Abstract
Abstract: In recent years, as the development of the modern technologies in information, communication, control and computing, our living environment is becoming ”smart.” Smart Home and Smart City have gradually become part of our lives, and are no longer merely future concepts for the public. Microgrids have been set up to try and bring electricity to people even living in remote and rural areas. Even though different techniques have been applied to solve this problem, nevertheless there seems to be scarcity in deep exploration of local and indigenous solar power generation and weather data unique to the African context while dealing with this issue. In this dissertation, an optimal configuration algorithm for sharing energy resources in a microgrid using data collected from a microgrid located in Nigeria has been developed. The microgrid was divided into three (3) blocks (containing different offices) with different load distribution. The system was analyzed to determine the degree of deviation of the supplied power from the load demand. Results showed that in one of the blocks, the developed system showed a 89.2% improvement in the amount of surplus energy generated by the system. Simulation results also showed that the conventional system suffered the worst draw down during peak load demands. The battery SoC in block A went below the acceptable 30% threshold, while at that instance the SoC for the developed system in this work was about 57%. The developed worked showed about 27% improvement on the existing system even at peak load periods. Simulation results showed that as at 100 seconds, the error percentage of the existing design spiked to 7% while that of the developed algorithm was tending to zero. The system response when switching between different scenarios was also examined, and it was discovered that the developed algorithm responded to the switch in just 80mS. Keywords: Microgrid, Solar PV, ELDI, Scheduling algorithm, Energy Management
Published Version
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