Market-oriented flow allocation for thermal solar plants: An auction-based methodology with artificial intelligence

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. • Auction-based mechanism for flow management in parabolic-trough plants. • Market price determined by predicted thermal power. • Efficient algorithms that rely solely on the data available in real plants. • Neural network that learns the flow rates to use only the available information. • Successful implementation and deployment in commercial solar power plants.