文中提出一种智能电网环境下可控负荷优化调度的双层优化模型。上层是可控负荷聚合器的优化问题,其目标是通过优化调度3类可控负荷的方式最小化购电成本;下层是电网优化问题,该问题提供电网实时电价给上层优化问题。文中将电网优化问题...文中提出一种智能电网环境下可控负荷优化调度的双层优化模型。上层是可控负荷聚合器的优化问题,其目标是通过优化调度3类可控负荷的方式最小化购电成本;下层是电网优化问题,该问题提供电网实时电价给上层优化问题。文中将电网优化问题的KKT条件作为可控负荷优化问题的均衡约束,双层优化问题转换为具有均衡约束的数学规划(Mathematical Program with Equilibrium Constraints,MPEC)问题来求解。算例仿真反映了提出的调度策略的基本特征。展开更多
An optimal dimension-down iterative algorithm （DDIA） is proposed for solving a mixed （continuous/ discrete） transportation network design problem （MNDP）, which is generally expressed as a mathematical programmin...An optimal dimension-down iterative algorithm （DDIA） is proposed for solving a mixed （continuous/ discrete） transportation network design problem （MNDP）, which is generally expressed as a mathematical programming with equilibrium constraints （MPEC）. The upper level of the MNDP aims to optimize the network performance via both the expansion of existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium （UE） model. The idea of the proposed DDIA is to reduce the dimensions of the problem. A group of variables （discrete/continuous） are fixed to altemately optimize another group of variables （continuous/discrete）. Some continuous network design problems （CNDPs） and discrete network design problems （DNDPs） are solved repeatedly until the optimal solution is obtained. A numerical example is given to demonstrate the efficiency of the proposed algorithm.展开更多
文摘文中提出一种智能电网环境下可控负荷优化调度的双层优化模型。上层是可控负荷聚合器的优化问题,其目标是通过优化调度3类可控负荷的方式最小化购电成本;下层是电网优化问题,该问题提供电网实时电价给上层优化问题。文中将电网优化问题的KKT条件作为可控负荷优化问题的均衡约束,双层优化问题转换为具有均衡约束的数学规划(Mathematical Program with Equilibrium Constraints,MPEC)问题来求解。算例仿真反映了提出的调度策略的基本特征。
基金Supported by National Science Foundation of China (10771162)Foundation of Shang-hai Municipal Education Commission (071605123)Shanghai Excellent Young Teacher Foundation(SDL08015)
基金The National Natural Science Foundation of China(No. 50908235 )China Postdoctoral Science Foundation (No.201003520)
文摘An optimal dimension-down iterative algorithm （DDIA） is proposed for solving a mixed （continuous/ discrete） transportation network design problem （MNDP）, which is generally expressed as a mathematical programming with equilibrium constraints （MPEC）. The upper level of the MNDP aims to optimize the network performance via both the expansion of existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium （UE） model. The idea of the proposed DDIA is to reduce the dimensions of the problem. A group of variables （discrete/continuous） are fixed to altemately optimize another group of variables （continuous/discrete）. Some continuous network design problems （CNDPs） and discrete network design problems （DNDPs） are solved repeatedly until the optimal solution is obtained. A numerical example is given to demonstrate the efficiency of the proposed algorithm.
