الفهرس | Only 14 pages are availabe for public view |
Abstract The optimal power flow (OPF) problem plays a very important role in power system planning and operational control. The solution of the OPF problem aims to optimize a selected objective function via optimal adjustments of the power system control variables while satisfying various equality and inequality constraints. Generally, the OPF problem is a large-scale, highly constrained, non-linear and non-convex multi-objective optimization problem. In this thesis, many case studies are considered. Starting from, the simplest form of power dispatch problem, where the fuel cost of generators is represented by a quadratic function of real power generation and the system constrains include only reliability constraint and generation limits constraint. Passing through, · The non-conventional fuel cost functions, where more accurate models are used to provide better results in the solution of the economic power dispatch problem, · The environmental-economic power dispatch, where the environmental issue is considered, · The security constrained power dispatch, where the security constraints are incorporated into the power dispatch problem, · Active OPF problem, where several objective functions are considered such as minimizing real power losses and improving voltage profile, and finally · Reactive OPF problem. Recently, with the progress in evolutionary optimization techniques, it is possible to deal with the real life multi-objective optimization problems. This has been reflected on the OPF with an aim to formulate it as a true multiobjective optimization problem. In this thesis, a new evolutionary computation technique, called differential evolution algorithm (DE), has been proposed and introduced. The algorithm is inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multi-modal surfaces. The DE algorithm is used as an optimization technique to find the optimal values of the objective functions in the different case studies. Also, a multi-objective differential evolution based approach is proposed, developed and successfully implemented to solve the multi-objective environmental-economic power dispatch, multi-objective OPF problem, and multi-objective reactive power flow problem. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, a technique based on fuzzy set theory is employed to extract the best compromise non-dominated solution. The different case studies are tested using three test systems, which are wale and hale 6-bus test system, IEEE 30-bus test system, and real power system for west delta region (52-bus test system). A comparison between results of single and multi-objective optimization problem is discussed. Also, the results are compared with the available literatures. The results obtained show the effectiveness of the proposed DE algorithm in solving single and multi-objective optimization problems. |