Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

Abstract

Abstract:With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem

“…An alternative constraint handling approach is based on the fitness function shown in Equation (14), named as F f 2 (fitness function 2). This function is an adaptation of the one proposed in [33] which was first introduced in the context of expansion planning for congestion management.…”

“…Genetic and evolutionary algorithms have also been used to approach the ORPD. These techniques mimic the process of natural selection using the concepts of inheritance, mutation selection and crossover [13,14]. In [15] reactive power optimization is performed by means of a Genetic Algorithm (GA) aiming at minimizing the total support cost from generators and reactive compensators.…”

Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

“…Mathematically, the present problem is a high-dimensional, nonlinear, multistage, and multi-objective optimization [18]. In the past decades, three kinds of techniques have been developed for this problem: Mathematical programming [19,20], dynamic programming (DP) and a DP-based method [21][22][23], and population-based algorithms [24][25][26]. In mathematical programming, linear programming and nonlinear programming methods are widely used.…”

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

“…A modified Realcoded Genetic Algorithm approach using mutation based on random transfer vectors (RCGA-RTVM) was presented in Haghrah, et al [10]. A combination of improved Mühlenbein mutation and RCGA is proposed in Nazari-Heris, et al [11] and the Parallel Multi-objective Genetic Algorithm (PMOGA) is proposed in Feng, et al [12]. A modified dynamic neighborhood learning-based particle swarm optimization (MDNLPSO) [13] and multi-objective quantumbehaved particle swarm optimization (MOQBPSO) [14] obtained better cost than most methods, including conventional PSO.…”