Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind ene...Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.展开更多
To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy stora...To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large dev...The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large deviation between the calculated results and experimental results of optimization scheduling.A flexibility evaluation methodology and an optimization model considering load associations characteristics are proposed for flexible loads in residential buildings.Temporal flexibility ratio,which is the ratio of temporal flexibility considering association characteristics to that without considering association characteristics,is defined in this study.The optimization model is solved using the CPLEX solver under three different scenarios,namely,a scenario only considering the temporal overlapping load associations,a scenario only considering the temporal non-overlapping load associations,and a scenario considering both types of load associations.It was shown that in the residential building case in this study,the cooking loads with association characteristics exhibit less temporal flexibility but higher temporal flexibility ratio of up to 71.21%,while laundry loads exhibit higher temporal flexibility,but their temporal flexibility ratio is only around 36.84%.Additionally,when the users adopted the time of use(TOU)price,their electricity costs under the three considered scenarios increased by 0.00%,7.57%,and 7.57%relative to the scenario without considering load associations,respectively.When installing a 3-kW household photovoltaic system,the electricity costs under the three scenarios increased by 0.00%,1.28%,and 1.28%,respectively.As highlighted in the results,temporal non-overlapping association characteristics greatly affect the optimal scheduling of flexible energy loads,especially under TOU,while temporal overlapping association characteristics have little effect on that.展开更多
Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users....Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.展开更多
To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and...To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and sc is the peak strength),extensive UC and uniaxial graded cyclical loading-unloading(GCLU)tests were performed on four rock types.In the GCLU tests,four unloading stress levels were designated when σ<0.9σc and six unloading stress levels were designated forσ≥0.9σc.The variations in the elastic energy density(ue),dissipative energy density(ud),and energy storage efficiency(C)for the four rock types under GCLU tests were analyzed.Based on the variation of ue whenσ≥0:9σc,a method for calculating the peak energy density was proposed.The energy evolution in rock under UC condition before the post-peak stage was examined.The relationship between C0.9(C atσ≥0:9σc)and mechanical behavior of rocks was explored,and the damage evolution of rock was analyzed in view of energy.Compared with that of the three existing methods,the accuracy of the calculation method of peak energy density proposed in this study is higher.These findings could provide a theoretical foundation for more accurately revealing the failure behavior of rock from an energy perspective.展开更多
The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has al...The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has also triggered energy crisis and ecological environment crisis,which is not compatible with the new demands of the new round of scientific and technological revolution,industrial transformation,and sustainable human development.It is in urgent need to research and develop a new-type energy system in the context of carbon neutrality.In the framework of"technique-dominated"new green and intelligent energy system with"three new"of new energy,new power and new energy storage as the mainstay,the"super energy basin"concepts with the Ordos Basin,Nw China as a representative will reshape the concept and model of future energy exploration and development.In view of the"six inequalities"in global energy and the resource conditions of"abundant coal,insufficient oil and gas and infinite new energy"in China,it is suggested to deeply boost"China energy revolution',sticking to the six principles of independent energy production,green energy supply,secure energy reserve,efficient energy consumption,intelligent energy management,economical energy cost;enhance"energy scientific and technological innovation"by implementing technique-dominated"four major science and technology innovation projects',namely,clean coal project,oil production stabilization and gas production increasing project,new energy acceleration project,and green-intelligent energy project;implement"energy transition"by accelerating the green-dominated"four-modernization development',namely,fossil energy cleaning,large-scale new energy,coordinated centralized energy distribution,intelligent multi-energy management,so as to promote the exchange of two 80%s"in China's energy structure and construct the new green and intelligent energy system.展开更多
The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of en...The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)values.The study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor.These models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the AI.The findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to perfection.In detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE.Such outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’explanation.In addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy solutions.The outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.展开更多
Electrifying end uses is a key strategy to reducing GHG emissions in buildings.However,it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system,leading to delays...Electrifying end uses is a key strategy to reducing GHG emissions in buildings.However,it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system,leading to delays in electrification and needs of significant investment.There is also concern that building electrification may cause an increase of energy costs,leading to further energy burden for low-income communities.This study uses the urban scale building modeling tool CityBES to assess the electrification impacts of more than 43,000 residential buildings in a neighborhood of Portland,Oregon,USA.Energy efficiency upgrades were investigated on their potential to mitigate the increase of peak electricity demand and energy burden.Simulation results from the calibrated EnergyPlus models show that electrification with heat pumps for space heating and cooling as well as for domestic water heating can reduce CO_(2)e emissions by 38%,but increase peak electricity demand by about 9%from the baseline building stock.Combining electrification measures and energy efficiency upgrades can reduce CO_(2)e emissions by 48%while reducing peak electricity demand by 6%and saving the median household energy costs by 28%.City and utility decision makers should consider integrating energy efficiency upgrades with electrification measures as an effective residential building electrification strategy,which significantly reduces carbon emissions,caps or even decreases peak demand while reducing energy burden of residents.展开更多
Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast ele...Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.展开更多
This paper provides an overview of the global wave resource for energy exploration.The most popular metrics and estimators for wave energy resource characterization have been compiled and classified by levels of energ...This paper provides an overview of the global wave resource for energy exploration.The most popular metrics and estimators for wave energy resource characterization have been compiled and classified by levels of energy exploration.A review of existing prospective wave energy resource assessments worldwide is also given,and those studies have been collated and classified by continent.Finally,information about forty existing open sea wave energy test sites worldwide and their characteristics is depicted and displayed on a newly created global map.It has been found that wave power density is still the most consensual metric used for wave energy resource assessment purposes among researchers.Nonetheless,to accomplish a comprehensive wave resource assessment for exploitation,the computation of other metrics at the practicable,technical,and socio-economic levels has also been performed at both spatial and temporal domains.Overall,regions in latitudes between 40°and 60°of both hemispheres are those where the highest wave power density is concentrated.Some areas where the most significant wave power density occurs are in offshore regions of southern Australia,New Zealand,South Africa,Chile,the British Isles,Iceland,and Greenland.However,Europe has been the continent where most research efforts have been done targeting wave energy characterisation for exploitation.展开更多
Mechanical energy produced by human motion is ubiquitous,continuous,and usually not utilized,making it an attractive target for sustainable electricity-harvesting applications.In this study,flexible magnetic-Juncus ef...Mechanical energy produced by human motion is ubiquitous,continuous,and usually not utilized,making it an attractive target for sustainable electricity-harvesting applications.In this study,flexible magnetic-Juncus effusus(M-JE)fibers were prepared from plant-extracted three-dimensional porous Juncus effusus(JE)fibers decorated with polyurethane and magnetic particles.The M-JE fibers were woven into fabrics and used for mechanical energy harvesting through electromagnetic induction.The M-JE fabric and induction coil,attached to the human wrist and waist,yielded continuous and stable voltage(2 V)and current(3 mA)during swinging.The proposed M-JE fabric energy harvester exhibited good energy harvesting potential and was capable of quickly charging commercial capacitors to power small electronic devices.The proposed M-JE fabric exhibited good mechanical energy harvesting performance,paving the way for the use of natural plant fibers in energy-harvesting fabrics.展开更多
Complex Field Theory (CFT) proposes that dark matter (DM) and dark energy (DE) are pervasive, complex fields of charged complex masses of equally positive and negative complex charges, respectively. It proposes that e...Complex Field Theory (CFT) proposes that dark matter (DM) and dark energy (DE) are pervasive, complex fields of charged complex masses of equally positive and negative complex charges, respectively. It proposes that each material object, including living creatures, is concomitant with a fraction of the charged complex masses of DM and DE in proportion to its mass. This perception provides new insights into the physics of nature and its constituents from subatomic to cosmic scales. This complex nature of DM and DE explains our inability to see DM or harvest DE for the last several decades. The positive complex DM is responsible for preserving the integrity of galaxies and all material systems. The negative complex charged DE induces a positive repelling force with the positively charged DM and contributes to the universe’s expansion. Both fields are Lorentz invariants in all directions and entangle the whole universe. The paper uses CFT to investigate zero-point energy, particle-wave duality, relativistic mass increase, and entanglement phenomenon and unifies Coulomb’s and Newton’s laws. The paper also verifies the existence of tachyons and explains the spooky action of quantum mechanics at a distance. The paper encourages further research into how CFT might resolve several physical mysteries in physics.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distributi...With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.展开更多
Large language models(LLMs)are gaining attention due to their potential to enhance efficiency and sustainability in the building domain,a critical area for reducing global carbon emissions.Built on transformer archite...Large language models(LLMs)are gaining attention due to their potential to enhance efficiency and sustainability in the building domain,a critical area for reducing global carbon emissions.Built on transformer architectures,LLMs excel at text generation and data analysis,enabling applications such as automated energy model generation,energy management optimization,and fault detection and diagnosis.These models can potentially streamline complex workflows,enhance decision-making,and improve energy efficiency.However,integrating LLMs into building energy systems poses challenges,including high computational demands,data preparation costs,and the need for domain-specific customization.This perspective paper explores the role of LLMs in the building energy system sector,highlighting their potential applications and limitations.We propose a development roadmap built on in-context learning,domain-specific fine-tuning,retrieval augmented generation,and multimodal integration to enhance LLMs’customization and practical use in this field.This paper aims to spark ideas for bridging the gap between LLMs capabilities and practical building applications,offering insights into the future of LLM-driven methods in building energy applications.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
The kinetic energy of the ejected fragments is an effective index for quantitatively evaluating the failure severity of rockburst.To improve the measurement accuracy of the kinetic energy,the total kinetic energy was ...The kinetic energy of the ejected fragments is an effective index for quantitatively evaluating the failure severity of rockburst.To improve the measurement accuracy of the kinetic energy,the total kinetic energy was divided into translational and rotational kinetic energy in this paper.An analysis method for translational and rotational kinetic energy was subsequently proposed by introducing a four-eye high-speed photography system.Moreover,the true triaxial rockburst experiments on granite samples after heat treatment at various temperatures were carried out to reveal the evolution characteristics of the kinetic energy of rockburst.The experimental results reveal that with increasing the particle size of the rockburst fragment,the correction coefficient of measurement error of the translational kinetic energy increases first but then decreases.A power function law is obtained between the ratio of the rotational kinetic energy to the translational kinetic energy and the particle size of the rockburst fragment.Compared to the uncorrected kinetic energy measured by the system,the total kinetic energy presents a decreasing trend.The maximum proportion of total kinetic energy to uncorrected kinetic energy is 0.9.The peak stress,failure intensity and total kinetic energy all initially increase but subsequently decrease as the heat treatment temperature increases.The research outcome is favourable to revealing the impact of initial thermal damage on the rockburst mechanism.展开更多
Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex syst...Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.展开更多
The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned gr...The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.展开更多
基金The APC was funded by Research Management Center, Multimedia University, Malaysia.
文摘Electrical energy can be harvested from the rotational kinetic energy of moving bodies,consisting of both mechanical and kinetic energy as a potential power source through electromagnetic induction,similar to wind energy applications.In industries,rotational bodies are commonly present in operations,yet this kinetic energy remains untapped.This research explores the energy generation characteristics of two rotational body types,disk-shaped and cylinder-shaped under specific experimental setups.The hardware setup included a direct current(DC)motor driver,power supply,DC generator,mechanical support,and load resistance,while the software setup involved automation testing tools and data logging.Electromagnetic induction was used to harvest energy,and experiments were conducted at room temperature(25℃)with controlled variables like speed and friction.Results showed the disk-shaped body exhibited higher energy efficiency than the cylinder-shaped body,largely due to lower mechanical losses.The disk required only two bearings,while the cylinder required four,resulting in lower bearing losses for the disk.Additionally,the disk experienced only air friction,whereas the cylinder encountered friction from a soft,uneven rubber material,increasing surface contact losses.Under a 40 W resistive load,the disk demonstrated a 17.1%energy loss due to mechanical friction,achieving up to 15.55 J of recycled energy.Conversely,the cylinder body experienced a 48.05%energy loss,delivering only 51.95%of energy to the load.These insights suggest significant potential for designing efficient energy recycling systems in industrial settings,particularly in manufacturing and processing industries where rotational machinery is prevalent.Despite its lower energy density,this system could be beneficially integrated with energy storage solutions,enhancing sustainability in industrial practices.
基金This study was supported by State Grid Corporation headquarters technology project(4000-202399368A-2-2-ZB).
文摘To address the excessive complexity of monthly scheduling and the impact of uncertain net load on the chargeable energy of storage,a reduced time-period monthly scheduling model for thermal generators and energy storage,incorporating daily minimum chargeable energy constraints,was developed.Firstly,considering the variations in the frequency of unit start-ups and shutdowns under different levels of net load fluctuation,a method was proposed to reduce decision time periods for unit start-up and shut-down operations.This approach,based on the characteristics of net load fluctuations,minimizes the decision variables of units,thereby simplifying the monthly schedulingmodel.Secondly,the relationship between energy storage charging and discharging power,net load,and the total maximum/minimum output of units was analyzed.Based on this,daily minimum chargeable energy constraints were established to ensure the energy storage system meets charging requirements under extreme net load scenarios.Finally,taking into account the operational costs of thermal generators and energy storage,load loss costs,and operational constraints,the reduced time-period monthly schedulingmodel was constructed.Case studies demonstrate that the proposedmethod effectively generates economical monthly operation plans for thermal generators and energy storage,significantly reduces model solution time,and satisfies the charging requirements of energy storage under extreme net load conditions.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
基金supported by the National Natural Science Foundation of China(52378109)Innovation Capability Support Program of Shaanxi(2023KJXX-043)+1 种基金Young Talent Fund of Association for Science and Technology in Shaanxi(20220425)the Key Research and Development Program of Shaanxi,General Project(2023-YBSF-177).
文摘The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large deviation between the calculated results and experimental results of optimization scheduling.A flexibility evaluation methodology and an optimization model considering load associations characteristics are proposed for flexible loads in residential buildings.Temporal flexibility ratio,which is the ratio of temporal flexibility considering association characteristics to that without considering association characteristics,is defined in this study.The optimization model is solved using the CPLEX solver under three different scenarios,namely,a scenario only considering the temporal overlapping load associations,a scenario only considering the temporal non-overlapping load associations,and a scenario considering both types of load associations.It was shown that in the residential building case in this study,the cooking loads with association characteristics exhibit less temporal flexibility but higher temporal flexibility ratio of up to 71.21%,while laundry loads exhibit higher temporal flexibility,but their temporal flexibility ratio is only around 36.84%.Additionally,when the users adopted the time of use(TOU)price,their electricity costs under the three considered scenarios increased by 0.00%,7.57%,and 7.57%relative to the scenario without considering load associations,respectively.When installing a 3-kW household photovoltaic system,the electricity costs under the three scenarios increased by 0.00%,1.28%,and 1.28%,respectively.As highlighted in the results,temporal non-overlapping association characteristics greatly affect the optimal scheduling of flexible energy loads,especially under TOU,while temporal overlapping association characteristics have little effect on that.
基金supported by National Key Research and Development Program of China(2022YFB4201003)the National Natural Science Foundation of China(52278104 and 52108076)the Science and Technology Innovation Program of Hunan Province(2023RC1042).
文摘Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.
基金the National Natural Science Foundation of China(Grant Nos.52104133 and 52304227)the Postdoctoral Foundation of Henan Province(Grant No.HN2022015)are appreciated.
文摘To obtain the precise calculation method for the peak energy density and energy evolution properties of rocks subjected to uniaxial compression(UC)before the post-peak stage,particularly at s0.9sc(s denotes stress and sc is the peak strength),extensive UC and uniaxial graded cyclical loading-unloading(GCLU)tests were performed on four rock types.In the GCLU tests,four unloading stress levels were designated when σ<0.9σc and six unloading stress levels were designated forσ≥0.9σc.The variations in the elastic energy density(ue),dissipative energy density(ud),and energy storage efficiency(C)for the four rock types under GCLU tests were analyzed.Based on the variation of ue whenσ≥0:9σc,a method for calculating the peak energy density was proposed.The energy evolution in rock under UC condition before the post-peak stage was examined.The relationship between C0.9(C atσ≥0:9σc)and mechanical behavior of rocks was explored,and the damage evolution of rock was analyzed in view of energy.Compared with that of the three existing methods,the accuracy of the calculation method of peak energy density proposed in this study is higher.These findings could provide a theoretical foundation for more accurately revealing the failure behavior of rock from an energy perspective.
文摘The essence of energy system transition is the"energy revolution':The development of the"resource-dominated"energy system with fossil energy as the mainstay has promoted human progress,but it has also triggered energy crisis and ecological environment crisis,which is not compatible with the new demands of the new round of scientific and technological revolution,industrial transformation,and sustainable human development.It is in urgent need to research and develop a new-type energy system in the context of carbon neutrality.In the framework of"technique-dominated"new green and intelligent energy system with"three new"of new energy,new power and new energy storage as the mainstay,the"super energy basin"concepts with the Ordos Basin,Nw China as a representative will reshape the concept and model of future energy exploration and development.In view of the"six inequalities"in global energy and the resource conditions of"abundant coal,insufficient oil and gas and infinite new energy"in China,it is suggested to deeply boost"China energy revolution',sticking to the six principles of independent energy production,green energy supply,secure energy reserve,efficient energy consumption,intelligent energy management,economical energy cost;enhance"energy scientific and technological innovation"by implementing technique-dominated"four major science and technology innovation projects',namely,clean coal project,oil production stabilization and gas production increasing project,new energy acceleration project,and green-intelligent energy project;implement"energy transition"by accelerating the green-dominated"four-modernization development',namely,fossil energy cleaning,large-scale new energy,coordinated centralized energy distribution,intelligent multi-energy management,so as to promote the exchange of two 80%s"in China's energy structure and construct the new green and intelligent energy system.
文摘The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)values.The study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor.These models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the AI.The findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to perfection.In detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE.Such outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’explanation.In addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy solutions.The outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.
文摘Electrifying end uses is a key strategy to reducing GHG emissions in buildings.However,it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system,leading to delays in electrification and needs of significant investment.There is also concern that building electrification may cause an increase of energy costs,leading to further energy burden for low-income communities.This study uses the urban scale building modeling tool CityBES to assess the electrification impacts of more than 43,000 residential buildings in a neighborhood of Portland,Oregon,USA.Energy efficiency upgrades were investigated on their potential to mitigate the increase of peak electricity demand and energy burden.Simulation results from the calibrated EnergyPlus models show that electrification with heat pumps for space heating and cooling as well as for domestic water heating can reduce CO_(2)e emissions by 38%,but increase peak electricity demand by about 9%from the baseline building stock.Combining electrification measures and energy efficiency upgrades can reduce CO_(2)e emissions by 48%while reducing peak electricity demand by 6%and saving the median household energy costs by 28%.City and utility decision makers should consider integrating energy efficiency upgrades with electrification measures as an effective residential building electrification strategy,which significantly reduces carbon emissions,caps or even decreases peak demand while reducing energy burden of residents.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
文摘This paper provides an overview of the global wave resource for energy exploration.The most popular metrics and estimators for wave energy resource characterization have been compiled and classified by levels of energy exploration.A review of existing prospective wave energy resource assessments worldwide is also given,and those studies have been collated and classified by continent.Finally,information about forty existing open sea wave energy test sites worldwide and their characteristics is depicted and displayed on a newly created global map.It has been found that wave power density is still the most consensual metric used for wave energy resource assessment purposes among researchers.Nonetheless,to accomplish a comprehensive wave resource assessment for exploitation,the computation of other metrics at the practicable,technical,and socio-economic levels has also been performed at both spatial and temporal domains.Overall,regions in latitudes between 40°and 60°of both hemispheres are those where the highest wave power density is concentrated.Some areas where the most significant wave power density occurs are in offshore regions of southern Australia,New Zealand,South Africa,Chile,the British Isles,Iceland,and Greenland.However,Europe has been the continent where most research efforts have been done targeting wave energy characterisation for exploitation.
基金National Natural Science Foundation of China(52303064 and U21A2095)the National Key Research and Development Program of China(2022YFB3805800)the Shandong Key Research and Development Program(2023CXGC010612).
文摘Mechanical energy produced by human motion is ubiquitous,continuous,and usually not utilized,making it an attractive target for sustainable electricity-harvesting applications.In this study,flexible magnetic-Juncus effusus(M-JE)fibers were prepared from plant-extracted three-dimensional porous Juncus effusus(JE)fibers decorated with polyurethane and magnetic particles.The M-JE fibers were woven into fabrics and used for mechanical energy harvesting through electromagnetic induction.The M-JE fabric and induction coil,attached to the human wrist and waist,yielded continuous and stable voltage(2 V)and current(3 mA)during swinging.The proposed M-JE fabric energy harvester exhibited good energy harvesting potential and was capable of quickly charging commercial capacitors to power small electronic devices.The proposed M-JE fabric exhibited good mechanical energy harvesting performance,paving the way for the use of natural plant fibers in energy-harvesting fabrics.
文摘Complex Field Theory (CFT) proposes that dark matter (DM) and dark energy (DE) are pervasive, complex fields of charged complex masses of equally positive and negative complex charges, respectively. It proposes that each material object, including living creatures, is concomitant with a fraction of the charged complex masses of DM and DE in proportion to its mass. This perception provides new insights into the physics of nature and its constituents from subatomic to cosmic scales. This complex nature of DM and DE explains our inability to see DM or harvest DE for the last several decades. The positive complex DM is responsible for preserving the integrity of galaxies and all material systems. The negative complex charged DE induces a positive repelling force with the positively charged DM and contributes to the universe’s expansion. Both fields are Lorentz invariants in all directions and entangle the whole universe. The paper uses CFT to investigate zero-point energy, particle-wave duality, relativistic mass increase, and entanglement phenomenon and unifies Coulomb’s and Newton’s laws. The paper also verifies the existence of tachyons and explains the spooky action of quantum mechanics at a distance. The paper encourages further research into how CFT might resolve several physical mysteries in physics.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金funded by the Science and Technology Project of State Grid Shanxi Electric Power Company(5205E0230001).
文摘With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.
基金supported by the U.S.National Science Foundation(Grant Number:2309030).
文摘Large language models(LLMs)are gaining attention due to their potential to enhance efficiency and sustainability in the building domain,a critical area for reducing global carbon emissions.Built on transformer architectures,LLMs excel at text generation and data analysis,enabling applications such as automated energy model generation,energy management optimization,and fault detection and diagnosis.These models can potentially streamline complex workflows,enhance decision-making,and improve energy efficiency.However,integrating LLMs into building energy systems poses challenges,including high computational demands,data preparation costs,and the need for domain-specific customization.This perspective paper explores the role of LLMs in the building energy system sector,highlighting their potential applications and limitations.We propose a development roadmap built on in-context learning,domain-specific fine-tuning,retrieval augmented generation,and multimodal integration to enhance LLMs’customization and practical use in this field.This paper aims to spark ideas for bridging the gap between LLMs capabilities and practical building applications,offering insights into the future of LLM-driven methods in building energy applications.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金supported by the National Natural Science Foundation of China(Grant Nos.41572334,41941018)the Fundamental Research Funds for the Central Universities(Ph.D.Top Innovative Talents Fund of CUMTB)(Grant No.BBJ2024078)。
文摘The kinetic energy of the ejected fragments is an effective index for quantitatively evaluating the failure severity of rockburst.To improve the measurement accuracy of the kinetic energy,the total kinetic energy was divided into translational and rotational kinetic energy in this paper.An analysis method for translational and rotational kinetic energy was subsequently proposed by introducing a four-eye high-speed photography system.Moreover,the true triaxial rockburst experiments on granite samples after heat treatment at various temperatures were carried out to reveal the evolution characteristics of the kinetic energy of rockburst.The experimental results reveal that with increasing the particle size of the rockburst fragment,the correction coefficient of measurement error of the translational kinetic energy increases first but then decreases.A power function law is obtained between the ratio of the rotational kinetic energy to the translational kinetic energy and the particle size of the rockburst fragment.Compared to the uncorrected kinetic energy measured by the system,the total kinetic energy presents a decreasing trend.The maximum proportion of total kinetic energy to uncorrected kinetic energy is 0.9.The peak stress,failure intensity and total kinetic energy all initially increase but subsequently decrease as the heat treatment temperature increases.The research outcome is favourable to revealing the impact of initial thermal damage on the rockburst mechanism.
文摘Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.
基金supported by the National Natural Science Foundation of China(No.62171486 and No.U2001213)the Guangdong Basic and Applied Basic Research Project(2022A1515140166)。
文摘The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.
