Modeling and Economic Evaluation of PV Net-Metering and Self-consumption Schemes
Due to the high rate of Photovoltaics (PV) installations in many countries, the need for data processing and exploitation is a crucial factor that determines the success of the economic profitability of the installation. Machine learning is a family of tools for information retrieval and knowledge extraction. In the present study, clustering is applied to a set of PV power generation curves that correspond to locational distributed PV installations with the aim of formulating the PV generation profiles and PV clusters. Next, a techno-economic assessment of different policy schemes is applied to selected cluster. The scope is to reduce the need for conducting economic analyses per PV site; grouping PV installations in homogenous clusters can lead to reduced effort in the phase of techno-economic evaluation of the overall operation of the PV technology.
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