Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks. Issue 2 (5th April 2019)

• Record Type: Journal Article
• Authors: Hussain, Akhtar; Shah, Syed Danial Ali; Arif, Syed Muhammad
• Is Part Of: IET smart grid. Volume 2:Issue 2(2019)
• Page Start: 269
• Page End: 282
• Publication Date: 2019-04-05
• Subjects: distributed power generation -- genetic algorithms -- particle swarm optimisation -- distribution networks -- load shedding; load shedding -- resiliency analysis -- transformed network -- total network power loss -- power loss calculations -- IEEE 69‐bus distribution systems -- autonomous microgrid network -- severe weather events -- power distribution network -- conventional power distribution systems -- resilient autonomous microgrid networks -- distributed generators -- heuristic optimisation -- climate change -- particle swarm optimisation -- genetic algorithm -- IEEE 33‐bus distribution systems; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- B8120J Distribution networks -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0209
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.