Utilization Of Energy Resources Research Articles

Performance prediction of a supercritical CO2 Brayton cycle integrated with wind farm-based molten salt energy storage: Artificial intelligence (AI) approach

Using energy storage technologies in combination with renewable energy sources can improve power generation efficiency and reliability. Here, we predict the performance of a wind farm-based molten salt energy storage system combined with a supercritical CO2 Brayton cycle (s-CO2BC) with the aid of artificial intelligence (AI). For this purpose, the Grasshopper Optimization Algorithm (GOA) and Particle Swarm Optimization (PSO) are applied to train a Multilayer Perceptron Artificial Neural Network (MLP-ANN). The proposed AI approaches model the complex interactions between the wind farm, the molten salt energy storage, and the s-CO2BC components. Integrated system simulations under various operating conditions are used for training the performance prediction model. Power generation, energy storage, and system efficiency can all be accurately predicted by a trained AI model. In addition to providing valuable insight into the optimal operation and control strategies of the integrated system, the proposed approach enables the efficient utilization of renewable energy resources and energy storage to generate sustainable power. This study contributes to the development of AI-based optimization approaches and predicts the performance of integrated renewable energy systems, resulting in improved grid integration and stability. According to the results, the PSO method yields average absolute percentage deviations (AAPDs) between 0.47% and 0.51% across different parameters, while GOA yields AAPDs between 0.05% and 0.27%.

A new type of velocity averaging tube vortex flow sensor and measurement model of mass flow rate

Accurate mass flow rate measurement of fluid is one of the key technologies to ensure product quality and utilization of energy resources. However, the accuracy of traditional combined measuring devices is limited. In this paper, a new type of velocity averaging tube vortex flow sensor (VATV) is designed, and the mass flow rate measurement model is proposed and analyzed to achieve the high precision measurement of mass flow rate. The size, shape and structure of the VATV are designed in this study and there are no moving parts inside the integrated VATV. The method of double differential pressure is proposed to achieve the measurement of the average differential pressure signal and differential pressure fluctuation signal. Then, the method of Empirical Wavelet Transform (EWT) is used to extract the vortex shedding frequency from the differential pressure fluctuation signal. It indicated that the proposed VATV achieves an accuracy of ±0.50%, and the repeatability is also lower than 0.07%. A remarkable improvement of accuracy is achieved compared to the original combined measurement devices. The proposed sensor shows good prospect for the detection of gas-liquid two-phase flow such as wet stream due to the feature of strong anti-interference, high stability and reliability.

Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence

The integration of wind power into the electricity grid faces a significant challenge due to the unpredictable nature of wind speed fluctuations. Therefore, ensuring precise short-term predictions of power output from wind turbines is vital for effectively incorporating wind energy into the grid and proficiently managing power systems. In this paper, an Artificial Neural Network (ANN)-based approach for the short-term power forecasting of wind turbines based on a swarm intelligence algorithm is proposed. Also, a simulation study of the wind power real system at different wind speeds is presented by using MATLAB/Simulink. The swarm intelligence algorithm is employed to optimize the forecasting model parameters. The performance of the proposed algorithm is evaluated using real data from a wind farm in Turkey. Three distinct methodologies are utilized to process the data efficiently: ANN, ANN with Firefly Algorithm (ANN-FA), and ANN with Particle Swarm Optimization (ANN-PSO). The results demonstrate that the swarm intelligence algorithm outperforms traditional forecasting methods, such as statistical approaches and machine learning techniques, in terms of accuracy and reliability. Furthermore, the computational efficiency of the algorithm is examined, and it is shown that the swarm intelligence-based approach offers a practical solution for real-time forecasting applications. The algorithm’s scalability and adaptability make it suitable for large-scale wind farms with multiple turbines, as it can handle the inherent variability and uncertainties associated with wind power generation. The proposed method offers an accurate and reliable forecasting tool that can assist power system operators and energy market participants in making informed decisions for the efficient utilization of wind energy resources.

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

AbstractEnergy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).

Transient stability enhancement in renewable energy integrated multi-microgrids: A comprehensive and critical analysis

Multi-microgrids offer various benefits including the decreased overloading of a single microgrid, more options during faulty conditions, and more utilization of renewable energy resources. However, the implementation of a multi-microgrid brings the challenges such as power system complexity, interconnection issues, bidirectional power flow management, and power flow balancing. In the presence of these challenges, the power flow stability of the multi-microgrids is a challenging problem. In this context, this study evaluates a transient stability analysis model in multi-microgrids using solar photovoltaics, wind power, and a unified power flow controller (UPFC). UPFC offers a more robust power flow control strategy compared with other flexible alternating current transmission systems (FACTS) devices. First, a multi-microgrid structure consisting of the two microgrids was designed in DIgSILENT PowerFactory software. Second, the load flow calculation was performed in the absence and presence of UPFC, short circuit fault, and grid connection. Third, the electromagnetic transients (EMT) simulation was performed for all these situations. The results exhibited that the UPFC would offer significant power flow stability in the multi-microgrids. It was observed that the UPFC resulted in more transient stability in the microgrid where it was located. However, it improved the power flow quality at all the locations in the multi-microgrids. In addition, UPFC offered significant transient stability during the fault occurrence. The results offer various insights into power flow management in multi-microgrids.

Artificial Neural Network with 3-Port Dc-Dc Converter Based Energy Management Scheme in Sustainable Energy Sources

In micro grids, energy management is referred to as an information and control system that offers the essential functionality to ensure that the energy supply from the generation and distribution systems occurs at the lowest possible operational cost. Energy management systems (EMS) support distributed energy resource utilization in micro grids, especially when variable generation and pricing are present. In this paper, an Artificial Neural Network (ANN)-based energy management approach for a hybrid wind, solar and Battery Storage System (BSS) is presented. To sustain the DC voltage, a 3 Port DC-DC Converter is also proposed. While renewable energy systems have numerous advantages, one of the challenges they face is the intermittency of power generation, leading to fluctuations in the power supply to the grid. Therefore, EMS aims to reduce these variations. Another goal is to maintain the battery state of charge (SOC) within the allowed ranges to extend the battery life. The implementation is carried out in Simulink/Matlab platform. To demonstrate the efficacy of the suggested approach, we compare the Total Harmonic Distortion (THD) of the proposed controller (1.52%) with that of conventional controllers, including the ZSI-based PID controller (3.05%), PI controller (4.02%), and FO-PI (3.32%) controller.

(Digital Presentation) Understanding the Electrochemical Stability of Potential Current Collectors in Zinc Sulfate Electrolyte for Rechargeable Aqueous Zinc Ion Battery Application

The need for clean and sustainable energy sources has witnessed a sudden upsurge in recent years owing to rising environmental degradation and climate disruption brought on by an overdependence on fossil fuels [1-3]. Electrochemical energy conversion technology is a crucial complement to the storage and on-demand utilization of renewable energy resources [4-6]. Rechargeable aqueous zinc ion batteries (AZIBs) have gained a significant research upsurge in recent times owing to their attractive potential for large-scale energy storage applications. In addition to low cost and naturally abundant raw materials, AZIBs technology offers competitive electrochemical performance and compatibility with water-based electrolyte systems, thereby eliminating the safety concerns associated with prevalent lithium-based battery technology. Moreover, multivalent AZIBs offer the opportunity to attain high energy and power density by permitting multiple electron transfers during reversible electrochemical operations [7]. In the context of AZIBs, significant research efforts are being actively pursued to develop high energy density cathode materials and to address the issue of Zn dendrite formation [8]. However, the selection of stable and low-cost current collectors is equally important as it serves as a bridge between the battery components and the external circuit, thereby influencing the battery capacity and its rate capability [9-10]. In this work, we have analyzed the electrochemical behavior of potential current collectors that are used globally in battery applications, namely Ni, Al, carbon paper, Cu mesh, graphite, and stainless steel in near-neutral aqueous ZnSO4 electrolyte (pH value = 5-6). The electrochemical stability of different current collectors has been investigated using linear sweep voltammetry and chronoamperometry techniques for their application as anode and cathode collectors. Scanning electron microscopy analysis has also been performed to understand the sign of corrosion post-electrochemical study. With stability up to 2.3 V w.r.t. Zn/Zn2+, Ni has proved to be the most corrosion-resistant current collector among the tested collectors on the cathode side. Although Zn itself is sufficiently stable at the anode side than any other current collector under the ZnSO4 electrolyte environment, Ni has shown considerable stability. Nonetheless, understanding the electrochemical stability of current collectors for AZIBs is a vital step in their design and future practical applications.

Investigation of a grid-integrated hybrid energy system for residential and electric vehicle (3-wheeler) loads under schedule grid outage

Due to the global energy crisis and the limited availability of conventional fuels and their reserves in certain areas, the utilization of renewable energy resources is significantly paramount for sustainable development. Grid disruptions have been discovered in areas where demand for electricity has surpassed supply. The purpose of this work is to size a grid-integrated hybrid renewable energy system (HRES) during a scheduled power outage. The HRES model was created to meet the requirements for residential load as well as EV charging load at midnight when the residential load is lower. Using the HOMER Pro software tool, the HRES system is developed to satisfy the residentialload demand of 1940 kWh/day and the electric vehicle (3-wheeler) charging load requirement of 645 kWh/day, respectively. This study examined the effects of grid purchasing capacity and grid scheduling outages on techno-economic and environmental performance. The results show that the most optimal configuration among the various configurations is the PV/WT/Batt with grid-integrated system, which has a COE of 0.0714 $/kWh, an NPC of $1,822,653, and emits 164,812 kg/yr of CO2 into the environment. The optimized system consists of a 377 kW PV module, a 2 kW wind turbine, an 836 kWh battery bank, and a 237 kW inverter. In addition, the impacts of sensitivity of the grid purchase and selling capacity, and the different tariffs on the techno-economic indicators of the HRES have been investigated. The daytime grid outage lowers environmental emissions and increases the renewable contribution compared to the grid outage at night. Reducing the grid’s purchase capacity while increasing the share of renewable energy provides significant advantages. The stand-alone PV/Batt has a higher COE (0.26 $/kWh) than the PV/Batt system (0.072 $/kWh) with the net metering option connected to the national grid.

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

AbstractWith the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

Optimal FPGA memory allocation for image processing

In the field of computer vision, Field Programmable Gate Array (FGPA) limited de on-chip memory is difficult to meet the power, size and other requirements. To address this phenomenon, the study constructs a partitioning algorithm to achieve a balance between energy consumption and resource utilisation based on the analysis of memory resource allocation, overall power consumption and resource utilisation from the perspective of image processing technology. The power consumption of the balancing algorithm is lower compared to the optimised utilisation algorithm HLS tool, with both Block Ramdom Access Memory (BRAM) power consumption taking the value of 0.005; the dynamic power consumption takes the value range of 0.014–0.082. Compared to the High Level Synthesis (HLS) tool, the overall power consumption of the balancing algorithm and the optimised utilisation algorithm is significantly lower, with the values of 0.251 and 0.252 respectively, both with a reduction rate of approximately 30%. The accuracy rate of the proposed memory optimisation allocation algorithm is the highest among the four memory optimisation allocation algorithms and strategies on all three types of target scales. FPGA memory optimisation allocation strategy can guarantee to have lower power consumption while satisfying the same resource occupancy, and the model has in-depth application value in visual image vision technology.

Bio‐inspired Design of Bidirectional Oxygen Reduction and Oxygen Evolution Reaction Molecular Electrocatalysts

AbstractThe proper utilization of renewable energy sources has emerged as a major challenge in our pursuit of a sustainable and carbon‐neutral energy landscape. Small molecule activation is a key component for proper utilization of renewable energy resources, where O2/H2O redox couple is reckoned to be a potential game changer. In this regard, electrocatalytic oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) have become the prime interest of catalyst designers. Typically, these ORR and OER electrocatalysts are developed distinctly; however, very soon, the requirement of a bidirectional ORR/OER electrocatalyst becomes obvious for practical applicability and rapid energy transduction purposes. A bidirectional catalyst is defined as a catalyst capable of driving a redox reaction in opposing directions. This review has portrayed the development of enzyme structure‐inspired design of molecular bidirectional ORR/OER catalysts. The strategic incorporation of secondary and outer coordination sphere features has significantly enhanced the performance of these catalysts, which can be monitored via the key catalytic parameters. These bifunctional OER/ORR catalysts are vital for metal‐air battery and fuel cell applications and appropriately poised to lay the foundation for an efficient, economical, and eco‐friendly pathway for sustainable energy usage with the rational assembly of energy converting and storage devices.

Optimization of Wind Farm Design Layout based on the Non-Uniform Spacing Algorithm between Wind Turbines

The optimization of wind turbine layout is an important step during the design phase of wind farms, which directly influences the overall power performance and the profitability of the wind plants. The spacing distance between the wind turbines, which is often inconsistent in the prevailing wind direction and other directions, is the critical factor of wind turbine array layout optimization. With the considerations of the wind turbines’ safety in operation and the maximum utilization of wind energy resources comprehensively, an elliptic-constrained wind turbine layout optimization model is proposed in this paper. The stochastic optimal algorithm including the constraint checking and random searching processes is established to conduct the iterative calculation. It is proved that the annual energy production of the elliptic-constrained model with non-uniform spacing has increased in comparison with the conventional method. The proposed approach provides an effective engineering solution for improving the space utilization and the power output estimation of the wind farm.

Deep learning to design Z-FFR device models

Z-Pinch fusion centre, encased by a fission envelope, serves as an individual neutron source. It can expeditiously catalyze fission reactions in 238U and 232Th nuclear materials, which are hard to use in current commercial nuclear reactors. This is the essence of the Z-Pinch Driven Fusion-Fission Hybrid Reactor (Z-FFR). The fusion core acts as a stand-alone neutron source, efficiently driving fission reactions in nuclear energy materials that are difficult to use in existing commercial nuclear reactors, such as 238U and 232Th. Then it can deliver enormous amounts of energy in a stable and controlled manner. This new type of reactor uses the fact that the fission discharges energy (∼200 Megaelectronvolts) and the neutrons’ number released is much greater than that released when the fusion discharge energy (∼17 Megaelectronvolts). Moreover, the neutrons’ number is released to achieve energy amplification and neutron amplification, significantly makes it less difficult in implementing fusion technology applications, and increases the utilisation of nuclear energy resources by more than one order of magnitude. The Z-FFR has a complex design and covers a wide range of physical processes. The use of deep learning to design the device model allows for a more closely engineered model. Deep learning allows the model design to be decomposed, the Z-FFR design data flow to be analysed and optimised, and the tedious physical process to be turned into a deep learning network layering so that we can obtain an accurate physical model. The deuterium-tritium combustion depth parameters obtained by deep learning reach around 30%, demonstrating the ability to achieve fusion self-sustained combustion.

Agricultural Greenhouses: Resource Management Technologies and Perspectives for Zero Greenhouse Gas Emissions

Resource management in agriculture is considered a pivotal issue because greenhouse farming and agriculture-related activities generate about 10–29% of all global greenhouse gas emissions. The problem of high greenhouse gas emissions is still unresolved due to the rapid expansion of arable land to meet global food demand. The purpose of this systematic literature review was to generate new perspectives and insights regarding the development of resource management and optimized environments in greenhouses, thereby lowering energy requirements and CO2 emissions. This review sought to answer what technologies and inventions could be used to achieve zero greenhouse gas emissions through efficient energy-saving mechanisms while considering their technical and economic viability. The synthesis of the findings led to several themes which included energy-saving techniques for greenhouses, systems that reduced unfavorable external conditions and renewable energy systems. Other themes identified regarded energy storage systems, systems for managing conditions in greenhouses, carbon capture and storage, and factors influencing the performance of different technologies to enhance resource management and ensure zero carbon emissions. The findings also revealed various technologies used in the design of energy-saving techniques in greenhouses including proportional–integral–derivatives (PID), fuzzy, artificial neural networks, and other intelligent algorithms. Additionally, technologies that were a combination of these algorithms were also examined. The systems that reduced unfavorable external conditions included the use of insulation panels and intelligent shading systems. Greenhouse covers were also optimized by smart glass systems, sensors, Internet of Things (IoT), and Artificial Intelligence (AI) systems. Renewable energy systems included PV (solar) panels, wind turbines, and geothermal electricity. Some of the thermal energy storage systems widely studied in recent research included underground thermal energy storage (UTES) (for seasonal storage), phase-change materials (PCMs), and water tanks, which are used to address short-term shortages and peak loads. The adoption of the various technologies to achieve the above purposes was constrained by the fact that there was no isolated technology that could enable agricultural producers to achieve zero energy, zero emissions, and optimal resource utilization in the short term. Future research studies should establish whether it is economical for large agricultural companies to install smart glass systems and infrastructure for slow fertilizer release and carbon capture in greenhouse structures to offset the carbon footprint.

Performance Analysis and Comprehensive Evaluation of Solar Organic Rankine Cycle Combined with Transcritical CO2 Refrigeration Cycle

In order to achieve efficient utilization of solar energy resources, this study combines the trans-critical organic Rankine cycle (ORC) power cycle (TORC) with the trans-critical CO2 refrigeration cycle (TCO2). Additionally, a comprehensive three-level index decision evaluation system is developed based on system safety and environmental protection, thermodynamics, and techno-economic performance. The evaluation focuses on typical medium- and high-temperature solar energy applications and considers six organic working gases. The evaluation results demonstrate that the R600 + CO2 solution outperformed the others. This solution achieved a maximum net output power (Pnet) of 1531.31 kW and 2306.43 kW, a maximum coefficient of performance (COP) of 3.16, a predicted payback period of 2.651 years and 2.033 years, and a benefit–investment ratio of 4.533 and 5.773.

Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations in managing an increasing demand and predicting future pricing. This paper presents a novel approach to address the limitations of existing methods for determining and controlling demand response (DR) in the context of dynamic pricing systems for sustainable energy development. By leveraging actor–critic agent reinforcement learning (RL) techniques, a dynamic pricing DR model is proposed for efficient energy management. The model’s learning framework was trained using DR and real-time pricing data extracted from the Australian Energy Market Operator (AEMO) spanning a period of 17 years. The efficacy of the RL-based dynamic pricing approach was evaluated through two predicting cases: actual-predicted demand and actual-predicted price. Initially, long short-term memory (LSTM) models were employed to predict price and demand, and the results were subsequently enhanced using the deep RL model. Remarkably, the proposed approach achieved an impressive accuracy of 99% for every 30 min future price prediction. The results demonstrated the efficiency of the proposed RL-based model in accurately predicting both demand and price for effective energy management.

Improving the Performance of Solar Thermal Energy Storage Systems

In recent times, renewable energy resources have been greatly researched because of the increasing concern to minimize global warming and meet energy demands. Energy storage systems have become useful tools for sustainability and meeting energy needs. Solar energy has proven in recent times to be the primary and most prevalent option due to its environmental friendliness, availability, and minimal pollution. Effective utilization of available energy resources has led to developing new alternative energy devices like the solar thermal energy storage system (STESS) with a solar energy source. Solar thermal energy systems are efficient systems that utilize solar energy to produce thermal and electrical energy. This review aims to give a detailed overview of solar TESS, different TES application systems, and effective methods of increasing the system performance to provide energy during deficient times. The various classifications, basic components, the principle of operation, application areas of STESSs, prospects, and extensive reviews on these aspects have also been discussed in this review. The different factors to be considered geared towards meeting energy demands and increasing the efficiency of solar TES systems have been duly detailed. This review is a single manuscript with a detailed overview of STESS, the principle of operation and components of STESS, thermal energy storage materials, a description of different application systems, and a discussion of factors responsible for improving the system efficiency.

Fundamental theory on multiple energy resources and related case studies

Herein, I methodically optimize a distributed energy resource in terms of the production, management, utilization, and/or transaction of renewable energies during the deployment process. I deliver a theoretical mathematical model that allows users to visualize three critical output functions of their energy preference, including output power, energy economy, and carbon footprint. The model delivers three eigenstates derived by a power utility matrix (PUM) model. PUM transforms three-input parameters (3i) into three-output functions (3o) through 3i3o-transformation. It is ubiquitous, and its systematic characterization is discussed. Moreover, I discover a mathematical conversion relationship translating energy generation to carbon emissions. Various case-studies demonstrate the optimal energy resource utilization. Furthermore, an energy blockchain approach is employed for microgrid design, development, and carbon reduction. Finally, the authors demonstrate the energy–matter conversion relationship that improves carbon emissions for energy production, reducing the beta factor of carbon emissions to 0.22 kg/kilowatt hour for carbon peak and to zero for carbon neutrality.

Optimizing Wind Farm Layouts with Genetic Algorithms (Enhancing Efficiency in Wind Energy Planning and Utilization in Bosnia and Herzegovina)

This paper proposes a genetic algorithm-based approach to optimize wind farm layouts in Bosnia and Herzegovina, a country with a high potential for wind energy production. The primary objective is to maximize the total power output derived from the wind farm while concurrently minimizing the wake effects resulting from turbine interactions. This balance is pivotal in ensuring the optimal utilization of wind energy resources. In this study, three different wind data scenarios are considered: a single wind direction with overall average velocity, the most prevalent wind direction with overall average velocity, and an all-encompassing wind direction analysis incorporating a weighted objective function. The results of the study suggest that the genetic algorithm is highly effective in identifying optimal solutions across each scenario. This serves as a testament to the algorithm's accuracy, robustness, and applicability in tackling real-world problems, thereby marking a significant step forward in the realm of wind farm optimization. The limitations of this research include the use of a simplified wake model and a fixed turbine type. The implications of this research include the potential for increasing the efficiency and profitability of wind farms in Bosnia and Herzegovina, as well as informing future research on more complex and realistic optimization problems.

Design and fabrication of TiO2-based dye sensitized solar cells using plant-derived organic dyes

The utilization of solar energy, an abundant and renewable resource, shows great promise. Various technologies have been employed to capture this vast energy potential. In this study, the effectiveness of natural dye-sensitized solar cells (DSSCs) was explored through experimental and computational methods. Organic dyes derived from specific plant species were examined, with a solvent mixture of ethanol, methanol, and tetrahydrofuran used for extraction. A UV–vis spectrophotometer was utilized to measure the sensitizers’ optical characteristics. The study employed density functional theory (DFT), implemented in Gaussian 09 W software, to perform computational calculations. The B3LYP method and a basis set of 6-31G++(d,p) were chosen for optimizing the geometries and energetics of dye molecules. The DFT results indicated that the dye molecules had a bandgap in the range of 2.16–2.38 eV. The photochemical performance of all four fabricated DSSC-based devices was evaluated to be between 0.33% and 1.04%, and the Euryops pectinatus sensitizer demonstrated the highest efficiency of 1.04% among the studied sensitizers.