Online dynamic security assessment of microgrids before intentional islanding occurrence

This paper presents a statistical learning-based method for security assessment of microgrids (MGs) in case of isolation from the main grid. Based on the stability criteria, the MG pre-islanding conditions are divided into secure and insecure regions. Critical system variables regarding the MG dynamic security are first selected via a feature selection procedure, known as minimum redundancy maximum relevance. An unsupervised learning method called pattern discovery method is then performed on the space of the critical features to extract the organization (patterns) among samples. Geometrically, the patterns are hyper-rectangles in the features space representing the system dynamic secure/insecure regions and can be effectively used for online MG security monitoring before islanding condition. Simulation results are carried out in the time domain, by using MATLAB, which demonstrate the effectiveness and accuracy of the proposed method in the MG security assessmen

Application of Microgrids in Supporting the Utility Grid

Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply-demand balance. In this dissertation, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues and flexibility support of utility grid. The proposed coordination is performed to capture both inter-hour and intra-hour net load variabilities. Furthermore, a microgrid optimal scheduling model is developed to demonstrate microgrid\u27s capability in offering ancillary services to the utility grid. The proposed microgrid optimal scheduling model coordinates the microgrid net load with the aggregated consumers/prosumers net load in its connected distribution feeder to capture both inter-hour and intra-hour net load variations in order to offer different ancillary services to the utility grid. The proposed models are developed through mixed-integer programming. In addition, a robust optimization model is applied to the proposed model in order to consider possible uncertainties in forecasting while supporting the utility grid. The microgrid value of ramping is further determined based on its available reserve using a cost-benefit analysis, which helps the microgrid owners for offering the flexibility support to the utility grid. In addition, a distribution market scheduling model is developed to capture and collect the ramping capability of participating microgrids in the distribution market as to offer it to the upstream network to address emerging ramping issues in the system associated with growing proliferation of variable renewable generation. Moreover, numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model