Development of a predictive model for enhanced grid densification and last mile connections in Uganda

Abstract

The United Nations sustainable development goal 7 ensures that by 2030, there is a universal access to affordable, reliable and modern energy services. Electricity access is key for the social and economic development of Uganda and the tracking of the load demand growth and electricity infrastructure locations is key to making informed decisions on grid densification and last mile connectivity. A case study of Nansana Municipality was chosen and the Namungoona Nansana 11kV feeder was analyzed. Data for the existing infrastructure, load profile data from Jan 2013 to Feb 2023, and the reliability statistics data was obtained from UMEME for model development and to understand the status and reliability of the existing network. Load flow analysis was carried out on the simulated network using the Newton Raphson algorithm and it converged with only three iterations indicating a good convergence behavior of the algorithm. Reliability analysis was also performed to determine using statistical methods, the total electric interruptions for loads on the feeder during an operating period and the reliability results were within the standards set by the Electricity Regulatory Authority in the S-factor mechanism. The loading on different line segments as well as the voltage levels on different busbars were also analyzed and it was noted that the voltages at different buses especially those that were far away from the substation and where the loads were concentrated were below the acceptable voltage limits set by the authority (below ±10% of the nominal voltage). The voltage levels were improved through network reconfiguration and conductor upgrade. Two forecasting models were successfully developed and their performances were compared. An LSTM model performed better than Random Forest model. The two models were combined to obtain the resulting model known as the LSTM-RF model in order to improve the accuracy of the predicted power and the LSTM-RF model outperformed both the LSTM and Random Forest models across all the three evaluation metrics. Using the developed LSTM-RF model, the load demand was predicated and it was observed that the maximum peak import power increased from 7.62MW to 8.46MW in a period of 3 years and this increased the loading on various line segments. Also, the power losses increased from 0.94MW to 1.18MW at the end of the period.