Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control DOI
Lian Shen, Ping Zhou, Han Yan

и другие.

Energy, Год журнала: 2025, Номер 324, С. 135989 - 135989

Опубликована: Апрель 23, 2025

Язык: Английский

Methods for analysing renewable energy potentials in energy system modelling: A review DOI Creative Commons

Alina Kerschbaum,

Lennart Trentmann, Andreas Hänel

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 215, С. 115559 - 115559

Опубликована: Март 12, 2025

Язык: Английский

Процитировано

0

Layout optimization for offshore wind farms considering both fatigue damage and power generation DOI

Wangxuan Peng,

Baoliang Li, Mingwei Ge

и другие.

Renewable Energy, Год журнала: 2025, Номер unknown, С. 122878 - 122878

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Under Complex Wind Scenarios: Considering Large-scale Wind Turbines in Wind Farm Layout Optimization via Self-adaptive Optimal Fractional-order Guided Differential Evolution DOI
Yu-Jun Zhang, Zihang Zhang, Rui Zhong

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135866 - 135866

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization DOI Creative Commons
Borui Zhang, Bo Liu

Energies, Год журнала: 2025, Номер 18(8), С. 2133 - 2133

Опубликована: Апрель 21, 2025

Standalone wind–solar–diesel–storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such rural regions islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable generation uncertainty load demand, this paper proposes an adaptive method (DQN-PSO) that integrates Deep Q-Network (DQN) with Particle Swarm Optimization (PSO). The proposed approach leverages DQN to assess operational state microgrid dynamically adjust key parameters PSO. Additionally, multi-strategy switching mechanism, incorporating global search, local adjustment, reliability enhancement, is introduced jointly optimize both clean utilization supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, low-load scenarios, improves by 3.2%, 4.5%, 10.9%, respectively, compared PSO algorithms while reducing risks 0.70%, 1.04%, 0.30%, respectively. These findings validate strong adaptability algorithm dynamic environments. Further, parameter sensitivity analysis underscores significance adjustment mechanism.

Язык: Английский

Процитировано

0

A comprehensive review of artificial intelligence applications in wind energy power generation DOI Creative Commons

Pouya Moshtaghi,

Najmeh Hajialigol,

Behnam Rafiei

и другие.

Sustainable Futures, Год журнала: 2025, Номер unknown, С. 100638 - 100638

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control DOI
Lian Shen, Ping Zhou, Han Yan

и другие.

Energy, Год журнала: 2025, Номер 324, С. 135989 - 135989

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0