Machine Learning-Based Dispatch Optimization of a Molten-Salt Concentrating Solar Power Plant Using Day-Ahead Electricity Price Forecasting

The energy transition poses substantial challenges for all actors in modern power systems, where the output of key renewable technologies is weather-dependent and electricity prices are increasingly volatile. This work presents a decision-support algorithm that optimizes the day-ahead dispatch of a parabolic-trough concentrating solar power (CSP) plant with two-tank molten-salt thermal energy storage (TES) connected to the Spanish grid. The algorithm couples a deep neural network (DNN) that forecasts hourly day-ahead electricity prices—trained on Spanish market data for 2016–2021 using only predictors available before day-ahead market closure: the previous-day natural-gas index, calendar variables, lagged hourly price profiles, and the previous-day generation mix—with a genetic algorithm (GA) that maximizes expected market revenues subject to the technical constraints of the plant, using a 10 min discretization of the TES operating trajectory. Under a strictly chronological evaluation, the forecasting module achieved a mean absolute error of 12.31 EUR/MWh on the held-out year 2021 and 3.57 EUR/MWh on 2020, outperforming persistence benchmarks by 22.1% and 32.0%, respectively. Across a 32-scenario benchmark spanning seasons, gas-price regimes, day types, and irradiance patterns, the optimizer adopted solutions within 3.25% of a perfect-foresight exact optimum (range: 0.70–6.94%), with five random seeds, increasing gross revenue by 20.5% over a no-storage baseline but by only 1.1% over a simple rule-based dispatch strategy, with no evidence of storage carry-over ahead of adverse meteorological days. The results illustrate how embedding machine-learning price forecasts in plant-control algorithms can increase gross market revenue for dispatchable solar generation.