基于可拓云和集成学习的电力现货市场运营风险评价与预警

廖建; 张贝西; 张成刚; 陈艺华; 李昀昊; 张耀

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电力建设 ›› 0

基于可拓云和集成学习的电力现货市场运营风险评价与预警

廖建¹, 张贝西¹, 张成刚², 陈艺华², 李昀昊², 张耀¹

作者信息 +

Operational Risk Assessment and Warning for Electricity Spot Market Based on Extension Cloud and Ensemble Learning

LIAO Jian¹, ZHANG Beixi¹, ZHANG Chenggang², CHEN Yihua², LI Yunhao², ZHANG Yao¹

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文章历史 +

摘要

【目的】全国统一电力市场体系构建加速,但复杂的市场机制和现有方法的实时性不足,导致现货市场运营风险动态预警困难。【方法】为此,提出基于出清前特征变量的运营风险评价模型,并采用Stacking集成学习模型进行预测。首先,选取对市场影响显著的特征变量,通过改进的拉丁超立方抽样法生成大量风险场景。其次,构建现货市场运营风险评价指标体系,现货市场出清后计算风险指标。随后,采用基于云熵优化算法的物元可拓云模型,平衡定量与定性转化中的模糊性与不确定性,实现合理的风险评级。最后,以随机森林、深度神经网络和XGBoost模型作为初级学习器进行五折交叉验证,提取市场特征,并采用Logistic回归作为次级学习器对市场综合风险和指标风险进行预警。【结果】基于IEEE 24节点系统的仿真结果表明,所提模型避免了少数类欠拟合问题,综合风险预测准确率为79.36%,Top-2准确率达97.01%,较传统模型提升4%~12%,单指标风险预警平均准确率达78.95%。【结论】所提模型可实现电力现货市场运营风险的合理评价、准确预警,有效识别风险来源,为实时风险决策提供有效辅助工具。

Abstract

[Objective] The construction of a unified national electricity market system is accelerating; however, the complexity of market mechanisms and the lack of real-time capabilities in existing methods make dynamic risk warning in spot market operations challenging. [Methods] To address this, an operational risk assessment model based on pre-clearing feature variables is proposed, utilizing a Stacking ensemble learning model for prediction. First, key feature variables with significant market impact are selected, and an improved Latin hypercube sampling method is used to generate numerous risk scenarios. Second, an operational risk evaluation index system for the spot market is established, and calculate risk indicators after the spot market is cleared. Subsequently, a cloud entropy-optimized matter-element extension cloud model is employed to balance the fuzziness and uncertainty in the transformation between quantitative and qualitative evaluations, enabling reasonable risk grading. Finally, random forest, deep neural network, and XGBoost are used as base learners for five-fold cross-validation to extract market features. Logistic regression is then employed as the meta-learner to provide early warnings for overall market risks and specific indicator risks. [Results] Simulation results based on the IEEE 24-bus system indicate that the proposed model effectively alleviates the underfitting issue for minority classes. It achieves an accuracy of 79.36% in overall risk prediction and a Top-2 accuracy of 97.01%, representing a 4%-12% improvement over traditional models. The average accuracy for single-indicator risk warnings reaches 78.95%. [Conclusions] The proposed model enables reasonable evaluation and accurate early warning of operational risks in electricity spot markets. It effectively identifies risk sources and provides reliable support for real-time risk management and decision-making.

导出引用

廖建, 张贝西, 张成刚, 陈艺华, 李昀昊, 张耀. 基于可拓云和集成学习的电力现货市场运营风险评价与预警[J]. 电力建设. 0

LIAO Jian, ZHANG Beixi, ZHANG Chenggang, CHEN Yihua, LI Yunhao, ZHANG Yao. Operational Risk Assessment and Warning for Electricity Spot Market Based on Extension Cloud and Ensemble Learning[J]. Electric Power Construction. 0

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