Open AccessProbabilistic–possibilistic model for a parking lot in the smart distribution network expansion planning
Author(s) -
Nasri Amirhossein,
Abdollahi Amir,
Rashidinejad Masoud,
Hadi Amini M.
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.0366
Subject(s) - probabilistic logic , parking lot , smart grid , sorting , genetic algorithm , mathematical optimization , computer science , grid , possibility theory , mathematics , engineering , algorithm , artificial intelligence , fuzzy logic , civil engineering , geometry , electrical engineering , fuzzy set
Conventional distribution network departs to the smart grid. The parking lot will have an important role in the smart grid as a distributed generation. Due to the output power of parking lots is uncertain, More accurate modeling of parking lot output power is necessary for the future of distribution network studies such as Distribution Network Expansion Planning (DNEP). In this paper, a systematic method based on the Z ‐number concept is utilized to represent the uncertainty of Vehicle to Grid's (V2G's) presence. In order to investigate the impact of V2Gs uncertainty on the DNEP, we proposed a Probabilistic–Possibilistic DNEP in the presence of V2Gs referred to as P‐PDNEPV2G. If the V2Gs historical data is incomplete, the proposed structure can significantly consider the effects of V2G on the DNEP. In P‐PDNEPV2G, parking lots output power is described as a probabilistic–possibilistic variable by Z ‐number method. The optimization of P‐PDNEPV2G is executed by the Non‐Dominated Sorting Genetic Algorithm (NSGA‐II). A 24‐bus test system and the real 20 kV distribution network of Ghale‐Ganj city of Kerman province in Iran are used to demonstrate the effectiveness of the proposed methodology. Eventually, several analyses are conducted to investigate the impact of probabilistic–possibilistic V2G model on the DNEP problem.