Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

Abstract

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems.

Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios

“…These functions, whether single-or multi-objective, encompass technical and/or economic goals and are subject to various equality and inequality constraints. Previous studies [20,[24][25][26][27][28][29][30] have predominantly concentrated on technical objectives, encompassing power losses (both active and reactive), power system load capability, voltage deviations, voltage stability, and emissions from generating units. In contrast, other investigations [18,[31][32][33][34] have emphasized economic objectives, including fuel costs, active and reactive power costs, and the investment costs associated with optimal DGs, often overlooking technical considerations.…”

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

“…Microgrid optimization is studied in 30 , utilizing a fuzzy decision-maker and Pareto Front beam method for hourly performance optimization. The strategic placement of renewable DGs is emphasized in 31 , while 32 proposes Improved Particle Swarm Optimization to optimize distribution system performance across various objectives. These endeavors collectively contribute to the advancement of multi-objective optimization techniques for enhancing the planning and operation of distribution systems.…”

Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms

“…To address the inherent limitations of radial ADNs, network reconfiguration (NR) has been widely employed in distribution system operations. This involves changing the open and closed statuses of the sectionalizing and tie switches of the feeders [2][3][4][5][6][7][8]. Regardless of topology changes, the distribution networks (DNs) must be maintained in a radial type.…”