their performance heavily depends on the specific energy source mix. This study offers insights into optimizing renewable energy systems for remote locations, highlighting the necessity of a balanced solar‐wind combination to achieve optimal sustainability and cost‐effectiveness. The findings are applicable to regions with similar climatic conditions and contribute to global sustainable energy solutions, providing crucial information for policymakers and investors focused on supporting sustainable energy projects in isolated areas. [ABSTRACT FROM AUTHOR]

Hydropower as a flexible regulation resource is a rare choice to suppress the ever-increasing penetration of wind power in electrical power systems. The complementary characteristics and performance improvement of a hydro–wind hybrid power system based on a mathematical model of the hybrid power system is studied in this paper. This established model takes into account the stochastic variation in wind speeds in the wind power subsystem and the hydraulic–mechanical–electrical coupling characteristics of the hydropower subsystem. The complementary analysis is conducted based on the evaluation variables outputted by the established model, such as the wind power, hydro-regulation power, hydraulic power, and frequency. To make full use of the regulation capability of the hydropower system, the optimization of parameter settings is also carried out to improve complementary performances of the hybrid power system. The results from the complementary analysis show the detailed characteristics of hydro–wind coordinated operation under different types of real wind speeds. Here, 95% of installed hydro-capacity is used to complement the power shortage of the intermittent wind energy under the low wind speed. Alternatively, only around 66% of the installed hydro-capacity can be utilized to cope with the fluctuation in wind power under the medium and high wind speeds before the optimization of parameter settings. The recommended values and change rules of the control, hydraulic, and electrical parameters for the hydropower system are subsequently revealed from the analysis of parameter settings to contribute to a stable and safe hybrid power system. The results show that the optimized parameter can increase the maximal regulating capacity of the hydropower system by nearly 9 MW, approximately a sixth of the total installed hydropower capacity. The method and results obtained in this paper provide theoretical and technical guidance for the safe and economical operation of power stations. [ABSTRACT FROM AUTHOR]

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid's ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy's volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC's involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer efficient, environmentally friendly power at a reasonable price while addressing the needs of both power providers and customers. This study also looks into the effects of V2G mixing on voltage stability, LMP (locational marginal price), and the effectiveness of a TCSC in a deregulated electrical system. MiPower software is used to simulate operation and find the optimal placement of the storage unit within the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

This paper examines the problem of synthesizing a two-loop control system for a three-phase voltage source inverter designed for an autonomous power supply system. This type of DC/AC converters is an essential part of power supply systems with renewable energy sources as primary sources, and, accordingly, must corresponds to the requirements for the electric power quality. These requirements are met through circuit design solutions, as well as through the construction of high-precision automatic control systems. The system integrates: a hydrogen electrolyzer, a hydrogen storage system, and a hydrogen fuel cell. The paper proposes a method for calculating the parameters of PI controllers with resonant components, based on the time-scale separation method and allowing independent adjustment of the controller components. The introduction of a resonant component ensures high tracking accuracy for a desired main voltage harmonic and selective suppression of external harmonic influences. The proposed approach allows to synthesize an automatic control system that significantly improves the quality of the voltage inverter output parameters. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFCs) play a critical role as a key technological component in the utilization of hydrogen energy. Extensive research has been conducted on pressurized SOFC hybrid systems, focusing on performance enhancement and novel system configurations, etc. However, the planar SOFC suitable for commercial application exists limitations that confine its operation solely to atmospheric pressure. Moreover, SOFC exhaust contained considerable potential for energy recovery. In light of these limitations, two hybrid power generation systems of SOFC and GT have been proposed: direct and indirect utilization of SOFC cycle exhaust. Parameter and exergy analysis has been conducted to investigate the characteristic of SOFC-GT system. Multi-objective optimization is performed by setting exergy efficiency and output power as objective. The results reveal that the combustion process contributes significantly to system losses, with exergy efficiency reaching a maximum of 49.48% and 41.61%, respectively. Additionally, the system's maximum output power reaches 92 kW. Notably, the indirect SOFC-GT system demonstrates an 8% increase in exergy efficiency compared to the direct SOFC-GT system at the same output power. To improve the overall system fire efficiency, it is recommended to develop planar SOFCs suitable for high pressure and slow down the reaction process inside the combustion chamber. • Two atmospheric pressure SOFC-GT systems are studied comparatively. • SOFC-IDGT configuration exhibits superior thermodynamic performance. • Multi-objective optimization is performed for this SOFC-GT system. [ABSTRACT FROM AUTHOR]

To meet the varying load demands and improve the efficiency of the power system during aircraft flights in the aviation field, a hybrid power system architecture combining direct ammonia solid oxide fuel cells (SOFCs) and lithium batteries is established. A rule‑based energy distribution strategy is adopted for the comparative analysis of parameters such as efficiency and power‑to‑mass ratio of the hybrid system within the typical flight envelope. The aim is to find an optimal energy supply scheme for the entire flight process of the aircraft. In the case of the BAe.146, a four‑engine turbofan short‑range transport aircraft, a net power generation efficiency of 53.32% and a power‑to‑mass ratio of 0.491 3 kW/kg are achieved. Furthermore, it has been found that the output power and power generation efficiency of the SOFC system decrease with increasing altitude. These results indicate that the direct ammonia SOFC‑lithium battery hybrid power system is capable of meeting the energy demands of different flight phases and holds promising application prospects in the aviation field. [ABSTRACT FROM AUTHOR]

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy. [ABSTRACT FROM AUTHOR]

In this paper, a novel series hydraulic hybrid powertrain is proposed for a three-axis all-terrain vehicle. The engine drives two variable displacement pumps responsible for driving and steering, respectively. A variable displacement motor is connected to the ring gear of the planetary coupling mechanism to drive the vehicle and a fixed-displacement motor is connected to the sun gear to steer the vehicle. The active disturbance rejection control with feedforward control is employed to control the engine speed. The engine speed is controlled in a close-looped manner by adjusting the engine throttle. The controller parameters are decided by analyzing the influence of each parameter on the controller performance by means of the control variable method. The simulation results indicate that the proposed control strategy enables the vehicle to obtain better engine speed following and anti-disturbance performance. An all-terrain prototype is established and field tests are carried out to verify the effectiveness of the design and control strategy of the series hydraulic hybrid powertrain for the all-terrain vehicle. [ABSTRACT FROM AUTHOR]

Decarbonization has significant operational implications for ships, fuel resources, and ports. Sustainable management is highly prioritized by ship owners and port authorities. The shipping industry must develop appropriate strategies to achieve zero carbon emissions by 2050 and support the 2030 United Nations sustainable development goals. Tugboats, a ubiquitous vessel in Taiwanese ports, were selected as the subject of this study. Implementing hybrid power systems in response to the growing trend of carbon reduction was explored. This study involves various stakeholders in Taiwan, including ship owners, port agency representatives, and other relevant parties. Assessing investment costs, market demand, and policy guidance is highly significant. Establishing a positive cycle is necessary to encourage tugboat operators to adhere to sustainable management practices and environmental protection measures. This entails providing incentives through policy measures that enable tugboat operators to gain cost advantages and balance environmental, economic, and societal needs within an expanding market. [ABSTRACT FROM AUTHOR]

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery's state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship's operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships. [ABSTRACT FROM AUTHOR]

Hybrid estimation architectures make it possible to combine the widespread availability of conventional SCADA data with the improved accuracy and higher sampling rates of synchronized phasor measurements. A particularly appealing hybrid scheme is the so-called APSI architecture, which comprises two sequential estimation stages: a conventional SCADA-based state estimator, whose outcome is then treated as a priori information by a second stage, where available phasor measurements are cumulatively processed. As desirable features, this architecture preserves existing SCADA-based estimators and waives PMU-observability requirements. On the other hand, bad data processing for APSI estimators is a topic that still deserves additional research efforts. In this paper, we set out to improve the resilience of APSI estimators against bad data occurring both within synchrophasor measurements and SCADA data. The proposed strategy comprises two distinct actions: (a) replacing conventional algorithms in the second APSI stage by a more robust one, capable of automatic suppressing outliers in both data classes, and (b) adopting a method for tracing down SCADA gross measurements from a priori data previously identified as erroneous. The performance of the proposed methodology is assessed through tests conducted on the IEEE 14-bus, 57-bus and 118-bus test systems. [ABSTRACT FROM AUTHOR]

This present article examines the frequency control of a dual-area interconnected hybrid power system that integrates conventional as well as non-conventional sources with additional support from electric vehicles. The complicated, non-linear behavior of the system adds to the grid's already high level of complexity. To navigate this complex environment, it becomes essential to develop a resilient controller. In this respect, a robust secondary controller is developed to handle the problem. The controller is developed while taking into account the intricate design of the contemporary power system. An extensive comparison between well-established controllers is presented to verify the efficacy of the proposed controller. An AI-based optimization technique, namely, COVID-19, is employed to obtain optimal values for different parameters of the controller. This work also investigates the effect of the FACTS device as a static synchronous series compensator (SSSC) on the dynamics of the system. Moreover, it also investigates the role of electric vehicles (EVs) and an SSSC on system stability. Further, the developed system is subjected to significant load variations and intermittent solar and wind disturbances to check the response of the optimal controller under dynamic conditions. The results demonstrate that the proposed controller reactions successfully handle system disturbances, highlighting the strength of the proposed controller design. Lastly, a case study on an IEEE-39 bus system is carried out to check the optimality of the proposed secondary controller. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs) into current power networks addresses rising global energy demand and mitigates greenhouse gas emissions. This transition shifts the role of frequency regulation from synchronous generators to power converters, serving as interfaces between the grid and RESs. This paper proposes a hybrid method for optimizing power flow in hybrid power-generating systems. The proposed hybrid technique is the combined execution of an Adaptive-Neural-Fuzzy-Inference-System (ANFIS) and Pelican Optimization Algorithm. Hence it is named as ANFIS-POA technique. POA optimizes current and voltage controllers, while ANFIS predicts optimal system parameters. Implemented in MATLAB, the proposed strategy achieves an impressive efficiency of 96%, outperforming existing strategies like genetic algorithm (GA) and particle swarm optimization (PSO). With a faster settling time (0.56 s), a shorter rising time (1.32 s), and a reduced peak time (5.78 s), the proposed approach performs better than existing methods. Additionally, it significantly lessens system costs (Rs. 99,450) and demonstrates superior statistical performance in terms of lower error rates and improved key metrics like accuracy, precision, and recall compared to existing methods. [ABSTRACT FROM AUTHOR]

Hybrid power systems are the contemporary solution to meet the stiff climate change targets and also hold future for implementing distributed generation and microgrids. These systems have renewable sources as their key elements which in turn have high level of intermittency due to weather conditions and other reasons. At the same time, these hybrid power systems are also vulnerable to cyber threats owing to the intense adoption of various IoT Technologies (Internet of Things). Load frequency control (LFC) is a formidable task for the system operators under such scenario and robust control strategies are required to achieve the load frequency within tolerable limits. In this work Linear quadratic controller (LQR) has been proposed to address the challenges of hybrid power systems. Two area interconnected power systems are considered in this work with each one having different configuration with respect to source of power generation. Solar, wind, geo-thermal, electric vehicle charging infrastructure and pumped storage power plant are integrated together with conventional thermal power plant for showcasing the LQR based controller under data integrity cyber-attack scenario. The frequency response of the hybrid power system is demonstrated through MATLAB simulations. [ABSTRACT FROM AUTHOR]

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management. • Hybrid Power System is formed using Fuel cell, battery, and supercapacitor. • This study presents PPOA algorithm to optimize fuel consumption of a fuel cell. • PPOA is hybridized with ECMS and EEMS energy management techniques. • PPOA is tested using MATLAB/Simulink and compered with OPAL-RT Real time simulator. • PPOA-ECMS attained least H 2 consumption (15.36 gm) compared to Existing algorithms. [ABSTRACT FROM AUTHOR]

Herein, the load power control of the stand‐alone photovoltaic‐battery hybrid power system (HPS) has been investigated. The underlying HPS consists of a boost DC‐DC converter, a non‐isolated bidirectional half‐bridge converter, a photovoltaic (PV) panel, and a battery pack. On the PV side, a disturbance observer‐based finite‐time terminal sliding mode control (FTSMC) is used to regulate the DC bus to the desired voltage, in the presence of irradiation variation and load changes. On the battery side, the load power control system is constructed, based on a model predictive control (MPC) algorithm, with constraints on state‐of‐charge (SOC) and maximum current value of the battery to improve the battery life cycle and high reliability of the system. To highlight the benefits of the closed‐loop system, the analytical proofs and numerical analysis are presented from a comparative viewpoint. The experimentally derived results, by implementation on TMS320F28335 digital signal processing (DSP), are also presented and discussed for practical justification. [ABSTRACT FROM AUTHOR]

The need to transmit energy in the most effective way arises in parallel with the demand for energy in developing countries. As the amount of transmitted power increases, transmission lines can become overloaded, causing a blackout. This study aims to increase the transmission capacity without experiencing right‐of‐way problems by converting Turkey's existing high voltage AC systems to hybrid AC/DC systems. With such a design, it is aimed at providing more efficient and continuous transmission by using the high‐voltage DC system's low‐loss feature at long distances. Using the 400 kV double circuit line structure, the existing electricity transmission system is modelled using real data in the DigSILENT PowerFactory. The electromagnetic fields that will occur due to the use of AC and DC circuits on the same tower in such a transformation situation are analysed with FEMM 4.2, based on the finite element method. The compliance of the electromagnetic fields that will occur with the use of hybrid 400 kV AC and 500 kV DC was determined in the study. In addition, according to the simulation results, it is predicted that in the case of hybrid AC/DC transmission, the current transmission capacity would increase and the losses would decrease. [ABSTRACT FROM AUTHOR]

To enhance the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs), we propose a hierarchical energy management strategy (HEMS) to efficiently allocate power to a hybrid system comprising a fuel cell and a battery. Firstly, the upper-layer supervisor employs a fuzzy fault-tolerant control and prediction strategy for the battery and fuel cell management system, ensuring vehicle stability and maintaining a healthy state of charge for both the battery and fuel cell, even during faults. Secondly, in the lower layer, dynamic programming and Pontryagin's minimum principle are utilized to distribute the necessary power between the fuel cell system and the battery. This layer also incorporates an optimized proportional-integral controller for precise tracking of vehicle subsystem set-points. Finally, we compare the economic and dynamic performance of the vehicle using HEMS with other strategies, such as the equivalent consumption minimization strategy and fuzzy logic control strategy. Simulation results demonstrate that HEMS reduces hydrogen consumption and enhances overall vehicle energy efficiency across all operating conditions, indicating superior economic performance. Additionally, the dynamic performance of the vehicle shows significant improvement. [ABSTRACT FROM AUTHOR]

Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS. [ABSTRACT FROM AUTHOR]

ABSTRACT Addressing issues of low input voltage in new energy generation systems such as fuel cells, a high step‐up three‐port DC–DC converter for fuel cell/battery hybrid power supply system is proposed. The proposed converter is evolved from Boost three‐port converter, and switch capacitor structure and diode capacitor voltage doubling unit are employed to achieve high voltage gain and low voltage stress. The three ports are connected to fuel cell, battery and load respectively, among which the flow of energy is realized. The steady state performance of this converter in various operating states and design of parameter are presented. Primary competitive advantages compared to similar converters include high voltage gain, continuous input current and its feature of common ground. Moreover, control strategy and compensators under three operating modes are designed to achieve stable output voltage and battery protection. Finally, a 100 W experimental prototype with 15 V input voltage and 24 V battery voltage is established to verify the stable and dynamic performance of the proposed converter. [ABSTRACT FROM AUTHOR]

Introduction: The increasing global demand for sustainable energy solutions highlights the urgency of exploring renewable resources, particularly sunlight, which is abundant and virtually limitless. This study reviews innovative technologies like solar trees, wind trees, and hybrid solar-wind trees, which are emerging as efficient structures for harnessing renewable energy. Methods: A comprehensive SWOT analysis was conducted to evaluate the strengths, weaknesses, opportunities, and threats associated with solar, wind, and hybrid trees. The analysis also considered sustainability aspects, focusing on the efficiency and practicality of these technologies in various settings. Results: Solar trees mimic natural foliage, utilizing solar modules to convert sunlight into electricity, while wind trees incorporate micro-wind turbines and solar panels, effectively harnessing both wind and solar energy. Hybrid solar-wind trees combine these technologies to provide a consistent energy supply. These structures are compact, cost-effective, and adaptable to urban landscapes. Discussion: Challenges such as land use, aesthetic considerations, and public perception were identified. The review emphasizes the need for future research to optimize configurations and address these challenges, ensuring the successful integration of these technologies into sustainable urban landscapes. Conclusion: This review provides critical insights for renewable energy researchers, particularly in the development of hybrid wind and solar power systems, promoting energy security and climate resilience. [ABSTRACT FROM AUTHOR]

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance. [ABSTRACT FROM AUTHOR]

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator's terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms. [ABSTRACT FROM AUTHOR]

Battery energy systems (BESs) assisted photovoltaic (PV) plants are among the popular hybrid power systems in terms of energy efficiency, energy management, uninterrupted power supply, grid-connected and off-grid availability. The primary objective of this study is to enhance the power quality of a grid-tied PV-BES hybrid system by developing an operational strategy based on artificial neural network (ANN) based maximum power point tracking (MPPT) method. A test system comprising a 10-kWh BES and a 12.4 kW PV plant is structured and simulated on the MATLAB/Simulink platform. The hybrid system is validated with three different cases: constant radiation, rapid changing radiation, and real-day solar radiation data from the Turkish State Meteorological Service of Tarsus (Mersin, Türkiye) employing the developed operational strategy. These cases involve the examination of three distinct MPPT methods, analyzing DC-link voltage, battery state of charge (SOC), current, voltage, and system total harmonic distortion (THD). The simulation results indicate that the developed operational strategy with the ANN-MPPT method yields superior THD results in output current and a more stable DC-link voltage. Furthermore, the strategy shows improved convergence speed and reduced oscillations to achieve diverse reference operating points under varying atmospheric conditions compared to conventional MPPT methods. Numerical results demonstrate that the developed operational strategy with the ANN-MPPT consistently maintains THD values below 3% and exhibits a stable DC-link voltage deviation of 1.42% in various charging modes for both rapidly changing radiation and real-day solar radiation data. [ABSTRACT FROM AUTHOR]

Environmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind and solar power, are increasingly contributing to electricity systems. Participants in the energy market need to understand the future electricity demand in order to plan their purchasing and selling strategies. To forecast the electricity demand, this study proposes a hybrid forecasting model. The method uses Kalman filtering to eliminate noise from the electricity demand series. After decomposing the electricity demand using an empirical model, a support vector machine optimized by a genetic algorithm is employed for prediction. The performance of the proposed forecasting model was evaluated using actual electricity demand data from the Australian energy market. The simulation results indicate that the proposed model has the best forecasting capability, with a mean absolute percentage error of 0.25%. Accuracy improved by 74% compared to the Support Vector Machine (SVM) electricity demand forecasting model, by 73% when compared to the SVM with empirical mode decomposition, and by 51% when compared to the SVM with Kalman filtering for noise reduction. Additionally, compared to existing forecasting methods, this study's accuracy surpasses LSTM by 63%, Transformer by 47%, and LSTM-Adaboost by 36%. The simulation of and comparison with existing forecasting methods validate the effectiveness of the proposed hybrid forecasting model, demonstrating its superior predictive capabilities. [ABSTRACT FROM AUTHOR]

The modern power markets introduce higher penetration levels of solar photovoltaic (PV) power generation units on a wide scale. Along with their environmental and economic advantages, these variable generation units exhibit significant challenges in network operations. The objective is to find critical observations based on available literature evidence reported by several researchers towards large-scale PV integration issues and important developments in the last two decades: Higher penetration issues, maximum power point tracking, grid integration, and solar PV regulations and policies are chosen as the primary attributes of the study. The work summarizes the significant outcomes of 122 research documents. These are mainly based on three focused areas: (i) solar PV systems with storage and energy management systems; (ii) solar power generation with hybrid system topology; and (iii) the role of artificial intelligence for the large-scale PV and storage integrated market. The review will support upcoming researchers and power engineering practitioners working on operation and planning, including large-scale PV integration. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low hydrogen consumption is an important objective for fuel cell power systems as it impacts both economic efficiency and continuous power supply. This paper addresses the optimization problem of minimizing hydrogen consumption in a hybrid power system comprising dual fuel cells and a single lithium battery. We introduce an energy management method for the multi-period synchronous optimization, incorporating a data processing module and power allocation module. The quasi-periodic loads are predicted using a modified gray-Markov chain model, while the optimization problem is solved using an adaptive constrained particle swarm optimization (ACPSO) algorithm. An initial swarm generation algorithm is introduced to enhance the effectiveness of ACPSO, and a power correction algorithm is integrated to address deviations between predicted and actual loads. The effectiveness and advantages in optimizing hydrogen consumption of the proposed strategy have been demonstrated through simulations and experiments. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The multi-degree-of-freedom characteristics of the planetary gear electronic continuously variable transmission (ECVT) configuration in series-parallel hybrid tractors impose more complex requirements for energy management strategies under variable load conditions. For a high-power hybrid tractor, this paper takes the hybrid tractor output-split (OS)-ECVT configuration as the research object and describes the principles of stepless transmission and power-splitting within the configuration. In order to improve the fuel economy of high-power hybrid tractors and the running status of power components, an energy management strategy focused on ploughing conditions based on the Bellman minimum dynamic programming (DP) algorithm is proposed in this paper. Second, equivalent fuel consumption is selected as the performance index for energy-saving control, and the solving principle of the energy management strategy based on the dynamic programming algorithm is established to facilitate the resolution process of the energy management strategy. Finally, the energy-saving control simulation is completed under ploughing conditions. The results show that compared with the energy management strategy based on the optimal operating line (OOL), the energy management strategy based on DP fully utilizes the benefits of low-cost electric energy and enables the hybrid power system to have a wider range of stepless transmission performance. In addition, the hybrid power system has the advantages of enhanced decoupling of speed and torque, higher efficiency, and more economical secondary energy conversion. As a result, the whole machine has enhanced power-split performance, greatly improving the running conditions of the power components. The equivalent fuel consumption values of the energy management strategies based on DP and OOL are about 3.1238 L and 4.2713 L, respectively. The equivalent fuel consumption based on DP is reduced by about 26.87%, which effectively improves the fuel efficiency of hybrid tractors. [ABSTRACT FROM AUTHOR]

Traditional internal combustion engines (ICEs) have garnered considerable attention due to their high emissions and low efficiency issues. In this study, a novel ICE–fuel cell hybrid power system based on a single‐methanol fuel is proposed to address these concerns. The system utilizes methanol as fuel, directly supplying it to the methanol engine, and generates hydrogen for the fuel cell through methanol reforming technology. The structural design of the system fully exploits engine exhaust, first using waste heat for methanol reforming to produce hydrogen and then utilizing exhaust inertial potential energy to drive a dual turbocharging structure for air compression entering the fuel cell, thereby achieving self‐thermal balance. Thermodynamic analysis and cost evaluation indicate that the thermal efficiency of this system is improved by 8.34% compared to traditional diesel engine setups. Compared to engine‐fuel cell hybrid systems that do not utilize waste heat, the thermal efficiency is increased by 5.81%. In terms of economics, the cost of the methanol engine system is ≈.1466$ kW h−1$\left(\text{kW h}\right)^{- 1}$, which is 44.05% lower than the 0.262 $ kW h−1$\left(\text{ kW h}\right)^{- 1}$ fuel cost of traditional diesel engine systems. This study presents an innovative solution that significantly enhances thermal efficiency and offers economic advantages, providing a viable approach to address the low efficiency and high emissions issues of traditional ICEs. [ABSTRACT FROM AUTHOR]

Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

Abstract This study investigates the low‐frequency oscillations (LFOs) in MMC‐MVDC systems with high photovoltaic (PV) penetration, considering the whole operating conditions of the grid‐connected MMC, including both the inverter and rectifier modes. First, the multi‐port impedance models of converters and networks are developed, upon which a stability criterion is defined. The mechanism of LFOs is then identified using bus participation analysis and parameter sensitivity analysis. In addition, the stable zones of the dominant parameters are identified, taking into account the whole operating conditions of the grid‐connected MMC. Interestingly, a phase‐locked loop may induce LFOs in two low‐frequency ranges. Finally, the experimental results confirm the correctness of the theoretical analysis.

Abstract Herein, the load power control of the stand‐alone photovoltaic‐battery hybrid power system (HPS) has been investigated. The underlying HPS consists of a boost DC‐DC converter, a non‐isolated bidirectional half‐bridge converter, a photovoltaic (PV) panel, and a battery pack. On the PV side, a disturbance observer‐based finite‐time terminal sliding mode control (FTSMC) is used to regulate the DC bus to the desired voltage, in the presence of irradiation variation and load changes. On the battery side, the load power control system is constructed, based on a model predictive control (MPC) algorithm, with constraints on state‐of‐charge (SOC) and maximum current value of the battery to improve the battery life cycle and high reliability of the system. To highlight the benefits of the closed‐loop system, the analytical proofs and numerical analysis are presented from a comparative viewpoint. The experimentally derived results, by implementation on TMS320F28335 digital signal processing (DSP), are also presented and discussed for practical justification.

Abstract The need to transmit energy in the most effective way arises in parallel with the demand for energy in developing countries. As the amount of transmitted power increases, transmission lines can become overloaded, causing a blackout. This study aims to increase the transmission capacity without experiencing right‐of‐way problems by converting Turkey's existing high voltage AC systems to hybrid AC/DC systems. With such a design, it is aimed at providing more efficient and continuous transmission by using the high‐voltage DC system's low‐loss feature at long distances. Using the 400 kV double circuit line structure, the existing electricity transmission system is modelled using real data in the DigSILENT PowerFactory. The electromagnetic fields that will occur due to the use of AC and DC circuits on the same tower in such a transformation situation are analysed with FEMM 4.2, based on the finite element method. The compliance of the electromagnetic fields that will occur with the use of hybrid 400 kV AC and 500 kV DC was determined in the study. In addition, according to the simulation results, it is predicted that in the case of hybrid AC/DC transmission, the current transmission capacity would increase and the losses would decrease.

This paper considers real-time parameter variations of dish Stirling solar thermal system (DSTS) and combined solar parabolic trough-thermal power system (HTP)-based hybrid power system pertaining to frequency containment. A maiden attempt has been made by considering a detailed modelling of variable speed DSTS system for load frequency control (LFC), which shares the total load along with a battery energy storage system (BESS) and HTP in the proposed hybrid power system. Frequency oscillations resulting from the variations in load demand and/or power output variation of DSTS due to intermittent solar insolation in the hybrid power system needs to be contained employing an appropriate technique. To this end, a novel tilt integral-sliding mode controller (TISMC) optimized by improved African vulture optimization algorithm (IAVOA) has been introduced. Improvement in the existing AVOA has been made by introducing a joint opposite selection (JOS) operator, which shows better performance in exploitation and exploration phases of minimizing frequency error. The performance of the proposed controllers is compared with proportional (P), proportional-integral (PI), proportional-integral-derivative (PID) and proportional-integral-Sliding mode controller (PISMC) for LFC. Finally, performance of the proposed technique is validated in real-time system using OPAL-RT OP4510. From the MATLAB simulation and OPAL-RT results, it has been observed that the IAVOA-based TISMC is the best controller for controlling frequency deviations of the proposed hybrid power system. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) nowadays are getting soaring importance in many aspects like agricultural and military fields. A hybrid power system is a promising solution toward high energy density and power density demands for UAVs as it integrates power sources like internal combustion engine (ICE), fuel cell (FC) and lowcapacity lithium-polymer (LIPO) batteries. For robust energy management, accurate state-of-charge (SOC) estimation is indispensable, which necessitates open circuit voltage (OCV) determination and parameter identification of battery. The presented research demonstrates the feasibility of carrying out incremental OCV test and even dynamic stress test (DST) by making use of the hybrid powered UAV system itself. Based on battery relaxation terminal voltage as well as current wave excitation, novel methods for OCV determination and parameter identification are proposed. Results of SOC estimation against DST through adaptive unscented Kalman filter (AUKF) algorithm show that parameters and OCV identified with longer relaxation time don't yield better SOC estimation accuracy. Besides, it also discloses that OCV played the vital role in affecting SOC estimation accuracy. A detailed analysis is presented showing that mean discharging rate and current wave amplitude are the major factors which affect the quality of OCV identified related to SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems. [ABSTRACT FROM AUTHOR]

Abstract Unleashing the potential of distributed renewable energy sources (RESs), intelligent and autonomous microgrids are becoming pivotal in attaining net‐zero carbon emission goals. Hybrid AC/DC microgrids rise as cutting‐edge microgrid topologies, capitalising on the best of both AC and DC systems. However, the integration of intermittent renewables and uncertainties in loading poses stability challenges. Advanced bidirectional converter controls provide efficient power exchange, but in extreme contingencies, a resilient supervisory control framework (load management/load curtailment approach) is inevitable to withstand/avoid unplanned renewable disruptions/blackouts. Moreover, the operational paradigm shift towards achieving net‐zero emissions, isolated operation of RESs, and conventional load shedding methods are anticipated to encounter substantial challenges, necessitating the development of alternative strategies. In order to improve the stability of hybrid microgrid systems in islanding scenarios, this research presents an energy balancing and load curtailment strategy. The proposed method aims at optimising resource utilisation, prioritising essential loads, and executing an optimal load curtailment strategy (if required), thereby augmenting the stability of systems. Unlike a meta‐heuristic or exhaustive search, which depends on 2n − 1 possible combinations and become unworkable as load numbers increase, the suggested methodology is based on a mathematically modelled load restriction method. By including load criticality, this strategy effectively prevents blackouts even with an increasing number of loads, providing a significantly more useful and practical solution. Additionally, the proposed charging algorithm ensures that the energy storage system imports energy from the grid during off‐peak hours and maximises power generation from the DC subgrid. The efficacy of the proposed strategy is validated using a modified IEEE‐33 bus system as a test case for a hybrid AC/DC microgrid. Simulation results demonstrate the effectiveness of the MILP‐based load curtailment approach in maintaining system stability and preventing blackouts during unforeseen events.

Abstract Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.