Photovoltaic System Performance Model for Output Power Forecasting

In the last few years, the total global photovoltaic installed capacity has been increasing. From 300 GW in 2016, it has increased to around 400 GW in 2017. In the Philippines, the installed capacity was 165 MW, 765 MW, and 885 MW, for the years 2015, 2016, and 2017, respectively. One of the challen...

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Bibliographic Details
Main Author: Chan, Aaron Keith
Format: text
Published: Archīum Ateneo 2019
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Online Access:https://archium.ateneo.edu/theses-dissertations/414
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Institution: Ateneo De Manila University
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Summary:In the last few years, the total global photovoltaic installed capacity has been increasing. From 300 GW in 2016, it has increased to around 400 GW in 2017. In the Philippines, the installed capacity was 165 MW, 765 MW, and 885 MW, for the years 2015, 2016, and 2017, respectively. One of the challenges of using PV energy is its intermittent nature. Due to this, managing the electrical grid network becomes difficult. Hence, it is important to be able to predict the output power of a PV system. In this study, a photovoltaic system model was developed consisting of a PV array model, a DC cable model, and an inverter model. A total of three inverter models were evaluated. The performance of each submodel was evaluated individually, followed by the performance of the system as a whole. Then, the system model was used in conjunction with an irradiance forecasting model to predict the output power of a PV system. To evaluate the model a 1 kW PV system was used, and the results show that the system model has an RMSE equal to 33.286 W when using inverter model 2 and an RMSE equal to 32.957 W when using inverter model 3, which are the best performing models. When using the system model in conjunction with an irradiance forecasting model RMSE increases to 137.519 W when using inverter model 2 and increases to 118.041 W when using inverter model 3. The large difference in error between inverter models 2 and 3 indicates that inverter model 3 is the better choice for forecasting output power.