A stochastic-MILP dispatch optimization model for concentrated solar thermal under uncertainty

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independent of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with weather and electricity price forecasts. A Stochastic Mixed-Integer Linear Program (SMILP) is proposed to maximize Sample Average Approximation (SAA) of expected profit within a specified scenario space. The SMILP exhibits robust performance, yet its computational time poses a challenge. Three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate Dispatch Plans (DPs). Subsequently, the DP with the best average performance on all profiles is selected. The new methods are applied to a hypothetical 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6–9 % higher profit than the closest heuristic when the DPs are applied to novel weather conditions. With a large historical weather dataset, the performance of the SMILP and closet heuristic becomes nearly identical since the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, the heuristic emerges a practical alternative, providing similar average profit in a reasonable time. Taken together, the results illustrate the importance of considering uncertainty in DP optimization and indicate that straightforward heuristics on a large database are a practical method for addressing uncertainty.