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Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties

Martins, Vinicius F., Borges, Carmen L. T.·2011·IEEE Transactions on Power Systems

Abstract

This paper presents a model for active distribution systems expansion planning based on genetic algorithms, where distributed generation (DG) integration is considered together with conventional alternatives for expansion, such as rewiring, network reconfiguration, installation of new protection devices, etc. The novel approach of planning DG integration together with network expansion is a requirement for the modern active distribution network. However, the uncertainties related to DG power generation and load response growth must be taken into account in order to plan a safe system at a minimum cost. Thus, two different methodologies for uncertainties incorporation through the use of multiple scenarios analysis are proposed and compared. The multiple objectives optimization algorithm applied in the model takes into account the costs of reliability, losses, power imported from transmission, and network investments.

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Metadata

Authors
Martins, V. F., & Borges, C. L. T.
Venue
IEEE Transactions on Power Systems
Published
2011-11
Volume
26
Issue
4
Pages
2164-2172
ISSN
  • 0885-8950 (ISSN-L)
  • 1558-0679
OpenAlex
W1997936011
Cited by
374
References
0
FWCI
12.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties. https://app.cochat.ai/papers/10.1109/tpwrs.2011.2122347
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Martins, V. F., & Borges, C. L. T. (2011). Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2011.2122347

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.