Renewable Energy Systems Research Articles

Performance evaluation of solar photovoltaic system based on perturb and observe (P&O) maximum power point tracking (MPPT) algorithm

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation.

Conceptual hybrid energy model for different power potential scales: Technical and economic approaches

This research attempts to address the gap between the theoretical fundamentals of hybrid renewable energy systems and their practical implementation at different scales through a new Conceptual Hybrid Energy Model (COHYBEM). The main objective was to develop a multi-variable model to allow a new complete and comprehensive techno-economic analysis of the performance of possible hybrid renewable power systems at different scales. The purpose is to evaluate the influence of critical parameters by changing key parameters in the developed model and identifying their impacts. It covers big data analyses, simulation and optimization of hybrid energy solutions, combining wind, solar and hydropower energy sources with the energy storage technology of pump hydropower storage. The research also denoted the Pareto front with the increasing power installed, for the maximum efficiency and total satisfied demand by Wind + PVSolar and by Hydro converges to a higher percentage, while a minimum waste by Wind + PVSolar is also progressing towards the increasing scales. In terms of investment costs for the 243 analyzed case studies, it varies between 45 k€ to 2.1 M€, resulting in a net present value (NPV) between 18 and 600 k€ and a payback period around 6–17 years depending on the power scale analyzed.

Prediction of Solar Radiation using Deep LSTM-based Machine Learning Algorithm

Solar radiation, a critical parameter for various applications such as solar energy systems and weather forecasting, exhibits complex temporal patterns influenced by numerous environmental factors. In the quest to enhance the accuracy of solar radiation predictions, this study introduces a novel approach utilizing Deep Long Short-Term Memory (Deep LSTM) networks, a type of recurrent neural network (RNN) known for its capability to model sequential data. Traditional prediction methods often struggle to capture these intricacies, leading to sub-optimal performance. This investigation is based on a Deep LSTM-based machine learning algorithm designed to predict solar radiation with elevated exactitude. The model is meticulously trained on copious datasets encompassing historical meteorological data, including temperature, humidity, wind velocity, and antecedent solar radiation metrics. Pivotal stages encompass data preprocessing, judicious feature selection, and hyperparameter optimization to enhance the model’s predictive efficacy. Empirical results clearly illustrate that the Deep LSTM model surpasses traditional methodologies, attaining superior accuracy and resilience across diverse meteorological scenarios. The ramifications of this endeavor portend significant advancements in the strategic planning and administration of solar energy resources, thereby contributing to more dependable and efficacious renewable energy systems.

OI url: https://doi.org/10.30574/wjarr.2024.23.3.2934

Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.

Optimization of Solar PV System Efficiency in Bangladesh

This paper presents a comprehensive review and analysis of Sarishabari Engreen Solar Plant Ltd., a 3.3 MW grid-connected solar photovoltaic (PV) system located in Sarishabari, Jamalpur, Bangladesh. The study evaluates the plant's economic and operational performance, revealing a competitive payback period of 10.1 years and a levelized cost of energy (LCOE) of 0.11 USD/kWh. These metrics highlight the plant's financial viability, largely due to the low cost of public land used for construction. However, profitability may be challenged if similar projects require significant investments in private land acquisition. Key areas for improvement identified include optimizing the tilt angle and integrating smart automation systems. Additionally, the potential for hybrid renewable energy systems combining solar and wind power is discussed. The paper also provides actionable recommendations for future renewable projects, emphasizing the importance of advanced technologies, and supportive policies. These insights aim to inform the optimization of existing solar PV systems and guide the development of future renewable energy projects in Bangladesh, contributing to the country's sustainable energy goals.

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

Synergetic UPQC Application for Power Quality Enhancement in Microgrid Distribution System: SCSO Approach

Nowadays, Hybrid renewable energy systems (HRES) are increasingly being integrated into grid-connected load systems to lower losses and boost dependability. When the HRES system is connected to the grid system to satisfy needed load demand, power quality issues arise because of imbalanced load circumstances, non-linear load, and critical load. So this manuscript proposed a synergetic Unified Power Quality Conditioner (UPQC) application for power quality enhancement in microgrid distribution systems. The Sand Cat Swarm Optimization (SCSO) technique is based on the proposed sand cat swarm optimization method. The proposed strategy's primary goal is to regulate power flow, minimize harmonic distortion in both voltage and current waveforms, and maximize UPQC. By then, the proposed method's performance has been implemented into practice on the MATLAB platform and contrasted with several currently used techniques. The proposed technique displays the low Total Harmonic Distortion (THD) and operation time in all existing approaches like Moth Flame Optimization (MFO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), Improved Bat Search Algorithm (IBSA), Lion Algorithm (LA), Artificial Bee Colony (ABC), Atom Search Optimization (ASO) and Crow Search Algorithm (CSA). The proposed method attains THD as before UPQC is 31%, after UPQC is 3%, load current is 1.42%, and load voltage is 2%. It also achieves a low operation time of 506ms and a low settling time of 0.15s compared to the existing methods. The significant improvement in power quality metrics demonstrates the effectiveness of the proposed SCSO technique.

Spinel-type calcium manganese oxide adorned on g-CN composite: A highly proficient electrocatalyst for oxygen evolution reaction (OER)

A comprehensive study was performed on water electrolysis to address environmental challenges and energy issues. Eco-friendly and renewable energy systems are required to produce cost-effective electro-catalysts with long-term durability and higher electrocatalytic efficiency to enhance OER. Spinel oxide has recently been used as a promising substitute for transition metal catalysts. They can function as very effective electrocatalysts in water oxidation processes. In current study, the CaMn2O4/g-CN composite was prepared using the hydrothermal technique in 1.0 M KOH in primary media. Distinctive physical and electrochemical analyses evaluated the prepared material's morphology, structure and electrocatalytic components. The composite material showed enhanced electrocatalytic efficacy for OER having a reduced overpotential (η) of 220 mV than pure CaMn2O4 278 mV and possesses a minimal Tafel value (35 mV/dec) contrasted with pristine CaMn2O4 (57 mV/dec). In case of overpotential, the composite material revealed good performance than its pure material while the Tafel slope of CaMn2O4/g-CN exhibited lower value than its individual material (CaMn2O4). Additionally, cyclic stability and chronoamperometric studies measured the electrodes' durability over 30 h. Electrochemical impedance spectroscopy (EIS) investigations indicated that charge transformation at an electrode-electrolyte surface might be feasible with minimal Rct value (0.19 Ω) and it showed excellent improved performance (63 %) better than its pure material, leading it to boost OER properties, attributed to its remarkable electrical conductivity, greater surface area and outstanding stability. These outstanding electrochemical features of the CaMn2O4/g-CN differentiate as an attractive choice for future OER utilization.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

A novel artificial neural network based selection harmonic reduction technique for single source fed high gain switched capacitor coupled multilevel inverter for renewable energy applications

Multilevel inverters (MLIs) are commonly used in renewable energy systems for their high-quality output, low total harmonic distortion (THD), and reduced component count. This study presents a high-gain, single-source MLI designed for renewable applications like solar or wind power. It features a novel topology with twice the voltage-boosting factor, utilizing a single DC source. The inverter achieves thirteen voltage levels using just 10 power switches and three switched capacitors. The voltage gain is achieved without the need for bulky DC-DC converters or transformers. This is accomplished by configuring the switched capacitors in series and parallel arrangements to attain the desired voltage boost. Additionally, the self-balancing capacitors eliminate the need for extra sensors. Both symmetric and asymmetric variants of the extensible configuration are investigated. The suggested design lowers the total standing voltage (TSV) while achieving high gain. A selective harmonic removal technique using artificial neural networks (ANN) reduces THD by up to 6.07 %. An extensive review of recent literature reveals significant advancements and applications of ANNs in this field. The proposed system's benefits, such as gain factor, total standing voltage (TSV), and minimized device count, are assessed. Comparative analysis reveals that the proposed topology employs fewer components and features a more simplified design. Additionally, the inverter achieves an efficiency of 96.9 %. The design is validated through an experimental prototype after being confirmed with MATLAB/SIMULINK.© YEAR The Authors. Published by Elsevier Ltd.Peer-review under responsibility of the scientific committee of the Name of the Conference, Conference Organizer Name, Year or Edition of Conference.

A Methodological Framework for the development of a hybrid renewable energy system with seawater Pumped storage Hydropower system under uncertainty in Karystos, Greece

The need to minimize energy reliance and its repercussions and accretivewater scarcity necessitates research into renewable energy resources. Hybrid renewable energy systems are an apparent solution for areas and countries like Greece, especially when combined with seawater-pumped storage hydropower systems, where wind potential and topography foster such approaches. Moreover, it is essential to understand the uncertainty of the involved natural processes and incorporate their stochasticity in these hybrid systems, moving towards a more realistic approach. This system is simulated under uncertainty and has a total capacity of 31.5 MW and aims to cover the water and energy needs of Karystos, combining 9 wind turbines of 3.5 MW each, a desalination plant of 9,600 m3/day, a desalinated water tank with a capacity of 100,000 m3, a 9 MW pumping station, and a seawater pumped storage hydropower system with a 1.7 hm3 storage tank. Some promising results of this system under study are 99 % reliability for drinking water needs and 63 % and 85 % for irrigation and energy needs respectively, with the wind park contributing 54.4 % and the hydropower system 31.3 %.

Impedance modeling and quantitative stability analysis of grid-connected voltage source converters under complex unbalanced conditions

The stability issues caused by voltage source converters (VSCs), which are commonly used in renewable energy systems, are investigated in this paper. Much literature has investigated the impedance models and quantitative stability analysis for converters under the weak and unbalanced grid. However, on the one hand, harmonic-transfer-function (HTF)-based modeling methods are complex, and the established infinite-order models may suffer from unreasonable truncation. On the other hand, these existing impedance models without considering all unbalance factors are inaccurate. Both of these may bring wrong stability analysis results. Therefore, this paper proposes a simple impedance extension method and considers all unbalance factors to derive the impedance model of the converter. However, due to the established higher-order impedance model, the traditional impedance-ratio based stability analysis method is not applicable to multi-input multi-output (MIMO) systems. The commonly used eigenvalue-based GNC and diagonalization-based methods applicable to MIMO systems are cumbersome and not suitable for quantitative analysis. Therefore, in this paper, a quantitative analysis tool of system stability based on the phase-frequency characteristics of determinants is developed. Then, stability regions in the multi-parameter space are obtained. Finally, the established impedance model and the quantitative analysis results are validated via both simulations and hardware experiments.© 2017 Elsevier Inc. All rights reserved.

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.

Evaluating the techno-economic potential of large-scale green hydrogen production via solar, wind, and hybrid energy systems utilizing PEM and alkaline electrolyzers

The study evaluates the potential of solar, wind, and hybrid PV/WT renewable energy systems for green hydrogen production in four Iraqi cities. Through a comparative analysis of six distinct scenarios involving the deployment of 60 MWp solar panels, 30 MWp wind turbines, and 45 MWp hybrid PV/WT systems, the research aims to ascertain the most energy-efficient and cost-effective strategy for hydrogen generation. This evaluation is aligned with the operational capacities of two types of water electrolyzers: Alkaline (AWE) and proton exchange membrane (PEM), each with a 17.5 MWp capacity. Employing the HOMER Pro software for system simulation and optimization, and considering a project timeline from 2022 to 2042, the study identifies Anbar City as the prime location for green hydrogen production, highlighting solar PV panels as the most economical option with the lowest levelized cost of energy at US $4.5/MWh. The analysis further demonstrates that hydrogen production costs are US $1.98/kg for AWE electrolyzers and US $2.72/kg for PEM electrolyzers, with net present costs of US $26.31 million and US $35.91 million, respectively. Moreover, the annual hydrogen output is estimated at 1.11 million kg for AWE and 1.19 million kg for PEM electrolyzers. These insights significantly contribute to the strategic planning and development of Iraqi green hydrogen sector, offering a valuable framework for policymakers and stakeholders invested in sustainable energy transitions.

Design and Control of Four-Port Non-Isolated SEPIC Converter for Hybrid Renewable Energy Systems

Design and Control of Four-Port Non-Isolated SEPIC Converter for Hybrid Renewable Energy Systems

Multi-objective optimal sizing and techno-economic analysis of on- and off-grid hybrid renewable energy systems for EV charging stations

Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate.

Techno economic analysis of PV pumping system for rural village in East Java

Renewable energy systems offer a sustainable alternative for addressing energy needs, especially in rural settings. This study investigates the optimization of a hybrid photovoltaic (PV) pumping system by minimizing the Cost of Energy (COE), maximizing the Renewable Fraction (RF), and reducing Carbon Dioxide Emissions (ECO₂). The optimization process was carried out using Python to implement the Queen Honeybee Migration (QHBM) algorithm, which evaluated three distinct scenarios to determine the most efficient system configuration. The optimal setup comprised 48 units of 600 Wp PV panels, 17 units of 250 Ah batteries, and 3 units of 8,000 W inverters. This configuration achieved a COE of $0.041 per kWh, an RF of 43.96 %, and an ECO₂ reduction of 118.49 kgCO₂e, with a cumulative objective value of -146.76. A comparative analysis with the Particle Swarm Optimization (PSO) algorithm indicated that QHBM excels in optimizing RF and ECO₂, whereas PSO demonstrates marginally superior performance in reducing COE. The economic evaluation reveals a Net Present Value (NPV) of $40,206.11 over a 35-year period, a Return on Investment (ROI) of 398 %, a Break-Even Point (BEP) of 6.45 years, and a Payback Period (PP) of 13.25 years. The system is projected to yield daily and annual cost savings of $6.33 and $2,310.90, respectively. These results underscore the efficacy of QHBM in achieving superior techno-economic and environmental performance in hybrid PV pumping systems.

Performance evaluation of sustainable energy alternatives to obtain efficient hybrid energy investments

This study evaluates the synergy of coalition for hybrid renewable energy (RWB) system alternatives. In this context, the alternative sources of hybrid RWB system are examined to illustrate the impact-relation directions among them with multi SWARA based on q-ROFs and golden cut. Next, the performances of renewable alternatives are measured in terms of the synergy of coalition with game theory and Shapley value. It is concluded that solar energy is the most suitable RWB alternative for synergy to increase efficiency in investments. However, biomass does not have a significant influence on providing synergy in energy investments. Therefore, solar energy should be prioritized for hybrid energy investments. Especially with the effect of technological developments, the efficiency of solar energy investments increases significantly. Thus, solar energy investments have become quite suitable for increasing the synergy in hybrid energy projects. Furthermore, necessary research should be conducted to make biomass energy more efficient.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Multi-objective optimisation of hybrid renewable energy systems for Colombian non-interconnected zones

Colombia’s Atlantic coast wind and solar resources could enhance energy mix and self-generation. Renewable clean energy has been studied in response to fossil fuel pollution. Wind and solar photovoltaic systems have low initial, operational, and levelized energy costs. Variable wind and sun radiation may limit availability. In this regard, hybrid renewable energy systems (HRES) and energy storage have become crucial. These systems can effectively meet load demand by utilising complementary renewable resources. This study deals with sizing a wind and photovoltaic HRES with storage, using a Particle Swarm Optimisation algorithm for yearly variable resources in a non-interconnected zone in La Guajira, Colombia. The study evaluates the LCOE, probability of load loss, and the system’s CO2 emission. It develops a sensitivity analysis to determine the importance of each objective factor. From a life cycle analysis, an HRES configuration with low LCOE and environmental emission rates is associated with the size of the wind resource; the HRES configuration with minimum LCOE values is close to obtaining higher equivalent CO2e emissions. The study highlights the configuration obtained for the Colombian context, giving more importance to the environmental factor and reaching an LCOE of 0.754 USD/kWh and emission of 18.97 tCO2e/year; this configuration also increases the wind energy generation, reaching 41 % more share, compared to the configuration obtained when the economic factor is a priority.