08:00 - 09:00 | Wed 16 Oct | Pacífico | W3-P1
Large-scale energy systems (such as heat, gas, electricity networks) play a crucial role in society and represent important challenges for researchers working on new solutions for the energy transition. These include data-driven modeling, prediction, and quantification of uncertainty and their incorporation in prescriptive models for distributed decision-making problems. In this talk, I will describe our research on distributed stochastic model predictive control (SMPC) for large-scale linear systems with additive disturbances and multiplicative uncertainties. Typical SMPC approaches for such problems involve formulating a large-scale finite-horizon chance-constrained optimization problem at each sampling time, which is in general non-convex and difficult to solve. Large-scale scenario programs instead use an approximation that allows to quantify the robustness of the obtained solution. I will show how such problems can be decomposed into distributed scenario programs that exchange a certain number of scenarios with each other in order to compute local decisions and address computational tractability issues with a-priori probabilistic guarantees for the desired level of constraint fulfillment. Finally, I will present an overview of open problems and our related research efforts.
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