Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant pe...Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
Dear Editor,This letter addresses the challenge of achieving robust global coordination in multi-agent systems(MASs)subject to heterogeneous actuator saturation and additive input disturbances.We develop a novel distr...Dear Editor,This letter addresses the challenge of achieving robust global coordination in multi-agent systems(MASs)subject to heterogeneous actuator saturation and additive input disturbances.We develop a novel distributed control framework that strategically integrates a redesigned saturation function to handle the nonlinear actuator constraint and a high-gain feedback mechanism for effective disturbance rejection.展开更多
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t...This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.展开更多
To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produce...To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.展开更多
We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the ...We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.展开更多
Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of soci...Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpi...As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.展开更多
Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges...Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).展开更多
Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and ...Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.展开更多
Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly im...Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.展开更多
In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model ave...In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.展开更多
Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in a...Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.展开更多
In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence ...In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence of pipeline hydraulic residence time(HRT)on disinfection efficiency,by-product formation,microbial activity,and biofilm growth were considered.The results show that both microbial activities and metabolite secretion were stimulated by increasing HRT,aggravating the potential risk of microbial pollution in DWDS.The enhanced microbial metabolism could further weaken disinfection efficiency by consuming extra residual Chlorine,after which the formation of disinfection by-products was facilitated.Residual Chlorine was found negatively correlated with HRT.With prolonging HRT from 5 to 40 h,the concentration of disinfection by-products(Chlorate,Chlorite,and Trichloromethane)was on a continuously increasing trend by 37%,140%,and 75%,respectively.But the water kept in pipeline still reliably satisfied the Standards for drinking water quality in China(GB5749–2022).Besides,more biofilm with denser morphologies developed on rubber pipeline gaskets rather than the iron/plastic ones.Rubber material was inappropriate for DWDS due to its potential risk of secondary biological pollution.Prolonging HRT also enhanced the accumulation of dominant bacteria(e.g.Bradyrhizobium and Obscuribacter)and decreased microbial diversity.展开更多
Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most species...Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is...The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.展开更多
This paper takes the water body of Daliao River-Liaodong Bay as the research object,divides it into three regions:river,estuary,and offshore,and analyzes the changes of antibiotics and antibiotic resistance genes(ARGs...This paper takes the water body of Daliao River-Liaodong Bay as the research object,divides it into three regions:river,estuary,and offshore,and analyzes the changes of antibiotics and antibiotic resistance genes(ARGs)from inland rivers to the sea and the environmental impact factors from this perspective.The results showed that in general,the pollution of antibiotics and ARGs in Daliao River-Liaodong Bay belonged to the medium-low level,and levels of antibiotics and ARGs were nd–106.23 ng/L and nd–1.95×10^(8)copies/L,respectively.The concentrations and types of antibiotics and ARGs decreased from inland to sea regions.Analysis of the distributional characteristics of antibiotics and ARGs from a regionalized perspective revealed significant differences among the three regions in sulfonamide antibiotics,tetracycline antibiotics,and dominant ARGs.Sulfonamide antibiotic levels were significantly higher in the estuarine zone than in the riverine and offshore zones;tetracycline antibiotic levels were significantly higher in the riverine and estuarine zones than in the offshore zone.Aminoglycosides were dominant in the riverine and estuarine zones,and macrolides were dominant in the offshore zone.We characterized the effects of environmental factors on the assignment of antibiotics and ARGs and found that overall temperature contributed the most to variation in antibiotics and ARGs;the contribution of dissolved oxygen was the lowest.The estuarine zone was most affected by these factors,followed by the offshore zone and finally the riverine zone.展开更多
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.12271034the Open Fund Project of Key Laboratory of Market Regulation under Grant No.2023SYSKF02003。
文摘Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
基金supported in part by the National Natural Science Foundation of China(62522313,62473207,U25A20301)the Fundamental Research Funds for the Central Universities(2024SMECP03)。
文摘Dear Editor,This letter addresses the challenge of achieving robust global coordination in multi-agent systems(MASs)subject to heterogeneous actuator saturation and additive input disturbances.We develop a novel distributed control framework that strategically integrates a redesigned saturation function to handle the nonlinear actuator constraint and a high-gain feedback mechanism for effective disturbance rejection.
基金supported by the National Key Research and Development Program of China(2025YFE0213100)the National Natural Science Foundation of China(62422315,62573348)+1 种基金the Natural Science Basic Research Program of Shaanxi(2025JC-YBMS-667)the“Shuang Yi Liu”Construction Foundation(25GH02010366)。
文摘This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.
基金National Natural Science Foundation of China(12125509,11961141003,12275361,U2267205,12175152,12175121)National Key Research and Development Project(2022YFA1602301)Continuous-support Basic Scientific Research Project。
文摘To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.
基金supported by Xinjiang Normal University Outstanding Young Teacher Research Launch Fund Project(Grant No.XJNU202116)。
文摘We study a finite number of independent random walks with subexponentially distributed increments and negative drifts.We extend the one-dimensional results to finite and fully general stopping times.Assuming that the distribution of the lengths of these intervals is relatively light compared to the distribution of the increments of the random walks,we derive the asymptotic tail distribution of the partial maximum sum over the random time interval.
基金supported by the Central Government Guidance Funds for Local Science and Technology Development Program(grant no.ZYYD2025ZY21)the Science and Technology Plan Project of the Xinjiang Production and Construction Corps(2023AB036)+1 种基金the Xinjiang Meteorological Bureau High-Level Key Talent Programthe Natural Science Foundation of the Xinjiang Uygur Autonomous Region(2023D01A17 and 2025D01A109).
文摘Low-visibility phenomena strongly impact the environment,as well as transportation,aviation and other fields that are closely related to people's livelihoods;thus,they represent important ecological issues of social concern.Based on observation data concerning low-visibility phenomena derived from 105 national meteorological stations in Xinjiang,China over the past 20 years,we systematically analyzed the differences between manual and instrument observations for six types of low-visibility phenomena,with a focus on exploring their spatiotemporal distribution characteristics using instrument data.The results revealed that low-visibility phenomena were dominated by fog-and haze-related events(mist,fog,and haze)in northern Xinjiang and dust-related events(dust storms,blowing sand,and floating dust)in southern Xinjiang,with transitional characteristics observed in eastern Xinjiang.Compared with manual observations,the instrument measurements significantly improved the fine-scale low-visibility phenomenon identification process.On the basis of the instrument observation data,spatial-dimension analysis results indicated that low-visibility phenomena in Xinjiang were significantly influenced by terrain factors.Constrained by the Tianshan Mountains,haze-like phenomena formed a core agglomeration area in northern Xinjiang,whereas dust-and sand-related phenomena radiated outward,with the Taklimakan Desert at the center.Moreover,the gripping effect of the terrain promoted dust transmission along low-altitude channels.Temporally,fog-and haze-related phenomena occurred mainly during autumn and winter,and the proportion of these events decreased from 76.7%to 55.1%.The fog-and haze-related phenomena demonstrated a U-shaped rebound trend,while the proportion of mist phenomena decreased by 34.2%.Dust storms occurred during spring,accounting for 23.3%to 44.9%of all storms.Instrument measurement technology has the advantages of high spatial and temporal resolutions and multiparameter coordination but provides a limited dust-haze mixed-pollution identification capacity.This study provides crucial reference data for enhancing the understanding of low-visibility events in Xinjiang and the potential responses while improving the accuracy of pollution source tracking and meteorological process diagnosis tasks.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
文摘As global climate change intensifies,alpine plants are facing dual pressures of habitat loss and rapid environmental degradation.As one of the world's most biodiverse countries,China's potential shifts in alpine plant distribution and their profound impact on fragile ecosystems have become a focus of ecological research and conservation efforts,with increasing urgency.Meconopsis,a typical representative of Chinese alpine plants,exhibits diverse adaptability,making it an ideal model for studying how alpine species respond to extreme environmental changes.A lack of comprehensive genus-level analyses may hinder the development of long-term conservation and management strategies.Given the genus's ecological importance,vulnerability,and the risk of trait homogenization in genus-level modeling,there is an urgent need to assess its future distribution patterns,migration trends,and adaptive mechanisms based on habitat classification.In this study,we employed the Maxent model,integrating multidimensional environmental variables,to develop genus-level models and representative habitat-based models(forest,meadows,and periglacial).Results indicate a northwestward expansion and southeastward contraction of suitable habitats under future climate scenarios,with migration patterns in latitude and elevation showing stage-specific characteristics.Key environmental factors varied across models but were mostly associated with seasonal growth traits and microhabitat conditions,highlighting both the universal ecological requirements and niche differentiation within Meconopsis.Based on these findings,we propose a dynamic conservation strategy framework informed by stage-specific responses and habitat differences.Future efforts should focus on incorporating alpine-specific environmental variables and optimizing specimen collection strategies to enhance model performance and support landscape planning and biodiversity conservation.
基金the Chinese Academy of Sciences Research Center for Ecology and Environment of Central Asia(RCEECA),the construction and joint research for the China-Tajikistan“Belt and Road”Joint Laboratory on Biodiversity Conservation and Sustainable Use(2024YFE0214200)the Shanghai Cooperation Organization Partnership and International Technology Cooperation Plan of Science and Technology Projects(2023E01018,2025E01056)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI)(2024VBC0006).
文摘Tajikistan represents a core region of the biodiversity hotspot in Central Asian mountains and has exceptional vascular plant diversity.However,the species diversity of the country faces urgent conservation challenges.There has been a lack of a comprehensive and multidimensional assessment to inform strategic conservation planning.Therefore,this study integrated 4 key biodiversity indices including species richness(SR),phylogenetic diversity(PD),threatened species richness(TSR),and endemic species richness(ESR)to map species diversity distribution patterns,identify conservation gaps,and elucidate their effects of climatic factors.This study revealed that species diversity shows a clear trend of decreasing from the western region to the eastern region of Tajikistan.The central–western mountains(specifically the Gissar-Darvasian and Zeravshanian regions)emerge as irreplaceable biodiversity hotspots.However,we found a severe spatial mismatch between these priority areas and the existing protected areas(PAs).Protection coverage for all hotspots was alarmingly low,ranging from 31.00%to 38.00%.Consequently,a critical 64.80%of integrated priority areas fall outside of the current PAs,representing a major conservation gap.This study identified precipitation seasonality and isothermality as the principal drivers,collectively explaining over 50.00%of the diversity variation and suggesting high vulnerability to hydrological shifts.Furthermore,we detected significant geographic sampling bias in the public biodiversity databases,with the most critical hotspot being systematically under-sampled.This study provides a robust scientific basis for conservation action,highlighting the urgent need to strategically expand PAs in the under-protected southwestern region and to mitigate critical sampling gaps through targeted data digitization and field surveys.These measures are indispensable for securing Tajikistan’s unique biodiversity and achieving the Kunming-Montreal Global Biodiversity Framework Target 3(“30×30 Protection”).
基金supported by ScientificResearch Fund of National Health Commission of the People’s Republic of China-Major Science and Technology Program for Medicine and Health in Zhejiang Province(WKJ-ZJ-2406).
文摘Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.
基金supported by the National Key R&D Program of China(2018YFB0904700).
文摘Large-scale access of distributed photovoltaic(PV)in distribution networks(DNs),if not properly evaluated,brings several operational problems.Uncertainties arising from both PV outputs and load demand significantly impact evaluation results.To address this issue,this paper proposes a possibilistic approach to evaluate PV hosting capacity(PVHC).First,possibility distribution is used to model load demand in order to reflect uncertainties associated with human factor,whereas the interval model is applied to deal with uncertainties of PV outputs.Second,a voltage deterioration index is proposed considering overvoltage risk of entire system on time scale.After that,possibilistic PVHC evaluation method based on this index is proposed.A 6-bus system is used to illustrate advantages of the proposed method,followed by a discussion of role of PVHC possibility distribution in actual decision-making of utilities.Moreover,sensitivity of simulation parameters is analyzed to reduce computational burden.Finally,the proposed method is tested on the IEEE 123-bus DN to validate adaptability to a larger system and to analyze impact of PVHC results against different acceptable values set by utilities.
基金supported by Youth Academic Innocation Team Construction project of Capital University of Economics and Business under Grant No.QNTD202303supported by the Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027the National Natural Science Foundation of China under Grant Nos.12031016,12531012 and 12426308。
文摘In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0208)the National Natural Science Foundation of China(Nos.42171148 and 42330512)the Key R&D Project from the Science and Technology Department of Tibet(No.XZ202501ZY0030).
文摘Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.
基金supported by the National Natural Science Foundation of China(Nos.52170070,52400022,and 52200088)the Youth S&T Talent Support Programme of Guangdong Provincial Association for Science and Technology(GDSTA)(No.SKXRC202406)+1 种基金the“One hundred Youth”Science and Technology Plan,Guangdong University of Technology,China(No.263113906)China Postdoctoral Science Foundation(No.2023M740754).
文摘In this study,the effects of low-dose sodium hypochlorite disinfection on water quality and biofilm growth in drinking water distribution systems(DWDS)after ultrafiltration pretreatment was investigated.The influence of pipeline hydraulic residence time(HRT)on disinfection efficiency,by-product formation,microbial activity,and biofilm growth were considered.The results show that both microbial activities and metabolite secretion were stimulated by increasing HRT,aggravating the potential risk of microbial pollution in DWDS.The enhanced microbial metabolism could further weaken disinfection efficiency by consuming extra residual Chlorine,after which the formation of disinfection by-products was facilitated.Residual Chlorine was found negatively correlated with HRT.With prolonging HRT from 5 to 40 h,the concentration of disinfection by-products(Chlorate,Chlorite,and Trichloromethane)was on a continuously increasing trend by 37%,140%,and 75%,respectively.But the water kept in pipeline still reliably satisfied the Standards for drinking water quality in China(GB5749–2022).Besides,more biofilm with denser morphologies developed on rubber pipeline gaskets rather than the iron/plastic ones.Rubber material was inappropriate for DWDS due to its potential risk of secondary biological pollution.Prolonging HRT also enhanced the accumulation of dominant bacteria(e.g.Bradyrhizobium and Obscuribacter)and decreased microbial diversity.
基金supported by the National Science Foundation of China(32201643)the Key Research Projects of Yibin,research and integrated demonstration and key technologies for smart bamboo industry(YBZD2024-1).
文摘Climate change disrupts the distribution of species and restructures their richness patterns.The genus of Asian bamboo,Phyllostachys,possesses significant ecological and economic values,and represents the most speciesrich genus in the Bambusoideae subfamily.Based on the distribution data of 46 species and 20 environmental variables,we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO_(2) emission scenarios.The results showed that the MaxEnt model had a good predictive ability,with a mean area under the working characteristic curve(AUC value)of 0.91 for all species.The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation,variations of seasonal precipitation,and mean diurnal range.Phyllostachys species are currently concentrated in southeastern China.Under future climate projections,18 species exhibited significant habitat contraction across three or more future climate scenarios,but suitable habitats for other species will expand.This enhancement is most pronounced under the extreme climate scenario(2090s-SSP585),primarily driven by high species gains contributing to elevated turnover values across scenarios.The center of maximum richness will progressively shift southwestward over time.Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China’s carbon sequestration capacity.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金part supported by the National Natural Science Foundation(62203034,62273126,62203035)the Ling-Yan Research and Development Project of Zhejiang Province of China(2023C03185)。
文摘The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.
基金supported by the Key R&D Projects in Hainan Province(No.ZDYF2024SHFZ085)Hainan Provincial Joint Project of Sanya Yazhou Bay Science and the Technology City(No.2021CXLH0009)the National Natural Science Foundation of China(No.42376234).
文摘This paper takes the water body of Daliao River-Liaodong Bay as the research object,divides it into three regions:river,estuary,and offshore,and analyzes the changes of antibiotics and antibiotic resistance genes(ARGs)from inland rivers to the sea and the environmental impact factors from this perspective.The results showed that in general,the pollution of antibiotics and ARGs in Daliao River-Liaodong Bay belonged to the medium-low level,and levels of antibiotics and ARGs were nd–106.23 ng/L and nd–1.95×10^(8)copies/L,respectively.The concentrations and types of antibiotics and ARGs decreased from inland to sea regions.Analysis of the distributional characteristics of antibiotics and ARGs from a regionalized perspective revealed significant differences among the three regions in sulfonamide antibiotics,tetracycline antibiotics,and dominant ARGs.Sulfonamide antibiotic levels were significantly higher in the estuarine zone than in the riverine and offshore zones;tetracycline antibiotic levels were significantly higher in the riverine and estuarine zones than in the offshore zone.Aminoglycosides were dominant in the riverine and estuarine zones,and macrolides were dominant in the offshore zone.We characterized the effects of environmental factors on the assignment of antibiotics and ARGs and found that overall temperature contributed the most to variation in antibiotics and ARGs;the contribution of dissolved oxygen was the lowest.The estuarine zone was most affected by these factors,followed by the offshore zone and finally the riverine zone.
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
