Renewable Energy Systems Research Articles

Power quality optimization in hybrid renewable energy system using MDNSOGI-based predictive control

Editorial: Assessment of renewable energy systems for energy conversion and storage

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system.

تتيح أنظمة الطاقة المتجددة الهجينة (HRESs) آفاقًا واعدة لتلبية الاحتياجات الحرارية في القطاع السكني في ليبيا. يقترح هذا البحث تصميم منظومة طاقة متجددة هجينة تتضمن الطاقة الشمسية الحرارية، وطاقة الكتلة الحيوية، وطاقة القشرة الأرضية. يطبق التحليل على حالة واقعية لحمل منزلي يقع في منطقة سمنو بجنوب ليبيا للاستفادة من الطبيعة الجغرافية والمناخية للمنطقة. أولاً، يتم بهذا البحث تحديد الحجم الأمثل لمكونات المنظومة الهجينة وفقًا للبيانات المناخية من منصة Solargis، وبيانات الطاقة الكتلة الحيوية، وبيانات الحمل الحراري المنزلي. ثم يتم نمذجة وتحليل مكونات المنظومة باستخدام برنامج SAM وبمساعدة كل من برنامج Excel و Jupyter Notebook. أظهرت النتائج أن المنظومة الهجينة المقترحة، قادرة على تلبية الاحتياجات الحرارية المنزلية، بتكلفة رأس مال للمشروع حوالي $ 3027، وتكلفة طاقة مستوية (LCOE) حوالي $/kWh 0.054. بالإضافة إلى ذلك، أسهمت المنظومة في خفض انبعاثات ثاني أكسيد الكربون بمقدار kg 425 سنويًا. ومن المتوقع أن تفتح نتائج هذا العمل آفاقًا جديدة للباحثين ومخططي أنظمة الطاقة المتجددة الهجينة واستخدامها للحد من الطلب المتزايد على الطاقة للأحمال الحرارية في المناطق الحضرية والريفية.

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems.

Energy plays a major task in the development of any nation’s economy. This may not be unconnected to its inter-relationship with countries’ national gross domestic product (GDP). In most developing countries, a reasonable percentage of national income initiates from the rural areas engaged in agricultural and related activities. Unfortunately, these communities depend predominantly on conventional sources of fuels which have detrimental environmental effects, as their source of supply of electricity. Consequently, there is little or no improvement in the standard of living in rural communities from their present low level without access to sustainable electricity. The aim of this study is to explore the prospect of meeting electricity demand from a solar wind-diesel hybrid system to a rural community isolated from the main utility grid in a coastal area of Nigeria. Based on the available renewable energy sources present at the site, a feasibility study has been carried out on the optimal hybrid renewable energy system (HRES) model to meet the electricity demand of a community of about 700 people. Hybrid Optimization Model for Electric Renewables (HOMER) is used to define the optimum configuration based on the available metrological resource. The outcome of the analysis is a list of feasible HRES configurations ranked according to their present cost (NPC). For this case study, the optimal configuration at the present interest rate of 12% in Nigeria is a combination of 20kW PV, 10 2.5kW, wind turbine, 50kW diesel generator, 17kW converter and 100 S4KS25P batteries. The NPC for this system configuration is N27,181,044 and LCOE is N71.676/kWh. The emission of CO2 the principal pollutant can be reduced by 97.1% using HRES instead of standalone diesel generator. Furthermore, relationship of economic indices and the interest rate is also evaluated. In all, HRES though has a higher initial cost; it has a lower NPC, Levelized Cost of Energy (LCOE) and reduced greenhouse gas emission compared to diesel only generator.

The integration of renewable energy in urban areas is critical for achieving sustainability and addressing climate change. This paper provides a comprehensive review of recent research on the integration of renewable energy systems in urban settings, focusing on a variety of technologies, strategies, and challenges. The review encompasses a range of renewable energy sources, including solar, wind, geothermal, and biomass, and examines their application in different urban contexts. Key findings from the 15 reviewed literature indicate that renewable energy systems significantly contribute to urban sustainability by enhancing energy efficiency, reducing greenhouse gas emissions, and promoting energy security. The research highlights the technical and economic feasibility of integrating multi-energy hybrid systems, the importance of energy storage solutions, and the potential of advanced technologies such as AI and nanomaterials in optimizing energy systems. Challenges such as intermittency, high initial costs, and the need for supportive policies are identified, along with innovative solutions and emerging trends that address these issues. The review underscores the need for supportive policy frameworks, investment in infrastructure, and public-private partnerships to overcome barriers and facilitate the widespread adoption of renewable energy. Recommendations include advancing hybrid energy systems, investing in R&D, and enhancing public awareness and regional cooperation. These actions are essential for promoting effective renewable energy integration and achieving sustainable urban development.

Carbon–neutral supercapacitors play an important role in renewable energy investments as environmentally friendly devices that both function as energy storage and aim to reduce carbon footprint. This situation can cause waste of resources and wrong prioritization decisions. In this context, the main problem is that the most important factors affecting the technical investment performance of carbon–neutral supercapacitors have not been determined. To fill this gap, this study proposes an original decision-making model to determine the importance levels of variables affecting the performance of these devices and to present appropriate investment strategies. The proposed model includes the integrated use of Entropy-game-based expert weighting method, Q-learning algorithm, molecular fuzzy intelligence algorithms, Bayesian network-based weighting (BANEW) and ant colony optimization (ACO) techniques. This study contributes to making more accurate and effective technical decisions for sustainable energy investments by filling an important gap in the literature with its original decision model. It is determined that recyclability rate is the most significant factor because it has the highest weight (0.316). On the other side, the best investment choice for carbon–neutral supercapacitors in renewable energy systems is gravity-based energy storage with the greatest fitness value of 4.044.

Knowing the battery SoC is important in battery usage, especially in renewable energy systems, electric vehicles, portable electronic devices, and other applications that use batteries as a power source. By knowing the battery SoC, users can manage battery usage, estimate the remaining time before needing to recharge or replace it. The BMS must ensure an efficient way for charging and discharging procedures. In addition, the BMS must maintain the proper SOC so that battery life can be maximized. In this study, a BMS designed for discharging protection on a battery as an energy storage that works to provide electrical energy to an electric load in the form of an electric vehicle, in carrying out its duties the BMS controls the electrical voltage limit to turn off the active switch on the load. The battery used is a Lead-Acid 12 V / 7.2 Ah, the C-rate used to serve the load of the battery discharging process is 0.15 C then the estimated battery capacity in an empty state is 6.4 Ah (SoC = 0%). The results of the study showed that the battery discharged for 6 hours and the cut-off voltage as protection was 10.6 Volts.

Bandgap engineering is the process of modifying a material's electronic structure to optimize its bandgap for specific applications. Applying pressure is an effective technique to alter a material's physical properties to meet device requirements. In this manuscript, we have investigated the impact of bandgap engineering through pressure application on the physical characteristics of AcGaO3. Using the Wien2K code and the FP-LAPW method, we evaluated the material's properties under pressures ranging from 0 to 30 GPa, with additions of 5 GPa in each calculation. The Modified Becke-Johnson approximation was employed to accurately account for exchange-correlation effects. The elastic constants show a significant decrease with increasing pressure, indicating a reduction in the material's resistance to external strain. Lower speed values of the elastic waves suggest that the atomic bonding becomes weaker as the pressure is enhanced. Similarly, the Debye and melting temperatures decline as pressure increases. Electronic properties reveal a reduction in the indirect bandgap, while optical properties exhibit a shift from the higher energy region to the lower energy region under elevated pressures. The optical properties report a significant reduction in the polarization ability, absorption, and conductivity as the pressure is increased. This approach opens new possibilities for technological applications, as AcGaO3's reduced bandgap and optical characteristics in the visible area make it an attractive contender for next-generation optoelectronic and energy storage devices.

Geothermal energy represents a crucial component of sustainable energy strategies due to its consistent availability and minimal emissions. However, the comprehensive assessment of sustainability for geothermal turbine systems remains challenging, primarily due to complex interactions among environmental, social, and economic dimensions. This study develops and applies an advanced Product Sustainability Index (PRODSI) framework, uniquely combining expert-driven fuzzy Analytic Hierarchy Process (fuzzy-AHP), data-driven Entropy methods, and Convolutional Neural Networks (CNN) for robust validation. Utilizing a detailed dataset derived from the System Advisor Model (SAM) Discounted Cash Flow (DCF), IPSEpro off-design performance models, Energy Information Administration (EIA) consumption data, and extensive supplementary tables, sustainability indicators were normalized and weighted systematically. The results indicate significant variation in sustainability scores across evaluated geothermal turbines, notably identifying the 5 MW turboexpander as the most balanced and sustainable choice, with a PRODSI score of 7.08, compared to 2.7 for the 1 MW turbine and 5.32 for the 20 MW steam turbine. This study contributes by integrating subjective expert insights with objective data analysis and validating this integration through CNN-driven machine learning, establishing a novel standard for sustainability assessments of renewable energy systems. The PRODSI framework offers a transparent, validated, and scalable tool for decision-making in geothermal sustainability. It provides actionable guidance for investors, developers, and policymakers, facilitating optimized technology selection and resource allocation. Additionally, it establishes a foundation for real-time decision support and broader applications in renewable energy.

This paper proposes an independent and flexible control strategy for Battery Energy Units (BEUs) in autonomous DC Nano Grids (DCNGs) using a communication-based system. The proposed strategy employs a hierarchical control approach, where the primary control manages power balance among BEUs, while the secondary control mitigates DC bus voltage deviations caused by droop operation. This ensures optimal power distribution while considering line resistance variations, State of Charge (SoC), and virtual power levels. The method prioritizes BEUs with higher SoC to contribute more power to the load while reducing the burden on lower SoC units. Additionally, a voltage recovery technique is implemented to maintain DC grid voltage stability. Beyond technical improvements, these findings can contribute to more efficient energy management in decentralized renewable energy systems, supporting the scalability of sustainable nanogrid infrastructures in remote or off-grid communities. An additional advantage is that in the event of a failure in the control of BEU1, the control of the unaffected BEU remains operational, ensuring system continuity. A comprehensive simulation comparison between conventional and the proposed method was conducted in MATLAB/Simulink under varying load power conditions. The results demonstrate that the proposed control strategy significantly improves power balancing accuracy and voltage stability.

The integration of clean energy sources into standalone power systems requires the selection of appropriate renewable resources based on the local weather conditions, geographic location, and installation costs. Photovoltaic (PV) panels and hydrogen-based systems, particularly those using Solid Oxide Fuel Cells (SOFCs), are often used in a complementary manner. The efficient operation of such hybrid systems necessitates an intelligent energy management capable of optimizing the power flow to the electrical loads and of storing the surplus energy. To achieve a high power quality and reduce the overall system costs, a suitable system architecture and advanced control strategies are essential. In this context, an intelligent supervisory control based on Multi-Input Multi-Output Fuzzy Logic Control (MIMO-FLC) is proposed. This controller addresses key challenges, such as enhancing the energy efficiency, ensuring a smooth production-consumption balance, and maintaining the service continuity and reliability. The proposed MIMO-FLC effectively manages the hybrid energy system by adapting to the changing weather conditions and load demands. The system was modeled and simulated using MATLAB/Simulink. The simulation results demonstrate that the fuzzy logic-based control significantly improves the system performance, ensuring high power quality and efficient energy usage. The controller successfully directs energy either to the load or to the battery storage without power loss or interruptions. This study emphasizes the simulation process and analyzes the evolution of key parameters—such as power, voltage, pressure, and current density—over time to estimate the electrical energy required for a 1 MW output load.

2nd International Conference on Smart and Sustainable Energy Systems (ICSSES 2025) was successfully held on March 14-15, 2025, via a virtual platform (MS-Teams), hosted by the Department of Electrical and Electronics Engineering at Vishnu Institute of Technology, Bhimavaram, Andhra Pradesh, India. This two-day 2nd International event aimed to gather students, faculty, researchers and stakeholders across the globe working within the realm of “Engineering Sciences and Particularly Sustainable Technologies for the Future Society,” fostering the exchange of knowledge and experiences. The conference specifically provided the scholars & all the participants in the event with a substantial technical platform to share their ideas and findings in the esteemed presence of renowned experts from nationally significant institutes (IISCs, IITs, NITs) and beyond. The conference also emerges as one of the premier platform featuring the technical sessions, keynote speeches and poster presentations focused on sustainability and the green energy forum. This gathering brought together industrial experts, scholars and enthusiasts in the field to share cutting-edge research, innovative solutions and impactful insights aimed at advancing smart and sustainable energy systems for a resilient future. The event not only showcased the latest advancements but also fostered collaborative discussions and networking opportunities, making it a pivotal moment in the journey towards a more sustainable world. The conference was organized into seven main tracks, each focusing on the critical research areas in environmental, sustainable, green & smart energy engineering. 1. Environmental Engineering and Sustainability 2. Renewable Energy Engineering and Technologies & Smart Energy Systems 3. Future Challenges and Opportunities in Energy Sectors & Sustainability for Industrial Applications 4. Applications of Power Converters in Renewable Energy 5. Artificial Intelligence and Machine Learning for Energy Optimization & IoT and Smart Grid Technologies in Renewable Energy Systems 6. Green Energy Innovations and Economy Integration & Energy Policy, Economics, and Social Impact List of Organizing Committee is available in this PDF.

In the modern world, renewable energy offers an alternative to traditional power generation. The environment and biological life are significantly impacted by the production of power using conventional energy. The universe is entirely of renewable energy. Renewable energy is stable, clean, and efficient for the environment. This study aims to model and simulate hybrid power generation via solar and wind turbines using MATLAB/Simulink for sustainable energy development. This article uses MATLAB/Simulink to examine a non-conventional hybrid energy system that uses wind and solar to generate power. The efficiency of their production methods is increased when optimal resource consumption is achieved. It also reduces dependence on a single source and increases reliability. Variability in sun irradiation and seasonal weather patterns causes fluctuations in solar array output. The DC/DC converter incorporates the maximum power point tracking approach to allow PV arrays to operate at maximum power. This hybrid solar and wind power-producing system is suitable for household and commercial use. There is an inadequate supply of energy in Nigeria, as most villages and towns are still disconnected from the grid. Because of the rugged terrain and financial limitations, grid connection is not feasible in these areas. The results from the simulated hybrid system produce 350 W of power, and the power generated is greatly affected by time variations and weather conditions. Renewable energy systems have significantly grown since they utilise readily available resources locally in recent years. It is one of the best ways to produce energy. Thus, installation requires more caution regarding simulation analysis and modelling for improved efficiency. This study will increase the availability of affordable electricity in the local community and efforts towards clean energy technology.

Abstract Triboelectrification is a phenomenon of transferring of electrical charges and accumulation among surfaces through material-specific interactions predominantly motivated by mechanisms such as ion movement and electron exchange. Triboelectrification, which is accomplished by material contact, is an approach that transforms mechanical motions such as wheel rotations, engine vibrations, or other dynamic motions into electrical energy. Without increasing the load or altering the rotational motion of the engine’s components, this method recovers mechanical energy that would otherwise be squandered. Auxiliary systems like sensors, lighting, and control systems may be powered by the gathered power to encourage sustainability for electric vehicle applications. This technique offers a cost-effective and ecologically friendly energy recovery option by reducing reliance on traditional power sources and improving system performance. This study presents a triboelectric generator’s design and modelling based on the Van de Graaff electrostatic generator’s principles. The basic mechanism of the suggested system, which generates electric charge by frictional contact between materials, is modelled after the Van de Graaff design. In addition to solar-powered mechanical activity, the generator makes use of easily available and affordable materials such as PVC tubing, rubber, and silk cloth to facilitate triboelectric interactions. The effective use of mechanical vibrations and rotational motion, the experimental results demonstrate a strong connection with computed values, validating the efficiency of energy generation. The potential arrangement of proposed triboelectric electrostatic generator design for everyday applications in renewable energy systems is augmented by the anticipated design. This in turn encourages the additional study to enhance its functionality ensuring larger electrical power output.

Comprehensive design of active damper for suppression of harmonic resonance in renewable energy system

Enabling sustainable and resilient supply chain expansion through technological advancements: Corporate policy insights from the Gulf petrochemical industry.

Techno-economic assessment of a smTMSR-based nuclear–renewable hybrid energy system with high-temperature steam electrolysis

Simulation and optimization of hybrid renewable energy system to achieve a net-zero and flexible-interconnected service area for highways