Primary Faculty Mentor Name

Mads Almassalkhi

Status

Graduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Electrical Engineering

Primary Research Category

Engineering & Physical Sciences

Presentation Title

Convex inner approximation for dispatchable demand side resources

Time

3:00 PM

Location

Silver Maple Ballroom - Engineering & Physical Sciences

Abstract

This work presents a linear control scheme that aims to improve the feasibility of linearized optimal power flow (OPF) models in distribution feeders. For a resistive distribution network, both real and reactive power effect the node voltages and this makes it necessary to consider both when designing optimal voltage control algorithms. Inaccuracy in linearized power flow models may lead to under and over voltages when implementing economic generation dispatch models. In order to guarantee feasibility, this paper develops bounds on power that each dispatchable resource can provide to the grid, based on its real and reactive power capabilities, that guarantees network voltages to be feasible. Test simulations are conducted on standard IEEE distribution test networks to show the validity of the approach.

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Convex inner approximation for dispatchable demand side resources

This work presents a linear control scheme that aims to improve the feasibility of linearized optimal power flow (OPF) models in distribution feeders. For a resistive distribution network, both real and reactive power effect the node voltages and this makes it necessary to consider both when designing optimal voltage control algorithms. Inaccuracy in linearized power flow models may lead to under and over voltages when implementing economic generation dispatch models. In order to guarantee feasibility, this paper develops bounds on power that each dispatchable resource can provide to the grid, based on its real and reactive power capabilities, that guarantees network voltages to be feasible. Test simulations are conducted on standard IEEE distribution test networks to show the validity of the approach.