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Indonesia’s power generation roadmap aspires to achieve 23%, 28%, and 31% of power from renewable energy by 2025, 2038, and 2050, respectively. This study presents a technoeconomic analysis of Indonesia’s power generation development plans using the LEAP model in the post-COVID-19 period, with a focus on achieving the renewable target. In this study, four scenarios were modeled: business as usual (BAU), cost optimization (CO), national plan (NP), and zero-carbon (ZC). The BAU scenario is based on the PLN Electricity Business Plan 2019–2028, which does not include a target for renewable energy. The CO scenario aims to meet the renewable energy mandate at the lowest possible cost. The NP scenario aims to achieve renewable energy, with an additional natural gas target of 22% by 2025 and 25% by 2038. The ZC scenario aims to achieve 100% renewable energy by 2050 at the lowest possible cost. In comparison to the other scenarios, the BAU scenario has the highest total cost of power production, with a total of 180.51 billion USD by 2050. The CO scenario has the lowest total cost of production with a total of 89.21 billion USD; however, it may not be practical to implement.

Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.

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