Open AccessElectricity scheduling optimisation based on energy cloud for residential microgrids
Author(s) -
Li Shenglin,
Yang Junjie,
Fang Jicheng,
Liu Ziqi,
Zhang Helong
Publication year - 2019
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.2018.5715
Subject(s) - microgrid , electricity , computer science , scheduling (production processes) , cloud computing , demand response , energy storage , environmental economics , electricity generation , cost of electricity by source , operations management , engineering , power (physics) , electrical engineering , operating system , economics , physics , control (management) , quantum mechanics , artificial intelligence
Nowadays with the development of smart residential microgrid (RMG), the distributed energy storage system (DESS) can help consumers to not only balance generation and consumption but also participate in demand respond. However, the unadjustable capacity of DESS and the lack of energy sharing among users have become the major challenges to the further development of RMG. This paper proposes a novel electricity scheduling architecture based on energy cloud (EC) for RMGs and designs an electricity scheduling optimisation. The EC is used in order to link different end‐users and promote coordination. In the proposed EC‐based electricity scheduling architecture, the mathematical model for the RMG is provided. Moreover, considering the depreciation cost of battery, the optimisation model is established with the objective of minimising the electricity cost. Compared with the traditional RMG, simulation results show that the proposed strategy can not only allow consumers to adjust their optimal energy storage capacity but also further reduce electricity payment costs. The designed strategy provides a new and effective research perspective for electricity scheduling of RMGs.