Objective With the accelerated development of China's unified national electricity market, a core mechanism now requires all renewable generation to participate in the spot market, where feed-in tariffs are determined by supply and demand. The inherent volatility and stochasticity of wind and solar resources, driven by meteorological conditions, lead to a deep spatiotemporal coupling of their spot market value. This study aims to provide a quantitative methodology for analyzing this spot value, offering a decision-making framework for market-based trading strategies and project investment.

Method Based on historical power data from renewable energy plants, a stochastic forecast error model featuring both time-varying variance and autoregressive characteristics was constructed. On this basis, and by integrating domestic and international market mechanisms, a quantitative formula for the spot value of renewable energy was proposed. Monte Carlo simulations were then employed to analyze the spatiotemporal distribution of this value, considering the dual dimensions of spatial differences in Locational Marginal Prices (LMPs) and stochastic output errors.

Result Taking the Guangdong electricity spot market as a case study, the analysis reveals that: (1) risk premiums increase significantly in high-volatility scenarios for forecast errors, with a notably wider confidence interval; (2) when the spread between day-ahead and real-time prices is right-skewed, a positive forecast deviation enhances the spot value; (3) due to supply-demand dynamics and grid congestion, high-value clusters for renewable energy emerge in the Pearl River Delta and Chaozhou regions; and (4) generation-based virtual power plants can significantly increase their spot market revenue by optimizing bidding strategies, even with a minor sacrifice in forecast accuracy.

Conclusion By constructing a stochastic forecast error model, this research quantifies and reveals the spatiotemporal distribution patterns of renewable energy's spot value. It provides a theoretical foundation for market trading, investment siting decisions, virtual power plant resource aggregation, and spot bidding strategies for renewable energy.