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Concentrated Solar Thermal (CST) offers a promising solution for large-scalesolar energy utilization as Thermal Energy Storage (TES) enables electricitygeneration independently of daily solar fluctuations, shifting to high-pricedelectricity intervals. The development of dispatch planning tools is mandatoryto account for uncertainties associated with solar irradiation and electricityprice forecasts as well as limited storage capacity. This study proposes theStochastic Mixed Integer Linear Program (SMILP) to maximize expected profitwithin a specified scenario space. The SMILP scenario space is generated bydifferent Empirical Cumulative Distribution Function percentiles of thepotential solar energy to accumulate in storage and the expected profit isestimated using the Sample Average Approximation (SAA) method. SMILP exhibitsrobust performance, however, its computational time poses a challenge. Thus,three heuristic solutions are developed which run a set of deterministicoptimizations on different historical weather profiles to generate candidatedispatching plans (DPs). The candidate DP with the best average performance onall profiles is then selected. The new methods were applied to a case study fora 115 MW CST plant in South Australia. When the historical database has alimited set of historical weather profiles, the SMILP achieves 6% to 9% higherprofit than the closest benchmark when the DP is applied to novel weatherconditions. With a large historical weather data, the performance of SMILP andHeuristic-2 becomes nearly identical because the SMILP can only utilize alimited number of trajectories for optimization without becomingcomputationally infeasible. In this case, Heuristic-2 emerges a practicalalternative, since it provides similar average profit in a reasonable time(saving about 7 hours in computing time).

A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty