Affiliations: Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Delhi, India
Correspondence:
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Corresponding author. Neeraj Kumar, Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India. E-mail: neeraj.kumar@bharatividyapeeth.edu.
Abstract: Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model.
