10:30 AM - 10:55 AM
Net power maximization in a beam-down solar concentrator
The reflectors of a beam-down solar concentrator are adjusted to maximize the net power collected at the receiver. The performance of the solar plant is predicted with a Monte Carlo ray-tracing model as a function of a feasible reflector geometry. Optimization is carried out with NOMAD, an instance of the mesh-adaptive direct search (MADS) blackbox algorithm. Challenges include reducing the number of optimization variables, dealing with the stochastic aspect of the blackbox, and the selection of effective MADS optimization parameters.
10:55 AM - 11:20 AM
Long-term planning of a flexible generation portfolio
To bridge the gap between long-term capacity planning and short-term intra-hour flexibility, our approach exploits the linear time-invariant feature of variable generation using historical phase planes of capacity(in MW) and ramp(in MW/min). Compared to other proposals, it is much more computationally tractable while adequately capturing the short-term operational features.
11:20 AM - 11:45 AM
Hydropower control with a reinforcement learning approach
We apply a reinforcement learning technique to optimize
hydropower at a site in British Columbia. The policies are generated
by a neural network which is tuned to maximize power while
satisfying a set of environmental constraints.
11:45 AM - 12:10 PM
A linear mixed-integer formulation of the short-term hydropower problem
We present a mixed integer model to solve the short-term hydropower problem. It determines the volumes for given pairs of maximum efficiency discharges and power production. The objective function is calculated using energy losses from maximum storage and penalizes unit startups. Constraints on the maximum number of turbine changes are imposed to find a viable solution in practice. Computational results are presented.