Open AccessImpact study of demand response program on the resilience of dynamic clustered distribution systems
Author(s) -
Khalili Tohid,
Bidram Ali,
Reno Matthew J.
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.2020.0068
Subject(s) - cluster analysis , resilience (materials science) , computer science , pareto principle , fuzzy logic , reliability engineering , electric power system , function (biology) , power (physics) , mathematical optimization , engineering , mathematics , artificial intelligence , quantum mechanics , evolutionary biology , biology , physics , thermodynamics
Natural disasters, faults, or the sudden outage of major energy resources can create resilience issues in power systems. Modern distribution systems can reconfigure due to the use of automated protection and control techniques and the proliferation of distributed generators (DGs). If there are several DGs located nearby, distribution systems can be clustered into microgrids in an emergency condition. Clustering of distribution systems offers many benefits to achieve high system resilience. Moreover, demand response (DR) is an efficient way of increasing the operation quality and improving the resilience of the power system. This study discusses the impact of DR on the resilience of dynamically‐clustered distribution systems. Accounting for the DR while clustering the distribution system can be beneficial for distribution system customers from the resilience and power quality points‐of‐view. To this end, the distribution clustering is performed using two different objective functions to improve its resilience and voltage profile. DR is formulated as new constraints applied to the distribution system clustering optimisation problem. This study proposes a multi‐objective optimisation function that is solved by using an exchange market algorithm, Pareto efficiency method, and fuzzy satisfying approach. The simulations are performed on IEEE 33‐bus test system.