A Heuristic Nonlinear Constructive Method for Electric Power Distribution System Reconfiguration

Abstract

The electric power distribution system usually operates

a radial configuration, with tie switches between circuits

to provide alternate feeds. The losses would be minimized

if all switches were closed, but this is not done because

it complicates the system's protection against overcurrents.

Whenever a component fails, some of the switches must be

operated to restore power to as many customers as possible.

As loads vary with time, switch operations may reduce losses

in the system. Both of these are applications for

reconfiguration.

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The problem is combinatorial, which precludes algorithms

that guarantee a global optimum. Most existing

reconfiguration algorithms fall into two categories. In

the first, branch exchange, the system operates in a

feasible radial configuration and the algorithm opens and

closes candidate switches in pairs. In the second, loop

cutting, the system is completely meshed and the algorithm

opens candidate switches to reach a feasible radial

configuration. Reconfiguration algorithms based on

linearized transshipment, neural networks, heuristics,

genetic algorithms, and simulated annealing have also been

reported, but not widely used. These existing

reconfiguration algorithms work with a simplified model of

the power system, and they handle voltage and current

constraints approximately, if at all.

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The algorithm described here is a constructive method,

using a full nonlinear power system model that accurately

handles constraints. The system starts with all switches

open and all failed components isolated. An optional

network power flow provides a lower bound on the losses.

Then the algorithm closes one switch at a time to minimize

the increase in a merit figure, which is the real loss

divided by the apparent load served. The merit figure

increases with each switch closing. This principle, called

discrete ascent optimal programming (DAOP), has been applied

to other power system problems, including economic dispatch

and phase balancing. For reconfiguration, the DAOP method's

greedy nature is mitigated with a backtracking algorithm.

Approximate screening formulas have also been developed for

efficient use with partial load flow solutions. This method's

main advantage is the accurate treatment of voltage and

current constraints, including the effect of control action.

One example taken from the literature shows how the

DAOP-based algorithm can reach an optimal solution, while

adjusting line voltage regulators to satisfy the voltage

constraints.

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