۱- Introduction

۲- Modeling of a radial MMG system oriented to performance sharing

۳- A BN-based reliability evaluation method for the radial MMG systems

۴- The system modeling and BN parameter modeling

۵- Case study

۶- Conclusion

References

Abstract

The multi-microgrid (MMG) system is studied as a rudiment of the smart grid, and the radial MMG system is regarded as the comprehensive and typical one. The reliability evaluation of MMG systems are widely discussed to ensure its reliable and steady operation. The power balance between supply and demand is the core criterion for the reliability evaluation of MMG systems, which will be broken by unexpected events or inherent limits, such as the failure of equipment and the insufficient transmission capacity of distribution network (DN). In existing research, few have established a reliability model considering partial failure and imperfect repair of the equipment, and the DN transmission capacity usually is ignored. In this study, we abstract the radial MMG system as a performance-sharing system and present a Bayesian-network-based (BN-based) unified modeling method for performance and reliability to solve the deficiencies in existing research. First, the operation of radial MMG systems is analyzed to establish an abstract model to simplify the problem. Afterwards, the executive program of the BN-based reliability evaluation method is given. Further, the system modeling and the BN parameter modeling are introduced. Finally, a radial MMG system including 9 microgrids (MGs) is studied as a case, the variation of the system reliability index in grid-connected and island modes is analyzed. The results show that the method supports the reliability evaluation and analysis of radial MMG systems considering unexpected events and inherent limits.

Introduction

The concept of an MMG is developed on the EU’s “More Microgrid” program proposed in 2006 (Xu et al., 2018), which forms at the medium-voltage (MV) level, and consists of several low-voltage (LV) MGs and distributed generation (DG) units connected to adjacent MV feeders (Gil & Lopes, 2007). With the popularization of renewable energy power generation, an increasing number of neighboring MGs interconnected to the DN will form a multilevel (and high-voltage) radial MMG system in a wide area (Madureira et al., 2011), which is more typical representative of the future grid. The radial MMG systems are regarded as the rudiment of the smart grid, which have the complex architecture, energy dispatch and failure modes. Compared to traditional power systems, the reliability evaluation of MMG systems is more difficult due to its more complex structure and operating modes. MG is an independently operating unit in an MMG system, and there are many reliability evaluation methods of MG. The typical characteristics of the MG, such as uncertainties, energy storage, and multiple operating modes, are taken into account in existing methods (Bae & Kim, 2008; Bai, Miao, Zhang, & Bai, 2015; Conti, Nicolosi, & Rizzo, 2012; Conti, Rizzo, El-Saadany, Essam, & Atwa, 2014; Costa & Matos, 2009; Moslehi & Kumar, 2010); which are the foundation of MMG reliability evaluation. The MMG reliability evaluation method has the following development trend: first, it only focused on the power balance between supply and demand, for example, Nikmehr and Najafi-Ravadanegh (2015, 2016) optimized power dispatch of an MMG system constructed by three interconnected MGs and evaluated the satisfaction of loads demands. However, equipment failure and maintenance will affect system reliability seriously, which is gradually taken into account in the latter methods. In general, the equipment has some intermediate states in addition to the states of “normal” and “failure”. For example, in a DG consisting of multiple diesel units, if one unit fails while the others generate power properly, the system is in the state of derating operation, which is an intermediate state. This state, which was ignored completely in the existing reliability model of the MMG, is called partial failure.