Congestion management of power system with uncertain renewable resources and plug-in electrical vehicle

• Published in: IET generation, transmission & distribution transmission & distribution, 2019-03, Vol.13 (6), p.927-938
• Main Authors: Prajapati, Vijaykumar K, Mahajan, Vasundhara
• Format: Article
• Language: English
• Subjects: beta function; congestion management problem; electric vehicles; GAMS environment; generation cost minimization; k‐means clustering algorithm; modified IEEE 39‐bus system; Monte Carlo methods; Monte Carlo simulation; normal distribution; normal distribution functions; PEV cost minimization; PEV uncertainty; plug‐in electrical vehicle; power generation economics; power generation scheduling; power markets; power system; power system management; Rayleigh distribution functions; renewable energy sources; RES; rescheduling cost minimization; Research Article; solar power stations; total power generation; uncertain renewable resources; uncertainty modelling; wind power plants
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2018.6820

The study describes the quantifying impact of plug-in electrical vehicles (PEVs) and renewable energy sources (RES) for congestion management of power system. The proposed congestion management problem is formulated considering uncertainties of wind, solar, number of PEVs and load condition over a day. The uncertainty modelling of solar, wind and PEVs is presented using beta, Rayleigh and normal distribution functions, respectively. The PEVs uncertainty is dependent on its number and increases with escalation in number. The degree of uncertainties of RES is dependent on corresponding variable (wind and solar). These uncertainties result in large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. The objective function is formulated to minimise generation cost, rescheduling cost and PEV cost for congestion management. This system is analysed by using Monte Carlo simulation. The proposed methodology relieves the congestion and reduces the generation cost, total power generation, total loss with increasing number of PEVs. The test result indicates that PEVs not only act as small storage unit but it also provides power during peak hours. The proposed approach is modelled in GAMS environment and implemented on modified IEEE 39-bus system.