Integration of distributed controllers: Power reference tracking through charging station and building coordination
2022
Wald, Dylan | King, Jennifer | Bay, Christopher J. | Chintala, Rohit | Johnson, Kathryn
An influx of controllable devices and sensor information can provide both barriers and opportunities for an improved electric grid. When operating by themselves, it has been shown that these devices can provide individual benefits, maintaining some level of convenience, grid stability, and quality of life. However, a clean and sustainable energy future requires the coordinated control of these edge devices on a large scale with state-of-the art control and optimization theory innovation integrated with abundant sensor information. In this work, we take a step toward this vision by proposing a method to explore whether two different types of devices can coordinate to reach consensus on a global objective while continuing to provide their individual benefits. Due to their high impact on the grid and quality of life, this proof-of-concept work focuses on the optimal control of buildings and electric vehicle charging. It is shown that the novel algorithm, termed Network Lasso - Alternating Direction Method of Multipliers - Limited Communication Distributed Model Predictive Control (NALD), successfully achieves the global objective by tracking a power reference from the grid. Simultaneously, the peak electric vehicle charging load is minimized while fully charging each electric vehicle and the internal building temperature is regulated within specified temperature bounds. Results indicate that this can be achieved in the selected example consisting of three charging stations and one large office building.
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