Analisa Prediksi Produksi Energi PLTA Menggunakan Metode Simulasi Monte Carlo
-
Sinda Ramadhana Akbar
*
Institut Teknologi PLN, DKI Jakarta,
Indonesia
- Musa Partahi Marbun Institut Teknologi PLN, DKI Jakarta, Indonesia (*) Corresponding Author
Abstract
Hydropower Plants (HPPs) are one of the most important renewable energy sources supporting the national electricity supply. However, electricity generation in HPPs is influenced by various factors, including hydrological conditions, water discharge, rainfall, equipment reliability, maintenance schedules, and power system demand, resulting in inherent uncertainty in energy production. This study aims to predict the electricity generation of the Kerinci Merangin Hydropower Plant over a 30-year period using the Monte Carlo Simulation method and to analyze its Capacity Factor (CF). A quantitative approach was employed using historical electricity generation data from the Kerinci Merangin Hydropower Plant. The research procedure included probability distribution analysis, cumulative distribution development, random number generation using Microsoft Excel, and 360 simulation iterations representing 30 years of plant operation. The simulation results indicate a total projected electricity generation of 42,950,284,934 kWh, with an average annual generation of 1,431,676,164 kWh and an average monthly generation of 119,306,347 kWh. The average Capacity Factor was 46.68%, with monthly values ranging from 14.91% to 92.22%, indicating a moderate to high level of plant capacity utilization. Evaluation under the annual Take-or-Pay scheme shows that most simulation results meet the contractual energy target, although seven years are predicted to fall below the target due to variations in hydrological conditions, water discharge, and plant operational reliability. The findings demonstrate that the Monte Carlo Simulation method is effective for long-term electricity generation forecasting and can serve as a valuable tool for operational planning, maintenance scheduling, water resource management, and business strategy development to enhance system reliability and support informed decision-making.
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