Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

Hybrid fuzzy particle swarm optimization approach for reactive power optimization

This paper presents a new approach to the optimal reactive power planning based on fuzzy logic and particle swarm optimization (PSO). The objectives are to minimize real power loss and to improve the voltage profile of a given interconnected power system. Transmission loss is expressed in terms of voltage increments by relating the control variables i.e. reactive var generations by the generators, tap positions of transformers and reactive power injections by the shunt capacitors. The objective function and the constraints are modeled by fuzzy sets. A term ‘sensitivity’ at each bus is defined which depends on variation of real power loss with respect to the voltage at that bus. Based on the Fuzzy membership values of the sensitivity, corrective action at a particular bus is taken i.e. shunt capacitors are installed at the candidate buses based on real power loss and sets of solution. Then, PSO is applied to get final solution. PSO is used for optimal setting of transformer tap positions and reactive generations of generators. The solutions obtained by this method is compared with the solutions obtained by other evolutionary algorithms like genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO)

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system