The aim of the study is to explore the financial feasibility of capital and return on investment under the Power Purchase Agreement (PPA) model in solar and wind energy projects using smart grid (SG) technology and to compare the production and consumption of energy.
Stratified sampling method was used to select a sample of 32 solar and 27 wind projects from Karnataka Renewable Energy Development Limited and Karnataka Electricity Regulatory Commission websites during the period between 2009 and 2025. The deep learning method, using Python’s Keras framework, was deployed to test hypotheses, predict patterns and extract insights from the secondary data.
The financial feasibility in the PPA model was found to be significant, showing a greater impact of investments on solar energy projects when compared to wind energy projects in the state of Karnataka. The results from deep learning prediction and heat maps indicate positive projections for it to continue from 2030 onwards. The trend analysis between production and consumption of energy indicates the growth and transition phase shifting towards renewable energy sources.
This study offers suggestions for the policymakers, including implementing a single clearance window to approve renewable energy projects, to involve private entities in distribution and transmission of electricity through SG technology. This technology helps the government in minimising the loss of energy in transmission.
PPAs are a reliable mechanism for procuring renewable energy and meeting the renewable energy targets. As there is a push for sustainability among corporates, it has led to an increased interest in PPAs to scale operations with renewable energy.
