Invited Session Wed.2.MA 550

Wednesday, 13:15 - 14:45 h, Room: MA 550

Cluster 18: Optimization in energy systems [...]

Stochastic optimization applied to power systems


Chair: Sara Lumbreras



Wednesday, 13:15 - 13:40 h, Room: MA 550, Talk 1

Sara Lumbreras
Efficient incorporation of contingency scenarios to stochastic optimization. Application to power systems.

Coauthors: Santiago Cerisola, Andrés Ramos


Many design problems include reliability as a sub-objective, which is evaluated through contingency scenarios. In particular, power system design problems usually incorporate reliability considerations of this kind in generation expansion or transport expansion problems. The incorporation of these scenarios to a stochastic optimization problem results in a special structure where each scenario is linked to the failure of a specific available component. We propose a Progressive Contingency Algorithm (PCI) to exploit this structure. This methodology is applied to the optimization of the electrical layout design of an offshore wind farm in a real case study. Time savings reached two orders of magnitude.



Wednesday, 13:45 - 14:10 h, Room: MA 550, Talk 2

Santiago Cerisola
Approximations of recourse functions in hydrothermal models. Numerical experiencies.

Coauthors: Sara Lumbreras, Andres Ramos


In this exposition we present some results about the application of stochastic programming techniques to a multistage hydrothermal model. We give an overview of extensions to use binary variables at every stage and
to use it for nonconvex models. Our current experiments of application of approximation techniques to the model are presented. We take advantage of the convexity and monotonicity of the recourse function in the computation of the expected recourse function and in its approximation in a Benders
type algorithm. Standard integration techniques are employed that involve the calculation of lower and upper bounds.



Wednesday, 14:15 - 14:40 h, Room: MA 550, Talk 3

Francisco D. Munoz
Using decomposition methods for wide-area transmission planning to accommodate renewables: A multi-stage stochastic approach

Coauthor: Benjamin F. Hobbs


Increasing environmental concerns have led authorities to promote the use of generation from renewable technologies. Although the type and location of future generation investments are still uncertain, transmission planners still need to make decisions "today'', in order to have enough network infrastructure available for "tomorrow''. Consequently, there is a need for tools to aid transmission planners to select robust transmission plans that will accommodate a broad range of generation configurations. We developed a two-stage stochastic program that considers transmission lumpiness, generators' response, uncertainty and Kirchhoff Voltage Laws. We apply our methodology to a 17-bus representation of California, and a 240-bus representation of the Western Interconnection in the US. We discuss the implementation and performance of Benders decomposition as an alternative approach for large-scale networks.


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