Carlos Olivos of Universidad Católica del Norte (UCN)

This work introduces a new modeling paradigm for the Stochastic Unit Commitment Problem (SUCP) that eliminates the need for scenario sampling. Traditional SUCP models rely on generating a large number of scenarios to capture uncertainty in demand and renewable generation, often leading to computational intractability. In contrast, this study derives an analytical expression for the expected dispatch cost using a merit-order dispatch rule and the probability distribution of net demand. This formulation embeds uncertainty directly within the optimization model, resulting in a nonlinear objective that is subsequently linearized via a piecewise approximation to yield a mixed-integer linear program (MILP). Numerical experiments on benchmark power systems show that the proposed approach achieves comparable or improved cost performance while drastically reducing computational and memory requirements. This statistical formulation provides a scalable and accurate alternative for large-scale power systems, marking a significant step forward in stochastic unit commitment modeling.

Biography:

Dr. Carlos Olivos is an Assistant Professor in the Department of Industrial Engineering at Universidad Católica del Norte (UCN) in Chile. He holds a B.S. in Industrial Engineering from UCN and an M.S. and Ph.D. in Industrial and Systems Engineering from Auburn University. His doctoral research focused on developing new stochastic optimization models for the Unit Commitment Problem in power systems. His current work combines operations research, energy systems optimization, and data-driven decision-making to support efficient and sustainable energy planning.

Email:

Address:Universidad Católica del Norte (UCN), Avenida Angamos, Antofagasta, Chile, 0610