Probabilistic Assessment of Hybrid Wind-PV Hosting Capacity in Distribution Systems

Dichen Liu, Chenxu Wang, Fei Tang and Yixi Zhou
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Dichen Liu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Chenxu Wang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Fei Tang: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Yixi Zhou: State Grid Hangzhou Electric Power Supply Company, Hangzhou 310000, China

Sustainability, 2020, vol. 12, issue 6, 1-19

Abstract: In recent years, hybrid wind-photovoltaic (PV) systems are flourishing due to their advantages in the utilization of renewable energy. However, the accurate assessment of the maximum integration of hybrid renewable generation is problematic because of the complex uncertainties of source and demand. To address this issue, we develop a stochastic framework for the quantification of hybrid energy hosting capacity. In the proposed framework, historical data sets are adopted to represent the stochastic nature of production and demand. Moreover, extreme combinations of production and demand are introduced to avoid multiple load flow calculations. The proposed framework is conducted in the IEEE 33-bus system to evaluate both single and hybrid energy hosting capacity. The results demonstrate that the stochastic framework can provide accurate evaluations of hosting capacity while significantly reducing the computational burden. This study provides a comprehensive understanding of hybrid wind-PV hosting capacity and verifies the excellent performance of the hybrid energy system in facilitating integration and energy utilization.

Keywords: Hybrid energy system; wind power; photovoltaic; hosting capacity; distribution system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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