The equator divides Indonesia into two parts, the areas located in North Latitude and South Latitude. This results in Indonesia having an abundant source of solar energy. Therefore, the use of hybrid power generation systems (solar cells and diesel generators) on ships can reduce generator fuel consumption, as well as reduce exhaust emissions from ships. This paper discusses the design of a hybrid power generation system on a tanker technically and economically. The total capacity of the solar panels installed on the ship can generate electrical energy of 885.2 kWh in a day. This electrical energy is used to supply electrical loads consisting of lighting, navigation equipment, radio communication, galley and laundry equipment, and air conditioner and refrigerator. The application of hybrid power plants on tankers can produce fuel consumption savings of 15.5% per year when compared to the use of conventional power generation systems. Meanwhile, the break event points due to the use of a hybrid generating system is less than 4 years or equivalent to the nominal total cost of Rp. 23,980,000,000.00 or $ 1,803,007.52.
To maximize the effectiveness of solar energy systems installed on the ship, the panels need to be positioned on their optimal inclination angle. To determine this angle, information on hourly average solar radiation is required in addition to position and direction data. However, the amount of hourly solar radiation is not continuously measured in Indonesia. This study proposed a method to estimate the hourly solar radiation for any month. This is based on Artificial Neural Network (ANN) with the month and the hour required for the estimate and the previous daily average solar radiation as the input. The proposed ANN method was validated by comparing the estimation results with the measured hourly average solar radiation in Surabaya from May to July 2020. Its effectiveness was affirmed with the coefficient of determination (R2) of 0.983 or higher for each of the three observed months.
This study aims to generate an accurate model for estimating the radiation of solar panels on different inclination angles. The output of this model is useful for determining the optimal installation angle of the solar panel either on land or on the ships. Furthermore, the amount of the hourly direct and diffuse radiation on the horizontal surface is estimated using Artificial Neural Networks (ANN), which were trained with the monthly radiation data of Surabaya from 2018 to 2019. Subsequently, the radiation on the inclined surface is estimated using a mathematical model. Also, the ANN accuracy was validated with a regression value higher than 99% for either direct or diffuse radiation estimate. A full-year evaluation based on the proposed model suggests an inclination angle of 25° for the solar panel installed in Surabaya. Meanwhile, the evaluation gives different angles for each month with the advantage compared with the fixed angle installation.
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