AC-Based Differential Evolution Algorithm for Dynamic Transmission Expansion Planning

This work proposes a method based on a mixed integer nonlinear non-convex programming model to solve the multistage transmission expansion planning (TEP). A meta-heuristic algorithm by the means of differential evolution algorithm (DEA) is employed as an optimization tool. An AC load flow model is used in solving the multistage TEP problem, where accurate and realistic results can be obtained. Furthermore, the work considers the constraints checking and system violation such as real and power generation limits, possible number of lines added, thermal limits and bus voltage limits. The proposed technique is tested on well known and realistic test systems such as the IEEE 24 bus-system and the Colombian 93-bus system. The method has shown high capability in considering the active and reactive power in the same manner and solving the TEP problem. The method produced improved good results in a fast convergence time for the test systems

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)

A novel differential evolution algorithmic approach to transmission expansion planning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Nowadays modern electric power systems consist of large-scale and highly complex interconnected transmission systems, thus transmission expansion planning (TEP) is now a significant power system optimisation problem. The TEP problem is a large-scale, complex and nonlinear combinatorial problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. The accurate solution of the TEP problem is essential in order to plan power systems in both an economic and efficient manner. Therefore, applied optimisation methods should be sufficiently efficient when solving such problems. In recent years a number of computational techniques have been proposed to solve this efficiency issue. Such methods include algorithms inspired by observations of natural phenomena for solving complex combinatorial optimisation problems. These algorithms have been successfully applied to a wide variety of electrical power system optimisation problems. In recent years differential evolution algorithm (DEA) procedures have been attracting significant attention from the researchers as such procedures have been found to be extremely effective in solving power system optimisation problems. The aim of this research is to develop and apply a novel DEA procedure directly to a DC power flow based model in order to efficiently solve the TEP problem. In this thesis, the TEP problem has been investigated in both static and dynamic form. In addition, two cases of the static TEP problem, with and without generation resizing, have also been investigated. The proposed method has achieved solutions with good accuracy, stable convergence characteristics, simple implementation and satisfactory computation time. The analyses have been performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm (CGA) procedures and a detailed comparison has also been presented. Finally, the sensitivity of DEA control parameters has also been investigated

A novel gradient based optimizer for solving unit commitment problem

Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique

Optimal design of single-tuned passive filters using response surface methodology

This paper presents an approach based on Response Surface Methodology (RSM) to find the optimal parameters of the single-tuned passive filters for harmonic mitigation. The main advantages of RSM can be underlined as easy implementation and effective computation. Using RSM, the single-tuned harmonic filter is designed to minimize voltage total harmonic distortion (THDV) and current total harmonic distortion (THDI). Power factor (PF) is also incorporated in the design procedure as a constraint. To show the validity of the proposed approach, RSM and Classical Direct Search (Grid Search) methods are evaluated for a typical industrial power system