Open AccessResearch on multi‐objective optimisation coordination for large‐scale V2G
Author(s) -
Zhou Tianpei,
Sun Wei
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0173
Subject(s) - microgrid , vehicle to grid , renewable energy , computer science , scheduling (production processes) , grid , state of charge , mathematical optimization , variable renewable energy , energy storage , electric vehicle , automotive engineering , engineering , power (physics) , electrical engineering , mathematics , physics , geometry , battery (electricity) , quantum mechanics
Vehicle‐to‐grid (V2G) technology plays an important role in solving the large‐scale disordered charging and discharging behaviour of the electric vehicles (EVs). V2G mode based on microgrid is used for multi‐objective optimisation coordination between the EVs and the power grid, and a multi‐objective optimisation model, in which minimum grid load fluctuation, maximum renewable energy utilisation and maximum benefits for the EV users as the optimisation objectives are established. In order to solve the optimisation model, searching valley scheduling algorithm, variable threshold optimisation algorithm and variable charge/discharge rate optimisation algorithm are proposed successively. In order to verify the control effect, the three proposed algorithms are compared with the without optimisation algorithm. The results show that the three proposed algorithms, in a manner, could improve the imbalance between power supply and demand of the microgrid; increase utilisation of renewable energy; and bring certain benefits to the EV users. By analysis of experimental data, the control effect of variable charge/discharge rate scheduling algorithm is the best in the three scheduling algorithms.