Recent advances in graphene-based phase change composites for thermal energy storage and management

Energy storage and conservation are receiving increased attention due to rising global energy demands.Therefore,the development of energy storage materials is crucial.Thermal energy storage(TES)systems based on phase change materials(PCMs)have increased in prominence over the past two decades,not only because of their outstanding heat storage capacities but also their superior thermal energy regulation capability.However,issues such as leakage and low thermal conductivity limit their applicability in a variety of settings.Carbon-based materials such as graphene and its derivatives can be utilized to surmount these obstacles.This study examines the recent advancements in graphene-based phase change composites(PCCs),where graphene-based nanostructures such as graphene,graphene oxide(GO),functionalized graphene/GO,and graphene aerogel(GA)are incorporated into PCMs to substantially enhance their shape stability and thermal conductivity that could be translated to better storage capacity,durability,and temperature response,thus boosting their attractiveness for TES systems.In addition,the applications of these graphene-based PCCs in various TES disciplines,such as energy conservation in buildings,solar utilization,and battery thermal management,are discussed and summarized.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Reinforcement Learning Model for Energy System Management to Ensure Energy Efficiency and Comfort in Buildings

This article focuses on the challenges ofmodeling energy supply systems for buildings,encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings.Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material,such as for thermal upgrades,which consequently incurs additional economic costs.It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions,considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in buildings.In addition,it explores the methods and mechanisms for modeling the operating modes of electric boilers used to collectively improve energy efficiency and indoor climatic conditions.Using the developed mathematical models,the study examines the dynamic states of building energy supply systems and provides recommendations for improving their efficiency.These dynamic models are executed in software environments such as MATLAB/Simscape and Python,where the component detailing schemes for various types of controllers are demonstrated.Additionally,controllers based on reinforcement learning(RL)displayed more adaptive load level management.These RL-based controllers can lower instantaneous power usage by up to 35%,reduce absolute deviations from a comfortable temperature nearly by half,and cut down energy consumption by approximately 1%while maintaining comfort.When the energy source produces a constant energy amount,the RL-based heat controllermore effectively maintains the temperature within the set range,preventing overheating.In conclusion,the introduced energydynamic building model and its software implementation offer a versatile tool for researchers,enabling the simulation of various energy supply systems to achieve optimal energy efficiency and indoor climate control in buildings.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Regenerative Braking Energy Recovery System of Metro Train Based on Dual-Mode Power Management

In order to fully utilize the regenerative braking energy of metro trains and stabilize the metro DC traction busbar voltage,a hybrid regenerative braking energy recovery system with a dual-mode power management strategy is proposed.Firstly,the construction of the hybrid regenerative braking energy recovery system is explained.Then,based on the power demand of low-voltage load in metro stations,a dual-mode power management strategy is proposed to allocate the reference power of each system according to the different working conditions,and the control methods of each system are set.Finally,the correctness and effectiveness of the dual-mode strategy are verified through simulation,and the proposed braking energy utilization scheme is compared with other singleform utilization schemes.The results illustrate that the hybrid system with the dual-mode strategy can effectively recycle the regenerative braking energy of metro train and inhibit the busbar voltage fluctuation;the proposed braking energy utilization scheme has certain advantages on energy recovery and DC bus voltage stabilization compared with other single-form schemes;the proposed power management strategy can correctly allocate the reference power of each system with a lower construction cost.

Encouraging circular economy and sustainable environmental practices by addressing waste management and biomass energy production

The current linear economy assumes abundant,easily accessible,and cost-effective natural resources.However,this assumption is unsustainable,especially considering the world’s current trajectory exceeding the Earth’s ecological limits.In contrast,circular economy(CE)reduces wastes and improves resource efficiency,making them a more sustainable alternative to the dominant linear model.Biomass energy generated from agricultural leftovers,forestry wastes,and municipal trash provides a renewable substitute for fossil fuels.This reduces greenhouse gas emissions and improves energy security.Proper waste management,including trash reduction,recycling,and innovative waste-to-energy technology,reduces the burden on landfills and incineration and creates renewable energy from materials that would otherwise go to waste.Although integrating these techniques is consistent with the CE’s resource efficiency and waste minimization principles,it requires addressing environmental,technical,and socioeconomic challenges.Given the pressing global issues,transitioning to a CE and implementing sustainable environmental practices are crucial to mitigate the current waste management crisis.The aim of this study is to emphasize the viability of biomass as a source of sustainable energy,the necessity of comprehensive strategies that prioritize ecological sustainability,community involvement,and innovation to achieve a circular principle based future,and the potential obstacles to the implementation of sustainable environmental practices.This study will aid in implementing CE practices to accomplish the Sustainable Development Goals(SDGs)by reducing greenhouse gas emissions and landfill loads.Beyond environmental benefits,it can also bring economic,social,and health improvements.Furthermore,this study will assist societies in addressing global issues,such as resource scarcity,pollution,and climate change,as well as transitioning to a more sustainable and resilient future.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

Energy Management

A new revised energy concept is proposed from relativistic approach using different kinds of spaces.It is not always feasible to implement a project for saving energy.It is not merely heating a stream and cooling another stream.The study includes other considerations.A total feasibility study should be performed.The cost and the income should be calculated.In this study it is shown how to perform such a feasibility study from scratch up to detailed implementation.It is an investment approach.There are two energy balances:one thermal and the other mechanical.They are not separated but they complete each other.The decision is upon rigorous energy balance.First the question where the energy will go shall be answered from 2nd law of thermodynamics.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

Enhancing Urban Intelligence Energy Management: Innovative Load Forecasting Techniques for Electrical Networks

Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks:A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.

Scenario-Based Assessment of the Water-Energy-Food Nexus in Kuwait: Insights for Effective Resource Management

The interdependency among water, food, and energy (WEF) in the GCC countries is strongly and closely interlinked, and is intensifying as demand for resources increases with population growth and changing consumption patterns, and are expected to be further compounded by the impacts of climate change. Therefore, integrated management of the three sectors is crucial to reduce trade-offs and build synergies among them. This paper presents a comprehensive framework to assess the WEF nexus in Kuwait as a representative case for the GCC countries. The framework consists of three main steps: 1) evaluating the influence of socio-economic development and climate change on water, energy, and food resources;2) generating scenario-based projections;and 3) conducting an extensive quantitative nexus analysis. The WEF interlinkages in Kuwait are modelled quantitatively using the Q-Nexus model, and current critical interdependencies are evaluated. Then, various WEF-Nexus scenarios were conducted for the year 2035 to explore the effects of management interventions in one sector on the other two sectors. The main findings are that per capita municipal water consumption is a major influencer on the WEF-nexus due to the heavy reliance on thermal desalination in municipal water supply in Kuwait, which is attributed to its energy intensity, financial cost, GHGs emissions, and environmental impacts on the marine and air environments. To reduce WEF trade-offs, mitigate risks, and build synergies among the three sectors, it is important to shift the current policy focus on supply-side management approach to the demand-side management and efficiency approaches.

Crosstalk between degradation and bioenergetics: how autophagy and endolysosomal processes regulate energy production

Cells undergo metabolic reprogramming to adapt to changes in nutrient availability, cellular activity, and transitions in cell states. The balance between glycolysis and mitochondrial respiration is crucial for energy production, and metabolic reprogramming stipulates a shift in such balance to optimize both bioenergetic efficiency and anabolic requirements. Failure in switching bioenergetic dependence can lead to maladaptation and pathogenesis. While cellular degradation is known to recycle precursor molecules for anabolism, its potential role in regulating energy production remains less explored. The bioenergetic switch between glycolysis and mitochondrial respiration involves transcription factors and organelle homeostasis, which are both regulated by the cellular degradation pathways. A growing body of studies has demonstrated that both stem cells and differentiated cells exhibit bioenergetic switch upon perturbations of autophagic activity or endolysosomal processes. Here, we highlighted the current understanding of the interplay between degradation processes, specifically autophagy and endolysosomes, transcription factors, endolysosomal signaling, and mitochondrial homeostasis in shaping cellular bioenergetics. This review aims to summarize the relationship between degradation processes and bioenergetics, providing a foundation for future research to unveil deeper mechanistic insights into bioenergetic regulation.

Imperative for long-term management and surveillance in Kawasaki disease

Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.

Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Motivational interviewing in postoperative rehabilitation and chronic disease management: Current findings and future research directions

This editorial highlights a recently published study examining the effectiveness of music therapy combined with motivational interviewing(MI)in addressing an-xiety and depression among young and middle-aged patients following percuta-neous coronary intervention.It further explores existing evidence and potential future research directions for MI in postoperative rehabilitation and chronic disease management.MI aims to facilitate behavioral change and promote healthier lifestyles by fostering a trusting relationship with patients and enhan-cing intrinsic motivation.Research has demonstrated its effectiveness in posto-perative recovery for oncological surgery,stroke,organ transplants,and gastroin-testinal procedures,as well as in managing chronic conditions such as diabetes,obesity,and periodontal disease.The approach is patient-centered,adaptable,cost-effective,and easily replicable,though its limitations include reliance on the therapist’s expertise,variability in individual responses,and insufficient long-term follow-up studies.Future research could focus on developing individualized and precise intervention models,exploring applications in digital health management,and confirming long-term outcomes to provide more compre-hensive support for patient rehabilitation.