(243h) Stochastic Optimization to Reduce Cost of Energy for Parabolic Trough Solar Power Plant for Different Weather Conditions

The need for clean and cheap renewable energy is on the rise. Solar energy is one of the cleanest and readily available technologies with almost zero carbon emissions. Optimizing the resources to produce efficient power at low costs is the need of the day. However, solar energy power plants face some uncertainties like the weather. Since the technologies are new, cost uncertainties are common. In this work, we optimize the cost of the solar thermal power plant of four different locations of USA (North East, North West, South East, South West) using the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm. We use the System Advisory Model (SAM) system from NREL to model performance and economics of the power plant. Since this is a black box model, optimization and optimization under uncertainty becomes difficult. Unlike the deterministic optimization problem, in stochastic programming (or stochastic optimization), one must consider the probabilistic functional of the objective function and constraints. The generalized treatment of such problems is to use computationally intensive probabilistic or stochastic models instead of the deterministic model, inside the optimization loop. BONUS can circumvent the problems associated with black box models, computational intensity of sampling, and perturbation derivative costs. Instead of the stochastic modeling loop, BONUS uses a statistical reweighting approach to obtain the probabilistic information. The results of our work show that using BONUS algorithm; we get 41% - 47% of savings on the Levelized Cost of Electricity (LCOE) for Parabolic Trough Solar Power Plant. The four regional cities show different optimal designs, and the optimal Levelized cost of electricity (LCOE) varies significantly. For example, the optimal LCOE of New York is 57.42% more, Jacksonville is 38.52% more, and San Diego 17.57% more than that of Las Vegas.