Microgrid Resiliency and Reliability Analysis

摘要

The damage assessment of severe weather events has attracted increased attention in recent years. Utilities need to know how much they can spend to improve the power infrastructure or build a micro-grid. By improving the power infrastructure, they can support more crews. Therefore, as a result of increased crew deployment, the cost of improving infrastructure can be compensated by decreased cost caused by fast restoration. An analysis framework is proposed in which utilities can assess the customer costs with different crew deployment models. The proposed procedure starts with deciding the severity of the storm and number of customer power outages. In the second step, an appropriate time dependent cost model for that region considering all of the factors is estimated. Then, a suitable crew deployment model is acquired from previous similar cases. In the next step, restoration rate is acquired from similar. In the fifth step, number of customers restored per day versus time is obtained for the new crew deployment case. Then, the developed stochastic models are utilized in order to estimate the benefits of increased crew deployment cases. Finally, the proposed framework is implemented on Potsdam microgrid and total benefits are evaluated for a practical case study.;Reliability of islanded microgrids has received increased attention in recent years. Therefore, a complete evaluation of reliability methodologies is a necessity for microgrids in which stochastic distributed generation (SDG) are utilized a lot. Evaluating microgrid reliability in which stochastic and non-dispatchable resources are utilized is a challenging issue which is addressed thoroughly in this study. The proposed framework for finding the reliability of a microgrid is implemented on a practical case study (Potsdam microgrid). The analysis depicts that it is not profitable to add PV to Potsdam microgrid. However, the results vary significantly in different regions and for each case a separate analysis is required to estimate the benefits. Also, different factors such as load-benefit curves have significant impact on the results of the analyses.