Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Unrelated parallel machine scheduling problem(UPMSP)is a typical scheduling one and UPMSP with various reallife constraints such as additional resources has been widely studied;however,UPMSP with additional resources,maintenance,and energy-related objectives is seldom investigated.The Artificial Bee Colony(ABC)algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources,among other factors.In this study,an energy-efficient UPMSP with additional resources and maintenance is considered.A dynamical artificial bee colony(DABC)algorithm is presented to minimize makespan and total energy consumption simultaneously.Three heuristics are applied to produce the initial population.Employed bee swarm and onlooker bee swarm are constructed.Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met.Dynamical employed bee phase is implemented by computing resource shifting and solution migration.Computing resource shifting and feedback are used to construct dynamical onlooker bee phase.Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given.The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP.

Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

Geological reservoir and resource potential(10^(13)m^(3))of gas hydrates in the South China Sea

A detailed understanding of the distribution and potential of natural gas hydrate(NGHs)resources is crucial to fostering the industrialization of those resources in the South China Sea,where NGHs are abundant.In this study,this study analyzed the applicability of resource evaluation methods,including the volumetric,genesis,and analogy methods,and estimated NGHs resource potential in the South China Sea by using scientific resource evaluation methods based on the factors controlling the geological accumulation and the reservoir characteristics of NGHs.Furthermore,this study compared the evaluation results of NGHs resource evaluations in representative worldwise sea areas via rational analysis.The results of this study are as follows:(1)The gas hydrate accumulation in the South China Sea is characterized by multiple sources of gas supply,multi-channel migration,and extensive accumulation,which are significantly different from those of oil and gas and other unconventional resources.(2)The evaluation of gas hydrate resources in the South China Sea is a highly targeted,stratified,and multidisciplinary evaluation of geological resources under the framework of a multi-type gas hydrate resource evaluation system and focuses on the comprehensive utilization of multi-source heterogeneous data.(3)Global NGHs resources is n×10^(15)m^(3),while the NGHs resources in the South China Sea are estimated to be 10^(13)m^(3),which is comparable to the abundance of typical marine NGHs deposits in other parts of the world.In the South China Sea,the NGHs resources have a broad prospect and provide a substantial resource base for production tests and industrialization of NGHs.

Progress in research on the exploration and evaluation of deep geothermal resources in the Fujian-Guangdong-Hainan region,China

Deep geothermal resources in the Fujian-Guangdong-Hainan region,China,offer significant potential for sustainable energy.The diverse igneous rock formations along the southeast coast present intricate geological challenges that impede exploration and evaluation efforts.In this study,we address critical concerns related to the Fujian-Guangdong-Hainan region's deep geothermal resources,encompassing heat source composition,formation conditions,strategic favorable areas,and exploration directions.Our methods involve the analysis of regional geothermal reservoirs and cap rocks.Major findings include:the primary heat sources in the Fujian-Guangdong-Hainan region consist of the radioactive heat generation from granites in the crust,heat conduction in the mantle,and,in specific areas like Yangjiang and Shantou,melts within the middle and lower crust;the deep,high-temperature geothermal resources in the region predominantly reside in basins'depressed areas.These areas are characterized by the confluence of triple heat sources:heat from the Earth's crust,mantle,and other supplementary sources;our analysis led to the identification of three strategic areas favorable for deep geothermal resources in the Fujian-Guangdong-Hainan region.These are the Beibu Gulf Basin's continental area,the Yuezhong Depression,and the Fuzhou-Zhangzhou area.

Applications of artificial intelligence in geothermal resource exploration: A review

Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

Wave Energy Resource Availability Assessment in the Philippines Based on 30-Year Hindcast Data

With the goal of evaluating the wave and wave energy conditions in the Philippines,the simulated wave nearshore(SWAN)model was used to estimate the wavefield using 30 years of cross-calibrated multi-platform(CCMP)wind field data(1987-2016).The spatiotemporal patterns of annual and monthly averaged significant wave heights and wave energy in the Philippines were analyzed based on the simulated data.Results showed that they had similar values;in particular,significant wave heights and wave energy were smaller in the south and southwest and higher in the north and northeast.A total of 12 representative points along the Philippine coast were selected to draw wave and wave energy roses.A directional analysis showed that the dominant wave was in the north north-east(NNE),northeast(NE),and east north-east(ENE)directions.Wave energy was mainly distributed in regions with an energy period between 1 and 10 s and significant wave heights between 0 and 4 m.To better utilize wave energy data in the Philippines,this paper studied the available and rich area of wave energy and analyzed the annual and monthly variability index of wave energy in the country.Moreover,the available significant wave heights of wave energy conversion devices(WECs)were set as 0.5-4 m,and the maximum annual average available wave energy occurred in the eastern Philippine Sea area,reaching 13 kW m^(-1).For the safety of WECs,extreme typhoon-induced wave conditions must be considered.Furthermore,the results showed that the maximum significant wave height and mean period over the 50-year return period reached 18 m and 15 s,respectively.

Prediction of Sedimentary Microfacies Distribution by Coupling Stochastic Modeling Method in Oil and Gas Energy Resource Exploitation

In view of the problem that a single modeling method cannot predict the distribution of microfacies, a new idea of coupling modeling method to comprehensively predict the distribution of sedimentary microfacies was proposed, breaking the tradition that different sedimentary microfacies used the same modeling method in the past. Because different sedimentary microfacies have different distribution characteristics and geometric shapes, it is more accurate to select different simulation methods for prediction. In this paper, the coupling modeling method was to establish the distribution of sedimentary microfacies with simple geometry through the point indicating process simulation, and then predict the microfacies with complex spatial distribution through the sequential indicator simulation method. Taking the DC block of Bohai basin as an example, a high-precision reservoir sedimentary microfacies model was established by the above coupling modeling method, and the model verification results showed that the sedimentary microfacies model had a high consistency with the underground. The coupling microfacies modeling method had higher accuracy and reliability than the traditional modeling method, which provided a new idea for the prediction of sedimentary microfacies.

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

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

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Sustainability analysis of SEEA indicators for non-renewable resources

Despite the fact that the non-renewable resources industry contributes greatly to regional and national gross domestic product(GDP),it casts massive negative impacts on the environment,which fails to be deducted from economic growth.Hence,sustainable development of non-renewable resources(extraction and processing)is playing an essential role in boosting economic growth continuously.The System of Integrated Environmental and Economic Accounting(SEEA)proposed by the United Nations Statistics Division(UNSD)provides a brand-new perspective for sustainability study.This paper designs a fundamental approach of green accounting for non-renewable resources based on SEEA.Three main aspects of the accounting are extracted to explore the way of analysis for sustainability indicators,which are not touched upon by SEEA.Main analyses are as follows:(1)the analysis on the influence of the change of the discount rate in user cost(UC);(2)correlation analysis between environmental degradation and pollutants emission intensity;(3)analysis of the accounting results of green GDP and green GCF(gross capital formation).Then taking petroleum resources in Shandong Province as an example,this paper will calculate and analyze green data based on the accounting and analytical approaches discussed above.However,sustainability indicators studied in the paper are just associated with past economic activities,while investigation into the factors of the change of sustainability indicators is the one most critical point in relevant policymaking.

Solar Energy Resource Characteristics of Photovoltaic Power Station in Shandong Province

[Objective] The aim was to analyze characters of solar energy in photo- voltaic power stations in Shandong Province. [Method] The models of total solar radiation and scattered radiation were determined, and solar energy resources in pho-tovoltaic power stations were evaluated based on illumination in horizontal plane and cloud data in 123 counties or cities and observed information in Jinan, Fushan and Juxian in 1988-2008. [Result] Solar energy in northern regions in Shandong proved most abundant, which is suitable for photovoltaic power generation; the optimal angle of tilt of photovoltaic array was at 35°, decreasing by 2°-3° compared with local latitude. Total solar radiation received by the slope with optimal angle of tilt exceeded 1 600 kw.h/（m2.a）, increasing by 16% compared with horizontal planes. The maximal irradiance concluded by WRF in different regions tended to be volatile in 1 020-1 060 W/m2. [Conclusion] The research provides references for construction of photovoltaic power stations in Shandong Province.

Methane hydrates:A future clean energy resource

Natural gas has been considered as the best transition fuel into the future carbon constraint world.The ever-increasing demand for natural gas has prompted expanding research and development activities worldwide for exploring methane hydrates as a future energy resource.With its vast global resource volume(~3000 trillion cubic meter CH4)and high energy storage capacity(170 CH4 v/v methane hydrate),recovering energy from naturally-occurring methane hydrate has attracted both academic and industry interests to demonstrate the technical feasibility and economic viability.In this review paper,we highlight the recent advances in fundamental researches,seminal discoveries and implications from ongoing drilling programs and field production tests,the impending knowledge gaps and the future perspectives of recovering energy from methane hydrates.We further emphasize the current scientific,technological and economic challenges in realizing long-term commercial gas production from methane hydrate reservoir.The continuous growth of the corresponding experimental studies in China should target these specific challenges to narrow the knowledge gaps between laboratory-scale investigations and reservoir-scale applications.Furthermore,we briefly discuss both the environmental and geomechanical issues related to exploiting methane hydrate as the future energy resource and believe that they should be of paramount importance in the future development of novel gas production technologies.

An assessment of global ocean wave energy resources over the last 45 a

Against the background of the current world facing an energy crisis,and human beings puzzled by the problems of environment and resources,developing clean energy sources becomes the inevitable choice to deal with a climate change and an energy shortage.A global ocean wave energy resource was reanalyzed by using ERA-40 wave reanalysis data 1957–2002 from European Centre for Medium-Range Weather Forecasts（ECMWF）.An effective significant wave height is defined in the development of wave energy resources（short as effective SWH）,and the total potential of wave energy is exploratively calculated.Synthetically considering a wave energy density,a wave energy level probability,the frequency of the effective SWH,the stability and long-term trend of wave energy density,a swell index and a wave energy storage,global ocean wave energy resources were reanalyzed and regionalized,providing reference to the development of wave energy resources such as wave power plant location,seawater desalination,heating,pumping.

A wave energy resource assessment in the China's seas based on multi-satellite merged radar altimeter data

Wave energy resources are abundant in both offshore and nearshore areas of the China＇s seas. A reliable assessment of the wave energy resources must be performed before they can be exploited. First, for a water depth in offshore waters of China, a parameterized wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power density. Second, wave heights and wind speeds on the surface of the China＇s seas are retrieved from an AVISO multi-satellite altim-eter data set for the period from 2009 to 2013. Three mean wave period inversion models are developed and used to calculate the wave energy period. Third, a practical application value for developing the wave energy is analyzed based on buoy data. Finally, the wave power density is then calculated using the wave field data. Using the distribution of wave power density, the energy level frequency, the time variability indexes, the to-tal wave energy and the distribution of total wave energy density according to a wave state, the offshore wave energy in the China＇s seas is assessed. The results show that the areas of abundant and stable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, southeast of Taiwan in the China＇s seas; the wave power density values in these areas are approximately 14.0–18.5 kW/m. The wave energy in the China’s seas presents obvious seasonal variations and optimal seasons for a wave energy utilization are in winter and autumn. Except for very coastal waters, in other sea areas in the China＇s seas, the energy is primarily from the wave state with 0.5 m≤Hs≤4 m, 4 s≤Te≤10 s whereHs is a significant wave height andTe is an energy period; within this wave state, the wave energy accounts for 80% above of the total wave energy. This characteristic is advantageous to designing wave energy convertors （WECs）. The practical application value of the wave energy is higher which can be as an effective supplement for an energy con-sumption in some areas. The above results are consistent with the wave model which indicates fully that this new microwave remote sensing method altimeter is effective and feasible for the wave energy assessment.

The temporal and spatial patterns and potential evaluation of China＇s energy resources development

The issue of China＇s energy supply security is not only the key problem which af- fects China＇s rapid and sustainable development in the 21st century, but also the one which international attention focuses on. Based on the notable characteristic of spatial imbalance between energy production and consumption in China, this paper takes the evolution of China＇s primary energy resources development（excluding hydropower） from 1949 to 2007 as the study object, with the aim to sum up the evolutive characteristics and laws of China＇s energy resources development in the past nearly 60 years. Then, based on comprehensive considerations of coal＇s, oil＇s and natural gas＇s basic reserves, qualities, geological conditions production status, and ecological service function of every province, this paper adopts development potential index （DP）to evaluate the development potential of every province＇s en- ergy resources, and divide them into different ranks. Conclusions are drawn as follows： （1） Generally speaking, China＇s gross energy production was increasing in waves from 1949 to 2007. From the viewpoint of spatial patterns, China＇s energy resources development has shown a characteristic of ＂concentrating to the north and central areas, and evolving from linear-shaped to ＂T-shaped＂ pattern gradually since 1949. （2） The structure evolution of China＇s energy resources development in general has shown a trend of ＂coal proportion is dominant but decreasing, while oil and gas proportions are increasing＂ since 1949. （3） At the provincial scale, China＇s energy resources development potential could be divided into large, sub-large, general and small ranks, four in all. In the future, the spatial pattern of China＇s energy production will evolve from ＂T-shaped＂ to ＂R-shaped pattern＂. These conclusions will help to clarify the temporal and spatial characteristics and laws of China＇s energy resources development, and will be beneficial for China to design scientific and rational energy development strategies and plans, coordinate spatial imbalance of energy production and consumption, ensure national energy supply, avoid energy resources waste and disorderly development, and promote regional sustainable development under the globalization back-ground with changeful international energy market.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Distribution,exploitation,and utilization of intermediate-to-deep geothermal resources in eastern China

The part of China,east of the Hu Huanyong Line,is commonly referred to as eastern China.It is characterized by a high population density and a well-developed economy;it also has huge energy demands.This study assesses and promotes the large-scale development of geothermal resources in eastern China by analyzing deep geological structures,geothermal regimes,and typical geothermal systems.These analyses are based on data collected from geotectology,deep geophysics,geothermics,structural geology,and petrology.Determining the distribution patterns of intermediate-to-deep geothermal resources in the region helps develop prospects for their exploitation and utilization.Eastern China hosts superimposed layers of rocks from three major,global tectonic domainsd namely Paleo-Asian,Circum-Pacific,and Tethyan rocks.The structure of its crust and mantle exhibits a special flyover pattern,with basins and mountains as well as well-spaced uplifts and depressions alternatively on top.The lithosphere in Northeast China and North China is characterized by a thin,low density crust and mantle,whereas the lithosphere in South China has a thin,low density crust and a thick,high density mantle.The middle and upper crust contain geobodies with high conductivity and low velocity,with varying degrees of development that create favorable conditions for the formation and enrichment of geothermal resources.Moderate-to-high temperature geothermal resources are distributed in the MesozoiceCenozoic basins in eastern China,although moderate temperature geothermal resources with low abundance dominate.Porous sandstone reservoirs,karstified fractured-vuggy carbonate reservoirs,and fissured granite reservoirs are the main types of geothermal reservoirs in this region.Under the currently available technical conditions,the exploitation and utilization of geothermal resources in eastern China favor direct utilization over large-scale geothermal power generation.In Northeast China and North China,geothermal resources could be applied for large-scale geothermal heating purposes;geothermal heating could be applied during winter along parts of the Yangtze River while geothermal cooling would be more suitable for summer there;geothermal cooling could also be applied to much of South China.Geothermal resources can also be applied to high value-added industries,to aid agricultural practices,and for tourism.

Spatiotemporal characteristics of wind energy resources from 1960 to 2016 over China

In the paper,daily near-surface wind speed data from 462 stations are used to study the spatiotemporal characteristics of the annual and seasonal mean wind speed(MWS)and effective wind energy density(EWED)from 1960 to 2016,through the methods of kriging interpolation,leastsquares,correlation coefficient testing,and empirical orthogonal function(EOF)analysis.The results show that the annual MWS is larger than 3 m s-1 and the EWED is larger than 75 W m-2 in northern China and parts of coastal areas.However,the MWS and EWED values in southern China are all smaller than in northern China.Over the past 50 years,the annual and seasonal MWS in China has shown a significant decreasing trend,with the largest rate of decline in spring for northern China and winter for coastal areas.The annual MWS in some areas of Guangdong has an increasing trend,but it shows little change in southwestern China,South China,and west of Central China.Where the MWS is high,the rate of decline is also high.The main spatial distributions of the annual MWS and the annual EWED show high consistency,with a decreasing trend year by year.The decreasing trend of wind speed and wind energy resources in China is mainly related to global warming and land use/cover change.

Discussion on the Assessment Method of Wind Energy Resources by Using Regional Meteorological Data

The exploitation status of wind energy resources was analyzed, and the distribution of wind energy resources and regional meteorological stations were introduced, and then the assessment method of wind energy resources by using data from regional meteorological station was studied taking Huangjin Regional Meteorological Station in Xinning County in Hunan Province for example, besides, corresponding software was compiled. By means of SQL database and program, the method was used simply and easily and had positive meaning for the development of wind energy resources and excavation of wind farm in inland region.