Open AccessCoordinating dynamic network reconfiguration with ANM in active distribution network optimisation considering system structure security evaluation
Author(s) -
Li Chao,
Miao Shihong,
Li Yaowang,
Zhang Di,
Ye Chang,
Liu Ziwen,
Li Lixing
Publication year - 2019
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.2018.6583
Subject(s) - control reconfiguration , computer science , mathematical optimization , flexibility (engineering) , active networking , demand response , distributed computing , computer network , engineering , electricity , mathematics , statistics , electrical engineering , embedded system
The growing penetration of renewable energy resources in distribution networks demands for more active management tools. As a new controllable resource, dynamic network reconfiguration (DNR) can improve the flexibility of active distribution network, and in turn decreasing the operation cost of the distribution network and mitigating renewable distributed generation (RDG) curtailment. In this study, an optimisation model coordinating DNR with active network management (ANM) strategies is established, and the system structure security is evaluated. Considering the coordination of scheduling resources such as DNR switches, RDG active/reactive outputs, demand response and static var compensator, the model aims to minimise the comprehensive operation cost of distribution network and improve the consumption rate of RDG while satisfying the distribution network operation constraints. The model transformation method based on the second‐order cone relaxation and variable substitution linearisation is proposed, and the original no‐convex optimisation model is transformed into the mixed‐integer second‐order cone programming problem. Finally, the extended IEEE 33‐node distribution network is utilised to conduct simulation calculation, and the results demonstrate the validity of the proposed model and its transformation method.