A multi-input power converter for hybrid renewable energy generation system

Renewable energy utilization is widely concerned all over the world. They are increasingly utilized in Thailand due to the Royal Thai government policy and subsidize that emphasize their utilization in potential areas. Chaiyaphum Province, located in northeastern part of Thailand, is a high potential area for renewable energy utilization. This paper presents the feasibility study of the potential of hybrid renewable energy sources, i.e. solar and wind energies, for the rural community under Chaiyaphum Provincial Organization. In this study, annual data of wind speed, wind direction, wind gust speed and solar irradiation taken from the meteorological department of Chaiyaphum provincial area, Thailand were used in the study. Optimum hybrid renewable energy generation sources, i.e. wind turbine and PV, were designed for Nongplathao Park Community, Muang District, Chaiyaphum province. Results showed the annual average wind speed of 7.02 m/s, and annual average solar energy of 4.6722 kWh/m 2 . Furthermore, the design of On-Grid hybrid renewable generation system was seen to be practical. Initial investment cost was analyzed by Homer Version 3.6.3.0. From the results, the total capacity for generation of electricity from hybrid renewable energy sources of 52 kW was obtained. By considering the community location and its potential, small wind turbines with a capacity of 3 kW/tower, 4 units and PV system of 40 kW were chosen for design in this study.

Microgrids serve as primary links for diverse loads, while numerous locally situated power supply systems are concurrently established in various locations. Grid-integrated hybrid renewable energy power conversion systems have gained significant popularity in recent years. However, a novel and effective control methodology must be implemented to supply high-quality power at the local load bus. The 3rd Order Degree of Freedom – PID (3DOF-PID) controllers stand out among numerous controllers, demonstrating exceptional performance across a wide range of operating conditions. In this paper, a hybrid system comprising a solar power plant and a wind power plant is examined, with a focus on its benefits to the power grid. Furthermore, Sliding Mode Control (SMC) techniques are applied to all photovoltaic (PV) and wind power conversion systems to facilitate the operation of the boost converter as Maximum Power Point Tracking (MPPT) devices. A shared DC bus is created by combining all converters in the solar power facility and the wind energy conversion plant. An inverter is integrated between the DC bus and the utility grid's point of common coupling (PCC). The study presents a robust control strategy utilizing 3DOF-PID controllers to ensure high-quality power delivery at the PCC while accommodating a diverse range of load combinations, including both linear and nonlinear loads, as well as single-phase and three-phase reactive power loads. 3DOF-PID controllers have been created for each converter and incorporated using OPAL-RT technology and modules within a Real-Time Simulator (RTS). The results are presented through RTS to evaluate the suggested control methodologies for the power supply system that integrates grid-connected hybrid renewable energy sources (RESs).

This paper presents a new approach to the multi-objective optimization for the hybrid renewable energy distributed generation system based on fuzzy satisfaction degree. Firstly, based on the analysis of the characteristics of the distributed power supply, three indexes, including economy cost, power supply reliability and power factor, are chosen. And then, the fuzzy satisfaction degree is proposed based on the concept of fuzzy mathematics to reflect the objective function with more comprehensively, veritably and naturally. And then the three individual indexes are converted and unified to overall satisfaction degree. Then, the event driven optimization process is established and global optimization is achieved by genetic algorithm. Finally, a scenario case study is demonstrated and indicates that the approach is a suitable solution for the multi-objective optimization to the distributed hybrid renewable energy generation system.

This paper introduces a controller model for the integration, of a wind system and photovoltaic system into the utility grid. The problem of integration of hybrid renewable energy based hybrid renewable energy generation system is that it relies heavily on weather conditions, PV and wind air speed. So, there is a necessity for developing control techniques for grid integration for such hybrid system, including a method for voltage and current control that stabilises the voltage and current. A PID control is developed in this work that is suitable for hybrid renewable energy system. Then, the system behaviour and performance are studied. For different weather conditions and fault condition systems, stability is also considered when there are change weather conditions in PV and air speed or a fault in the system. This paper advocates that the proposed PID controller gives a good performance. In this paper, solar energy is hybrid with wind energy. The developed MATLAB/SIMULINK design improves for integration of hybrid system performance and the results are presented.

In this paper, we investigate the PSO-tuned hybrid SMC performance based VSC-STATCOM under different conditions of fault using hybrid renewable energy sources (HRES). A hybrid renewable energy resource system (HRES) consists of PV, wind power, and batteries. Here the Irradiance is the PV input and the wind energy is Wind Input. The storage of energy is used for battery. The battery is used for changing weather condition or the changing the condition of the environment. Hybrid VSC-STATCOM controller based on SMC to reduce power quality issues like sag, swell, harmonics etc. associated with HRES system mainly due to non-linear load conditions. The novelty of our proposed PSO-tuned hybrid Sliding Mode Controller (SMC) method lies in its integration of Particle Swarm Optimization (PSO) as a tuning mechanism within the SMC framework. The Harmonic reduction and efficiency improvement is verified by using the Simulation. Therefore, the proposed system performance is simulated and to optimize the terms are real and reactive power, Total Harmonic Distortion (THD) and Voltage Sag.

This work presents a battery storage based renewable energy generation system (REGS) as solar power plant (SPP), wind power plant (WPP) and battery storage system (BSS) supported by electrolizer for delivering the demanded power to utility bus/load bus through common converter technology. Proposed system is capable to managing the power at AC load bus up to 30 kW under highly transient condition on utility bus by application of solar power generator and wind power generator with supported electrolizer battery storage device (SEBSD). The pulse width modulation (PWM) based converter is responsible/manage the ac voltage supply at utility node by changing the voltage at DC/RE node bus as application of converter technology implement as own generation system. Battery management system (BMS) is used for the power management at utility bus. The battery storage based hybrid renewable energy generation system is developed on platform of MATLAB with utility network loads L1 is 25 kW, L2 is 20 kW & 5 kVAr reactive (+ve) and L3 is 20 kW & 5 kVAr reactive (-ve) and perform different-different events by ON/OFF the electric loads with the help of circuit breakers, so we compare the input power which supplied by our proposed system at dc bus with output power taken by the utility load of network at load bus for the effectiveness of work and also evaluate power quality index (PQI) for the performance of grid parameters which affect by renewable energy penetration at electric grid.

The article analyzes the efficiency of using renewable energy sources as part of autonomous hybrid power plant to increase throughput on long sections of railways. The introduction considers Russian and world experience of autonomous hybrid power plants and renewable energy sources systems using in railway electrification systems. Mathematical models of photovoltaic converters and wind power plants as the main sources of generation for autonomous hybrid power plants presents in Methods of modelling section. The analysis of the energy potential was performed on the basis of initial data from various information resources for estimating the influence of the averaging interval on the accuracy of the calculation results. In Results section, a quantitative assessment of the energy potential of photovoltaic panels and wind turbines for the section of West Siberian Railway was made with recommendations on the feasibility of using systems and the optimal ratio of renewable energy sources as part of the autonomous hybrid power plant.

To meet the expanding energy demand, all available energy sources must be utilized. Renewable energies are both eternal and natural, but their major downside is their inconsistency. Due to the rising costs of fossil fuels and the CO2 they emit, hybrid renewable energy (HRE) sources have gained popularity as an alternative in remote and rural areas. To address this issue, a hybrid renewable energy system (HRES) can be developed by combining several energy sources. In order to build modern electrical grids that have advantages for the economy, environment, and society, the hybrid system is preferable. A summary of various optimization methods (modeling techniques) of an HRES is presented in this paper. This study offers an in-depth analysis of the best sizing, control methodologies, and energy management strategies, along with the incorporation of various renewable energy sources to form a hybrid system. Modern hybrid renewable energy system utilities rely more on an optimal design to reduce the cost function. Reviews of several mathematical models put out by various academicians are presented in this work. These models were created based on reliability analyses incorporating design factors, objective functions, and economics. The reader will get familiar with numerous system modelling optimization strategies after reading this study, and they will be able to compare different models based on their cost functions. Numerous modeling approaches and software simulation tools have been created to aid stakeholders in the planning, research, and development of HRES. The optimal use of renewable energy potential and the meticulous creation of applicable designs are closely tied to the full analysis of these undoubtedly complicated systems. In this field, as well, several optimization restrictions and objectives have been applied. Overall, the optimization, sizing, and control of HRES are covered in this paper with the energy management strategies.

The adverse effects of coal and gas energy production with the subsequent rapid increase in energy consumption emphasize the importance for Australia to adopt more renewable energy sources to counteract these dismissive contributions to climate change. This work presents a data mining approach for optimally selecting the best locations for installing a distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the constructed dataset into clusters. In total, 69 locations were selected for the overall dataset, proceeding with the filtering process. The returned cluster data were graphically rendered on a Western Australia map for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher degree than K-Medoids, given our dataset's nature. After passing the generated clusters to HOMER software to generate the potential wind and solar energy output for each centroid, K-Medoids produced a set of locations that generated higher solar and wind energy on average. However, due to the reduced internal validation, K-Medoids might not be as valuable as K-Means, it does not cluster the data points very well, and within-cluster location energy requirements are not considered in our study.

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

Optimum sizing of hybrid renewable power systems for on-site hydrogen refuelling stations: Case studies from Türkiye and Spain

This paper presents a comprehensive study on power management and control strategies for hybrid renewable energy microgrids. The research focuses on a DC microgrid system that integrates photovoltaic (PV) arrays with a hybrid energy storage system (HESS) comprising batteries and supercapacitors. The proposed system addresses key challenges in renewable energy integration, including power fluctuations, energy balance maintenance, and DC bus voltage stability. We develop and analyze a multilayer control strategy for power sharing among the PV system, battery, and supercapacitor components. This strategy enables supercapacitors to handle fast transients while batteries manage steady-state power variations, enhancing overall system stability and extending battery lifespan by preventing overcharging and deep discharging. Three maximum power point tracking (MPPT) techniques—Perturb and Observe (P&O), Incremental Conductance (INC), and Fuzzy Logic Control (FLC)—are implemented and compared through MATLAB/SIMULINK simulations under varying irradiance conditions. The comparative analysis reveals that while P&O and INC methods offer simplicity and ease of implementation, the FLC approach achieves higher accuracy in maximum power tracking despite increased operational complexity. The simulation results confirm the effectiveness of the proposed control methods in ensuring DC-bus voltage stability and power balance throughout the system. This research contributes valuable insights for improving the scalability, resilience, and performance of microgrid systems with hybrid renewable energy sources and storage solutions.

Sustainable Energy Systems: Planning, Integration and Management provides integrated perspectives on engineering, management, and technological perspectives to examine how renewable energy sources, smart grids, artificial intelligence, and advanced forecasting methods can be effectively planned, integrated, and managed within contemporary energy infrastructures. The book begins by introducing AI-enabled smart grid systems and their role in improving energy efficiency, reliability, and grid optimisation. The book highlights the technology, storage and working principles behind major renewable energy sources, including solar, wind, hydro, geothermal, and biomass. Chapters also focus on the use of computational intelligence in forecasting, management and optimisation of major energy sources. The book examines frameworks, principles, supply chains, and policy-driven approaches to sustainable energy governance. Real-world case studies on energy management provide practical insights into scalable and implementable solutions. By combining theoretical foundations with applied case studies and modern digital technologies, this book serves as an essential resource for understanding the planning, integration, and management of sustainable energy systems in the era of intelligent and renewable-powered infrastructure. Key Features: -Comprehensive coverage of renewable energy technologies, including solar, wind, hydro, geothermal, biomass, and hydrogen energy -Exploration of AI-enabled smart grids, energy forecasting, and intelligent energy management systems -Integration of Industry 4.0, machine learning, and digital technologies in energy optimisation and planning -Detailed discussion on hybrid renewable systems, energy storage, and grid integration strategies

Due to the lack of grid power availability in rural areas, hybrid renewable energy sources are integrated with microgrids to distribute reliable power to remote locations. This optimal hybrid system is created using a solar photovoltaic system, wind turbine, diesel generator, battery storage system, converter, electrolyzer and hydrogen tank to provide uninterrupted power and meet different load demands of different communities in Doddipalli village, Chittoor, Andhra Pradesh, India. Optimization and techno-economic analysis are performed to design the proposed system using HOMER Software. Various configurations are obtained from the software among which the best four combinations are considered for case studies. This research article aims to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%). In this research, the proposed system would be more economical when solar energy becomes the primary source and is integrated with the battery. This research also presents a sensitivity analysis of the off-grid HRES system with various electrical load demands, project lifetime, and derating factors.

Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.