This work develops microgrid dispatch algorithms with a unified approach to model predictive control (MPC) to (a) operate in grid-connected mode to minimize total operational cost, (b) operate in islanded mode to maximize resilience during a utility outage, and (c) utilize.
Different from the mainstream single case costing studies in the existing literature, which produces results highly specific to the grid configuration and gives limited reference value for future projects, this paper contributes to the knowledge base by gathering publicly available data.
Modern storage systems can respond to grid demands in under 100 milliseconds - faster than traditional thermal plants. That's exactly what Dodoma's predictive charge management achieves through machine learning.
The IEEE 2030 series of standards advances sustainability of the modern power grid through reliable aggregation of diverse energy sources in microgrids and virtual power plants.
Resilience, efficiency, sustainability, flexibility, security, and reliability are key drivers for microgrid developments. These factors motivate the need for integrated models and tools for microgrid planning, design, and operations at higher and higher levels of complexity.