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用于电力虚拟竞价的价格敏感型机器学习

文章 arXiv papers · 作者: Yinglun Li et al.
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总结

本文提出一种用于美国批发电力市场虚拟竞价的风险约束投资组合优化方法。研究利用市场出清所产生的依赖关系，通过循环神经网络预测小时之间的节点边际电价价差。受约束的梯度提升树则对净虚拟报价如何影响价差建模，使策略能够考虑自身头寸可能产生的价格影响。

针对 PJM、ISO-NE 和 CAISO 的实证分析发现，明确对价格敏感度建模的投资组合表现优于忽略该因素的组合。论文还报告称，三个市场的夏普比率均高于标普 500，并发现 CAISO 的双结算系统效率低于 PJM 和 ISO-NE。这些结果将策略设计与市场效率评估联系起来。所提供的摘要未说明样本日期、交易成本假设、风险约束细节或样本外验证，因此不足以判断报告的表现能否在实盘交易中持续。

核心观点

  • 虚拟竞价被建模为自营交易公司的风险约束投资组合优化问题。
  • 循环神经网络预测小时之间的节点边际电价价差。
  • 受约束的梯度提升模型估计净虚拟报价如何影响价差价格。
  • 在 PJM、ISO-NE 和 CAISO 中，明确对价格敏感度建模的投资组合报告表现更好。
  • 分析报告称，CAISO 的双结算系统效率低于 PJM 和 ISO-NE。

标签

机器学习期货投资组合构建风险管理
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# Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis


# Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis









This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity. The algorithmic trading strategy is developed from the perspective of a proprietary trading firm to maximize profit. A recurrent neural network-based Locational Marginal Price (LMP) spread forecast model is developed by leveraging the inter-hour dependencies of the market clearing algorithm. The LMP spread sensitivity with respect to net virtual bids is modeled as a monotonic function with the proposed constrained gradient boosting tree. We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U.S. wholesale electricity markets. The comprehensive empirical analysis on PJM, ISO-NE, and CAISO indicates that the proposed virtual bid portfolio optimization strategy considering the price sensitivity explicitly outperforms the one that neglects the price sensitivity. The Sharpe ratio of virtual bid portfolios for all three electricity markets are much higher than that of the S&P 500 index. It was also shown that the efficiency of CAISO's two-settlement system is lower than that of PJM and ISO-NE.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。