“双碳”背景下,为促进能源绿色低碳转型,提出一种考虑绿证-碳交易联合机制与混合博弈的综合能源系统(integrated energy system, IES)低碳经济调度模型。首先,构建混合博弈双层模型,上层模型将系统运营商作为领导者,以最大化自身收益...“双碳”背景下,为促进能源绿色低碳转型,提出一种考虑绿证-碳交易联合机制与混合博弈的综合能源系统(integrated energy system, IES)低碳经济调度模型。首先,构建混合博弈双层模型,上层模型将系统运营商作为领导者,以最大化自身收益为目标制定购售能价格策略,下层模型将氢能多元利用的综合能源系统(hydrogen multienergy integrated energy system, HMIES)联盟作为跟随者,以联盟整体经济性最优为目标优化自身出力;然后,在HMIES联盟的合作博弈模型中引入绿证-碳联合交易机制,提高整体低碳经济性,同时结合合作博弈理论分析合作成立条件,并基于Shapley值法对合作剩余进行合理分配,实现联盟内部碳捕集电厂主体与新能源发电主体的协同互动;最后,采用遗传算法联合混合整数线性规划(genetic algorithm and the mixed integer linear programming, GAMILP)进行求解。通过算例仿真验证了所提策略的有效性,结果表明所提模型可有效降低碳排放、提升系统经济性。展开更多
This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objec...This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.展开更多
The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used...The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used to transport the jobs from the processing shop to the assembly shop,where they are assembled.Therefore,studying the integrated scheduling problem with its processing,transportation,and assembly stages is extremely beneficial and significant.This research studies the three-stage flexible job shop scheduling problem with assembly and AGV transportation(FJSP-T-A),which includes processing jobs,transporting them via AGVs,and assembling them.A mixed integer linear programming(MILP)model is established to obtain optimal solutions.As the MILP model is challenging for solving large-scale problems,a novel co-evolutionary algorithm(NCEA)with two different decoding methods is proposed.In NCEA,a restart operation is developed to improve the diversity of the population,and a multiple crossover strategy is designed to improve the quality of individuals.The validity of the MILP model is proven by analyzing its complexity.The effectiveness of the restart operator,multiple crossovers,and the proposed algorithm is demonstrated by calculating and analyzing the RPI values of each algorithm's results within the time limit and performing a paired t-test on the average values of each algorithm at the 95%confidence level.This paper studies FJSP-T-A by minimizing the makespan for the first time,and presents a MILP model and an NCEA with two different decoding methods.展开更多
The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a nove...The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.展开更多
文摘“双碳”背景下,为促进能源绿色低碳转型,提出一种考虑绿证-碳交易联合机制与混合博弈的综合能源系统(integrated energy system, IES)低碳经济调度模型。首先,构建混合博弈双层模型,上层模型将系统运营商作为领导者,以最大化自身收益为目标制定购售能价格策略,下层模型将氢能多元利用的综合能源系统(hydrogen multienergy integrated energy system, HMIES)联盟作为跟随者,以联盟整体经济性最优为目标优化自身出力;然后,在HMIES联盟的合作博弈模型中引入绿证-碳联合交易机制,提高整体低碳经济性,同时结合合作博弈理论分析合作成立条件,并基于Shapley值法对合作剩余进行合理分配,实现联盟内部碳捕集电厂主体与新能源发电主体的协同互动;最后,采用遗传算法联合混合整数线性规划(genetic algorithm and the mixed integer linear programming, GAMILP)进行求解。通过算例仿真验证了所提策略的有效性,结果表明所提模型可有效降低碳排放、提升系统经济性。
文摘This framework proposes a heuristic algorithm based on LP （linear programming） for optimizing the electricity cost in large residential buildings, in a smart grid environment. Our heuristic tackles large multi-objective energy allocation problem （large number of appliances and high time resolution）. The primary goal is to reduce the electricity bills, and discomfort factor. Also, increase the utilization of domestic renewable energy, and reduce the running time of the optimization algorithm. Our heuristic algorithm uses linear programming relaxation, and two rounding strategies. The first technique, called CR （cumulative rounding）, is designed for thermostatic appliances such as air conditioners and electric heaters, and the second approach, called MCR （minimum cost rounding）, is designed for other interruptible appliances. The results show that the proposed heuristic algorithm can be used to solve large MILP （mixed integer linear programming） problems and gives a decent suboptimal solution in polynomial time.
基金Supported by National Natural Science Foundation of China(Grant Nos.52205529 and 62303204)the Youth Innovation Team Program of Shandong Higher Education Institution(Grant No.2023KJ206)the Guangyue Youth Scholar Innovation Talent Program support received from Liaocheng University(Grant No.LCUGYTD2022-03)。
文摘The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used to transport the jobs from the processing shop to the assembly shop,where they are assembled.Therefore,studying the integrated scheduling problem with its processing,transportation,and assembly stages is extremely beneficial and significant.This research studies the three-stage flexible job shop scheduling problem with assembly and AGV transportation(FJSP-T-A),which includes processing jobs,transporting them via AGVs,and assembling them.A mixed integer linear programming(MILP)model is established to obtain optimal solutions.As the MILP model is challenging for solving large-scale problems,a novel co-evolutionary algorithm(NCEA)with two different decoding methods is proposed.In NCEA,a restart operation is developed to improve the diversity of the population,and a multiple crossover strategy is designed to improve the quality of individuals.The validity of the MILP model is proven by analyzing its complexity.The effectiveness of the restart operator,multiple crossovers,and the proposed algorithm is demonstrated by calculating and analyzing the RPI values of each algorithm's results within the time limit and performing a paired t-test on the average values of each algorithm at the 95%confidence level.This paper studies FJSP-T-A by minimizing the makespan for the first time,and presents a MILP model and an NCEA with two different decoding methods.
基金Fundamental Research Funds for the Central Universities(2024JBZX038)National Natural Science F oundation of China(62076023)。
文摘The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.
