Configuration of Microgrids Considering State Estimation, Service Restoration, and Integration with Natural Gas Systems

The interest in the adoption of smart grid technologies as a means for digitalization and automation of power distribution systems has increased rapidly in the last few years. This interest can be explained by the common belief that smart grid technologies greatly enhance the system reliability, power quality, overall efficiency, and most importantly the accommodation of distributed generations (DGs). As DG penetration levels increase, distribution networks are divided into a new set of management layers based on a microgrid structure. A typical microgrid is formed of a cluster of DG units feeding a group of loads that operates in parallel to or isolated from the main grid. Microgrids are the building blocks of smart distribution grids (SDG). The concept of microgrid brings numerous benefits; among which, the improvement of system reliability is the most salient. However, the realization of such benefit is strongly dependent on the implementation of appropriate design and operation methodologies that take into account the special philosophy and operational characteristics of microgrids. Accordingly, this thesis introduces new methodologies to enhance the operation and reliability of SDGs clustered into microgrids. In particular, three main functions are dealt with in this research work: optimum configuration, self-healing restoration, and the integration between power and natural gas microgrids. First, an optimal zone clustering (i.e. configuration) algorithm is proposed for dynamic state estimation in islanded microgrids (IMG) considering the supply adequacy of each zone. Second, a centralized-based optimization model with multi-objective functions is formulated to perform the service restoration process for microgrids operating in both grid-connected and islanded modes of operation. Further, to obviate the need for a central unit and reduce the problem complexity, the optimization problem is reformulated using distributed automated agents. Third, a new model is proposed for optimal scheduling of power-to-gas (PtG), gas-fired generation (GfG), and gas storage units in a multi-carrier energy system (MCES)-based microgrid. The model aims to facilitate the integration of renewable DGs, utilize gas and power price arbitrage, provide regulation services to the real-time market, and contribute to the restoration of power and gas loads during unplanned outages.