用拍卖机制+AI优化太阳能集热场流分配，提升发电效率。
Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence
- 通过拍卖机制分配集热管流量，自动平衡温度差异。
- 在多种光照条件下，热功率和截获因子均优于无分配方案。
- 已在13个商用电站落地运行，适合大规模太阳能热发电厂。
本文提出一种新型方法，优化抛物槽式集热器（PTC）电站的热平衡。该方法结合基于市场的流量分配机制与人工神经网络（ANN），降低计算量与数据需求。拍卖机制可动态调节各回路流量，适应不同的热损失和集热效率。在晴天、部分多云和阴天等不同辐照条件下验证，相比无分配系统，热功率输出与截获因子均有提升。该方法首先在真实电站模型上仿真验证，后应用于50 MW槽式电站并成功运行。目前，相关算法已部署于13个商业槽式电站，实现规模化应用，显著提升整体性能。
原文摘要 · Abstract (English)
This paper presents a novel method to optimize thermal balance in parabolic trough collector (PTC) plants. It uses a market-based system to distribute flow among loops combined with an artificial neural network (ANN) to reduce computation and data requirements. This auction-based approach balances loop temperatures, accommodating varying thermal losses and collector efficiencies. Validation across different thermal losses, optical efficiencies, and irradiance conditions-sunny, partially cloudy, and cloudy-show improved thermal power output and intercept factors compared to a no-allocation system. It demonstrates scalability and practicality for large solar thermal plants, enhancing overall performance. The method was first validated through simulations on a realistic solar plant model, then adapted and successfully tested in a 50 MW solar trough plant, demonstrating its advantages. Furthermore, the algorithms have been implemented, commissioned, and are currently operating in 13 commercial solar trough plants.
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