| Electrical energy can be more efficiently and reliably generated, transmitted, and consumed over electricity grids as smart grids evolve. This dissertation studies the transactive energy management on the residential side via microgrid operation and message-passing based demand response. A microgrid is an independent section of the electrical distribution grid with capabilities to transmit, produce, and distribute power within a localized area. The implementation of the microgrid increases the reliability and quality of power supply through various means including the self-healing paradigm. A microgrid operating under a self-healing paradigm can automatically and intelligently detect and reroute the power flow around an unexpected line fault. This dissertation presents the formulations and the methodologies of the self-healing process, which is incorporated into the microgrid operation for real-time scheduling. The self-healing process tries to find the best topology of the microgrid including radial and closed-loop configurations that minimize the total operation cost while respecting all security constraints. The dissertation also considers the AC solution of optimal power flow for self-healing applications, which enhance the reactive power flow for mitigating any bus voltage violations and for alleviating any real and reactive loop flows. The message-passing based demand response scheme relies on dynamic pricing of electricity to regulate electricity consumption. To achieve this goal, load serving entities via messages-passing gather the information such as consumers' usage of electricity from smart meters, and set the dynamic price level appropriately in order to reduce the peak electricity demand through the cooperation of customers. In response to the dynamic price signals, customers can shift their demands automatically, with the help of a home energy management system, or manually to the off-peak hours so as to minimize their electricity payment and maximize its utility function. The message-passing based demand response scheme is applied in this dissertation to residential household scheduling, which is a key component of a future smart grid that can help reduce peak loads and adjust elastic demands to provide economic and emergency demand responses. A decentralized and iterative message-passing method is developed for solving the residential household scheduling problem. Under the context of a competitive retail electricity market, this dissertation analytically models load serving entities' production function based on well-known economic theory, analytically models household behaviors based on the ordinal and cardinal concepts of the utility function and using a static game strategy, and efficiently calculates retail electricity price with pure ex-ante or combinatorial pricing strategies. A simple yet effective price stabilization strategy for retail electricity price is proposed to mitigate the potential price and consumption spike caused by uncertainties in wholesale electricity prices. |
