Search Results (51)

This study investigates a hybrid renewable energy system combining the municipal solid waste (MSW) gasification and solar photovoltaic (PV) for electricity generation in Lobito, Angola. A fixed-bed downdraft gasifier was selected for MSW gasification, where the thermal decomposition of waste under controlled air flow produces syngas rich in CO and H₂. The syngas is treated to remove contaminants before powering a combined cycle. The PV system was designed for optimal energy generation, considering local solar radiation and shading effects. Simulation tools, including Aspen Plus v11.0, PVsyst v8, and HOMER Pro software 3.16.2, were used for modeling and optimization. The hybrid system generates 62 GWh/year of electricity, with the gasifier contributing 42 GWh/year, and the PV system contributing 20 GWh/year. This total energy output, sufficient to power 1186 households, demonstrates an integration mechanism that mitigates the intermittency of solar energy through continuous MSW gasification. However, the system lacks surplus electricity for green hydrogen production, given the region’s energy deficit. Economically, the system achieves a Levelized Cost of Energy of 0.1792 USD/kWh and a payback period of 16 years. This extended payback period is mainly due to the hydrogen production system, which has a low production rate and is not economically viable. When excluding H₂ production, the payback period is reduced to 11 years, making the hybrid system more attractive. Environmental benefits include a reduction in CO₂ emissions of 42,000 t/year from MSW gasification and 395 t/year from PV production, while also addressing waste management challenges. This study highlights the mechanisms behind hybrid system operation, emphasizing its role in reducing energy poverty, improving public health, and promoting sustainable development in Angola. Full article

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid energy solutions to ensure reliability, sustainability, and economic viability. However, optimizing the design of hybrid renewable energy systems, particularly those incorporating both hydrogen and battery storage, remains challenging due to system complexity and fluctuating energy trading conditions. This study addresses these gaps by proposing a novel framework that combines the Chimp Optimization Algorithm (ChOA) with a rule-based energy management strategy (REMS) to optimize component sizing and operational efficiency in a grid-connected microgrid. The proposed system integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), hydrogen storage, fuel cells (FC), and battery storage (BAT), while accounting for seasonal variations and dynamic energy trading. Each contribution in the Research Contributions section directly addresses critical limitations in previous studies, including the lack of advanced metaheuristic optimization, underutilization of hydrogen-battery synergy, and the absence of practical control strategies for energy management. Simulation results show that the proposed ChOA-based model achieves the most cost-effective and efficient configuration, with a PV capacity of 1360 kW, WT capacity of 462 kW, 164 kWh of BAT storage, 138 $H_2$ tanks, a 571 kW ELZ, and a 381 kW FC. This configuration yields the lowest cost of energy (COE) at $0.272/kWh and an annualized system cost (ASC) of $544,422. Comparatively, the Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) produce slightly higher COE values of $0.274, $0.275, and $0.276 per kWh, respectively. These findings highlight the superior performance of ChOA in optimizing hybrid energy systems and offer a scalable, adaptable framework to support future renewable energy deployment and smart grid development. Full article

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact. Full article

This paper deals with the design and control of an enhanced grid-tied photovoltaic (PV) cascaded H-Bridge (CHB) inverter, which suffers from issues related to operation in the overmodulation region in the case of a deep mismatch configuration of PV generators (PVGs). This can lead to reduced system performance in terms of maximum power point tracking (MPPT) efficiency, or even instability (i.e., a lack of control action). The proposed solution is to insert into the cascade a power cell fed by a battery energy storage system (BESS) with the aim of providing an additional power contribution. The latter is useful to reduce the modulation index of the cell, delivering more power than the others when a preset threshold is crossed. Moreover, a suitable hybrid modulation method is used to achieve the desired result. A simulated performance in a PLECS environment proves the viability of the proposed solution and the effectiveness of the adopted control strategy. Full article

The urgency to decarbonize fuels has contributed to a rise in biofuel production, which has culminated in a significant increase in the waste quantity of glycerol produced. Therefore, to convert glycerol waste into high-value products, electrochemical oxidation (EO) is a viable alternative for the co-generation of carboxylic acids, such as formic acid (FA) and green hydrogen (H₂), which are considered energy carriers. The aim of this study is the electroconversion of glycerol into FA by EO using a divided electrochemical cell, driven by a photovoltaic (PV) system, with a dimensionally stable anode (DSA, Ti/TiO₂-RuO₂-IrO₂) electrode as an anode and Ni-Fe stainless steel (SS) mesh as a cathode. To optimize the experimental conditions, studies were carried out evaluating the effects of applied current density (j), electrolyte concentration, electrolysis time, and electrochemical cell configuration (undivided and divided). According to the results, the optimum experimental conditions were achieved at 90 mA cm⁻², 0.1 mol L⁻¹ of Na₂SO₄ as a supporting electrolyte, and 480 min of electrolysis. In this condition, 256.21 and 211.17 mg L⁻¹ of FA were obtained for the undivided and divided cells, respectively, while the co-generation of 6.77 L of dry H₂ was achieved in the divided cell. The electroconversion process under the optimum conditions was also carried out with a real sample, where organic acids like formic and acetic acids were co-produced simultaneously with green H₂. Based on the preliminary economic analysis, the integrated-hybrid process is an economically viable and promising alternative when it is integrated with renewable energy sources such as solar energy. Full article

In the context of the accelerated global energy transition, power fluctuations caused by the integration of a high share of renewable energy have emerged as a critical challenge to the security of power systems. The goal of this research is to improve the accuracy and reliability of short-term photovoltaic (PV) power forecasting by effectively modeling the spatiotemporal coupling characteristics. To achieve this, we propose a hybrid forecasting framework—GLSTM—combining graph attention (GAT) and long short-term memory (LSTM) networks. The model utilizes a dynamic adjacency matrix to capture spatial correlations, along with multi-scale dilated convolution to model temporal dependencies, and optimizes spatiotemporal feature interactions through a gated fusion unit. Experimental results demonstrate that GLSTM achieves RMSE values of 2.3%, 3.5%, and 3.9% for short-term (1 h), medium-term (6 h), and long-term (24 h) forecasting, respectively, and mean absolute error (MAE) values of 3.8%, 6.2%, and 7.0%, outperforming baseline models such as LSTM, ST-GCN, and Transformer by reducing errors by 10–25%. Ablation experiments validate the effectiveness of the dynamic adjacency matrix and the spatiotemporal fusion mechanism, with a 19% reduction in 1 h forecasting error. Robustness tests show that the model remains stable under extreme weather conditions (RMSE 7.5%) and data noise (RMSE 8.2%). Explainability analysis reveals the differentiated contributions of spatiotemporal features. The proposed model offers an efficient solution for high-accuracy renewable energy forecasting, demonstrating its potential to address key challenges in renewable energy integration. Full article

Hybrid renewable-hydrogen energy systems offer a promising solution for meeting the globe’s energy transition and carbon neutrality goals. This paper presents a new multi-objective dynamic system model for the optimal sizing and simulation of hybrid PV-H₂ energy systems within grid-connected buildings. The model integrates a Particle Swarm Optimisation (PSO) algorithm that enables minimising both the levelised cost of energy (LCOE) and the building carbon footprint with a dynamic model that considers the real-world behaviour of the system components. Previous studies have often overlooked the electrochemical dynamics of electrolysers and fuel cells under transient conditions from intermittent renewables and varying loads, leading to the oversizing of components. The proposed model improves sizing accuracy, avoiding unnecessary costs and space. The multi-objective model is compared to a single-objective PSO-based model that minimises the LCOE solely to assess its effectiveness. Both models were applied to a case study within Robert Gordon University in Aberdeen, UK. Results showed that minimising only the LCOE leads to a system with a 1000 kW PV, 932 kW electrolyser, 22.7 kg H₂ storage tank, and 242 kW fuel cell, with an LCOE of 0.366 £/kWh and 40% grid dependency. The multi-objective model, which minimises both the LCOE and the building carbon footprint, results in a system with a 3187.8 kW PV, 1000 kW electrolyser, 106.1 kg H₂ storage tank, and 250 kW fuel cell, reducing grid dependency to 33.33% with an LCOE of 0.5188 £/kWh. Full article

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H₂ energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER software. In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H₂ energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. The developed novel model allows consideration of the dynamic behaviour of the hybrid PV-H₂ energy system while optimizing its sizing within grid-connected buildings to minimize the levelized cost of energy and maintain energy management across the hybrid system components and the grid in feeding the building load demands. The developed model was applied on a case-study grid-connected building to allow benchmarking of its results versus those from HOMER. Benchmarking showed that the developed model’s optimal sizing results as well as the corresponding levelized cost of energy closely match those from HOMER. In terms of energy management, the benchmarking results showed that the strategy implemented within the developed model allows maximization of the green energy supply to the building, thus aligning with the net-zero energy transition target, while the one implemented in HOMER is based on minimizing the levelized cost of energy regardless of the green energy supply to the building. Another privilege revealed by benchmarking is that the developed model allows a more realistic quantification of the hydrogen output from the electrolyser because it considers the dynamic behaviour of the electrolyser in response to the varying PV input, and also allows a more realistic quantification of the electricity output from the fuel cell because it considers the dynamic behaviour of the fuel cell in response to the varying hydrogen levels stored in the tank. Full article

A global energy transition is crucial to combat climate change, involving a shift from fossil fuels to renewable sources and low-emission technologies. Solar photovoltaic technology has grown exponentially in the last decade, establishing itself as a cost-effective and sustainable option for electricity generation. However, its large-scale integration faces challenges due to its intermittency and lack of dispatchability. This study evaluates, from an energy perspective, the case of hybrid photovoltaic (PV) plants with battery storage systems. It addresses an aspect little explored in the literature: the sizing of battery storage to maintain a steady and constant 24 h power supply, which is usually avoided due to its high cost. Although the current economic feasibility is limited, the rapidly falling price of lithium batteries suggests that this solution could be viable in the near future. Using Matlab simulations, the system’s ability to deliver a constant energy production of electricity is assessed. Energy indicators are used to identify the optimal system size under different scenarios and power setpoints. The results determine the optimal storage size to supply a constant power that covers all or a large part of the daily PV generation, achieving steady and reliable electricity production. In addition, the impact of using setpoints at different time horizons is assessed. This approach has the potential to redefine the perception of solar PV, making it a dispatchable energy source, improving its integration into the electricity grid, and supporting the transition to more sustainable and resilient energy systems. Full article

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management. Full article

A new methodology for hybrid energy systems (HESs) was developed, namely the HY4RES model, tailored for the water sector, covering hybrid energy objective functions and grid or battery support using optimization algorithms in Solver, MATLAB, and Python, with evolutionary methods. HOMER is used for hybrid microgrids and allows for comparison with HY4RES, the newly developed model. Both models demonstrated flexibility in optimizing hybrid renewable solutions. This study analyzed an irrigation system for 3000 m³/ha (without renewables (Base case) and the Proposed system—with PV solar and pumped-hydropower storage to maximize cash flow over 25 years). Case 1—3000 m³/ha presented benefits due to PV supplying ~87% of energy, reducing grid dependency to ~13%. Pumped-hydropower storage (PHS) charges with excess solar energy, ensuring 24 h irrigation. Sensitivity analyses for Case 2—1000—and Case 3—6000 m³/ha—highlighted the advantages and limitations of water-energy management and system optimization. Case 2 was the most economical due to lower water-energy needs with noteworthy energy sales (~73.4%) and no need for the grid. Case 3 led to increased operating costs relying heavily on grid energy (61%), with PV providing only 39%. PHS significantly lowered operating costs and enhanced system flexibility by selling excess energy to the grid. Full article

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN’s powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant’s running expenses. Full article

The purpose of this study was to analyze the economical suitability of numerous on-site renewable electricity generation technologies which were intended to be used in a recently built industrial facility designed and utilized as a warehouse. The facility was located in the vicinity of Riga, Latvia. Data were collected and calculations were performed within the scope of the project “Mitigating Energy Poverty through Innovative Solutions” as part of several planned activities to address the broad spectrum of energy poverty and self-reliance issues in both the residential sector and small-scale industrial facilities. During the project, evaluations of various renewable energy technologies, including PV installations, wind energy installations, battery storage solutions, and hybrid technologies, were carried out. The aim of these evaluations was to develop an electricity production–consumption model for efficient and cost-effective energy use and to reduce greenhouse gas emissions from the test facility. A model was created and subsequent research scenarios were developed based on a payback period instead of the net present value criterion. The project was carried out over several steps to develop a calculation methodology. The open access databases of energy resource providers were used to evaluate statistical data and make forecasts for the analysis of the electricity consumption of companies. MATLAB/Simulink 23/2 was used for the data analysis, and the H-TEC method was employed. This made it possible to modulate the required production capacity as the model allowed for the addition of new modules to modules already installed. The project results proved that despite high initial investment costs, renewable energy sources and efficient storage systems can provide cost-effective solutions and reduce dependence on fossil fuels in the long term. Full article

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid. Full article

A novel hybrid CSP-PV power plant is presented. Instead of the integration used in current hybrid power plants, where part of the PV production is charged into the thermal energy storage system through electrical resistors, the proposed system integrates both PV and thermal solar fields using a high-temperature heat pump. Both the heat pump and the heat engine are based on Brayton supercritical CO₂ thermodynamic cycles. Such integration allows for charging the molten salt storage as if a central tower receiver field supplied the thermal energy, whereas parabolic trough collectors are employed. Unlike conventional hybrid plants, where the storage of PV production leads to a decrease in power injected into the grid throughout the day, the power injected by the proposed system remains constant. The heat engine efficiency is 44.4%, and the COP is 2.32. The LCOE for a 50 MWe plant with up to 12 h of storage capacity is USD 171/MWh, which is lower than that of existing CSP power plants with comparable performance. Although the cost is higher compared with a PV plant with batteries, this hybrid system offers two significant advantages: it eliminates the consumption of critical raw materials in batteries, and all the electricity produced comes from a synchronous machine. Full article