Non-Gaussian multivariate modeling of plug-in electric vehicles load demand

• Published in: International journal of electrical power & energy systems Vol. 61; pp. 197 - 207
• Main Authors: Pashajavid, E., Golkar, M.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2014; Elsevier
• Subjects: Applied sciences; Battery charging profile; Charging; Computer simulation; Demand; Direct energy conversion and energy accumulation; Electric power generation; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Exact sciences and technology; Ground, air and sea transportation, marine construction; Load modeling; Mathematical models; Miscellaneous; Monte Carlo simulation; Operation. Load control. Reliability; Power networks and lines; Probabilistic methods; Probability theory; Road transportation and traffic; Smart grid; Uncertainty; Electric vehicles; Uncertainty; Smart grid; Monte Carlo simulation; Battery charging profile; Load modeling; Stochastic model; Power distribution planning; Electric vehicle; Power system load; State of charge; Data structure; Distribution network; Random variable; Monte Carlo method; Power demand; Probabilistic approach; Initial condition; Decision support system; Power system planning; Stochastic simulation; Secondary cell; Scheduling; Battery charging; Algorithm; Forecasting management; Electrical network; Numerical simulation; Energy demand
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.03.021

•An organized stochastic methodology to model the power demand of PEVs is proposed.•A probabilistic decision making algorithm is developed to infer charging occurrence.•A state transition model to describe the charging profile of a PEV battery is presented.•The correlation structure is modeled using a student’s t copula distribution. This paper proposes an organized stochastic methodology to model the power demand of plug-in electric vehicles (PEVs) which can be embedded into probabilistic distribution system planning. Time schedules as well as traveling and refueling information of a set of commuter vehicles in Tehran are utilized as the input dataset. In order to generate the required synthetic data, the correlation structure of the aforesaid random variables is taken into account using a multivariate student’s t function. Afterwards, a Monte Carlo based stochastic simulation is provided to extract the initial state-of-charge of batteries. Further, a non-Gaussian probabilistic decision making algorithm is developed that accurately infers whether the PEVs charging should take place every day or not. Then, through presenting a state transition model to describe the charging profile of a PEV battery, hourly demand distributions of the PEVs are derived. The obtained distributions can be used to generate the random samples required in probabilistic planning problems. Eventually, the extracted distributions are employed to estimate demand profile of a fleet that can be efficiently utilized in various applications.