Generation expansion planning in the presence of wind power plants using a genetic algorithm model

TL;DR

This paper introduces a genetic algorithm-based method for generation expansion planning that maximizes wind power integration while minimizing costs, considering different wind regimes and investment cost variations.

Contribution

It presents a novel GA model for GEP that effectively incorporates wind power constraints and assesses the impact of wind regimes and cost reductions.

Findings

Maximum wind power utilization increases robustness of wind regimes.

Reducing wind power plant costs by 10% lowers overall system costs.

The model effectively integrates wind energy constraints into GEP.

Abstract

One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on Genetic Algorithm (GA) for GEP in the presence of wind power plants. Since it is desired to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow obtaining the maximum reasonable amount of wind penetration in the network. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.