在能源互联网的背景下,电力系统与天然气系统互联以实现能量双向流动,此时对电-气耦合系统的电-气能量流进行协同优化很有必要。同时,为协调电力系统与天然气系统在多个目标下的矛盾冲突,需考虑如何实现多目标下系统最优调度运行。针对...在能源互联网的背景下,电力系统与天然气系统互联以实现能量双向流动,此时对电-气耦合系统的电-气能量流进行协同优化很有必要。同时,为协调电力系统与天然气系统在多个目标下的矛盾冲突,需考虑如何实现多目标下系统最优调度运行。针对以上问题,并考虑到电力系统与天然气系统通常隶属于分布自治的经营主体,文章提出一种基于并行交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)的分布式电-气能量流多目标协同优化算法,利用分解协同交互机制实现电力流与天然气流的分布式多目标并行优化,并针对算法的原理、收敛性能以及相关参数对算法的影响对该算法进行深入探讨。最终,在基于IEEE 39节点电力网络和Belgian 20天然气网络搭建的电-气耦合能源系统上进行仿真测试,仿真结果验证了所述算法的有效性。展开更多
A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust c...A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.展开更多
文摘在能源互联网的背景下,电力系统与天然气系统互联以实现能量双向流动,此时对电-气耦合系统的电-气能量流进行协同优化很有必要。同时,为协调电力系统与天然气系统在多个目标下的矛盾冲突,需考虑如何实现多目标下系统最优调度运行。针对以上问题,并考虑到电力系统与天然气系统通常隶属于分布自治的经营主体,文章提出一种基于并行交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)的分布式电-气能量流多目标协同优化算法,利用分解协同交互机制实现电力流与天然气流的分布式多目标并行优化,并针对算法的原理、收敛性能以及相关参数对算法的影响对该算法进行深入探讨。最终,在基于IEEE 39节点电力网络和Belgian 20天然气网络搭建的电-气耦合能源系统上进行仿真测试,仿真结果验证了所述算法的有效性。
文摘A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.
