# Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence

arXiv: https://arxiv.org/abs/2504.01652 · HTML: https://mlsanity.com/paper/2504.01652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Approximate non-linear model predictive control with safety-augmented neural networks](https://mlsanity.com/paper/2304.09575/) (2023)

## Abstract (arXiv)

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.