Final Technical Report: Sparse Grid Scenario Generation and Interior Algorithms for Stochastic Optimization in a Parallel Computing Environment

Mehrotra, Sanjay¹ 

[1] Northwestern Univ., Evanston, IL (United States)

关键词: Scenario generation;    interior point algorithms;    robust optimization;    unit commitment;    power grid design;    convex optimization;   

DOI  :  10.2172/1321178
RP-ID  :  None
PID  :  OSTI ID: 1321178

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The support from this grant resulted in seven published papers and a technical report. Two papers are published in SIAM J. on Optimization [87, 88]; two papers are published in IEEE Transactions on Power Systems [77, 78]; one paper is published in Smart Grid [79]; one paper is published in Computational Optimization and Applications [44] and one in INFORMS J. on Computing [67]). The works in [44, 67, 87, 88] were funded primarily by this DOE grant. The applied papers in [77, 78, 79] were also supported through a subcontract from the Argonne National Lab. We start by presenting our main research results on the scenario generation problem in Sections 1???2. We present our algorithmic results on interior point methods for convex optimization problems in Section 3. We describe a new ???central??? cutting surface algorithm developed for solving large scale convex programming problems (as is the case with our proposed research) with semi-infinite number of constraints in Section 4. In Sections 5???6 we present our work on two application problems of interest to DOE.