Trends in the optimal location and sizing of electrical units in smart grids using meta-heuristic algorithms

TL;DR

This paper reviews meta-heuristic algorithms for optimally locating and sizing electrical units in smart grids, highlighting challenges in multi-objective handling and suggesting directions for future research.

Contribution

It provides a comprehensive overview of existing methods, emphasizing the integration of objective functions and Pareto fronts in meta-heuristic optimization for smart grid applications.

Findings

Meta-heuristic algorithms are effective but face challenges with multiple objectives.

Techniques like voltage stability index and power loss index help reduce search space.

Pareto fronts enable non-dominating solutions for multi-objective problems.

Abstract

The development of smart grids has effectively transformed the traditional grid system. This promises numerous advantages for economic values and autonomous control of energy sources. In smart grids development, there are various objectives such as voltage stability, minimized power loss, minimized economic cost and voltage profile improvement. Thus, researchers have investigated several approaches based on meta-heuristic optimization algorithms for the optimal location and sizing of electrical units in a distribution system. Meta-heuristic algorithms have been applied to solve different problems in power systems and they have been successfully used in distribution systems. This paper presents a comprehensive review on existing methods for the optimal location and sizing of electrical units in distribution networks while considering the improvement of major objective functions. Techniques such as voltage stability index, power loss index, and loss sensitivity factors have been implemented alongside the meta-heuristic optimization algorithms to reduce the search space of solutions for objective functions. However, these techniques can cause a loss of optimality. Another perceived problem is the inappropriate handling of multiple objectives, which can also affect the optimality of results. Hence, a recent method such as Pareto fronts generation has been developed to produce non-dominating solutions. This review shows a need for more research on (i) the effective handling of multiple objective functions, (ii) more efficient meta-heuristic optimization algorithms and/or (iii) better supporting techniques.