Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

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. ω i weight function for velocity of particle i ω max , ω min min. and max. weights for velocity of particles k max , k maximum and current iteration numbers

Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

“…Stochastic mixed-integer non-linear programming [151,166] Stochastic mixed-integer linear programming [80,130,133,143,160] Heuristic methods [151] ratio of power deficiency after the occurrence of an event.…”

“…According to Table 5, many optimization methods are proposed to evaluate and improve resilience that they can be divided into deterministic and stochastic approaches. The deterministic methods such as linear programming (LP) [141], mixed-integer linear programming (MILP) [72,76,101,135,136,[142][143][144][145][146][147], mixed-integer second-order cone programming (MISOCP) [41,110,148,149], mixed-integer non-linear programming (MILNP) [98], stochastic methods such as stochastic MILNP [150], stochastic MILP [80,130] and heuristic methods such as particle swarm optimization and genetic algorithm [151]. The mentioned optimization problems are complex and need to be simplified.…”

Rightsizing the Design of a Hybrid Microgrid

“…Many papers have been written on the topic of selecting the sizes of distributed energy resources (DERs) for both grid-connected and stand-alone microgrids [1,2]. Most propose optimization or heuristic methods focused on financial impact [3][4][5][6][7][8][9][10][11], environmental impact, resilience and reliability [12] or a multi-objective combinations of these [13][14][15][16][17]. Although each paper proposes interesting analyses and methods, the design space they address is narrowed to the point of limiting their ability to leverage decision makers' expertise in practice.…”

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

“…Authors of [19] merged three goal functions (i.e., load shedding, total power, and voltage deviations) and presented them as a multi-objective index with different predilection weightages by using particle swarm optimization and genetic algorithm approaches to find the best position and size of DGs. The studies in [20] used PSO, GSA, and hybrid PSO-GSA algorithms to address OFs such as actual power loss and voltage profile adjustment.…”