  Concentrated Solar Thermal (CST) offers a promising solution for large-scale
solar energy utilization as Thermal Energy Storage (TES) enables electricity
generation independently of daily solar fluctuations, shifting to high-priced
electricity intervals. The development of dispatch planning tools is mandatory
to account for uncertainties associated with solar irradiation and electricity
price forecasts as well as limited storage capacity. This study proposes the
Stochastic Mixed Integer Linear Program (SMILP) to maximize expected profit
within a specified scenario space. The SMILP scenario space is generated by
different Empirical Cumulative Distribution Function percentiles of the
potential solar energy to accumulate in storage and the expected profit is
estimated using the Sample Average Approximation (SAA) method. SMILP exhibits
robust performance, however, its computational time poses a challenge. Thus,
three heuristic solutions are developed which run a set of deterministic
optimizations on different historical weather profiles to generate candidate
dispatching plans (DPs). The candidate DP with the best average performance on
all profiles is then selected. The new methods were applied to a case study for
a 115 MW CST plant in South Australia. When the historical database has a
limited set of historical weather profiles, the SMILP achieves 6% to 9% higher
profit than the closest benchmark when the DP is applied to novel weather
conditions. With a large historical weather data, the performance of SMILP and
Heuristic-2 becomes nearly identical because the SMILP can only utilize a
limited number of trajectories for optimization without becoming
computationally infeasible. In this case, Heuristic-2 emerges a practical
alternative, since it provides similar average profit in a reasonable time
(saving about 7 hours in computing time).
