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<p>The uncertainty of renewable energy is a major factor that decreases the reliability and economic efficiency of market operation. Therefore, various approaches, including stochastic, robust, and hierarchical optimization methods utilizing flexibility resources such as energy storage systems (ESSs) have been extensively studied; however, the statistical guarantee of forecast uncertainty risk has not been sufficiently addressed. This study proposes a conformal prediction-based uncertainty risk quantification model for photovoltaic (PV) generation, along with a hierarchical day-ahead market (DAM) operation mechanism based on this model. The quantified risk is incorporated into the upstream DAM bidding plan to determine the hourly potential flexibility of the ESS, serving as an upper limit on ESS output for uncertainty response in the downstream operational stage. A case study based on measured PV data demonstrates that the proposed method achieves an approximately 7% improvement in monthly average profit compared to a deterministic operation method and a 5.8% higher net profit than a Gaussian chance-constrained method. These results demonstrate that the proposed method can support the reliable and cost-effective operation of flexibility resources under renewable energy forecast uncertainty.</p>

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Conformal Prediction-Based Photovoltaic Uncertainty Risk Quantification for Securing Energy Storage System Flexibility in Day-Ahead Market Operation

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Sung-Won Park

摘要

The uncertainty of renewable energy is a major factor that decreases the reliability and economic efficiency of market operation. Therefore, various approaches, including stochastic, robust, and hierarchical optimization methods utilizing flexibility resources such as energy storage systems (ESSs) have been extensively studied; however, the statistical guarantee of forecast uncertainty risk has not been sufficiently addressed. This study proposes a conformal prediction-based uncertainty risk quantification model for photovoltaic (PV) generation, along with a hierarchical day-ahead market (DAM) operation mechanism based on this model. The quantified risk is incorporated into the upstream DAM bidding plan to determine the hourly potential flexibility of the ESS, serving as an upper limit on ESS output for uncertainty response in the downstream operational stage. A case study based on measured PV data demonstrates that the proposed method achieves an approximately 7% improvement in monthly average profit compared to a deterministic operation method and a 5.8% higher net profit than a Gaussian chance-constrained method. These results demonstrate that the proposed method can support the reliable and cost-effective operation of flexibility resources under renewable energy forecast uncertainty.