Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distributio...

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Vydané v:IEEE transactions on power systems Ročník 36; číslo 5; s. 4126 - 4139
Hlavní autori: Jha, Rahul Ranjan, Dubey, Anamika
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Approximation
Approximation algorithms
conic constraints
current operating point
D-OPF solution
distribution-level optimal power flow
Electric power distribution
equivalent NLP D-OPF model
feasible power flow solution
Flow equations
Iterative second order cone programming
Linear programming
linear programming problem
Load flow
Mathematical model
multiple iterations
network-level optimization
nonlinear power flow equations
Nonlinear programming
nonlinear programming problem
Optimization
Power distribution
Power flow
POWER TRANSMISSION AND DISTRIBUTION
scalable D-OPF algorithms
second-order cone programming problem
SOCP problem
successive linear approximation
successive linear programming
Three-phase electric power
three-phase optimal power flow
Unbalance
unbalanced power distribution system
ISSN:0885-8950, 1558-0679
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Popis
Shrnutí:The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system.
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content type line 14
USDOE
AC05-76RL01830
PNNL-SA-168277
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3066146