Open AccessCo‐optimisation for distribution networks with multi‐microgrids based on a two‐stage optimisation model with dynamic electricity pricing
Author(s) -
Hu Xiaotong,
Liu Tianqi
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1602
Subject(s) - mathematical optimization , computer science , latin hypercube sampling , particle swarm optimization , renewable energy , population , electricity , economic dispatch , matching (statistics) , dynamic pricing , power (physics) , electric power system , engineering , mathematics , economics , electrical engineering , microeconomics , statistics , physics , demography , quantum mechanics , sociology , monte carlo method
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.