- Title
- A new optimal power flow approach for wind energy integrated power systems
- Creator
- Rahmani, Shima; Amjady, Nima
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197510
- Identifier
- vital:18890
- Identifier
-
https://doi.org/10.1016/j.energy.2017.06.046
- Identifier
- ISSN:0360-5442
- Abstract
- Penetration of wind generation into power systems in recent years has greatly affected optimal power flow (OPF) because of the uncertain behavior of this new energy resource. In this research work, at first, a novel scenario generation approach is proposed to model wind power (WP) uncertainty. The proposed scenario generation approach includes construction of probability density function (PDF) pertaining to WP forecast error, segmentation of the PDF by an efficient clustering approach to obtain both the optimal number and the optimal arrangement of the clusters, and the generation of WP scenarios using the optimized clusters through roulette wheel mechanism. Secondly, this paper presents a new OPF framework based on DC network modeling for wind generation integrated power systems. Thirdly, a new out-of-sample analysis is presented to evaluate the long-run performance of the proposed OPF approach encountering various realizations of uncertain WPs. Finally, the performance of the proposed method for solving WP-integrated OPF problem is extensively illustrated on the IEEE 30-bus and the IEEE 118-bus test systems and compared with the performance of the deterministic method and the Weibull PDF method. These comparisons illustrate better performance of the proposed method, while it has reasonable computation times. •A new scenario generation approach is presented.•A new wind power integrated optimal power model is proposed.•A new out-of-sample analysis is presented.•The effectiveness of the proposed model is extensively illustrated.
- Publisher
- Elsevier
- Relation
- Energy Vol. 134, no. (2017), p. 349-359
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Elsevier
- Subject
- Cluster analysis; Clustering; Clusters; Computation; Electric power system; Electrical & electronic engineering; Electrical engineering, electronic Engineering, information Engineering; Energy; Engineering; Engineering and technology; Mathematical optimization; Network model; Optimal power flow (OPF); Power flow; Probability density function; Probability density function (PDF); Probability density functions; Reliability engineering; Scenario; Segmentation; Studies; Uncertainty; Weibull distribution; Wind power; Wind power (WP); 4012 Fluid mechanics and thermal engineering; 4017 Mechanical engineering
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