A review of grid parity assessment for solar photovoltaics

Grid parity is considered the tipping point of economic competitiveness of PV systems. However, accurately determining when grid parity is achieved hinges on the reliability and precision of the methodologies and data employed. This paper systematically reviews existing methods for assessing PV grid parity, proposes a structured three-step framework for grid parity assessment, and identifies the potential enhancements for more accurate evaluation outcomes. The framework begins with the calculation of PV costs using the Levelized Cost of Electricity (LCOE) method, continues with predicting PV cost trends through learning curves, and is completed by benchmarking PV costs against electricity prices. Our findings reveal that most current PV cost calculations for grid parity primarily rely on the LCOE method, which can be enhanced by incorporating modifications for integration costs, revenues, PV performance metrics, regional-specific characteristics, and uncertainties. Moreover, learning curve models used to predict PV cost trends can be refined by tailoring learning rates and model formulations to reflect specific stages of technological development and regional differences. Additionally, the results suggest that electricity prices used in grid parity assessment can be adjusted to reflect the impact of policies and market dynamics. This comprehensive review provides a robust framework for assessing grid parity and serves as an essential reference for conducting more precise techno-economic feasibility assessment of PV systems. • Assessing grid parity can be conducted by calculating PV costs, predicting cost trends and benchmarking electricity prices. • Existing PV cost computations are primarily based on the LCOE method. • Existing PV cost prediction are primarily based on learning curve method. • Enhance LCOE by including integration costs, revenues, PV performance factors, regional-specifics, and uncertainty. • Tailoring learning curves to specific technological stages and regions for better accuracy.