TY - JOUR
T1 - Decentralised hierarchical coordination of electric vehicles in multi-microgrid systems
AU - Zou, Suli
AU - Ma, Zhongjing
AU - Yang, Nan
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2019
PY - 2019/7/9
Y1 - 2019/7/9
N2 - Here, the authors study the optimal coordination of electric vehicles (EVs) in a multi-microgrid (MMG) system with respect to a given time-of-use (TOU) price trajectory over a multi-time period. The authors firstly formulate a class of EV charging/discharging coordination problems for each individual microgrid (MG) to minimise the electricity cost of this MG, while the implemented strategy may result in high variations of the total demand and even build new peaky demands. To mitigate these negative effects, the authors build an aggregate optimisation problem with quadratic cost function under certain bounds on the electricity costs of each MG. The authors further propose a decentralised method for the underlying optimisation problem and verify the convergence of the system to the optimal strategy with a logarithmic convergence rate. Furthermore, the authors consider the power exchange capacity between the MMG system and the main grid, and present a decentralised algorithm to obtain an optimal strategy that minimises the system cost under this capacity constraint. Also, the convergence, the optimality, and the convergence rate of the proposed algorithm are shown.
AB - Here, the authors study the optimal coordination of electric vehicles (EVs) in a multi-microgrid (MMG) system with respect to a given time-of-use (TOU) price trajectory over a multi-time period. The authors firstly formulate a class of EV charging/discharging coordination problems for each individual microgrid (MG) to minimise the electricity cost of this MG, while the implemented strategy may result in high variations of the total demand and even build new peaky demands. To mitigate these negative effects, the authors build an aggregate optimisation problem with quadratic cost function under certain bounds on the electricity costs of each MG. The authors further propose a decentralised method for the underlying optimisation problem and verify the convergence of the system to the optimal strategy with a logarithmic convergence rate. Furthermore, the authors consider the power exchange capacity between the MMG system and the main grid, and present a decentralised algorithm to obtain an optimal strategy that minimises the system cost under this capacity constraint. Also, the convergence, the optimality, and the convergence rate of the proposed algorithm are shown.
UR - http://www.scopus.com/inward/record.url?scp=85068576135&partnerID=8YFLogxK
U2 - 10.1049/iet-gtd.2018.6767
DO - 10.1049/iet-gtd.2018.6767
M3 - Article
AN - SCOPUS:85068576135
SN - 1751-8687
VL - 13
SP - 2899
EP - 2906
JO - IET Generation, Transmission and Distribution
JF - IET Generation, Transmission and Distribution
IS - 13
ER -