Reliability Assessment of Power Systems with Photovoltaic Power Stations Based on Intelligent State Space Reduction and Pseudo-Sequential Monte Carlo Simulation

Wenxia Liu, Dapeng Guo, Yahui Xu, Rui Cheng, Zhiqiang Wang and Yueqiao Li
Additional contact information
Wenxia Liu: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Dapeng Guo: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Yahui Xu: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Rui Cheng: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Zhiqiang Wang: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Yueqiao Li: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2018, vol. 11, issue 6, 1-14

Abstract: As the number and capacity of photovoltaic (PV) power stations increase, it is of great significance to evaluate the PV-connected power systems in an effective, reasonable, and quick way. In order to overcome the challenge of PV’s time-sequential characteristic and improve upon the computational efficiency, this paper presents a new methodology to evaluate the reliability of the power system with photovoltaic power stations, which combines intelligent state space reduction and a pseudo-sequential Monte Carlo simulation (PMCS). First, a non-aggregate Markov model of photovoltaic output is established, which effectively retains some time-sequential representation of the PV output. Then, the differential evolution algorithm (DE) is introduced into the sampling stage of PMCS to carry out an intelligent state space reduction (ISSR). By using the DE algorithm, success states are searched out and removed, thus the state space is reduced and formed with a high density of loss-of-load. Hence, unnecessary samplings are avoided, which optimizes the PMCS sampling mechanism and improves the computational efficiency. Finally, the proposed method is tested in the modified IEEE RTS-79 system. The results indicate that this new method has a better computational efficiency than the time-sequential Monte Carlo simulation method (TMCS) and pure PMCS. In addition, the effectiveness and feasibility of this method are also verified.

Keywords: photovoltaic power stations; power systems reliability; non-aggregate Markov model; pseudo-sequential Monte Carlo simulation; intelligent state space reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/6/1431/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/6/1431/ (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:jeners:v:11:y:2018:i:6:p:1431-:d:150422

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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