Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Ziqiang Zhou, Fei Tang, Dichen Liu, Chenxu Wang and Xin Gao
Additional contact information
Ziqiang Zhou: State Grid Zhejiang Electric Power Research Institute, Hangzhou 310014, Zhejiang Province, China
Fei Tang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China
Dichen Liu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China
Chenxu Wang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China
Xin Gao: School of Electrical Engineering and Automation, Wuhan University, Wuhan 420072, Hubei Province, China

Sustainability, 2020, vol. 12, issue 5, 1-20

Abstract: Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.

Keywords: distributed generators; forecast error; K-means clustering; cumulant method; probabilistic load flow (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/5/1709/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/5/1709/ (text/html)

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:gam:jsusta:v:12:y:2020:i:5:p:1709-:d:324888

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().