A Novel Improved Particle Swarm Optimization Approach for Dynamic Economic Dispatch Incorporating Wind Power

Abstract

In solving the electrical power systems dynamic economic dispatch problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. However, new challenges about dynamic economic dispatch arise with large amounts of wind power integrated into the system. In this article, a dynamic economic dispatch model with wind power is formulated first, and then an improved particle swarm optimization approach is developed for solving the dynamic economic dispatch problem. In the optimization model, the constraints of up-spinning reserve and down-spinning reserve are introduced to deal with the influence of wind power on dynamic economic dispatch, and valve point effect is taken into account in the objective function. The proposed method combines a solutionsharing strategy with an elitist learning strategy based on the basic particle swarm optimization. The effectiveness of the proposed approach is demonstrated by comparing its performance with other approaches, including basic particle swarm optimization and the genetic algorithm. The simulation results show that the proposed method has good convergence and great economic effect. All simulations are conducted based on the 6-unit system and the 15-unit system.

A Dynamic Economic Dispatch Model Incorporating Wind Power Based on Chance Constrained Programming

“…In recent decades, many salient approaches have been developed to solve such problems, such as genetic algorithm [20,21], differential evolution [22], evolutionary programming [25,26], and PSO [6,[27][28][29][30][31][32]. PSO, first introduced by Kennedy and Eberhart, is a population-based optimization technique, and conducts its search using a population of particles [27,28].…”

“…Each particle has its own best position ( , 1,2, k j pbest j J =  ) corresponding to the personal best objective value obtained at generation k. The global best particle is denoted by k gbest , which represents the best particle found so far at generation k among the whole population. The new velocity and position of each particle at generation k + 1 are calculated as shown below [6,29]:…”

“…Chen [4] proposed a method to incorporate wind generators into the generation scheduling problem. Special reserve constraints are established to operate the power system within the required stability margin, which is also adopted by Zhou et al [5] and Jiang et al in [6]. These models are deterministic and cannot accurately describe the effect of wind fluctuations on system operation.…”

“…Up to now, different authors have proposed models to solve economic dispatch (or unit commitment) problems for wind-penetrated power systems [4][5][6][7][8][9][10][11][12][13][14][15][16]. Chen [4] proposed a method to incorporate wind generators into the generation scheduling problem.…”

Nonconvex Dynamic Economic Power Dispatch Problems Solution Using Hybrid Immune-Genetic Algorithm

“…Wind power generation is the fastest growing renewable energy resources in the world [40]. The effect of the wind power generation is also considered in simulations using the methods proposed in [41], [42]. The remainder of the paper is organized as follows: Section II gives the mathematical formulation of the DED problem considering POZs, ramp-rate limits, valve-point effects and transmission losses.…”