Co-optimisation for distribution networks with multi-microgrids based on a two-stage optimisation model with dynamic electricity pricing

被引:38
|
作者
Hu, Xiaotong [1 ]
Liu, Tianqi [1 ]
机构
[1] Sichuan Univ, Sch Elect Engn & Informat, Chengdu, Peoples R China
关键词
distributed power generation; particle swarm optimisation; power distribution economics; power markets; power generation economics; pricing; sampling methods; search problems; distribution networks; multimicrogrids; two-stage optimisation model; renewable sources; DNs; MG economic load dispatch; dynamic electricity pricing strategy; lifetime characteristic; storage device system; rain-flow-counting method; multiobjective particle swarm optimisation; Latin hypercube sampling; global best position; adaptive selection; nonuniform mutation operator; search efficiency; ENERGY MANAGEMENT-SYSTEM; FUEL-CELL; STORAGE; PLACEMENT; OPERATION; LIFETIME;
D O I
10.1049/iet-gtd.2016.1602
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Microgrids (MG) with renewable sources and storage devices play an important role in distribution networks (DNs). MG economic load dispatch normally does not support DN, especially when they belong to different companies and share different interests. To achieve optimal operation of DN with multi-MGs, a two-stage optimisation model is established based on the dynamic electricity pricing strategy. The dynamic electricity pricing strategy gives incentive for MG to participate in the operation of DN, and it is determined by the matching degree between exchange power expected by DN and the real exchange power. Moreover, the life time characteristic of a storage device system is taken into consideration in the second stage via the rain-flow-counting method. The multi-objective particle swarm optimisation is applied to solve the two-stage optimisation problem. Besides, initial particles generated based on Latin hypercube sampling, global best position obtained based on adaptive selection, and non-uniform mutation operator are applied to improve search efficiency as well as maintain the population diversity of the algorithm. Simulation results show that the power sharing between MGs' and DNs' can smooth the load curve of DN as well as reduce the total operation cost of MGs.
引用
收藏
页码:2251 / 2259
页数:10
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