摘要
在一致性和模型预测控制理论的基础上,提出了一种本地集中优化、全局分布式迭代的分布式优化框架。该框架可以很好地解决大规模的微电网集群(MGC)分布式优化求解问题。在每次迭代中,各个微电网平行地求解对应的子问题,并用求解得出的互联功率不断更新拉格朗日乘法子直至聚合。同时,通过调节惩罚因子的大小,可以控制本地微电网消耗本地分布式能源的程度,以使得MGC具有强的灵活性。尤其地,所提分布式框架可通过调整辅助聚合常数的值实现子微电网的即插即用功能。仿真结果表明,所提方法仅需要少量迭代即可达到全局最优,同时证明了即插即用功能的有效性。
On the basis of the theory of consistence and model predictive control,this paper proposes a distributed optimization framework of local centralized optimization and global distributed iteration,which can solve the problem of large-scale optimization of microgrid cluster（MGC）.At each iteration,each microgrid solves its own sub-problem in parallel,and Lagrangian multiplier is updated by the solution of interconnecting variables until the convergence is achieved.Meanwhile,local distributed energy levels consumed by the local microgrid can be controlled by adjusting the penalty factor,so that the MGC has a strong flexibility.Especially,the proposed distributed framework can achieve the function of plug and play of submicrogrids by adjusting auxiliary convergence constants.The simulation results show that the proposed distributed framework can achieve the global optimal only by a small number of iterations,and prove the validity of plug and play functionality.
作者
周晓倩
艾芊
王皓
ZHOU Xiaoqian;AI Qian;WANG Hao(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第18期106-113,共8页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2016YFB0901302)
国家自然科学基金资助项目(51577115)~~
关键词
微电网集群
分布式优化
一致性
模型预测控制
即插即用
惩罚因子
microgrid cluster
distributed optimization
consistence
model predictive control
plug and play
penalty factor
