Multi-objective distributed generation planning in distribution network considering correlations among uncertainties

Shenxi Zhang, Haozhong Cheng, Ke Li, Nengling Tai, Dan Wang and Furong Li

Applied Energy, 2018, vol. 226, issue C, 743-755

Abstract: This paper proposes a novel multi-objective distributed generation planning methodology in distribution network considering correlations among uncertainties, i.e., wind speed, light intensity and load demand. First, under the framework of chance constrained programming, a multi-objective distributed generation planning model with the objective functions of minimizing both the annual total cost and the risk is established. The constraints of the model contain not only the restrictions of distributed generation investment and various electrical limitations, but also the restrictions of correlations among uncertainties. Second, an efficient solving strategy is employed to solve the planning model, in which the correlation-handled probabilistic power flow is used to deal with the correlated uncertainties, and non-dominated sorting genetic algorithm II is applied to achieve the Pareto optimal set of the model. Last, case studies are carried out on two test distribution networks, and the results show that a balance between the economy and the security can be achieved by non-dominated sorting genetic algorithm II. The case studies also verify that the correlations among uncertainties can influence the multi-objective distributed generation planning results, and the stronger the correlation is, the bigger the influence will be.

Keywords: Distributed generation planning; Correlations; Uncertainties; Chance constrained programming; Probabilistic power flow; Non-dominated sorting genetic algorithm II (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626191830919X
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:appene:v:226:y:2018:i:c:p:743-755

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.06.049

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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