<![CDATA[The rising demand for electrical energy, driven by population growth and technological advancements, presents challenges for PT PLN (Persero), Indonesia's main electricity provider. One key issue is reducing power losses in the distribution network, which affects both system efficiency and company revenue. This study explores the use of the Gauss-Seidel method for power flow analysis on the 20 kV distribution network at PLN UP3 Semarang, aiming to decrease losses and improve revenue. Using data on line impedance and load from PLN UP3 Semarang, the Gauss- Seidel method is applied via a Python script in Google Colab. The findings show that this method effectively reduces network losses, with potential financial benefits for PLN UP3 Semarang. This research also lays the groundwork for future network optimization strategies and contributes to the field of power flow analysis. The study is focused on the 20 kV network and does not compare the Gauss-Seidel method with other approaches. Growing demand for electricity and the need for reliable distribution motivate continuous improvement of power-flow analysis and loss-reduction strategies in Indonesia’s medium-voltage networks. This paper applies the classical Gauss–Seidel (GS) load-flow to the 20 kV feeders of PLN UP3 Semarang using field parameters (line R/X, substation data, and aggregated loads) and an open computational workflow in Python/Google Colab. We build a Ybus model, adopt the per-unit system, and implement standard GS updates for PQ buses with a practical convergence tolerance. The study evaluates baseline conditions and several optimization scenarios (e.g., modest R/X adjustments reflecting conductor upgrades, improved feeder balancing, and initial-voltage tuning). Results show consistent reduction of technical losses across representative ULPs and at the UP3 level; monthly loss percentages also trend downward during the observation horizon. Voltage profiles improve at non-slack buses while remaining within typical planning limits. The analysis highlights how low- complexity, data-driven GS studies can support day-to-day planning decisions for feeder reconfiguration and targeted reinforcement. We discuss implementation limits (data quality, simplifications, and scenario dependence) and outline follow-up steps, including PV-bus modeling, comparison with Newton–Raphson, and integration with economic screening curves. The findings strengthen the case for using GS-based what-if analyses as a lightweight decision aid for utilities operating medium- voltage distribution networks.]]>
