Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

Renewable Polymers in Biomedical Applications:From the Bench to the Market

Polymers from renewable resources have been used for a long time in biomedical applications and found an irreplaceable role in some of them.Their uses have been increasing because of their attractive properties,contributing to the improvement of life quality,mainly in drug release systems and in regenerative medicine.Formulations using natural polymer,nano and microscale particles preparation,composites,blends and chemical modification strategies have been used to improve their properties for clinical application.Although many studies have been carried out with these natural polymers,the way to reach the market is long and only very few of them become commercially available.Vegetable cellulose,bacterial cellulose,chitosan,poly(lactic acid)and starch can be found among the most studied polymers for biological applications,some with several derivatives already established in the market,and others with potential for such.In this scenario this work aims to describe the properties and potential of these renewable polymers for biomedical applications,the routes from the bench to the market,and the perspectives for future developments.

Characteristics of Biopellets Manufactured from Various Lignocellulosic Feedstocks as Alternative Renewable Energy Sources

The increased valorization of renewable and cost-effective lignocellulosic feedstocks represents a viable,sustainable,and eco-friendly approach toward the production of biopellets as alternative energy sources.The aim of this research work was to investigate and evaluate the feasibility of using various lignocellulosic raw materials,i.e.,raru(Cotylelobium melanoxylon),mangrove(Rhizophora spp.),sengon(Paraserianthes falcataria),kemenyan toba(Styrax sumatrana),oil palm(Elaeis guineensis),manau rattan(Calamus manan),and belangke bamboo(Gigantochloa pruriens)for manufacturing biopellets with different particle sizes.The raw materials used were tested for their moisture content,specific gravity,ash,cellulose,and lignin content.In addition,thermal analyses,i.e.,calorific values,thermogravimetric analysis(TGA),and differential scanning calorimetry(DSC),were performed.The following properties of the biopellets produced were investigated:moisture content,volatile matter,ash content,fixed carbon,density,and thermal analyses.Based on an analysis of the raw materials,raru had the lowest moisture content(12%)and ash content(1.5%)and the highest specific gravity(1.2).Markedly,palm oil stem had the highestα-cellulose(55%)and lignin(37%)content.In accordance with the SNI 8675:2018 standard requirements,biopellets with optimal properties(moisture content of 1.4%,ash content of 0.79%,density of 1.09 g/m^(3),calorific value of 4672 cal/g,and TGA residue of 13.9%),were manufactured from raru wood.

A Review on the Advancement of Renewable Natural Fiber Hybrid Composites: Prospects, Challenges, and Industrial Applications

Naturalfibre(NFR)reinforced functional polymer composites are quickly becoming an indispensable sustainable material in the transportation industry because of their lightweight,lower cost in manufacture,and adaptability to a wide variety of goods.However,the major difficulties of using thesefibres are their existing poor dimensional stability and the extreme hydrophilicity.In assessing the mechanical properties(MP)of composites,the interfacial bonding(IB)happening between the NFR and the polymer matrix(PM)plays an incredibly significant role.When compared to NFR/syntheticfibre hybrid composites,hybrid composites(HC)made up of two separate NFR are less prevalent;yet,these hybrid composites also have the potential to be valuable materials in terms of environmental issues.A new dimension to theflexibility of composites reinforced with NFR is added by the cost-effective manufacture of hybrid composites utilising NFR.The purpose of this study is to offer an over-view of the keyfindings that were presented on hybrid composites.The emphasis was focused on the factors that influence the performance of the naturalfiber composites,diverse approaches to enhancing MP,physical,electri-cal,and thermal characteristics of the HC.HC study in polymer science gains interest for applications in con-struction and automotive industries.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

ANovel Non-Isolated Cubic DC-DC Converter with High Voltage Gain for Renewable Energy Power Generation System

In recent years,switched inductor(SL)technology,switched capacitor(SC)technology,and switched inductor-capacitor(SL-SC)technology have been widely applied to optimize and improve DC-DC boost converters,which can effectively enhance voltage gain and reduce device stress.To address the issue of low output voltage in current renewable energy power generation systems,this study proposes a novel non-isolated cubic high-gain DC-DC converter based on the traditional quadratic DC-DC boost converter by incorporating a SC and a SL-SC unit.Firstly,the proposed converter’s details are elaborated,including its topology structure,operating mode,voltage gain,device stress,and power loss.Subsequently,a comparative analysis is conducted on the voltage gain and device stress between the proposed converter and other high-gain converters.Then,a closed-loop simulation system is constructed to obtain simulation waveforms of various devices and explore the dynamic performance.Finally,an experimental prototype is built,experimental waveforms are obtained,and the experimental dynamic performance and conversion efficiency are analyzed.The theoretical analysis’s correctness is verified through simulation and experimental results.The proposed converter has advantages such as high voltage gain,low device stress,high conversion efficiency,simple control,and wide input voltage range,achieving a good balance between voltage gain,device stress,and power loss.The proposed converter is well-suited for renewable energy systems and holds theoretical significance and practical value in renewable energy applications.It provides an effective solution to the issue of low output voltage in renewable energy power generation systems.

Renewable Energy: Prospects and Challenges in Bangladesh

Among expert scientists and politicians, there is increasing agreement that it is absolutely necessary to reduce the emission of greenhouse gas (GHG) to lessen the severity of climate change. Although little, renewable energy sources currently reduce GHG that are being emitted from the energy industries. According to the majority of long-term energy estimates, renewable energy will be a substantial addition to the supply of energy worldwide by the end of this century, as capacity of renewable energy is gradually increasing in the early decades. However, developing nations like Bangladesh are largely reliant on pricey imported energy supplies (coal, gas, and oil) that lay a heavy weight on the country’s economy. Also, air pollution growing in importance as a national and international environmental issue. Regarding the development of clean and sustainable energy, renewable energy sources seem to be among the most practical and efficient alternatives, in both Bangladesh and globally. The geographic advantages of Bangladesh allow for widespread usage of the majority of such renewable energy sources. The comparative potential and use of fossil fuels against renewable energy sources globally and in Bangladesh is explored in this review.

Renewable Energy Development in OAPEC Countries: History, Current Status, and Outlook

The renewable energy industry has grown its contribution to the global energy mix, particularly in terms of electricity generation. This study investigates the implications of an increasing renewable energy share on OAPEC countries and proposes a comprehensible policy strategy for the region. Four main topics are discussed: scientific and engineering principles of renewable energy utilization, current strategies for electricity generation in each OAPEC member country, economic and environmental implications of the energy transition under two future scenarios, as well as political interactions between oil-consuming and oil-producing countries. Based on this study, realistic and cost-effective strategies are proposed for OAPEC countries to better leverage their significant renewable energy resources while stabilizing fossil fuel supplies and strengthening their position in the global energy market. To mitigate the negative impacts of the energy transition, OAPEC countries are encouraged to take the following steps: 1) Developing renewable energy in conjunction with fossil fuel resources to reduce local demand for fossil fuel and increase the supply for exportation;2) Reviewing economic policies, environmental regulations, and carbon taxes imposed by oil-consuming countries;3) Increasing investment in renewable energy infrastructure;4) Cooperating to achieve a balance between economic development and environmental protection.

Application of Power Electronics Converters in Renewable Energy

Against the backdrop of global energy shortages and increasingly severe environmental pollution,renewable energy is gradually becoming a significant direction for future energy development.Power electronics converters,as the core technology for energy conversion and control,play a crucial role in enhancing the efficiency and stability of renewable energy systems.This paper explores the basic principles and functions of power electronics converters and their specific applications in photovoltaic power generation,wind power generation,and energy storage systems.Additionally,it analyzes the current innovations in high-efficiency energy conversion,multilevel conversion technology,and the application of new materials and devices.By studying these technologies,the aim is to promote the widespread application of power electronics converters in renewable energy systems and provide theoretical and technical support for achieving sustainable energy development.

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

With increasing renewable energy utilization,the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans.This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas.First,we provide a detailed formulation to calculate the renewable energy demand based on total energy demand.Second,we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems,after which we apply the differential evolution algorithmto solve the optimization result.Finally,we conduct a case study in Qingdao,China,to demonstrate the effectiveness of this optimizationmodel.Compared to the baseline design,the proposedmodel reduced annual costs and annual carbon emissions by 14.39%and 72.65%,respectively.These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions.Overall,this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.

Evolving National Security: A Roadmap from Present to Future in Renewable and Nonrenewable Energy Policies (A Technical Review)

This comprehensive exploration delves into the intricate dynamics of national security policies in the realm of renewable and nonrenewable energy sources.From the present landscape characterized by the diversification of energy portfolios to the long-term vision encompassing nuclear fusion,this article navigates through the nuanced interplay of technology,resilience,and environmental responsibility.The synthesis of established nuclear fission technologies and evolving renewable sources forms the cornerstone of a strategic approach,addressing challenges and opportunities to ensure a secure,sustainable energy future.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

A Comparative Analysis of the Sustainable Growth of Global Hydro, Solar, and Wind Power Systems (Renewable Energy Systems)

The unfettered reliance on fossil fuels for centuries has pushed the world to the brink of severe environmental crises. While individual studies on renewable energy generation capacity have been conducted, a comprehensive analysis is lacking. This study aims to address this gap by providing a comparative analysis of three major renewable energy sources—hydro, solar, and wind— and their current global utilization statistics. Additionally, it will examine the efficacy of fossil fuels and their detrimental impact on the environment. Global warming and its associated health consequences on the ecosystem are rapidly escalating. Without a complete decarbonization of our energy systems, environmental deterioration is poised to continue at an alarming rate. Fortunately, a plethora of traditional and renewable energy resources exist that have minimal or no environmental impact and have been available for years. However, these resources remain largely untapped. The full potential of RE resources hinges on the development of sustainable technologies to harness their energy to their fullest capacity. This study delves into the current global and regional RE utilization from 2013 to 2022, based on data from the International Renewable Energy Agency (IRENA) 2023. The focus is limited to the three primary renewable energy sources with the highest harnessing capacity in recent times. Employing appropriate mathematical analyses, the results reveal exponential growth in renewable energy, with an average annual generating capacity of 2353550.7 MW over the past decade. Hydroelectric power, solar power, and wind, among others, have played a significant role in the global penetration of renewable energy systems. The changing dynamics have propelled these RE resources into the spotlight in recent years, owing to their sustainability and environmental friendliness.

Air Pollution, Global Warming and Difficulties to Replace Fossil Fuel with Renewable Energy

Since the Industrial Revolution, greenhouse gas (GHG) emissions have greatly increased with the increased use of fossil fuels, leading to air pollution and global warming. We present the researches on air pollution and the use of fossil fuels in north China, the economic zone of Changsha-Zhuzhou-Xiangtan and the economic zone of the Pearl River Delta region. Researches indicate that the use of fossil fuels has been the main source of air pollution in the three regions. We present researches on global mean surface temperature (GMST) with the rise of carbon dioxide concentration (CDC) and global fossil fuel consumption (GFFC);researches indicate that the rise in CDC can account for 91% of the rise in GMST, and GFFC can account for 90% of the rise in GMST. We analyse the factors that bring about air pollution and temperature rise, they are the use of fossil fuels and deforestation. It is critically important to replace fossil fuels with clean energy, but renewable energy has also disadvantages. The world faces difficulties in solving air pollution and global warming, so governments of the world should cooperate to solve the technologies of clean energy, and preserve the forests and the natural environment.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Influence of Hydrodynamic Pore Pressure Damage on the Performance of Hot-Mixed Renewable Asphalt Mixture

For evaluating the water stability of hot-mixed renewable asphalt mixture(HRM),the traditional methods are all tested under still water conditions.Except for damage in still water conditions,the hydrodynamic pore pressure generated by the tire driving on the surface water has a great impact.Thus,the RAP contents of the HRMs were designed at 0%,30%,45%and 60%with AC-25 gradation.Then,the self-designed evaluation methods of water stability and dynamic modulus were studied.Finally,the mechanism of the influence of hydrodynamic pore pressure damage on HRMs was studied.The results show that the water stability of HRM containing 30%RAP is equivalent to that of 45%RAP,and the water stability of HRM containing 60%RAP decreases significantly.The Contabro test after MIST treatment can be used as an evaluation method for hydrodynamic pore pressure damage on HRM.Low-speed,heavy-load traffic and larger RAP content have greater damage to the mixture after hydrodynamic pore pressure damage.The performance differences between the aged bitumen and pure bitumen,as well as the aged minerals and new minerals,are continuing to be enlarged in hydrodynamic pore pressure conditions,finally affecting the water stability and dynamic modulus of the HRMs.

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.