Open AccessReliability constraint stochastic UC by considering the correlation of random variables with Copula theory
Author(s) -
Yu Dongmin,
Ghadimi Noradin
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2019.0485
Subject(s) - copula (linguistics) , monte carlo method , mathematical optimization , random variable , wind power , power system simulation , computer science , wind speed , reliability (semiconductor) , stochastic programming , stochastic optimization , stochastic process , electric power system , stochastic modelling , mathematics , power (physics) , econometrics , engineering , statistics , physics , quantum mechanics , electrical engineering , meteorology
This essay performs a reliability constraint stochastic model for unit commitment problem by considering generation and transmission constraints with high wind penetration and volatility of load demands. This query is expressed as a MILP that is based on the linear direct current model. The proposed approach models uncertainty of wind generators output power, load demand fluctuations and stochastic elements outage of the system like generators and transmission lines. In this paper, stochastic interdependence between random variables like wind speed and load demand is recognized. To establish the probability distribution of these correlated random variables, Copula theory is applied. Correlation structure between wind speed of different locations and a group of loads existing in the same area is investigated and studied based on historical data. For representing these uncertainties in the stochastic unit commitment problem, possible scenarios are generated with Monte Carlo simulation method. The reliability constraints are utilized in each scenario to evaluate the feasibility of solutions from a reliability point. The introduced stochastic UC is executed on the RTS 96‐bus test system. Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy.