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Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs 认领 引用 被引量：5
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作者 Chao Liu Weiru Wang +2 位作者 Jing Li Xinyuan Liu Yongning Chi 《Global Energy Interconnection》 EI CSCD 2024年第4期377-390,共14页
With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This st... With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits. 展开更多
关键词 Demand response Combined cooling Heating and power system Carbon transaction costs Flexible electric and thermal loads Optimal scheduling
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Cost-Optimal Building Energy System Scheduling Integrating Solar Irradiance Forecasting via LSTM-Attention-TCN Model 认领 引用 被引量：1
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作者 Zhengtian Wu Jianyu Li +7 位作者 Yang Gao Chuangyin Dang Chao Tang Yuansheng Li Xinmiao Wang Jinpeng Chen Hongbo Gao Xinyin Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期695-708,共14页
Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps acco... Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps accommodate the increasing share of renewables;however,the stochastic and intermittent nature of solar power still poses challenges to supply reliability.This study proposes a photovoltaic(PV)‐oriented storage scheduling strategy,in which short‐term PV generation forecasts are applied to guide the operation of a building power supply network consisting of photovoltaic panels,the grid,and energy storage systems.The forecasting approach employs a hybrid framework combining a Long Short‐Term Memory(LSTM)network to capture temporal dependencies,an attention mechanism to emphasise critical time steps,and a Temporal Convolutional Network(TCN)to map the enhanced features to PV outputs.Experimental evaluation using historical datasets under multiple weather conditions and time periods shows that the proposed LSTM‐Attention‐TCN model achieves a mean absolute error(MAE)of 20.45 W/m2 and a Nash–Sutcliffe efficiency(NSE)of 0.94,outperforming both standalone LSTM and TCN models as well as their hybrid variants in terms of accuracy and robustness.By providing high‐accuracy solar irradiance forecasts to guide energy storage operation and grid interaction,the proposed model enables more efficient and economical scheduling of building energy systems.Compared with an uncontrolled scenario,the LSTM‐Attention‐TCN‐based scheduling reduces the total operating cost by approximately 52.1%,and achieves an additional 16.5%reduction compared to a conventional strategy without predictive coordination.In addition,compared to other hybrid forecasting models such as LSTM‐TCN and TCN‐Attention,the proposed model achieves the lowest total cost of CNY 14.83 and demonstrates superior scheduling efficiency,thereby enhancing the stability and flexibility of building energy utilization. 展开更多
关键词 cost optimization deep learning energy storage systems optimal scheduling solar irradiance forecasting
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
3
作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Optimization and Scheduling Method for Wind-Solar-Thermal-Storage Power System of Multiple Energy Stations Using Correlation-IGDT 认领 引用
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作者 Yang Liu Yinguo Yang +4 位作者 Pingping Xie Qiuyu Lu Yue Chen Zhanpeng Xu Zejie Huang 《Energy Engineering》 EI 2026年第5期154-170,共17页
With the large-scale integration of wind and solar energy into the power grid,the power system is facing uncertainty challenges in multiple links,such as source,grid,and load.How to efficiently dispatch flexible resou... With the large-scale integration of wind and solar energy into the power grid,the power system is facing uncertainty challenges in multiple links,such as source,grid,and load.How to efficiently dispatch flexible resources,such as energy storage,has become an urgent problem to be solved.To this end,this paper considers the correlation between new energy stations due to natural conditions,uses Vine-Copula theory to describe the correlation characteristics of the output of multiple new energy stations,and proposes a wind solar new energy output scenario generation method based on Vine-Copula theory;Then,to develop the optimal scheduling and operation plan,considering the goal of minimizing operating costs within a scheduling cycle,combined with the scenario of output of wind and solar energy,an optimization and scheduling model for wind-solar-thermal-storage power system operation of multiple energy stations was constructed;On this basis,considering the difficulty in obtaining the probability distribution of load uncertainty,a risk-averse model and a risk-seeking model based on information Gap Decision Theory(IGDT)were constructed,and a multi energy station power system operation optimization scheduling method based on correlation-IGDT was proposed.By setting risk strategies and risk deviation factors,the power system operation scheduling scheme under this strategy can be obtained.Simulation experiments were conducted based on an improved IEEE39 node system for verification,and the results showed that compared to traditional methods that do not consider correlation,this method can reduce thermal power costs by 0.63%and energy storage costs by 10.56%.Meanwhile,Monte Carlo sampling analysis shows that the model has good accuracy and stability within the range of load disturbances.Further analysis shows that under the risk avoidance strategy,the maximum power variation of thermal power is controlled at 284 MW,with an average of 172 MW;while under the risk acceptance strategy,the maximum variation is 198 MW,with an average of 127 MW,significantly improving the system’s adaptability and operational efficiency to uncertain environments.The main contribution of this article is to integrate the modeling of new energy correlation with information gap decision-making and construct a power system scheduling optimization framework for multiple uncertain factors,which has good promotion value and practical application potential. 展开更多
关键词 Vine copula information gap decision theory(IGDT) wind and solar energy optimal scheduling
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Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling 认领 引用
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作者 Srinivasa Gowda G.K Hayder M.A.Ghanimi +5 位作者 Sudhakar Sengan Kolla Bhanu Prakash Meshal Alharbi Roobaea Alroobaea Sultan Algarni Abdullah M.Baqasah 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期210-222,共13页
Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(I... Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE. 展开更多
关键词 Cyber-physical systems Internet of things Task scheduling optimization Gated linear unit Machine learning
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Aero-engine fleets scheduling optimization and task assignment under task-constraints 认领 引用
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作者 Xiangzhao XIA Xuyun FU Shisheng ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期355-388,共34页
The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Fram... The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Framework(DCOF)to address these challenges.The problem is decomposed into two interrelated sub-problems,scheduling optimization and task assignment,with distinct mathematical models formulated for each.For scheduling optimization,this study proposes an Improved Gravity Particle Swarm Optimization(IGPSO)algorithm.The algorithm enhances global search capability and convergence speed through dynamic weight adjustment and constraint processing repair strategies,effectively handling dynamic variations in engine health and remaining life.For task assignment,an Improved Branch-and-Price(IB&P)method is used.This method combines column generation with branch-and-bound strategies,while integrating heuristic rules and parallel computing techniques to efficiently find optimal solutions under multi-dimensional constraints.By clearly distinguishing between operational and maintenance tasks and considering their interdependencies,the proposed DCOF better captures real operational needs,improving fleet scheduling efficiency and reliability.Experimental validation and engineering simulations confirm the method’s effectiveness,showing advantages in repair balance,task assignment balance,and minimizing engine life waste.The approach enhances both usage efficiency and maintenance management of aero-engine fleets. 展开更多
关键词 Aero-engine fleets Improved branch-and-price Improved gravity particle swarm optimization Scheduling optimization Task assignment
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The Unbounded Parallel-Batching Bicriteria Scheduling with Two-Component Jobs 认领 引用
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作者 Cheng He Jing Wu +2 位作者 Hao Lin Yuan Zhang Yan Zhao 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期547-564,共18页
This paper studies a bicriteria scheduling problem on a parallel-batching machine to minimize maximum cost and makespan simultaneously.Each job has two components:standard component and specific component.Standard com... This paper studies a bicriteria scheduling problem on a parallel-batching machine to minimize maximum cost and makespan simultaneously.Each job has two components:standard component and specific component.Standard components are processed in batches.Specific components are processed individually.The processing order of two components of a job has no constraint.A job is completed only when its two components are completed.For the simultaneous optimization scheduling problem,we design an O(n4)-time algorithm. 展开更多
关键词 Bicriteria scheduling Two-component Makespan Maximum cost Pareto optimal schedules
CVaR-Based Optimal Scheduling of an Integrated Energy System with Battery Storage,Hydrogen Storage,and Demand Response 认领 引用
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作者 Delong Yan 《Journal of Electronic Research and Application》 2026年第6期154-164,共11页
High renewable penetration improves the low-carbon performance of integrated energy systems,but it also increases scheduling uncertainty and renewable curtailment risk.This paper proposes a CVaR-based optimal scheduli... High renewable penetration improves the low-carbon performance of integrated energy systems,but it also increases scheduling uncertainty and renewable curtailment risk.This paper proposes a CVaR-based optimal scheduling model for an electric-heat-hydrogen integrated energy system with battery energy storage,hydrogen storage,and demand response.The proposed model minimizes a weighted objective that combines expected operating cost and tailrisk cost,while considering electricity purchase and sale,gas consumption,carbon emissions,battery degradation,hydrogen conversion,demand response compensation,and renewable curtailment penalty.Wind power,photovoltaic generation,and electric load uncertainty are represented by multiple scenarios,and the same scenario set is used for all comparative cases to ensure fairness.Five operation schemes are studied,including no storage,battery energy storage only,hydrogen storage only,battery-hydrogen storage,and the proposed battery-hydrogen-demand response scheme.The numerical results show that the proposed scheme achieves the lowest weighted objective,expected cost,and CVaR cost.Compared with the no-storage case,the proposed scheme reduces the weighted objective from 12337.22 to 9677.39,increases renewable utilization from 92.3%to 99.8%,and reduces expected carbon emissions from 4771.88 to 2994.13.These results indicate that coordinated scheduling of battery storage,hydrogen storage,and demand response can improve economic performance,reduce operational risk,and enhance renewable energy accommodation in highrenewable integrated energy systems. 展开更多
关键词 Integrated energy system Optimal scheduling CVaR Battery energy storage Hydrogen storage Demand response Renewable curtailment
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Optimization of scheduling management for vertical transportation equipment used in the construction of high-rise buildings 认领 引用
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作者 HAN Yi 《外文科技期刊数据库（文摘版）工程技术》 2026年第6期026-030,共5页
This paper conducts a comprehensive analysis and proposes solutions to address critical challenges in the scheduling management of vertical transportation equipment during high-rise building construction. It clearly d... This paper conducts a comprehensive analysis and proposes solutions to address critical challenges in the scheduling management of vertical transportation equipment during high-rise building construction. It clearly demonstrates how high-rise construction heavily relies on vertical transportation systems, detailing the specific functional characteristics of common equipment such as construction elevators and tower cranes, while emphasizing the pivotal role of scheduling management in ensuring project timelines, controlling costs, and maintaining safety standards. The study identifies significant shortcomings in current scheduling approaches—including mismatches between equipment demand and actual availability, prolonged waiting times, and inefficient resource allocation—attributing these issues primarily to poor communication, inadequate planning, and limited flexibility in responding to changes. To address these challenges, the paper introduces an innovative scheduling optimization framework aimed at minimizing total transportation time and optimizing equipment load distribution. It elaborates on the application of genetic algorithms for multi-objective optimization calculations and operational procedures, while introducing a dynamic adjustment mechanism that adapts to real-time demand fluctuations. Additionally, the paper explores practical implementations of IoT technology in this field, proposing an integrated scheduling platform that combines equipment status data, material requirements, and worker positioning information. The study evaluates the platform's capabilities in achieving data-driven monitoring, intuitive management, and intelligent decision support, along with preliminary achievements and outcomes obtained to date. 展开更多
关键词 vertical transportation equipment scheduling high-rise building construction dynamic scheduling optimization genetic algorithm Internet of Things
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Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage 认领 引用 被引量：1
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作者 Jiacheng Guo Yimo Luo +1 位作者 Bin Zou Jinqing Peng 《Engineering》 SCIE EI CSCD 2025年第3期331-347,共17页
Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.... Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage. 展开更多
关键词 Industrial park energy system Hybrid energy storage Active energy storage Configuration optimization Day-ahead optimal scheduling
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Centralized-Distributed Scheduling Strategy of Distribution Network Based on Multi-Temporal Hierarchical Cooperative Game 认领 引用 被引量：2
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作者 Guoqing Li Jianing Li +1 位作者 Kefei Yan Jing Bian 《Energy Engineering》 EI 2025年第3期1113-1136,共24页
A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimizatio... A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas,as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally.A scheduling architecture combiningmulti-temporal scales with a three-level decision-making hierarchy is established:the overall approach adopts a centralized-distributed method,analyzing the operational characteristics and interaction relationships of the distribution network center layer,cluster layer,and transformer area layer,providing a“spatial foundation”for subsequent optimization.The optimization process is divided into two stages on the temporal scale:in the first stage,based on forecasted electricity load and demand response characteristics,time-of-use electricity prices are utilized to formulate day-ahead optimization strategies;in the second stage,based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships,rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory.Finally,a distributed optimization algorithm using the bisection method is employed to solve the constructed model.Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy. 展开更多
关键词 Photovoltaic consumption distribution area optimal scheduling cooperative game
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Bilevel Optimal Scheduling of Island Integrated Energy System Considering Multifactor Pricing 认领 引用 被引量：1
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作者 Xin Zhang Mingming Yao +3 位作者 Daiwen He Jihong Zhang Peihong Yang Xiaoming Zhang 《Energy Engineering》 EI 2025年第1期349-378,共30页
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys... In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified. 展开更多
关键词 Bilevel optimal scheduling load aggregator integrated energy operator carbon emission dynamic pricing mechanism
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Coordinated Scheduling of Electric-Hydrogen-Heat Trigeneration System for Low-Carbon Building Based on Improved Reinforcement Learning 认领 引用
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作者 Jiayun Ding Bin Chen +1 位作者 Yutong Lei Wei Zhang 《Energy Engineering》 EI 2025年第11期4561-4577,共17页
In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupli... In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling.In light of this,this study focuses on electro-thermal-hydrogen trigeneration systems,first modelling the system's scheduling optimization problem as a Markov decision process,thereby transforming it into a sequential decision problem.Based on this,this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement,aiming to minimize system operating costs and enhance the system's sustainable operation capability.Experimental results show that compared to traditional reinforcement learning algorithms,the reinforcement learning algorithm based on deep deterministic policy gradient improvement achieves improvements of 12.5%and 22.8%in convergence speed and convergence value,respectively.Additionally,under uncertainty scenarios ranging from 10%to 30%,cost reductions of 2.82%,3.08%,and 2.52%were achieved,respectively,with an average cost reduction of 2.80%across 30 simulated scenarios.Compared to the original algorithm and rule-based algorithms in multi-uncertainty environments,the reinforcement learning algorithm based on improved deep deterministic policy gradients demonstrated superiority in terms of system operating costs and continuous operational capability,effectively enhancing the system's economic and sustainable performance. 展开更多
关键词 Trigeneration low carbon building I-DDPG optimized scheduling
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Optimization of microgrid scheduling based on multi-strategy improved MOPSO algorithm 认领 引用
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作者 Yang Xue Shiwei Liang +1 位作者 Fengwei Qian Jinyi Tang 《Global Energy Interconnection》 EI CSCD 2025年第6期959-968,共10页
A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protect... A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protection.A grid-connected microgrid model containing photovoltaic cells,wind power,micro gas turbine,diesel generator,and storage battery is constructed with the aim of optimizing the multi-objective grid-connected microgrid economic optimization problem with minimum power generation cost and environmental management cost.Based on the optimization of the standard multi-objective particle swarm optimization algorithm,four strategies are introduced to improve the algorithm,namely,Logistic chaotic mapping,adaptive inertia weight adjustment,adaptive meshing using congestion distance mechanism,and fuzzy comprehensive evaluation.The proposed IMOPSO is applied to the microgrid optimization problem and the performance is compared with other unimproved multi-objective gray wolf algorithm(MOGWO),multi-objective ant colony algorithm(MOACO),and MOPSO algorithms,and the total cost of the proposed method is reduced by 3.15%,8.34%,and 10.27%,respectively.The simulation results show that IMOPSO can more effectively reduce the cost and optimize power distribution,and verify the effectiveness of the proposed method. 展开更多
关键词 Microgrid Multi-objective particle swarm System economic operation Optimal scheduling
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Two-Stage Scheduling Model for Flexible Resources in Active Distribution Networks Based on Probabilistic Risk Perception 认领 引用
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作者 Yukai Li Ruixue Zhang +3 位作者 Yongfeng Ni Hongkai Qiu Yuning Zhang Chunming Liu 《Energy Engineering》 EI 2025年第2期681-707,共27页
Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase sch... Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network. 展开更多
关键词 Core operation boundary probabilistic power flow risk perception optimize scheduling flexible resource
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Optimal Sensor Scheduling for Remote State Estimation With Partial Channel Observation 认领 引用
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作者 Bowen Sun Xianghui Cao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1510-1512,共3页
Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)fram... Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach. 展开更多
关键词 truncating simplifying remote state estimation systems optimal transmission scheduling problem threshold structure sensor scheduling optimal solution partially observable markov decision process partially observable markov decision process pomdp framework
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Optimization and Scheduling of Green Power System Consumption Based on Multi-Device Coordination and Multi-Objective Optimization 认领 引用
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作者 Liang Tang Hongwei Wang +2 位作者 Xinyuan Zhu Jiying Liu Kaiyue Li 《Energy Engineering》 EI 2025年第6期2257-2289,共33页
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of... The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels. 展开更多
关键词 Multi-objective optimization scheduling model multi-objective particle swarm optimization algorithm consumption capacity of green power wind and solar curtailment coordinated optimization of multiple devices
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A Survey of Spark Scheduling Strategy Optimization Techniques and Development Trends 认领 引用
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作者 Chuan Li Xuanlin Wen 《Computers, Materials & Continua》 SCIE EI 2025年第6期3843-3875,共33页
Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demand... Spark performs excellently in large-scale data-parallel computing and iterative processing.However,with the increase in data size and program complexity,the default scheduling strategy has difficultymeeting the demands of resource utilization and performance optimization.Scheduling strategy optimization,as a key direction for improving Spark’s execution efficiency,has attracted widespread attention.This paper first introduces the basic theories of Spark,compares several default scheduling strategies,and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency.Subsequently,existing scheduling optimization schemes are summarized based on three scheduling modes:load characteristics,cluster characteristics,and matching of both,and representative algorithms are analyzed in terms of performance indicators and applicable scenarios,comparing the advantages and disadvantages of different scheduling modes.The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments.Finally,the limitations of the existing schemes are analyzed,and prospects are envisioned. 展开更多
关键词 Spark scheduling optimization load balancing resource utilization distributed computing
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Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm 认领 引用
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作者 Zhaoyu Zeng Fuyin Ni 《Energy Engineering》 EI 2025年第3期1153-1174,共22页
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ... Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function. 展开更多
关键词 Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm
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Optimal Operation of Virtual Power Plants Based on Revenue Distribution and Risk Contribution 认领 引用
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作者 Heping Qi Wenyao Sun +2 位作者 Yi Zhao Xiaoyi Qian Xingyu Jiang 《Energy Engineering》 EI 2026年第1期373-392,共20页
Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic effici... Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation. 展开更多
关键词 Virtual power plant carbon trading green certificate trading CVaR shapley risk contribution optimal scheduling
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