A Time-Dependent Approach to Evaluate Capacity Value of Wind and Solar PV Generation

• Published in: IEEE transactions on sustainable energy Vol. 7; no. 1; pp. 129 - 138
• Main Authors: Mosadeghy, Mehdi, Yan, Ruifeng, Saha, Tapan Kumar
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2016
• Subjects: Capacity value; Correlation; Data models; Generators; Load modeling; Monte Carlo methods; Photovoltaic cells; Power system reliability; Reliability; reliability assessment; Renewable energy; Renewable resources; Solar cells; South Australia (SA) power system; Time dependence; wind and photovoltaic (PV); Wind farms; Wind power generation; Australia; Capacity value; reliability assessment; wind and photovoltaic (PV); South Australia (SA) power system
• DOI: 10.1109/TSTE.2015.2478518
• ISSN: 1949-3029

Contribution of renewable energies in power systems is increasing due to continuous growth of wind and solar generators. Because of intermittency and uncertainty of these resources, conventional reliability evaluation methods are not applicable and different techniques have been developed to model these generators. However, most of these methods are time-consuming or may not be able to keep time dependency and correlations between renewable resources and load. Therefore, this paper intends to improve the existing methods and proposes a fast and simple approach. In this approach, wind power, photovoltaic (PV) generation, and electricity demand have been modeled as time-dependent clusters, which not only can capture their time-dependent attributes, but also are able to keep the correlations between these data sets. To illustrate the effectiveness of this framework, the proposed methodology has been applied on two different case studies: 1)IEEE RTS system and 2)South Australia (SA) power network. The developed technique is validated by comparing results with sequential Monte Carlo technique.