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Title: Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables

Abstract

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.

Authors:
; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), NREL Laboratory Directed Research and Development (LDRD); Grid Modernization Laboratory Consortium
OSTI Identifier:
1376662
Report Number(s):
NREL/JA-5D00-67689
Journal ID: ISSN 0885-8950
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Power Systems; Journal Volume: 32; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distribution systems; renewable integration; optimal power flow; voltage regulation; model predictive control

Citation Formats

DallAnese, Emiliano, Baker, Kyri, and Summers, Tyler. Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables. United States: N. p., 2017. Web. doi:10.1109/TPWRS.2017.2656080.
DallAnese, Emiliano, Baker, Kyri, & Summers, Tyler. Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables. United States. doi:10.1109/TPWRS.2017.2656080.
DallAnese, Emiliano, Baker, Kyri, and Summers, Tyler. Fri . "Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables". United States. doi:10.1109/TPWRS.2017.2656080.
@article{osti_1376662,
title = {Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables},
author = {DallAnese, Emiliano and Baker, Kyri and Summers, Tyler},
abstractNote = {This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.},
doi = {10.1109/TPWRS.2017.2656080},
journal = {IEEE Transactions on Power Systems},
number = 5,
volume = 32,
place = {United States},
year = {Fri Sep 01 00:00:00 EDT 2017},
month = {Fri Sep 01 00:00:00 EDT 2017}
}