Invited Session Mon.1.MA 549

Monday, 10:30 - 12:00 h, Room: MA 549

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

Optimization models to manage risk and uncertainty in power systems operations


Chair: Raphael Martins Chabar and Luiz Augusto Barroso



Monday, 10:30 - 10:55 h, Room: MA 549, Talk 1

Alexandre Street
Energy and reserve scheduling under a joint GT n-K security criterion: An adjustable robust optimization approach

Coauthors: Arroyo M. Jose, Alexandre Moreira


This presentation shows a new approach for energy and reserve scheduling in electricity markets under a general n-K security criterion. It extends previous robust optimization based works that
only considered generation faults to consider a joint GT criterion. A Benders decomposition is applied in combination with a set of valid constraints based on a single-bus reduction of the problem. Such constraints provide a tighter formulation for the master problem resulting in significant improvements in the method computational burden.



Monday, 11:00 - 11:25 h, Room: MA 549, Talk 2

Jinye Zhao
Adaptive robust optimization for the security constrained unit commitment problem


Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources. To meet these challenges, we propose a two-stage adaptive robust unit commitment model and a practical solution method. We present a numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.



Monday, 11:30 - 11:55 h, Room: MA 549, Talk 3

Anthony Papavasiliou
Applying high performance computing to multi area stochastic unit commitment for high wind penetration

Coauthor: Shmuel S. Oren


We use a two-stage stochastic programming formulation in order to schedule locational generation reserves that hedge power system operations against the uncertainty of renewable power supply. We present a parallel implementation of a Lagrangian relaxation algorithm for solving the stochastic unit commitment problem. The model we present addresses the uncertainty of wind power supply, the possibility of generator and transmission line outages and transmission constraints on the flow of power over the network. We present a scenario selection algorithm for representing uncertainty in terms of a moderate number of appropriately weighted scenarios and use a high performance computing cluster in order to validate the quality of our scenario selection algorithm. We compare the performance of our approach to N-1 reliable unit commitment. We examine the dependence of the Lagrangian duality gap on the number of scenarios in the model and relate our results to theoretical bounds provided in the literature. We finally report results regarding speedup and efficiency.


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