Open AccessData‐driven distributionally robust joint planning of distributed energy resources in active distribution network
Author(s) -
Gao Hongjun,
Wang Renjun,
Liu Youbo,
Wang Lingfeng,
Xiang Yingmeng,
Liu Junyong
Publication year - 2020
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.2019.1565
Subject(s) - mathematical optimization , distributed generation , column generation , microgrid , computer science , integer programming , linear programming , piecewise , relaxation (psychology) , joint probability distribution , renewable energy , engineering , mathematics , control (management) , mathematical analysis , statistics , electrical engineering , psychology , social psychology , artificial intelligence
With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two‐stage joint planning model considering doubly‐fed induction generator, photovoltaics (PVs) with the ancillary services of PV inverter, distributed energy storage systems and different types of controllable loads in the ADN. To address the uncertainties of DGs, a two‐stage data‐driven distributionally robust planning model is constructed. The proposed model is solved in a ‘master and sub‐problem’ framework by column‐and‐constraint generation algorithm, where the master problem is to minimise the total cost and find the optimal planning decision under the worst probability distributions, and the sub‐problem is to find the worst probability distribution of given uncertain scenarios. Besides, the original mixed‐integer non‐linear planning problem is converted into a mixed‐integer second‐order cone programming problem through second‐order cone relaxation, Big‐M and piecewise linearisation method. The numerical results based on 33‐bus system verify the effectiveness of the proposed model.