Open AccessBi‐level operational planning of microgrids with considering demand response technology and contingency analysis
Author(s) -
Haghifam Sara,
Zare Kazem,
Dadashi Mojtaba
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.2018.6516
Subject(s) - microgrid , demand response , contingency , reliability (semiconductor) , mathematical optimization , computer science , operations research , renewable energy , reliability engineering , contingency plan , investment (military) , risk analysis (engineering) , engineering , electricity , mathematics , business , power (physics) , linguistics , philosophy , computer security , electrical engineering , physics , quantum mechanics , politics , law , political science
Considering financial, ecological and also reliability challenges in planning and operation of microgrids is an indispensable issue. On the other hand, an appropriate design of microgrids in planning stages has a major impact on the daily operation of the system. According to these matters, in this study, a hierarchical decision‐making framework has been presented for both planning and operation of microgrids with wide ranges of means and concepts. The proposed model has been formulated as a bi‐level optimisation problem in which every level optimises its own objectives independently, however, in interaction with each other. The upper level (leader) problem, which is related to the planning of microgrids, minimises the utility's demand, investment, and emission costs, while the lower level (follower) problem minimises the operation and maintenance costs through the implementation of an energy management system. The mentioned model is a non‐linear bi‐level problem, which is transformed into a linear single‐level problem through Karush–Kuhn–Tucker conditions. Moreover, the contingency based energy management, demand response programme and uncertain nature of renewable resources have been taken into account. Finally, the proposed method has been applied to a typical microgrid and its results are compared with the weighted‐sum multi‐objective approach to depict its efficiency.