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Stochastic Look-Ahead Commitment: A Case Study in MISO

Conference ·

This paper introduces the Stochastic Look Ahead Commitment (SLAC) software prototyped and tested for the Midcontinent Independent System Operator (MISO) look ahead commitment process. SLAC can incorporate hundreds of wind, load, and net scheduled interchange (NSI) uncertainty scenarios. It uses a progressive hedging method to solve a novel two-stage stochastic unit commitment. The first stage commitment decisions, made only for those generators whose decision to commit or not in each time period cannot be deferred, can cover the uncertainties within the next three hours. The second stage includes both the dispatch for each of the scenarios and the commitment decisions that can be deferred. Study results on 15 MISO production days show that SLAC may bring economic and reliability benefits under uncertainty.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
DOE Contract Number:
AC36-08GO28308
OSTI ID:
2229085
Report Number(s):
NREL/CP-2C00-88230; MainId:89005; UUID:f8ccfc88-40cd-44d3-bfce-8b1cc0772886; MainAdminID:71241
Resource Relation:
Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida; Related Information: 84539
Country of Publication:
United States
Language:
English

References (18)

Pyomo: modeling and solving mathematical programs in Python journal August 2011
Pyomo — Optimization Modeling in Python book January 2021
Combining Progressive Hedging with a Frank--Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming journal January 2018
On Mixed-Integer Programming Formulations for the Unit Commitment Problem journal June 2020
Stochastic Unit Commitment Performance Considering Monte Carlo Wind Power Scenarios conference June 2018
Real-Time Markets for Flexiramp: A Stochastic Unit Commitment-Based Analysis journal March 2016
Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs journal April 2016
Incorporating Post Zonal Reserve Deployment Transmission Constraints Into Energy and Ancillary Service Co-Optimization journal March 2014
Toward scalable stochastic unit commitment: Part 2: solver configuration and performance assessment journal April 2015
Applying robust optimization to MISO Look-Ahead commitment conference July 2014
Resource transition model under MISO MIP based Look Ahead Commitment conference July 2012
A stochastic model for the unit commitment problem journal January 1996
Scenarios and Policy Aggregation in Optimization Under Uncertainty journal February 1991
Stochastic unit commitment via Progressive Hedging — extensive analysis of solution methods conference June 2015
Toward scalable, parallel progressive hedging for stochastic unit commitment conference January 2013
Fundamentals and recent developments in stochastic unit commitment journal July 2019
Stochastic Optimization for Unit Commitment—A Review journal July 2015
Large-Scale Stochastic Mixed-Integer Programming Algorithms for Power Generation Scheduling book January 2016