A Comparison of Heuristic Methods for Optimum Power Flow Considering Valve Point Effect

Optimum Power Flow (OPF) is one of the key considerations for planning, generation control and management of electric utility. Hence it is of major importance to solve OPF with minimum cost within reasonable computing time. This paper presents solutions of OPF with Valve Point Effect (OPF-VPE) using Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). When steam valve starts to open in a turbine it changes generation curve. The valve point effect is considered by adding sine component to the quadratic cost function for OPF-VPE. Also, penalty function is added for generator violations. The common parameters of algorithms such as population size and the iteration number are selected same values for the comparison of algorithms for solving OPFVPE. Specific parameters are stated and used for each algorithm. The heuristic algorithms are examined on IEEE-30 bus system and convergence curves are demonstrated with the system results. Performances of each algorithm are discussed as regards optimizing fuel cost, iteration time and other system results

Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem

A new approach for solving the optimal power flow (OPF) problem is established by combining the reduced gradient method and the augmented Lagrangian method with barriers and exploring specific characteristics of the relations between the variables of the OPF problem. Computer simulations on IEEE 14-bus and IEEE 30-bus test systems illustrate the method. (c) 2007 Elsevier Inc. All rights reserved

Optimal spatial and temporal demand side management in a power system with large penetration of renewable energy sources

Предмет истраживања докторске дисертације је оптимално просторно и временско управљање потрошњом у електроенергетском систему са високим степеном пенетрације обновљивих извора енергије (ОИЕ)...The subject of the doctoral dissertation is optimal spatial and temporal demand side management in a power system with large penetration of renewable energy sources..