Fuzzy Logic-Based Planning and Operation of a Resilient Microgrid

Abstract

The uncertain nature of natural, man-made, and complex phenomena poses a challenge to microgrid (MG) functioning. In the face of such unexpected events, the power supply to the customer degrades and it becomes necessary to manage the performance of MG components. Therefore, the resilience of MG should be a priority. Resilience prepares the system to handle the operational loss and recover quickly to its pre-disturbance state. It is the ability to adapt to changing conditions, withstand it, and rapidly recover from uncertain natural disasters, man-made interruptions, and complex events termed as high-impact low-probability (HILP) events. MG planning and operation strategy for such HILP events enhances resilience. To analyze this strategy, the uncertain nature of MGs needs to be addressed. However, resilience study can be extended throughout the power system but is more suitable for MGs. It is due to the location of MGs at different terrains that makes it more vulnerable to HILP events. Crisp value of resilience parameter fails to capture the wide range of variations in MG behavior. To incorporate these significant variations, fuzzy-based resilience is required. The fuzzy-based resilience planning and operation is flexible and allows variabilities associated with changing environment. This chapter provides a comprehensive analysis of fuzzy-based resilience assessment for MG planning and operation against windstorm. Weibull wind assessment estimates the maximum likelihood of wind speed distribution in a particular region. Distribution lines are the exposed component during windstorm, so the probability of impacting the MG connectivity is very high. Therefore, this chapter focuses on distribution line fragility. The fragility curve of distribution lines depicts the wind speed-dependent failure probability. The region-specific wind profile of windstorms is mapped to the fragility curve of lines to obtain the time and hazard-dependent operational status. The Monte-Carlo probabilistic assessment measures this disruption status of lines by comparing the failure of lines as a function of weather parameter. To evaluate the influence of uncertain parameters on the operation and planning of MG, fuzzy-based system average interruption frequency index (FSAIFI), fuzzy-based system average interruption duration index (FSAIDI), and fuzzy-based average service availability index (FASAI) are calculated. For MG resilience planning, it is essential to assess the time-varying nature of these indices. The characteristics of these indices are thus assessed using the resilience triangle. It describes the resilience level of a system during each specific phase of the windstorm, which are pre-disturbance, degraded, and restorative stages. This analysis is tested on IEEE 33-bus system. Also, a comparative assessment of the resilience triangle and trapezoid approach for the IEEE 33-bus system is provided. This graphical representation of fuzzy-based performance parameters provides an insight into the impact of uncertainties on the MG under HILP events.