Open AccessRisk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: an information gap decision theory approach
Author(s) -
Nikkhah Saman,
Rabiee Abbas,
MohseniBonab Seyed Masoud,
Kamwa Innocent
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.2019.0472
Subject(s) - control reconfiguration , distributed generation , renewable energy , computer science , wind power , mathematical optimization , energy management , stochastic programming , probability density function , reliability engineering , operations research , risk analysis (engineering) , energy (signal processing) , engineering , mathematics , electrical engineering , business , statistics , embedded system
Distributed energy resources (DERs) and distribution network reconfiguration have considerable effects on both the economic and operational performance of distribution networks. However, the uncertain nature of renewable energy sources (RESs), wind energy, for instance, can bring about serious challenges to the distribution system operators and distribution companies (DisCos). Therefore, a suitable methodology is a matter of the utmost importance to handle the uncertainty of RESs. In addition, DisCos can benefit from the utilisation of energy storage technologies to increase the penetration of RESs into the system. In this regard, this study proposes a risk‐averse energy management strategy (RA‐EMS) in the presence of DERs, while the impact of uncertainties of RESs on the optimal configuration of the network is investigated. The uncertainty of RESs is modelled through the information gap decision theory, which has significant advantages such as low computational burden, no need for probability density function, and exact results compared to other methodologies for uncertainty handling. The proposed RA‐EMS model is implemented on the IEEE 33‐bus distribution system, and its superiority over the scenario‐based stochastic programming is demonstrated. The robust configuration of the system against RESs’ uncertainty is obtained for different levels of uncertainty radius.