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.

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.

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.

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 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.

Investigating and predicting the role of photovoltaic, wind, and hydrogen energies in sustainable global energy evolution

The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels.This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar,wind,and hydrogen.This transition economically challenges traditional energy sectors while fostering new industries,promoting job growth,and sustainable economic development.The transition to renewable energy demands social equity,ensuring universal access to affordable energy,and considering community impact.The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint.This study highlights the rapid growth of the global wind power market,which is projected to increase from$112.23 billion in 2022 to$278.43 billion by 2030,with a compound annual growth rate of 13.67%.In addition,the demand for hydrogen is expected to increase,significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions.By 2028,renewables are predicted to account for 42%of global electricity generation,with significant contributions from wind and solar photovoltaic(PV)technology,particularly in China,the European Union,the United States,and India.These developments signify a global commitment to diversifying energy sources,reducing emissions,and moving toward cleaner and more sustainable energy solutions.This review offers stakeholders the insights required to smoothly transition to sustainable energy,setting the stage for a resilient future.

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%.

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.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

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.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

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.

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.

Renewable Energy Seawater Desalination Technology and Application Analysis

This paper proposed a new technology way for seawater desalination which used renewable energy(wind energy and solar energy).The effects of practical application showed that remote islands and cage culture zones in the bay that lack electricity and water are very suitable for using small seawater desalination devices that do not require consumption of conventional energy.

Anthropogenic Climate Change, Deforestation and Renewable Energy

Since the Industrial Revolution, greenhouse gas (GHG) emission, especially global CO2 emission (GCE) has greatly increased with the growth of global human population (GHP), which has caused climate change. Both GCE and GHP are positively related with the rise of global mean surface temperature (GMST). Our empirical research shows that the impact from GCE on GMST is 7.72 times of that from TSI, and the impact from GHP on GMST is 7.9 times of that from TSI. The growth rate of global surface temperature was slower from 1998 to 2012, which was mainly caused by the decadal cooling of the tropical Pacific Ocean-La Niña. Deforestation and vegetation degradation affect climate change, feedback radiation between the atmosphere and the biosphere account for 30% of the variation in global surface radiation and precipitation. The share of renewable energy remains small in primary energy consumption due to their disadvantages.

Research on Equivalent Modeling Method of AC-DC Power Networks Integrating with Renewable Energy Generation

Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.