# Energy Management of a Multi-Source Power System
## 5 Conclusions  
This paper is concerned with multi source power systems consisting of batteries, super capacitors, a hydrogen fuel cell, and a photovoltaic cell. The usage of each of the sources is controlled by turning connected switches on or off as needed to supply the needed power demand of the system. A mathematical model was developed for the efficient energy management of the integrated sources, generating an optimal switching sequence between the sources. Two methods have been developed to solve for the optimal switching sequence. The first method uses a dynamic mathematical model solved using Lingo to minimize the running cost of the system by generating a switching sequence. The second method uses a heuristic approach, where a set of rules were used to generate the switching sequence. The heuristic algorithm was primarily tested on Matlab. The switching sequences generated by the dynamic approach resulted in power consumption that was on average 9% lower than those of the heuristic approach. However, the main advantage introduced by the heuristic algorithm was the short computational time needed to generate the switching sequence between the sources. The developed approaches were implemented offline before running the system, but can be implemented online. To be implemented online, the algorithms would require readings of the system's behavior and demand to generate the optimal switching sequence. Additionally, both approaches were tested in simulations of a drone flight scenario, and also experimentally tested on a multi-energy source ground robot. The developed dynamic model was capable of generating a switching sequence that minimized the power dissipation of the energy sources for the illustrative simulation examples of a drone, while the standard mode of operation was failed to provide the needed power. Also, the model was able to prolong the simulated flight time of the drone by charging the batteries and the super capacitor as needed depending on the demand profile. The switching sequences generated by the heuristic algorithm were also able to prolong the flight time in the  
Figure 12: Power consumption comparison. simulation tests related to the drone, and minimize the power dissipation of the energy sources; but not as well as those of the dynamic modeling approach. Both the dynamic modeling, and heuristic approaches, when tested on a multi-energy source ground robot, were able to generate switching sequences that minimize the power dissipation by reducing the average power consumption across the sources due to the voltage dynamics of the different sources. However, the dynamic approach's switching sequence resulted in the most significant reduction in the average power consumption; 5.5% lower average power consumption compared to the standard mode of operation of the robot. The switching sequence of the heuristic approach was also able to reduce the average power consumption by 2.5% compared to the standard mode of operation. The limitations faced in this work include the length of the simulations conducted. Due to the large computing power required by the dynamic model based approach, the simulations were conducted for only fifty seconds. However, with access to more computing power, better switching sequences could be generated that provide a further reduction in the running cost of the system. Additionally, future work could include the real-time management of the sources integrated into the system, alongside continuous readings of the behavior of the system while it is being used. By implementing the real-time management of the sources, the system could become more responsive to fluctuations in demand. O.S. : Conceptualization, methodology, programming and writing the original draft. A.S. and S.M. : Conceptualization, methodology, and finalizing the manuscript. All authors have read and agreed to the published version of the manuscript. The authors received no specific funding for this work. Not applicable.