Coordinated DG-Tie planning in distribution networks based on temporal scenarios

Yue Xiang, Lili Zhou, Yunche Su, Jichun Liu, Yuan Huang, Junyong Liu, Xia Lei, Zhang Sun, Weiting Xu and Wentao Zhang

Energy, 2018, vol. 159, issue C, 774-785

Abstract: Optimal planning of distributed generation (DG) in distribution networks is key for improving energy utilization efficiency and system operation benefits. Considering the uncertainties and temporal correlations of DG output and load demand, a multi-scenario chance-constrained economic model for DG planning is established in this paper. The model considers the comprehensive benefits of environmental, reliability and other aspects, as well as the active curtailment management of DGs. Thereafter, the model is extended to the coordinated planning of both DGs and tie lines, which is formulated as a multi-objective model. An improved genetic algorithm based solving strategy integrated with game multi-objective decision method is proposed. The feasibility and effectiveness of the proposed models are verified on the IEEE 33-bus distribution system. The impacts of natural resource distribution, confidence level, unit environmental cost and other parameters are investigated as well. The case studies also prove the integration of complementary DG generation could help improve the maximum capacity of DG and indicate the environmental benefit is the vital incentive to introduce DG into the distribution network.

Keywords: Distributed renewable generation; Tie lines; Temporal scenarios; Chance constrained; Genetic algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218312246
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:159:y:2018:i:c:p:774-785

DOI: 10.1016/j.energy.2018.06.159

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().