Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

Abstract

In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.

A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources

“…iter Iter max (10) where ω max and ω min are the maximum and minimum inertia weights, respectively. iter is the current iteration number, and Iter max is the maximum number of iterations.…”

“…In [3], the author undertook a comprehensive review in combination with previous studies on the reactive power optimization of power systems, and noted the advantages and limitations of various artificial intelligence algorithms in solving reactive power optimization problems of power systems. Various artificial intelligence algorithms, such as elephant herding optimization (EHO) [4], slime mold algorithm (SMA) [5], tabu search (TS) [6], earthworm optimization algorithm (EWA) [7], Harris hawks optimization (HHO) [8], enhancement of the general DE algorithm (NSODE and C-DEEPSO) [9,10], and particle swarm optimization (PSO) [11][12][13].…”

“…However, the particle swarm optimization (PSO) algorithm also has the problem of being easily trapped in a local optimum. Based on this, many researchers have improved the particle swarm optimization (PSO) algorithm [10][11][12][13][14]. Jiang et al [14] proposed an improved adaptive particle swarm algorithm with guiding strategy (GSAPSO), which was applied to reactive power optimization (RPO).…”