Energy Generation System Research Articles

Reliability Evaluation of Multi-State Solar Energy Generating System with Inverters Considering Common Cause Failures

To ensure the efficient functioning of solar energy generation systems, it is crucial to have dependable designs and regular maintenance. However, when these systems or their components operate at multiple working levels, optimizing reliability becomes a complex task for models and analyses. In the context of reliability modeling in solar energy generation systems, researchers often assume that random variables follow an exponential distribution (binary-state representation) as a simplification, although this may not always hold true for real-world engineering systems. In the present paper, a multi-state solar energy generating system with inverters in series configuration is investigated, in which unreliable by-pass changeover switches, common cause failures (CCFs), and multiple repairman vacations are also considered. Furthermore, the arrivals of CCFs and the repair processes of the failed system due to CCFs are governed by different Markovian arrival processes (MAPs), and the lifetimes and repair times of inverters and by-pass changeover switches and the repairman vacation time in the system have different phase-type (PH) distributions. Therefore, the behavior of the system is represented using a Markov process methodology, and reliability measures for the proposed system are derived utilizing aggregated stochastic process theory. Finally, a numerical example and a comparison analysis are presented to demonstrate the findings.

Artificial Mitochondria Nanoarchitectonics via a Supramolecular Assembled Microreactor Covered by ATP Synthase.

Abiotic stress tends to induce oxidative damage to enzymes and organelles that in turns hampers the phosphorylation process and decreases the adenosine triphosphate (ATP) productivity. Artificial assemblies can alleviate abiotic stress and simultaneously provide nutrients to diminish the oxidative damage. Here, we have integrated natural acid phosphatase (ACP) and ATP synthase with plasmonic Au clusters in a biomimetic microreactor. ACP immobilized on the Au clusters is harnessed to generate proton influx to drive ATP synthase and concurrently supply phosphate to improve phosphorus availability to combat phosphorus-deficiency stress. In tandem with the reactive oxygen species (ROS) scavenging and the photothermal functionality of Au clusters, such an assembled microreactor exhibits an improved abiotic stress tolerance and achieves plasmon-accelerated ATP synthesis. This innovative approach offers an effective route to enhance the stress resistance of ATP synthase-based energy-generating systems, opening an exciting potential of these systems for biomimicking applications.

Design and optimization of zero-carbon integrated energy system for highway service area

Abstract The challenge of achieving dual-carbon targets arises from the issue of high carbon emissions in Chinese highway service areas. To address this, a comprehensive electric-thermal-hydrogen energy system is proposed, consisting of a new energy generation system, heat pumps, electrolyzers, hydrogen fuel cells, energy storage devices, and charging stations. A dispatch model for the integrated energy system is established, with the objective of minimizing the total operating cost while satisfying the constraints of each device and the electric-thermal-hydrogen balance. The model is implemented in MATLAB, and the ideal optimal solution for each decision variable is obtained using the Gurobi solver. The investment and operating costs, as well as the scheduling results for electricity, heat, and cooling, are analyzed. The results indicate that the electrolyzer-fuel cell system can improve the system’s operational stability and flexibility in dispatch optimization. Due to the instability of photovoltaic and wind power devices and the fluctuating nature of loads, energy storage devices are required for peak load shaving in the system. The zero-carbon integrated energy system model can calculate the most economical investment plan based on the load and meteorological conditions of different service areas.

Assessment of the Optimal Energy Generation and Storage Systems to Feed a Districting Heating Network

Assessment of the Optimal Energy Generation and Storage Systems to Feed a Districting Heating Network

Energy efficiency in medium-scale airports: Two Mexican airports as case of study

Reducing energy consumption in the operation of airports has been identified as one of the approaches to achieve the commitments of the countries in reducing their greenhouse gas (GHG) emissions. The first step in this approach is the development of an energy diagnostic. However, multiple practical aspects remain unresolved when applying the existing methodologies to perform energy diagnostics, especially in the case of small and medium-scale airports. Seeking to address these issues, this work presents energy diagnostics of two Mexican international airports so that it can be used to carry out energy diagnostics in other airports with similar characteristics. Emphasis is given to identifying and prioritizing, from a sustainable point of view, the strategies to reduce energy consumption and GHG emissions. The Ciudad del Carmen Airport (CME) is located in a nearshore region with high ambient temperatures (27 °C) and humidities. It was found that in 2019, the CME airport consumed 123 MWh with an average of 577 Wh per passenger, with the HVAC system being the primary energy consumer. Critical strategies for the CME airport include photovoltaic systems and HVAC renovation. In contrast, the Puebla airport (PBC) is located in a region with comfortable ambient conditions (16 °C). In 2019, the PBC airport consumed 61.31 MWh/year and 442 Wh per passenger. The main strategies for PBC include expanding its photovoltaic energy generation system, employee awareness programs, and renewing the vehicle fleet with electric vehicles.

Aerodynamic effects of an offshore floating photovoltaic platform undergoing pitch and heave motions

The offshore floating photovoltaic (OFPV) platform is a renewable energy generation system that has gained attention in recent years due to several advantages. OFPV exhibits higher sensitivity to wind loads than other offshore platforms, owing to its lightweight design and large aerial coverage. In contrast to the fixed photovoltaic systems, the OFPV platforms experience continuous oscillations induced by waves, which significantly influence the aerodynamic characteristics. In this study, the aerodynamic effects of an oscillating OFPV are investigated using the computational fluid dynamics (CFD) simulation method, including the three-dimensional effects, the Reynolds number effects, and the aerodynamic loads with various kinematic parameters. Some major findings are as follows. Firstly, a large-scale tip vortex exists when OFPV oscillates in pitch and heavy directions, which cannot be detected in two-dimensional simulations. Consequently, the lift coefficient, CL, and the drag coefficient, CD, are highly overestimated in 2-D simulation. Secondly, CL and CD decrease significantly as the Reynolds number increases for cases where Re < 106 while there is only a slight decrease for Re > 106. Thirdly, the aerodynamic forces are highly related to the amplitude of the pitch motion and the phase lag between the pitch and the heave motions. A fitting formula is proposed to calculate CL and CD based on the maximum angle of attack. Lastly, the evolution of the flow fields around the oscillating OFPV is analyzed. The separation of the leading edge mainly causes the aerodynamic force.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

Assessment and selection of a micro-hybrid renewable energy system for sustainable energy generation in rural areas of Zambia

This research focuses on the implementation of micro-hybrid renewable energy systems (MHRES) in rural Zambia, where a large part of the population lacks adequate electrical infrastructure. Using a multi-criterion decision-making approach (MCDM), specifically the Technique for Preference Order by Similarity to an Ideal Solution (TOPSIS) with an entropy-weighted method, this research aims to identify an optimal MHRES solution to replace dependence on wood and fossil fuels. Analysis is carried out in 14 scenarios, taking into account technical, economic, social, and environmental criteria. The results show that a biogas-solar photovoltaic integrated with Energy System Power Safe Storage Battery Systems 190 (SBS 190) is the most visible option, demonstrating a relative proximity of 0.98556. In contrast, a diesel generator-solar PV system is the least favorable, with a relative proximity of 0.02576. Validation of the results through a convergent distance assessment (CODAS), an evaluation based on the distance from the average solution (EDAS), and the weighted aggregate sum of the product assessment (WASPAS) confirm its robustness. The proposed MHRES solution is designed to meet the energy needs of rural communities using local biomass and solar resources, free of obstacles by high buildings and structures. This research highlights the potential of MHRES to meet energy needs and promote sustainable development in developing countries, providing valuable insights for policymakers and energy stakeholders.

A novel improved harbor seal whiskers algorithm for solving hybrid dynamic economic environmental dispatch considering uncertainty of renewable energy generation

The originality of this work is introducing a novel Improved Harbor Seal Whiskers Algorithm (IHSWA) as an innovative and improved optimizer for solving complex hybrid dynamic economic-environmental dispatch (HDEED) considering uncertainty of renewable energy generation (REG) and various system constraints. IHSWA is implemented by hybridizing opposition-based algorithm (OBA) and local search algorithm (LSA) approaches to enhance the algorithm's search efficiency and overcome the challenges of other counterpart approaches. IHSWA has an optimal balancing feature between searching modes (exploring and exploiting modes). This feature makes the proposed algorithms more advanced than their counterpart optimizing techniques. The HDEED is modeled using a non-convex and non-smooth objective function, considering various equality and inequality constraints such as ramp rate bounds (RRLs), valve-point effects (VPE), production limits (PL), spinning reserves (SRLs), prohibited operating zones (POZs), and power balance (PB). The proposed approach is verified on a hybrid modified IEEE 57-bus 7-unit power system incorporating solar and wind energy generation. The proposed approach is compared with various modern optimization techniques, such as the traditional Harbor Seal Whiskers Algorithm (HSWA), the Lemurs Optimization Algorithm (LOA), the Nutcracker Optimization Algorithm (NOA), and the Artificial Hummingbird Algorithm (AHA). The incorporation of REG sources produces a significant reduction in operative costs and a remarkable decline in emissions. The convergence rate of the proposed approach indicates that the novel hybrid algorithm converges rapidly towards the optimal solution, achieving the best global optimum solution. The ANOVA test and the Tukey test are used to analyze the results statistically. The IHSWA approach shows superior advanced features with a best fuel cost of 1640.5890 $ and a minimum std. value of 1708.4895. While for emission objective minimizing, IHSWA attained a minimum emission cost and std. value of 1080.1629 kg and 1777.0770, respectively. The statistical analysis of the results approves the stability, reliability, and consistency of the proposed strategy with significant convergence. The appreciated contribution of this work is introducing a novel hybrid approach for solving HDEED efficiently, incorporating REG, and considering various system constraints. IHSWA has advanced features over its counterpart approaches. The contribution holds specific importance for the economic development of countries. It creates the path for increased self-sufficiency by reducing their need for fossil fuels for energy production. The study emphasizes the transition from being reliant on fossil fuels to the development of sustainable urban economies.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Clean energy technologies and energy systems for industry and power generation: Current state, recent progress and way forward

This vision article accompanies a Special Issue of Applied Thermal Engineering dedicated to the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series held during 2022, including the 5th SEE SDEWES Conference Vlore, 3rd LA SDEWES Conference Sao Paulo, and 17th SDEWES Conference Paphos. The article discusses a selection of papers presented at these conferences and selected for publication in Applied Thermal Engineering, all connected by a focus on clean energy technologies and systems for the advancement of sustainable thermal energy systems. This research area covers a wide range of technologies but is primarily focused on the power generation sector, energy storage and utilization, efficiency improvements, sustainable technical solutions, and the facilitation of the robust integration of renewable energy resources into wider energy systems. Additional work on previously established research topics is also present, but new directions have crystallized, especially research into thermal management for power devices and microelectronics. Based on the recent contributions to the research field presented in the included papers, this vision article discusses the development and application of cutting-edge technological solutions and identifies the challenges that require focused attention. Current shortcomings of developing and established technologies will be developed by the underlying fundamental research, reduction of prohibitively high costs, expansion of operating conditions, and the inclusion of advanced control approaches, sophisticated combinations of renewable energy sources, and modeling tools for system investigations. Scientists, researchers, and engineers can benefit from the summary of the topics at the forefront of the research field and find guidelines and ways forward for future research activities.

Harvesting Sunlight: The Promise of Agro-Photovoltaic Fusion Systems for Sustainable Agriculture and Renewable Energy Generation

Harvesting Sunlight: The Promise of Agro-Photovoltaic Fusion Systems for Sustainable Agriculture and Renewable Energy Generation

Zoning of Degraded Areas Suitable for Implementation of Renewable Energy Generation Systems: Systematic Review

Objective: The objective of this study is to investigate and carry out a systematic review of the literature, with the aim of delving deeper into the topic of zoning and selection of areas for the implementation of renewable energy generating plants. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. The elaboration of the research protocol in four stages: elaboration of questions, choice of databases, definition of the search strategy and inclusion/exclusion criteria, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises the choice of databases and definition of the search string based on the research protocol. Data collection was made by searching Scopus, Science Direct, Web of Science and Energy Citations Database - OSTI. Results and Discussion: The results obtained revealed that 309 studies met the research protocol, of which only 29 studies, after reading the abstract, were ready for the full article reading stage. Seven of these were accepted as relevant to the objective, being classified as accepted and participating in the information gathering to meet the proposed objective. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of georeferencing. These implications may include public and private enterprises that aim for a less environmentally aggressive methodology for selecting sites for renewable energy generation. Originality/Value: This study contributes to the literature by showing the most relevant points and the main gaps in current selection methodologies. The relevance and value of this research are highlighted by representing a new way of thinking about choosing new locations for power plants.

A piezoelectric vibration sensor with excellent performance based on Bi12SiO20 crystal for high-temperature applications

High-temperature piezoelectric vibration sensors play a crucial role in the accurate monitoring of the dynamic mechanical conditions in aerospace, automotive, and energy generation systems. However, the use of conventional piezoelectric materials in high-temperature environments is restricted owing to their limited Curie temperatures. In this study, we grew a piezoelectric crystal Bi12SiO20 (BSO) with the crystal cut optimized for high longitudinal piezoelectric coefficient and low piezoelectric crosstalk behaviors. Subsequently, a compression-type piezoelectric vibration sensor utilizing the BSO bulk crystal was developed and fabricated for structural health monitoring under high temperatures. The impact of pre-tightening torques on the sensor performance was investigated. Moreover, the sensor performance was analyzed under temperatures up to 650 °C. The BSO-based sensor exhibited an average sensitivity of ∼3.89 pC/g between 25 and 650 °C under 160 Hz frequency, with a variation of 5.5%. Additionally, the BSO-based sensor demonstrated ultra-stable sensitivity at 600 °C, highlighting its strong sensing capabilities and reliability under high temperatures. Thus, the BSO-based vibration sensor is a promising option for structural health monitoring applications under high temperatures.

COMBINATION OF PVT-COLLECTORS WITH USING OF CONTROLLED CONNECTIONS

Abstract. The paper shows the energy characteristics and efficiency of photovoltaic thermal solar collectors. The possibility of combining photovoltaic thermal collectors in series and in parallel is considered. Calculations have been made. The connection of the thermal and photovoltaic parts of the collectors using a switching device is shown, and the possibility of dynamic connection of both the thermal and electric parts in one energy-generating system is shown. Emphasis is placed on the possibility of integrating a photovoltaic thermal collector with an automatic control system, for effective operation and joint thermal and electrical regulation in autonomous and grid modes. Conclusions are made.

Hybrid system for electrical energy generation for the sustainable functioning of the transport infrastructure facility

Hybrid system for electrical energy generation for the sustainable functioning of the transport infrastructure facility

Iot Based Smart Renewable Energy Generation and Irrigation System with Moisture Detection

This research introduces an innovative IoT-based system combining renewable energy with smart irrigation. It uses solar panels and wind turbines to power an irrigation setup while feeding excess energy back to the grid. Soil moisture sensors and machine learning algorithms dynamically adjust watering schedules, optimizing water use and preventing over-irrigation. The system offers remote monitoring and control via mobile devices, enhancing resource efficiency and sustainability. Real-world tests show improved crop yields, reduced resource consumption, and increased energy sustainability, presenting a scalable solution for sustainable agriculture.

ANALISIS KECEPATAN KELUARAN ANGIN KONDENSOR AC TERHADAP JENIS–JENIS BLADE GENERATOR LISTRIK DENGAN KOMPUTASI FLUIDA DINAMIK

Electrical energy is widely used for household and industrial purposes, so the need for electrical energy will continue to increase every year. Therefore, it is necessary to develop an electrical energy generation system that comes from renewable energy such as wind. Currently, not much is known about the use of wasted wind from air conditioner (AC) condensers. The blow of wind can be used to produce electrical energy, so it is necessary to make wind-powered electric generator blades from the condenser output. The blade that will be designed is a three-blade model, so it is hoped that it can rotate the generator optimally. The purpose of this research is to design a three-blade type that can optimize the rotation of the electric motor/generator and get the maximum number of revolutions with a three-blade design because this design provides better stability and more efficiency. One way of making the design is to make simulation variations with computational fluid dynamics (CFD) in order to find out the right blade design to rotate the generator and find out the wind blowing from the air conditioner (AC) condenser that crosses the blade. In this study, three simulations were carried out to get maximum results, namely 390 rpm at 42 °C with a velocity of 10.7 m/s and a pressure of 34.4 Pa. To be able to determine the effectiveness of the AC condenser wind output speed with a three-blade design.

A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting

Growing energy demand and increasing environmental challenges underscore the importance of precise forecasts for photovoltaic (PV) operations in renewable energy generation systems. At this stage, it is mainstream to combine both temporal and spatial factors to forecast PV power generation. However, there are fewer studies that consider factors at very large spatial scales. This paper proposes Hybrid Dual Stream ProbSparse Self-Attention Network (HDSPAN), a novel spatial–temporal photovoltaic power forecasting network architecture that can solve the above limitations. The model implements an encoder–decoder approach that extracts the required spatial–temporal information through a dual stream distilling mechanism. In addition, the ProbSparse self-attention mechanism is employed to improve model efficiency and reduce repetitive and redundant information processing. The hyperparameters are optimized using Tree-structured Patzen estimator to improve forecasting outcomes. This paper demonstrates the effectiveness of spatial–temporal PV forecasting by using ERA5 reanalyzed PV data as a case study. Our results show that the HDSPAN model achieves a 10% higher forecasting accuracy compared to the baseline models, significantly advancing PV power forecasting.

Design of a reliable electrical photovoltaic generator based on power converters control

This work proposes a renewable energy generation system that operates with a similar behavior as a synchronous generator in terms of reliability in the power supply and inertial response properties against power changes and grid voltage disturbances. The main contributions of this work are: (1) the renewable energy variability managing by means of a battery energy storage strategy, whose charge and discharge are determined by a forecasted-based power adjustment algorithm, (2) the dynamical modeling of an inverter that includes physical inertia and its corresponding response to power and voltage variations, and (3) the synthesis of a nonlinear optimal control for the inertia-based inverter system where the reactive power injection is regulated. Simulation results for a photovoltaic-based generator are presented to demonstrate the effectiveness of the proposed methodologies.