A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks. (1st December 2016)

• Record Type: Journal Article
• Authors: Mo, Hua-Dong; Li, Yan-Fu; Zio, Enrico
• Is Part Of: Applied energy. Volume 183(2016)
• Page Start: 805
• Page End: 822
• Publication Date: 2016-12-01
• Subjects: Reliability analysis -- Distributed generation system -- Degraded communication networks -- System-of-systems -- Energy management; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.09.041
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A system-of-systems framework is proposed for reliability analysis of DG system. The impact of degraded communication networks is included and quantified. Various uncertainties and contingencies in the DG system are considered. A Monte Carlo simulation-optimal power flow computational framework is developed. The results of the application study show the power of the proposed framework. Abstract: Distributed generation (DG) systems install communication networks for managing real-time energy imbalance. Different from previous research, which typically assumes perfect communication networks, this work aims to quantitatively account for the impact of degraded communication networks on DG systems performance. The degraded behavior of communication networks is modeled by stochastic continuous time transmission delays and packet dropouts. On the DG systems side, we consider the inherent uncertainties of renewable energy sources, loads and energy prices. We develop a Monte Carlo simulation-optimal power flow (MCS-OPF) computational framework that is capable of generating consecutive time-dependent operating scenarios of the integrated system. Quantitative analysis is carried out to measure the impact of communication networks degradation onto the DG systems. For illustration, the framework is applied to a modified IEEE 13 nodes test feeder . The results demonstrate that the degraded communication networks can significantly deteriorate the performance of the integrated system. A grey differential model-based prediction method for reconstructing missing data is effective in mitigating the influence of the degraded communication networks.