Stochastic modeling of intraday photovoltaic power generation |
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| Affiliation: | 1. Escuela Universitaria Politécnica, Departamento de Matemática Aplicada, Universidad del País Vasco, UPV/EHU, Plaza de Europa 1, 20018 San Sebastián, Spain;2. Departamento de Automática y Computación, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain;1. Beijing Research & Development Center for Grass and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China;2. Institute of Desertification Studies, Chinese Academy of Forestry, Beijing, China;1. Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates;2. Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON N2M2C7, Canada;3. Department of Electronics and Communications Engineering, Faculty of Engineering, Cairo University, Giza 12613, Egypt;1. Unité de Développement des Equipements Solaires UDES, Centre de Développement des EnergiesRenouvelables EPST / CDER, Algeria;2. Commissariat Aux Energies Renouvelables et à L’Efficacité Energétique, CEREFE, Algiers, Algeria |
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| Abstract: | Renewable energies play an increasing role in power generation worldwide. Electricity generated by photovoltaic power plants is an important factor here. The fact that no solar power is generated at night makes modeling for high resolution difficult. Previous work has therefore been limited to daily variation. However, this obviously leads to a lack in description of the data, a gap which we will fill in this work. To do this, first we filter a cloud cover component from the infeed data by using physical relationships. This variable incorporates the complete stochastic and can be modeled as a non-linear continuous-time autoregression as defined by Brockwell and Hyndman (1992). We fit our model to infeed data in Germany and show that it describes the data better than other comparable approaches. The model enables pricing of derivatives, which is illustrated by a new future contract. This product allows the volume risk of photovoltaic power plants to be hedged. |
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