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—This paper presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this(More)
—This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using an improved particle swarm optimization (IPSO). Although the particle swarm optimization (PSO) approaches have several advantages suitable to heavily constrained nonconvex optimization problems, they still can have the drawbacks such as(More)
paper proposes a new binary particle swarm optimization (BPSO) approach inspired from quantum computing, so-called quantum-inspired BPSO (QBPSO), for solving the unit commitment (UC) problems. Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems of power systems, the BPSO algorithm has some drawbacks such(More)
—This paper presents a development of an improved genetic algorithm (IGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in(More)
This paper presents an efficient approach for solving the economic dispatch (ED) problems with valve-point effects using a hybrid particle swarm optimization (PSO) technique. Although PSO-based algorithms are easy to implement and have been empirically shown to perform well on many power system optimization problems, they may get trapped in a local optimum(More)
– This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary(More)
The demise of the native franchise markets and emergence of competitive markets for electricity generation service is changing the way that electricity is and will be priced and is making increasingly difficult for market participants to appraise the prospects for the future electricity market. As a result, conventional generation expansion planning (GEP)(More)
This paper presents a windows-based educational simulator with user-friendly graphical user interface (GUI) for the education and training of particle swarm optimization (PSO) technique for mathematical optimization problems and economic dispatch (ED) applications. The main objective for developing the simulator is to provide information with the electrical(More)
This paper proposes an Artificial Bee Colony (ABC) algorithm to Generator Maintenance Scheduling (GMS) in competitive market. In the regulated market the problem of generating optimal maintenance schedules of generating units for the purpose of maximizing economic benefits and improving reliable operation of a power system, subject to satisfying system
market environment. In order to find the Nash equilibriums it is necessary to search all the feasible combinations of generators' outputs which satisfy various constraints. The procedure to eliminate the dominated strategies can be formulated using Bellman's optimality principle of dynamic programming problem and hence the backward or forward search(More)