Energy Storage Prognostic and Management in Resilient Microgrids

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Using distributed renewable resources combined with battery energy storage provides a self-contained energy system as a backup during power outage. Such solutions may even become vital when harsh weather like the Great Ice Storm of 1998 limits fuel transportation for diesel generators in remote areas in Canadian territories or even in large cities. A microgrid is a combination of loads and distributed resources such as renewable resources which are interconnected at the distribution power system. In a grid-tied operation mode, the microgrid can improve power grid resiliency by providing grid supports. Stand-alone operations has become feasible through recent advances in battery technologies, which enables reliable power supply to critical loads such as hospitals, gas stations, and military bases during grid power outage due to faults, severe weather events, and/or cyber-attacks to grid infrastructure. Currently, grid battery energy storages with modular structures are among the fastest and most expensive solutions for improving grid resiliency. This project first aims to quantify the level of resilience obtained by using batteries in terms of avoided damage costs of power outage by investigating and defining quantitative indicators to measure the resiliency of a microgrid with/without battery energy storage. To achieve this, a method will be developed for the cost-benefit analysis considering the value of microgrid resiliency for potential investment on energy storage systems. The result of this research will also help determine the appropriate size of energy storage in microgrids. To effectively maintain an expensive asset such as battery, this project also aims to investigate and develop on-line prognostic battery health condition monitoring methods for the estimation of remaining useful life of battery systems in microgrids. Challenges include higher thermal tensions and irregular partial/full discharge patterns, which adversely affect the reliability and maintenance schedules. To address these challenges, advanced analytical and statistical approaches will be used for the estimation of health and capacity states of batteries.