Multiobjective Planning of Recloser-Based Protection Systems on DG Enhanced Feeders

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In the last years, distribution automation has gathered a

significant relevance in distribution systems planning and

operation. The network operator (NOp) looks for a suitable

configuration of the feeder topology as well as the

system, pursuing the reliability enhancement and a full energy

demand supply. Nevertheless, an efficient protection system

requires an adequate investment in such devices as reclosers,

fuses and sectionalizers. Thus, two conflictive objectives arise,

namely, NOp investment minimization and reliability maximization.

In this sense, the number and location of devices

in the system are critical variables to accomplish preceding

objectives. Here, we focus on recloser-based protection systems.

Specifically, we analyze the planning of normally closed

reclosers (NCRs), located within the distribution system (DS),

providing capability to isolate a fault section and restoring the

grid service.

This decision-making may be modeled as a multiobjective

optimization problem (MOOP), i.e., minimization of investment

costs along with reliability maximization. As reported

by Deb [1], the aggregating function approach and the "-

constraint method have been the most popularly mechanisms

to adequate a MOOP into a single-objective optimization

problem (SOOP). Thus, the traditional and widely studied

methods to solve a SOOP can be applied. In the first case,

the aggregating function approach combines all the objectives

into a single one using arithmetical operators, such as addition,

multiplication, among others [1]. Commonly, a weighted sum

of the objectives is performed, where a set of weights are

determined in order to state the importance of every objective.

Evidently, the output of such optimizat...

... middle of paper ...

...cision-making process

must to be developed taking into account several objectives

such as high levels of reliability together with low costs.

As stated by Deb [1], a multiobjective optimization problem

(MOOP) has a vector composed by objective functions which

are to be minimized or maximized. Besides, a set of constraints

describes the feasible region of solutions. On the other hand,

Coello et al. [8] bring the words of Osyczka [9] to define

the MOOP: “a vector of decision variables which satisfies

constraints and optimizes a vector function whose elements

represent the objective functions. These functions form a

mathematical description of performance criteria which are

usually in conflict with each other. Hence, the term optimize

means finding such a solution which would give the values of

all the objective functions acceptable to the decision maker.

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