为提高风电并网效益,减少弃风,在电网中接入电池储能电站(battery energy storage power station,BESPS)。首先,建立了考虑损耗成本的BESPS调度特性模型,该损耗成本由BESPS投资成本与调度区间内的充放电循环次数估算;然后,在考虑风电可...为提高风电并网效益,减少弃风,在电网中接入电池储能电站(battery energy storage power station,BESPS)。首先,建立了考虑损耗成本的BESPS调度特性模型,该损耗成本由BESPS投资成本与调度区间内的充放电循环次数估算;然后,在考虑风电可信容量的基础上构建了BESPS调度模型。某些情况下,系统很难全额消纳风电,因此,调度模型具有运行成本最小与风电接纳最大2个不同维度的优化目标。为求解此问题,基于隶属度函数将2个子优化目标模糊化,构建了基于最大满意度的单目标优化模型,并采用GAMS软件提供的CPLEX求解器对其进行求解。基于我国东北某实际省级电网的仿真实验说明:BESPS接入可显著提升系统风电接纳能力,且基于最大满意度的优化调度可给出更为合理的结果。展开更多
To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated ...To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.展开更多
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.展开更多
文摘为提高风电并网效益,减少弃风,在电网中接入电池储能电站(battery energy storage power station,BESPS)。首先,建立了考虑损耗成本的BESPS调度特性模型,该损耗成本由BESPS投资成本与调度区间内的充放电循环次数估算;然后,在考虑风电可信容量的基础上构建了BESPS调度模型。某些情况下,系统很难全额消纳风电,因此,调度模型具有运行成本最小与风电接纳最大2个不同维度的优化目标。为求解此问题,基于隶属度函数将2个子优化目标模糊化,构建了基于最大满意度的单目标优化模型,并采用GAMS软件提供的CPLEX求解器对其进行求解。基于我国东北某实际省级电网的仿真实验说明:BESPS接入可显著提升系统风电接纳能力,且基于最大满意度的优化调度可给出更为合理的结果。
基金The National Natural Science Foundation of China(No60673054,90412012)
文摘To achieve high quality of service （QoS） on computational grids, the QoS-aware job scheduling is investigated for a hierarchical decentralized grid architecture that consists of multilevel schedulers. An integrated QoS-aware job dispatching policy is proposed, which correlates priorities of incoming jobs used for job selecting at the local scheduler of the grid node with the job dispatching policies at the global scheduler for computational grids. The stochastic high-level Petri net （SHLPN） model of a two-level hierarchy computational grid architecture is presented, and a model refinement is made to reduce the complexity of the model solution. A performance analysis technique based on the SHLPN is proposed to investigate the QoS-aware job scheduling policy. Numerical results show that the QoS-aware job dispatching policy outperforms the QoS-unaware job dispatching policy in balancing the high-priority jobs, and thus enables priority-based QoS.
基金Supported by the National Natural Science Foundation of China(21276078)"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500)the Shanghai Science and Technology Program(13QH1401200)
文摘Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
