融合气象预测与在线校准，提升风光混合发电的预报与交易收益
A Hybrid Strategy for Probabilistic Forecasting and Trading of Aggregated Wind-Solar Power: Design and Analysis in HEFTCom2024
- 用多源天气预报叠加生成风力预测，提升准确性
- 通过在线模型校准解决光伏数据分布偏移问题
- 基于概率聚合与随机策略，实现电力市场高收益交易
未来能源系统中，准确的概率化能源预测与应对多重不确定性下的有效决策仍是常态挑战。本文介绍团队GEB在2024年IEEE混合能源预测与交易竞赛（HEFTCom2024）中的获奖方案，该方案在交易环节排名第三，预测环节排名第四，学生组排名第一。方案针对风光混合发电系统，提出高精度概率预测，并在日前电力市场实现显著交易收益。核心包括：(1) 基于堆叠法融合多种数值天气预报（NWPs）生成风力预测；(2) 在线光伏后处理模型，缓解因光伏装机增加导致的测试集分布偏移；(3) 概率聚合方法，实现混合发电量的精准分位数预测；(4) 考虑电价不确定性的随机交易策略，以最大化预期收益。论文还探讨端到端学习对进一步提升交易收益的潜力。通过详细案例验证了方法有效性，所有方法代码均已开源，供产业与学术界复现与研究。
原文摘要 · Abstract (English)
Obtaining accurate probabilistic energy forecasts and making effective decisions amid diverse uncertainties are routine challenges in future energy systems. This paper presents the winning solution of team GEB, which ranked 3rd in trading, 4th in forecasting, and 1st among student teams in the IEEE Hybrid Energy Forecasting and Trading Competition 2024 (HEFTCom2024). The solution provides accurate probabilistic forecasts for a wind-solar hybrid system, and achieves substantial trading revenue in the day-ahead electricity market. Key components include: (1) a stacking-based approach combining sister forecasts from various Numerical Weather Predictions (NWPs) to provide wind power forecasts, (2) an online solar post-processing model to address the distribution shift in the online test set caused by increased solar capacity, (3) a probabilistic aggregation method for accurate quantile forecasts of hybrid generation, and (4) a stochastic trading strategy to maximize expected trading revenue considering uncertainties in electricity prices. This paper also explores the potential of end-to-end learning to further enhance the trading revenue by shifting the distribution of forecast errors. Detailed case studies are provided to validate the effectiveness of these proposed methods. Code for all mentioned methods is available for reproduction and further research in both industry and academia.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。