A Stochastic Programming Approach to Power Portfolio Optimization

Authors:
Suvrajeet Sen;Lihua Yu;Talat Genc
Affiliations:
SIE Department, MORE Institute, University of Arizona, Tucson, Arizona 85721;SIE Department, MORE Institute, University of Arizona, Tucson, Arizona 85721;SIE Department, MORE Institute, University of Arizona, Tucson, Arizona 85721
Venue:
Operations Research
Year:
2006

Citing 9
Cited 2

Kendall's advanced theory of statistics

Kendall's advanced theory of statistics
Formulating two-stage stochastic programs for interior point methods

Operations Research
Bundle Methods in Stochastic Optimal Power Management: A Disaggregated Approach Using Preconditioners

Computational Optimization and Applications
A Heuristic for Moment-Matching Scenario Generation

Computational Optimization and Applications
Branch-And-Price: Column Generation for Solving Huge Integer Programs

Operations Research
Formulation of the Russell-Yasuda Kasai Financial Planning Model

Operations Research
Incorporating Fuel Constraints and Electricity Spot Prices into the Stochastic Unit Commitment Problem

Operations Research
Generating Scenario Trees for Multistage Decision Problems

Management Science
A Branch-and-Price Algorithm for Multistage Stochastic Integer Programming with Application to Stochastic Batch-Sizing Problems

Management Science

A two-stage stochastic programming model for electric energy producers

Computers and Operations Research
Stochastic Power Generation Unit Commitment in Electricity Markets: A Novel Formulation and a Comparison of Solution Methods

Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a power portfolio optimization model that is intended as a decision aid for scheduling and hedging (DASH) in the wholesale power market. Our multiscale model integrates the unit commitment model with financial decision making by including the forwards and spot market activity within the scheduling decision model. The methodology is based on a multiscale stochastic programming model that selects portfolio positions that perform well on a variety of scenarios generated through statistical modeling and optimization. When compared with several commonly used fixed-mix policies, our experiments demonstrate that the DASH model provides significant advantages.