发表机构
LIST, Luxembourg; LIST, University of Luxembourg（卢森堡列表研究所; 卢森堡大学列表研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对BESS多市场竞价中的尾部风险，提出随机均值-CVaR优化框架，利用KDE生成联合价格情景，实现收益与稳健性的权衡，显著提升收入稳定性。
AI 中文摘要
电池储能系统（BESS）运营商在参与多个电力市场时面临重大挑战，原因是价格波动与随机备用激活之间存在复杂耦合。传统的确定性调度模型忽视了与极端市场实现相关的“尾部风险”，可能导致技术不可行或严重的经济损失。本文提出了一种风险感知的随机优化框架，用于BESS在日前（DA）能源市场和手动频率恢复备用（mFRR）市场参与中的协同优化。该模型通过利用非参数核密度估计（KDE）生成保留欧洲平衡市场经验特征的联合价格情景，显式捕获多维不确定性。采用两阶段随机规划方法管理高影响价格事件的财务风险，将条件风险价值（CVaR）指标整合到均值-CVaR目标函数中。这使得决策者能够调整其风险厌恶水平，并识别盈利能力与稳健性之间的有效前沿。仿真结果表明，与确定性基准相比，所提出的联合CVaR方法显著增强了收入稳定性。
英文摘要
Battery Energy Storage Systems (BESS) operators face significant challenges when participating in multiple electricity markets due to the complex coupling of price volatility and stochastic reserve activation. Traditional deterministic dispatch models neglect the "tail risks" associated with extreme market realizations, potentially leading to technical infeasibility or severe economic losses. This paper proposes a risk-aware stochastic optimization framework for the co-optimization of BESS participation in the Day-Ahead (DA) energy market and the manual Frequency Restoration Reserve (mFRR) market. The model explicitly captures multi-dimensional uncertainties by utilizing non-parametric Kernel Density Estimation (KDE) to generate joint price scenarios that preserve the empirical characteristics of European balancing markets. A two-stage stochastic programming approach is employed.To manage the financial exposure to high-impact price events, the Conditional Value-at-Risk (CVaR) metric is integrated into a Mean-CVaR objective function. This allows decision-makers to tune their risk-aversion levels and identify an efficient frontier between profitability and robustness. Simulation results demonstrate that the proposed joint CVaR approach significantly enhances revenue stability compared to deterministic benchmarks.
CommentsThe paper has been accepted in IECON26 without any comments
