Optimal Energy Scheduling in Microgrids with Photovoltaic (PV) Generation and Energy Storage Systems

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This award supports fundamental research to address two major threats to public health and welfare, namely global warming and air pollution, by enabling the optimal use of solar photovoltaic energy. This project seeks to create optimal scheduling of energy storage systems incorporated in microgrids, which is a key part of a solar energy solution, and to overcome two critical limits of solar energy, intermittency and uncertainty, by addressing the degradation process of energy storage systems and the uncertainty in solar output and power demand at loads. The research results can help to fully utilize energy storage systems and to minimize their lifetime cost, enabling solar energy to become a vital source of renewable energy. Additionally, the project will engage, motivate, and train US students preparing them for the workforce in the interdisciplinary area of energy, power, and control through the development of interdisciplinary curricula and various science activities for diverse youth. This project addresses a fundamental trade-off in energy storage systems for power grid applications, which involves the integration of PV generation requiring more frequent charge/discharge cycles and the corresponding degradation process. The research will find answers to the following unsolved questions that are essential for optimal energy scheduling of a microgrid: (1) how multiple degradation mechanisms of energy storage systems are coupled, and how battery capacity fade is eventually affected by loading and environmental conditions; (2) what is the impact of uncertainty of solar radiation, market prices, and load on battery loading profiles, and how can uncertainty be addressed in optimal scheduling of microgrids. Specifically, the project has the following four objectives: (1) gain a fundamental understanding of battery degradation mechanisms and how they couple, and integrate that knowledge into a high-fidelity battery model; (2) create a low-order battery model that can generate the predicted results quickly without losing accuracy; (3) understand uncertainty phenomena in microgrids, and create a model to incorporate all of the phenomena; and (4) develop algorithms for optimal scheduling by considering battery degradation and microgrid uncertainty. The proposed research will discover how the uncertain nature of a microgrid can affect individual components, including energy storage degradation; the results are expected to constitute critical knowledge for the power engineering community. In addition, this project will develop a stochastic optimization technique for near-real-time optimal scheduling. It is expected that the general framework developed here will be adaptable for applications beyond power engineering.