In recent years, microgrids have drawn increasing attention from both academic and industrial sectors due to their enormous potential benefits to the power systems. Microgrids are essentially highly-customized small-scale power systems. Microgrids’ islanding capability enables microgrids to conduct more flexible and energy-efficient operations. Microgrids have proved to be able to provide reliable and environmental-friendly electricity to quality-sensitive or off-grid consumers. In addition, during the grid-connected operation mode, microgrids can also provide support to the utility grid. World-widely continuous microgrid deployments indicate a paradigm shift from traditional centralized large-scale systems toward more distributed and customized small-scale systems. However, microgrids can cause as many problems as it solves. More efforts are needed to address these problems caused by microgrids integration. Considering there will be multiple microgrids in future power systems, the coordination problems between individual microgrids remain to be solved. Aiming at facilitating the promotion of microgrids, this thesis investigates the system-level modeling methods for coordination between multiple microgrids in the context of participating in the market. Firstly, this thesis reviews the background and recent development of microgrid coordination models. Problems of existing studies are identified. Motivated by these problems, the research objectives and structure of this thesis are presented. Secondly, this thesis examines and compares the most common frameworks for optimization under uncertainty. An improved unit commitment model considering uncertain sub-hour wind power ramp behaviors is presented to illustrate the reformulation and solution method of optimization models with uncertainty. Next, the price-maker bidding strategy for collaborative networked microgrids is presented. Multiple microgrids are coordinated as a single dispatchable entity and participate in the market as a price-maker. The market-clearing process is modeled using system residual supply/demand price-quota curves. Multiple uncertainty sources in the bidding model are mitigated with a hybrid stochastic-robust optimization framework. What’s more, this thesis further considers the privacy concerns of individual microgrids in the coordination process. Therefore a privacy-preserving solution method based on Dantzig-Wolfe decomposition is proposed to solve the bidding problem. Both computational and economic performances of the proposed model are compared with the performances of conventional centralized coordination framework. Lastly, this thesis provides suggestions on future research directions of coordination problems among multiple microgrids
