Please use this identifier to cite or link to this item:
- Time-series-based maximization of distributed wind power generation integration
- Univ Edinburgh
- Universidade Estadual Paulista (UNESP)
- Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.
- IEEE Transactions on Energy Conversion. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 23, n. 3, p. 968-974, 2008.
- Institute of Electrical and Electronics Engineers (IEEE)
- distributed generation (DG)
- distribution networks
- multiobjective programming
- Pareto's optimality
- wind power
- Acesso restrito
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.