Energy Management of a Multi-Source Power System

Abstract

Many industries are heavily dependent on fossil fuels to carry out their daily operations. The transportation industry alone is responsible for consuming two thirds of the oil used around the world. As fossil fuel deposits deplete, the need for transportation via sustainable energy solutions such as electric vehicles and battery-powered drones is rising. Battery- operated drones are being targeted by the product delivery industry. However, the use of drones is limited due to constraints on their flight time and distance. This work proposes an energy management system consisting of multiple energy sources integrated into a drone, to optimize the switching between the sources, in an effort to increase the drone’s maximum flight time and distance. A mathematical model representing the energy sources in the drone is presented, taking into account the different constraints on the system, i.e. primarily the state of charge of the battery, and super capacitor. In addition to the model, a heuristic approach is developed and compared with the mathematical model. The results generated using both methods are analyzed and compared to a standard mode of the operation of a drone; demonstrating that the dynamic approach provides a superior switching sequence, while the heuristic approach provides the advantage of low computational time. Additionally, the switching sequence provided by the dynamic approach was able to meet the power demand of the drone for all simulations performed and showed that the average power consumption across all sources is minimized. However, switching sequences provided by the heuristic approach and standard mode of operation failed in some simulations. Both the dynamic approach and heuristic approach are also tested on a multi-energy source ground robot built at AUS. The results of the tests are compared to the standard mode of operation of the ground robot; validating that the average power consumption across all sources is minimized by both proposed approaches. Moreover, the concept of scheduling different components in a system to generate the optimal operating sequence, can be used in areas like electric vehicles, and smart homes, by altering the inputs and constraints.