Day ahead scheduling model of wind power system based on fuzzy stochastic chance constraints—considering source-load dual-side uncertainty case
Year: 2025
Type: article
Abstract: As global energy tensions increase, the demand for clean energy is growing exponentially. Although wind power is growing rapidly, it introduces significant stability challenges to power system dispatch due to its intermittency and variability. To address this challenge, a nonparametric kernel density was employed to model wind power output, and a multi-objective optimization model was proposed for day-ahead scheduling of wind power generation systems. First, by comparing the fitting effects of parameter distribution and kernel density function on wind power prediction errors, a kernel density function-based wind power output model was established. At the same time, the fuzzy stochastic constraint rule was introduced to constrain the uncertainty of the source and load sides of the wind power system, with the aim of minimizing the system operation cost and carbon emissions. The experimental results show that in the multi-objective optimization experiment, the system cost of multi-objective optimization increased by 17.19%, and the carbon emissions decreased by 51.99% compared with the single cost optimization goal. Compared with the single environmental optimization objective, the system cost of the multi-objective optimization decreased by 16.11%, and the carbon emissions increased by 15.15%. The above data indicate that the optimized scheduling scheme adopted in the study can not only save economic costs but also consider certain environmental protection measures. This research result can provide a new direction for the scheduling research of power systems, including wind power, and has an important reference value for the scheduling of actual power systems.
Source: AIMS energy
Author: Bitian Wu
Institution: Nanchang University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $300
OpenAlex ID: W4410599554