Day-Ahead Bidding Strategy for Photovoltaic Power Plants Based on Dynamic Error-Band Optimization

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

To address the limitations of traditional day-ahead bidding strategies in handling the time-varying uncertainty of photovoltaic output, and considering that single-point forecasts are insufficient for reliable risk-based decision-making, this paper proposes a day-ahead bidding strategy for PV power plants based on dynamic error-band optimization. First, a dynamic uncertainty quantification method based on dual-model prediction discrepancy is proposed. It couples two complementary forecasting mechanisms—Long Short-Term Memory, and Seasonal Autoregressive Integrated Moving Average—and utilizes the Dynamic Time Warping algorithm to extract their discrepancy as a dynamic input for subsequent risk assessment and decision-making. Secondly, based on this uncertainty indicator, a probabilistic mapping model is constructed to link prediction uncertainty to the risk of power violation, translating the abstract prediction discrepancy into a concrete economic risk probability. Finally, considering the trade-off between economic benefits and security, a dynamic error-band optimization mechanism is introduced to adaptively determine the bidding margin at different time periods. Case results for a 20 MW PV plant show that the dynamic strategy reduces the number of violation events to zero in the tested daily bidding case, compared with four violations under a fixed 5% error band and one violation under a fixed 10% error band. The corresponding economic revenue increases by 5.3% and 11.2% relative to the fixed 5% and fixed 10% strategies, respectively.

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