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A new multi-objective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem based on Particle Swarm Optimization (PSO) is proposed in this paper. The new algorithm has adopted the maintenance method of Pareto candidate solution set based on the max-min distance density. The algorithm effectively guarantees the convergence of the algorithm and the diversity solutions. The performance...
This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called hybrid particle swarm optimization - differential evolution (PSO-DE). The mutation operators...
Economic power dispatch problem plays an important role in the operation of the power systems. The objective of economic dispatch problem is to schedule output of the committed units such that the total fuel cost is minimized while meeting a set of operating constraints. In this paper, two modified particle swarm optimization algorithms with one of the accelerating coefficients being constant are...
Vehicle-to-Grid (V2G) technology has drawn great interest in the recent years. Success of the V2G research depends on efficient scheduling of gridable vehicles in limited parking lots. V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning...
This paper proposes a new hybrid meta-heuristic method for profit-based unit commitment (PBUC) that considers units with nonlinear cost function. The proposed method aims at global optimization to carry out profit maximization under competitive environment. The objective of the traditional UC is to minimize operation-cost while satisfying the constraints. However, power system operation needs reformulate...
An improved particle swarm optimization approach is introduced in this paper, the improvements involves the dasiaworstpsila particlepsilas impact on the particles in addition to that of the dasiabestpsila one. Meanwhile, a self-adaptive inertia weight is adopted to enhance the performance of the approach. With nonlinear constraints handled by a penalty function, the proposed approach is applied to...
In this paper, a new technique based on particle swarm optimization (PSO) is proposed to incorporate voltage stability limits into traditional optimal power flow (OPF) formulations. Two objective functions such as fuel cost and voltage stability margin (loadability) are considered to be optimized. In proposed method systempsilas maximum loading margin (loadability) is calculated by continuation power...
A new approach based on particle swarm optimization technique is proposed for the design of load following controller for microturbines in a competitive distributed generation power system. The objective is to avoid any power change from upper system when there is small load change in the local distribution system. A case study for IEEE-37 Node Feeder is presented to illustrate the proposed approach...
This paper presents an effective method for solving economic dispatch problem (EDP) with nonsmooth cost function using a hybrid method that integrates particle swarm optimization (PSO) with sequential quadratic programming (SQP). PSO is the main optimizer to find the optimal global region while SQP is used as a fine tuning to determine the optimal solution at the final stage. The proposed hybrid PSO-SQP...
This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator are considered for practical generator operation, such as ramp rates, prohibited operating zones, and non-smooth cost functions. The feasibility of the proposed method is demonstrated for two different systems, and is Compared...
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