根据风电预测精度随时间尺度的减小逐级提高的固有特性,建立了多时间尺度多目标协调调度的滚动优化模型。依据风电并网标准与分布式电池储能系统(distributed battery energy storage system,DBESS)能快速修正风电波动的低频分量,以系...根据风电预测精度随时间尺度的减小逐级提高的固有特性,建立了多时间尺度多目标协调调度的滚动优化模型。依据风电并网标准与分布式电池储能系统(distributed battery energy storage system,DBESS)能快速修正风电波动的低频分量,以系统经济性最优和弃风电量最小为目标函数建立优化模型,采用加入4个风电场(wind farm,WF)和2个电池储能系统(battery energy storage systems,BESSs)的IEEE-39节点标准系统进行算例分析,遗传算法(genetic algorithm,GA)对目标函数进行迭代求解。结果证明,本研究提出的基于DBESS的风储有功滚动优化调度模型,可以有效降低系统运行经济性以及提高电网对风电的接纳能力。展开更多
With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high ef...With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high efficiency and flexibility. In this paper, the problem of seru production system formation under uncertain demand is researched. A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic demand. Sample average approximation （SAA） is used to approximate the expected objective of the stochastic programming. The non-dominated sorting genetic algorithm II （NSGA-II） is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoffbetween cost and service level, and how the performance of the seru production system varies with the number of product types, mean and deviation of product volume, and skill-level-based cost.展开更多
文摘根据风电预测精度随时间尺度的减小逐级提高的固有特性,建立了多时间尺度多目标协调调度的滚动优化模型。依据风电并网标准与分布式电池储能系统(distributed battery energy storage system,DBESS)能快速修正风电波动的低频分量,以系统经济性最优和弃风电量最小为目标函数建立优化模型,采用加入4个风电场(wind farm,WF)和2个电池储能系统(battery energy storage systems,BESSs)的IEEE-39节点标准系统进行算例分析,遗传算法(genetic algorithm,GA)对目标函数进行迭代求解。结果证明,本研究提出的基于DBESS的风储有功滚动优化调度模型,可以有效降低系统运行经济性以及提高电网对风电的接纳能力。
文摘With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high efficiency and flexibility. In this paper, the problem of seru production system formation under uncertain demand is researched. A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic demand. Sample average approximation （SAA） is used to approximate the expected objective of the stochastic programming. The non-dominated sorting genetic algorithm II （NSGA-II） is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoffbetween cost and service level, and how the performance of the seru production system varies with the number of product types, mean and deviation of product volume, and skill-level-based cost.
