Smart Grid Reconﬁguration based on Prediction Model for Technical

Teams Intervention Integration and Recovery Enhancement

Leila Ziouche

1,3

, Syrine Ben Meskina

2

, Mohamed Khalgui

1

and Laid Kahoul

4

1

National Institute of Applied Sciences and Technology, Carthage University, Tunis, Tunisia

2

Computer Science Department, ESPRIT - School of Business, Tunis, Tunisia

3

Computer Science Department, Science Faculty of Tunis, Tunis, Tunisia

4

LINFI LAB, Computer Science Department, Biskra University, Algeria

Keywords:

Smart Grid, Reconﬁguration, Technical Team, Prediction Model, Recovery Enhancement.

Abstract:

To overcome the problem of critical failures recovery and improve reliability, quality of service and recovery

performance, it is essential to provide and apply a new oriented solution for smart grid reconﬁguration. This

solution allows for resolving the problem of the late intervention of technical teams and the insufﬁciency of

energy for recovery, by implementing a prediction model that assists the integration of a number of technical

teams. In addition, it estimates the newly added number of emergency lines coming from new integrated

renewable sources. This heuristic is programmed based on the linear programming and the simplex algorithm.

This approach is implemented in python as a tool called SGREP, then tested and validated at run-time on four

real different smart grids. Thereby, the proposed solution improves the guaranteed gains in terms of power

availability, waiting time and ﬁnancial cost.

1 INTRODUCTION

The smart grid is deﬁned as a real meshed power net-

work of electrical components and lines, speciﬁcally

it includes three voltage levels: high, medium and

low. In the context of failures detection and restora-

tion, a study in (Iqbal and Kütt, 2018) has investi-

gated the power demands of clients by establishing

a new model that estimates power quality characteris-

tics in residential buildings. They have measured the

consumer energy demand using the web of things for

personalized loads forecasting in (Ding et al., 2014).

Authors in (Ben Meskina et al., 2016) have su-

pervised and controlled smart grids through a multi-

agent system with variant smart devices and sensors

and integrates new emergency lines coming from a

central power plant. In (James et al., 2017), a pro-

posed scheme is provided for determining the fault

type, phase, and location for the protection of micro-

grid and the recovery of failures. In (Ben Meskina

et al., 2018), a reconﬁguration solution is proposed to

identify consumers that may play the role of feeders

by adding new emergency lines between consumers.

However, researchers in (Bhalshankar and Thorat,

2016) have addressed the effective role of the integra-

tion of renewable sources to improve the growth of

power demand. Researchers in (Abidi et al., 2017)

have created a mathematical model that works for en-

ergy balancing between the production and the con-

sumption of loads in micro-grids. In (Worighi et al.,

2019), authors have brieﬂy proposed a virtual system

for the integration of solar power generation units and

battery storage systems with the proposed architec-

ture along with control strategies.

Authors in (Nefedov et al., 2018) have developed

an electric vehicle that uses renewable energy sources

to support great prosumer buildings in case of in-

terruptions and outages. In (Ziouche et al., 2019a),

workers have provided a linear regression prediction

model that deﬁnes the accurate number of new inte-

grated renewable sources to a studied smart grid ac-

cording to a given budget by the electricity market.

In (Ziouche et al., 2019b), researchers have imple-

mented a new cloud-based architecture for smart grid

rebuilding where we have worked on scheduling tech-

nical teams interventions and estimation of available

renewable energy in sources for failures recovery.

In (Vaidya and Kasturiwale, 2016), researchers

have dealt with giving the solution of solving two-

phase problems with the simplex methods that work

for the resolution of linear programming problems.

Nowadays, smart power systems aim to enhance

382

Ziouche, L., Ben Meskina, S., Khalgui, M. and Kahoul, L.

Smart Grid Reconﬁguration based on Prediction Model for Technical Teams Intervention Integration and Recovery Enhancement.

DOI: 10.5220/0009406703820389

In Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), pages 382-389

ISBN: 978-989-758-421-3

Copyright

c

2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

security, reliability, recovery performance and quality

of service. Critical clients such as hospitals, commer-

cial buildings and research laboratories suffer from a

miss of electricity in case of blackouts and outages,

which causes a set of problems such that:

• The insufﬁcient number and late intervention of

technical teams because they are not specialized

or busy in repairing other urgent failures.

• The slow intervention of technical teams that

causes the growth of total waiting time.

• The self-insufﬁciency of available energy for re-

covery of critical failures in repairing.

In order to resolve the above problems, we propose a

global reconﬁguration that is applied in two steps:

• The ﬁrst step is to integrate and predict the ac-

curate number of expert and technician teams

needed to ﬁx all the failures in the smart grid, then

apply run-time scheduling.

• The second step is to provide a new number of

emergency lines to reduce clients waiting time

without electricity.

Based on this contribution, we propose in this paper a

new approach that permits (i) to cover multiple un-

covered failures, (ii) to bring a deﬁned number of

technical teams: « expert teams that works for hard

failures maintenance and technician teams that repair

easy failures », and (iii) to integrate new emergency

lines at real-time from renewable sources. We imple-

ment a tool called SGREP for smart grid reconﬁgura-

tion and to demonstrate the effectiveness and robust-

ness of this strategy in order to guarantee best recov-

ery in terms of power availability and waiting time.

The remainder of this paper is organized as fol-

lows: Section 2 surveys the formalized requirements

of the problematic. Section 3 discusses the contribu-

tion of reconﬁguring strategy. Section 4 considers a

case study and shows the obtained gains in terms of

power availability and total waiting time. Section 5

concludes the paper and provides a brief suggestions

for the future work.

2 SMART GRID MODEL

In this section, we formalize the smart grid structure

and parameters.

2.1 Smart Grid Formalization

A smart grid SG is considered as a graph of electrical

components and electrical lines, i.e.,

SG = G(C,L) (1)

where C is the set of electrical components that in-

cludes a set of power generators PG, a set of trans-

formers T RANS and a set of consumers CONS, i.e.,

C = PG ∪ T RANS ∪CONS

PG = PS ∪ RS

T RANS = MV T ∪ LV T

CONS = MC ∪ LC

(2)

where PS is the central power plant and RS is the set

of existing renewable sources in the smart grid, MV T

and LVT are respectively the sets of medium and low

voltage transformers, MC and LC refer to the sets of

medium and low consumers. L is the set of electrical

lines, it involves a set of principal lines PL that are

activated pertinently and a set of emergency lines EL

activated in case breakdowns and interruptions, i.e.,

L = PL ∪ EL. (3)

2.1.1 Failures

Smart grids can face a global list of failures

GFL(SG). A failure f (x

k

) ∈ GFL(SG) represents the

malfunction of a component or line x

k

that may cause

multiple propagated failures. Hence, the recorded

failures list GFL(SG) is categorized into «dominant»

or «equivalent» that allows reducing the number of

failures to be maintained and resolved as cited in

(Ben Meskina et al., 2016) where we deﬁne a short

fault list SFL(SG) ⊆ GFL(SG), with |SFL(SG)| ≤

|GFL(SG)| as mentioned in Equation (4).

SFL(SG) = { f (x

1

),.., f (x

k

),.., f (x

N

f

)} (4)

where N

f

is the number of recorded failures in SG.

Let us suppose that f (x

k

) ∈ SFL(SG) is characterized

by: (i) a required load ReqL

f (x

k

)

, (ii) a priority com-

paring with other failures Pr

f (x

k

)

, (iii) a needed re-

covery time RT

f (x

k

)

, and (iv) the waiting time with-

out electricity W T

f (x

k

)

. Also, every failure f (x

k

) ∈

SFL(SG) is deﬁned by: (i) its class in classiﬁcation,

(ii) a ﬁxing time FT

f (x

k

)

, (iii) the proposed technical

team for repairing, (iv) its waiting time for the arrival

of technical team W TA

f (x

k

)

, (v) the recovery available

time RTA

rs

j

and (vi) recovery available energy de-

mand E

rs

j

by recovery source rs

j

. Thereby, SFL(SG)

can be classiﬁed into:

(i) uncritical failures list NCF(SG): includes the to-

tally covered failures by an emergency line coming

from a renewable source or central power plant, i.e.,

NCF(SG) = { f (x

k

)/RT

f (x

k

)

≤ RTA

rs

j

} (5)

(ii) critical failures list CF(SG): contains the uncov-

ered or insufﬁciently covered failures by a renewable

source, i.e.,

CF(SG) = { f (x

k

)/RT

f (x

k

)

> RTA

rs

j

} (6)

Smart Grid Reconﬁguration based on Prediction Model for Technical Teams Intervention Integration and Recovery Enhancement

383

2.1.2 Failures Recovery Sources

Every failure f (x

k

) ∈ SFL(SG) may have 0, 1 or

N

EL

f (x

k

)

recovery sources, in the form of electrical

emergency lines coming from the power plant or ex-

isting renewable sources. We suppose that EL

PS

is

the set of emergency lines coming from power plant

and EL

RS

is the set of emergency lines coming from

existing renewable sources, i.e.,

EL = EL

PS

∪ EL

RS

(7)

Thus, the existing renewable sources are in the form

of solar panels, wind turbines, and batteries, i.e.,

RS = {pv

1

,..., pv

N

1

,wt f

1

,...,wt f

N

2

,bt

1

,...,bt

N

3

} (8)

where N

1

, N

2

, and N

3

are respectively the existing

numbers of solar panels, wind turbines, and batteries.

The total recovery time T RAT

RS

and the total recov-

ery energy TRA

RS

of the existing renewable sources

in the smart grid are deﬁned both in Equation (9).

(

T RTA

RS

=

∑

N

RS

j=1

RTA

rs

j

T RA

RS

=

∑

N

RS

j=1

E

rs

j

(9)

where N

RS

is the number of renewable sources: N

RS

=

N

1

+N

2

+N

3

, with j = 1,.., N

RS

. Every proposed sub-

set of recovery source rs

j

⊆ RS is deﬁned by: (i) the

generated energy E

rs

j

= E

pv

∗a

1

+E

wt f

∗a

2

+E

bt

∗a

3

,

and (ii) the recovery available time RTA

rs

j

= RT

pv

∗

a

1

+ RT

wt f

∗ a

2

+ RT

bt

∗ a

3

, where E

pv

, E

wt f

and E

bt

refer respectively to generated loads by a solar panel,

a wind turbine or a battery. RT

pv

, RT

wt f

and RT

bt

are

recovery times of a solar panel, wind turbine and a

battery respectively. a

1

, a

2

and a

3

refer to the percent-

age of energy transformed through emergency lines to

cover assigned components by f (x

k

). Hence, we ver-

ify the sufﬁciency of available energy by calculating

the remaining load Q

k, j

= E

rs

j

− ReqL

f (x

k

)

.

2.1.3 Failures Maintenance

The maintenance phase is applied by specialized

groups, working together for failures repairing, in

smart grid called technical teams T T (SG), i.e.,

T T (SG) = {T T

1

,..,T T

i

,..,T T

N

tt

}

T T (SG) = Exp(SG) ∪Tech(SG)

(10)

Every single technical team T T

i

∈ T T (SG) is deﬁned

by: (i) a scheduling table Sched

T T

i

, (ii) a total ﬁxing

time T FT

T T

i

and (iii) a budget P

T T

i

that deﬁnes the

salary to be paid and the materials to be used dur-

ing failures maintenance, with i = 1,..,N

tt

and N

tt

is

the total number of teams: N

tt

= N

Exp

+ N

Tech

, where

N

Exp

and N

Tech

are the numbers of expert and techni-

cian teams.

Expert Teams Exp(SG): they are experted teams in

repairing critical and hard failures with the highest

priorities over components and lines in SG.

Technician Teams Tech(SG): they are the teams that

move to maintain medium, easy and fully recovered

failures over lines in SG.

After deﬁning the chosen technical team to repair

a determined failure f (x

k

), we apply the proposed

scheduling algorithm in (Ziouche et al., 2019b) to

compute the total ﬁxing time of failures for each tech-

nical team that must be equivalent or almost closest.

2.2 Problems

According to the existing researches on smart grids

recovery enhancing, the insufﬁcient number of tech-

nical teams that may intervene quickly to maintain

failures causes a problem of: (i) long waiting time

for the arrival of technical teams, and (ii) critical

failures increasing due to the growth of the waiting

time of clients without energy regarding covered fail-

ures with renewable energy sources, (iii) the inabil-

ity of technician teams to repair some failures that

need the fast intervention of an expert team, (iv) the

least number of expert teams and their inability to

intervene because of their occupation in other loca-

tions, and (v) the insufﬁciency of energy for recov-

ery causes power and ﬁnancial losses (e.g., We sup-

pose that f (x

k

) is a recorded failure and it has no re-

covery source RTA

rs

j

= 0h, it needs a recovery time

from an emergency source for RT

f (x

k

)

= 5h. Also, the

number of teams in this smart grid is N

tt

= 2 where

N

Exp

= 1 and N

Tech

= 1. The technician team tried

to ﬁx this failure, but it needs the fast intervention of

an expert team with experted material while the ex-

pert team is busy in repairing another failure. In this

case, f (x

k

) should wait for the arrival of technical

team for W TA

f (x

k

)

= 3h, it stays without electricity

for W T

f (x

k

)

= 5h).

3 CONTRIBUTION: SMART

GRID RECONFIGURATION

In order to reduce the waiting time for the arrival of

teams and to minimize the waiting time without elec-

tricity, we brought in Figure 1 an approach that en-

hances failures recovery and permits to guarantee bet-

ter power availability during failures maintenance.

3.1 Failures Classiﬁcation

The categorization of global list of failures GFL(SG)

into « dominant or equivalent » failures allows re-

ENASE 2020 - 15th International Conference on Evaluation of Novel Approaches to Software Engineering

384

Figure 1: Smart grid Reconﬁguration Architecture for availability Enhancement.

ducing the number of failures into a short fault list

SFL(SG). These failures are then classiﬁed into: (i)

critical CF(SG) and (ii) uncritical NCF(SG) failures

as explained in formulas (5) and (6) to determine the

failures that need the fast intervention of technical

teams and new recovery emergency lines.

3.2 Technical Teams Prediction Model

In order to realize the fast maintenance of failures,

we aim to build a prediction model based on linear

programming and simplex (Vaidya and Kasturiwale,

2016) that allows deﬁning the new integrated number

of technical teams according to a given budget by the

electrical market. We deﬁne using equation system

(11) the minimized accurate numbers of expert and

technician teams.

Minimize N

m

tt

= N

m

Tech

+ N

m

Exp

N

m

Tech

∗ P

Tech

+ N

m

Exp

∗ P

Exp

≤ T cost

T T

N

m

Tech

∗ T FT

Tech

+ N

m

Exp

∗ T FT

Exp

≤ β

N

m

Tech

,N

m

Exp

≥ 0

(11)

where N

m

Tech

and N

m

Exp

refer respectively to the num-

bers of technician and expert teams, P

Tech

and P

Exp

refer respectively to the total salaries and used mate-

rials prices of a technician team and an expert team,

T cost

T T

is the given budget to pay new integrated

technical teams. T FT

Tech

and T FT

Exp

are the total

ﬁxing times spent by a technician team and an expert

team, and β = T FT

m

SFL(SG)

+ TW TA

m

SFL(SG)

where

T FT

m

SFL(SG)

is the total ﬁxing time of failures and

TW TA

m

SFL(SG)

is the total waiting time for the arrival

of teams. Then, we adjust the constraints by adding

the slack variables e

1

and e

2

, i.e.,

N

m

Tech

∗ P

Tech

+ N

m

Exp

∗ P

Exp

+ e

1

= T cost

T T

N

m

Tech

∗ T FT

Tech

+ N

m

Exp

∗ T FT

Exp

+ e

2

= β

N

m

Tech

,N

m

Exp

,e

1

,e

2

≥ 0

(12)

This system is resolved at each month using the sim-

plex algorithm where after a number of iterations I,

we get the values of N

m

Tech

and N

m

Exp

.

After that, we compute the accurate threshold num-

bers of technician and expert teams to be integrated,

respectively T hresh

Tech

and T hresh

Exp

, i.e.,

T hresh

Tech

=

∑

n

m=1

N

m

Tech

n

T hresh

Exp

=

∑

n

m=1

N

m

Exp

n

(13)

where n refers to the number of months that deﬁnes

the historic period of study, N

m

Tech

and N

m

Exp

are the

numbers of technician and expert teams at m

th

month.

After getting the accurate values of thresholds, we

build for each team a scheduling table.

3.3 Integration of Emergency Lines

To deﬁne the number of new added emergency lines

into a smart grid, we take the set of critical failures

CF(SG) that suffer from electricity missing during

blackouts, and sort them according to failure prior-

ity. We brieﬂy indicate the set of new integrated re-

newable sources NRS(SG) that includes respectively

Smart Grid Reconﬁguration based on Prediction Model for Technical Teams Intervention Integration and Recovery Enhancement

385

Table 1: Detailed information about observed critical failures.

f (x

k

) ReqL

f (x

k

)

RT

f (x

k

)

FT

f (x

k

)

W T

f (x

k

)

T RA

RS

T RTA

RS

T RA

NRS

T RTA

NRS

a

1

+ a

2

+ a

3

f (L

24

) 220W 2h 2h 2h 0 0h 220W 2h 0.67+0+0

f (ML

6

) 2150W 9h 9h 9h 0 0h - - -

f (LV T

5

) 1507W 14h 8h 8h 690W 6h 817W 6h 2.12+0+0.51

f (L

21

) 351W 4h 3h 4h 0 0h 185W 2h 0.56+0+0

f (L

6

) 442W 6h 3h 6h 0 0h 442W 6h 1+0.49+0

f (L

31

) 262W 4h 4h 4h 0 0h 262W 4h 0.03+0+1.21

f (L

15

) 535W 4h 4h 4h 0 0h 535W 4h 1.62+0+0

f (LV T

1

) 558W 8h 5h 2h 400W 6h 158W 2h 0+0+0.79

y

1

solar panels, y

2

wind turbines and y

3

batteries as

reported in (Ziouche et al., 2019a). In order to max-

imize the number of added emergency lines coming

from renewable sources that satisfy the required load

of f (x

k

) and cover it during the intervention of tech-

nical teams, we certify the following constraints, i.e.,

Maximize N

EL

f (x

k

)

= a

1

+ a

2

+ a

3

a

1

+ a

2

+ a

3

≤ N

NRS

E

pv

∗ a

1

+ E

wt f

∗ a

2

+ E

bt

∗ a

3

≥ ReqL

f (x

k

)

RT

pv

∗ a

1

+ RT

wt f

∗ a

2

+ RT

bt

∗ a

3

≥ α

a

1

,a

2

,a

3

≥ 0

(14)

where α = W TA

f (x

k

)

+ FT

f (x

k

)

is the needed time for

coverage by renewable sources.

To adjust the proposed constraints, we add the

three slack variables respectively e

1

, e

2

and e

3

, i.e.,

a

1

+ a

2

+ a

3

+ e

1

= N

NRS

E

pv

∗ a

1

+ E

wt f

∗ a

2

+ E

bt

∗ a

3

− e

2

= ReqL

f (x

k

)

RT

pv

∗ a

1

+ RT

wt f

∗ a

2

+ RT

bt

∗ a

3

− e

3

= α

a

1

,a

2

,a

3

,e

1

,e

2

,e

3

≥ 0

(15)

We apply simplex algorithm to resolve the equation

systems. We re-compute the remaining numbers of

y

1

, y

2

, y

3

and the remaining total available load of new

renewable sources T RA

NRS

of the existing failure in

the critical failures list. The used percentage of energy

load from a new renewable source indicates adding a

new emergency line going from the deﬁned source to

its ﬁnal destination. Finally, we can build the new set

of added emergency lines EL

new

= {eL

k, j

/Q

k, j

≥ 0}

with eL

k, j

is the new emergency line that connects the

k

th

failure and j

th

integrated renewable source.

Then, we apply the scheduling algorithm pro-

posed in (Ziouche et al., 2019b) to build ﬁnal schedul-

ing tables, and we re-deﬁne the value of TW T

SFL(SG)

and the critical CF(SG), uncritical failures NCF(SG).

4 IMPLEMENTATION &

RESULTS

In order to evaluate the proposed prediction model,

we have implemented a tool called SGREP that is ap-

plied to a real case study. Then, we have validated the

obtained gains through real-time experimental study.

4.1 Case Study

The proposed contribution is demonstrated and illus-

trated through the following real-time study. We con-

sider the real meshed power network -North Urban

Center SG (Tunis, Tunisia)- as presented in Figure 2.

Figure 2: Case study on North Urban center smart grid.

SG is composed of a power generator PG, eight trans-

formers: 3 MV T and 5 LV T , thirty-eight consumers:

4 MC and 34 LC, with nine emergency lines EL: 3

EL

PS

and 6 EL

RS

. We assume that there are two

technical teams that intervene to repair failures in

ENASE 2020 - 15th International Conference on Evaluation of Novel Approaches to Software Engineering

386

SG (N

Exp

= 1 and N

Tech

= 1). SG is supervised and

controlled through smart agents and devices that de-

tect and isolate malfunctioned lines as explained in

(Ben Meskina et al., 2016).

Table 2: Observed values of studied smart grid.

Month Jan Feb Mar Apr May Jun

N

f

15 9 13 14 12 10

N

CF

8 4 7 8 6 5

T ReqL

CF

6.03 2.65 5 5.02 2.29 4.06

TW T

CF

39h 21h 30h 14h 21h 25h

T FT

CF

53h 38h 41h 50h 32h 40h

T RTA

RS

12h 12h 6h 0h 0h 10h

After a deep supervision of six successive months,

we consider (i) the number of recorded failures N

f

,

(ii) the number of critical failures N

CF

, (iii) the

total required load (in kilowatts), (iv) total ﬁxing

time (hours), (v) total waiting time (hours), and (vi)

the total available recovery time as shown in Ta-

ble 2 (e.g., In January, the recorded number of crit-

ical failures is N

CF

= 8. These last need a total

required load of T ReqL

CF

= 6.03kWh. Technical

teams spend T FT

SFL(SG)

= 53h to ﬁx these failures

where the initial total waiting time without energy

is TW T

SFL(SG)

= 39h). Thereby, we suppose that

the given budget by the electricity market to inte-

grate a new number of renewable sources in SG is

T

cost

= 6000$. The given budget to pay the newly

added number of technical teams is T cost

T T

= 3000$

and the paid salaries to a technician and an expert

team are in order: P

Exp

= 1200$, P

Tech

= 770$. Also,

we suppose that the quantity of generated energy by a

solar panel, a wind turbine, and a battery are succes-

sively: E

pv

= 330W , E

wt f

= 230W and E

bt

= 200W ,

the prices of a solar panel, a wind turbine and a

battery are respectively: P

pv

= 710$, P

wt f

= 600$,

P

bt

= 450$, and the recovery times available by a so-

lar panel, a wind turbine and a battery are in order:

RT

pv

= 3h, RT

wt f

= 2h and RT

bt

= 3h.

Step 1: Failures Classiﬁcation. Let us suppose that

SFL(SG) includes ﬁfteen failures, we use the formu-

las (5) and (6) to classify failures into:

CF(SG) = { f (L

24

), f (L

21

), f (ML

6

), f (LV T

1

), f (L

6

),

f (L

31

), f (LV T

5

), f (L

15

)}, NCF(SG) = { f (LV T

3

),

f (ML

1

), f (ML

2

), f (L

19

), f

(

L

24

), f (L

4

), f (L

38

)}. The

proposed scheduling tables allow computing the total

waiting times without electricity and for the arrival

of technical teams respectively: TW T

CF

= 39h and

TW TA

SFL(SG)

= 115h.

Step 2: Technical Teams Prediction Model. The

growth total waiting time without electricity of

recorded failures and total ﬁxing time of failures show

that the existing two technical teams for failures main-

tenance in SG are insufﬁcient. Thus, we apply the

proposed prediction model in Section 3.2 on January

to deﬁne the minimum number of teams to be inte-

grated into SG. Based on the proposed linear pro-

gramming system in formulas (11) and (12), we com-

pute using simplex algorithm the accurate values of

expert and technician teams. We ﬁnd: N

Exp

= 1 and

N

Tech

= 2. We re-apply the same process on the ﬁve

remaining months to predict the accurate thresholds

of expert and technician teams with the help of for-

mula (13), we ﬁnd: T hresh

Exp

= 1, and Thresh

Tech

=

2. The total numbers of technician and expert teams

are respectively N

Tech

= 3 and N

Exp

= 2.

Step 3: Integration of Emergency Lines. Based

on the implemented tool FROSLR in (Ziouche et al.,

2019a), we can predict the accurate threshold of in-

tegrated renewable sources T hresh

NRS

= 9 with the

accurate values of added solar panels y

1

= 6, wind tur-

bines y

2

= 1 and batteries y

3

= 2. The total recovery

time available T RTA

NRS

= 26h and total available en-

ergy for recovery T RA

NRS

= 2610W . Then, we com-

pute the maximized number of emergency lines using

formulas (14) and (15) and simplex algorithm.

Table 1 illustrates the detailed information of criti-

cal failures recovery after integrating a new number

of renewable sources in January month (e.g., A fail-

ure occurred on LV T

1

requires a load of 558W for 8h.

Technical teams must spend 5h to ﬁx this failure, the

clients of this region wait without electricity for 2h, it

is covered through available renewable sources for 6h

and after adding one emergency line coming from a

battery N

EL

new

( f (LV T

5

)) = 1, where T RA

NRS

= 158W

and T RTA

NRS

= 2h). Hence, the application of the

proposed reconﬁguring strategy is applied in the same

way on the ﬁve rest months to determine the numbers

of critical and uncritical failures and emergency lines.

They are respectively equal to N

NCF

= 13, N

CF

= 2

and N

EL

new

= 13.

The system applies a scheduling algorithm to assign

priorities and teams to all existing failures. We get at

the end ﬁve scheduling tables where the total ﬁxing

times are almost equal. The obtained scheduling ta-

bles are: Sched

Exp

1

= { f (ML

6

), f (LV T

3

), f (L

1

)}

Sched

Exp

2

= { f (LV T

5

), f (ML

2

), f (L

19

)}

Sched

Tech

1

= { f (L

24

), f (L

31

), f (LV T

3

), f (L

4

)}

Sched

Tech

2

= { f (L

21

), f (LV T

1

), f (ML

1

)}

Sched

Tech

3

= { f (L

6

), f (L

15

), f (L

38

)}

Hence, we get: TWT

CF

= 11h and TW TA

CF

= 5h.

4.2 Performance Evaluation

To illustrate the obtained gains using simulated tool

SGREP, we deﬁne for every smart grid SG two met-

rics that are the total availability rate and the total

Smart Grid Reconﬁguration based on Prediction Model for Technical Teams Intervention Integration and Recovery Enhancement

387

Table 3: The structure of investigated smart grids.

SG |CONS| N

f

|EL| T ReqL

CF

N

RF

N

NRF

N

RS

σ

init

(SG) σ

RB

(SG) σ

Froslr

(SG)

SG

1

34LC+4MC 38 12 2.06 kWh 16 18 3 47.06% 70.58% 85.29%

SG

2

75LC+8MC 83 25 4.25 kWh 41 42 5 49.36% 71.08% 79.51%

SG

3

122LC+28MC 150 54 5.67 kWh 78 72 7 52% 71.33% 78%

SG

4

142LC+38MC 180 65 6.87 kWh 98 62 10 54.44% 72.78% 81.11%

waiting time rate.

4.2.1 Total Availability Rate

-Denoted by σ

SG

-, it represents the rate of the total

number of covered failures thanks to the proposed re-

conﬁguration for integrating a new number of techni-

cal teams and emergency lines, i.e.,

σ

SG

=

N

RF

N

f

(16)

where N

RF

is the number of recovered failures.

4.2.2 Total Waiting Time Rate

-Denoted by ρ

SG

-, it refers to the rate between the ini-

tial and ﬁnal total waiting time before and after apply-

ing the new reconﬁguring strategy, i.e.,

ρ

SG

=

TW T

END

(SFL(SG))

TW T

init

(SFL(SG))

(17)

where TW T

init

(SFL(SG)) refers to the initial total

waiting time and TW T

END

(SFL(SG)) refers to the

waiting time after applying the new heuristic.

In case study, we have σ

SG

= 80% and ρ

SG

= 47%.

In order to reduce the number of non-recovered fail-

ures through smart grids, an experimental study is

applied to four real meshed power networks of the

Tunisian power systems cited in (Ziouche et al.,

2019b). They have different sizes: SG

1

and SG

2

are

two small smart grids, SG

3

is a medium one, and SG

4

is large. They include different numbers of electri-

cal components and lines, the structure of this power

grids is investigated completely in Table 3 by deﬁning

(i) the number of consumers |CONS|, (ii) the num-

ber of existing emergency lines |EL|, (iii) the num-

ber of recorded failures N

f

, (iv) the total required

load of critical failures T ReqL

CF

, (v) N

RF

is the num-

ber of recovered failures, (vi) N

NRF

is the number of

non-recovered failures, and (vii) N

RS

is the number

of available renewable sources in the smart grid (e.g.,

SG

2

is a small smart grid, it includes 83 consumers,

the number of recovered consumers in smart grid is

about 41 and non-recovered is 42, and the number of

existing renewable sources is 5).

Table 4: The structure of simulated smart grids by SGREP.

SG |EL

new

| N

tt

N

NRS

N

RF

σ

SGREP

(SG)

SG

1

13 5 9 28 87.8%

SG

2

20 7 12 61 81.49%

SG

3

28 10 18 110 83.33%

SG

4

36 12 21 135 85%

Table 4 represents the structure of simulated smart

grids using the software tool SGREP, we deﬁne (i)

the new number of emergency lines |EL

new

|, (ii)

the predicted number of technical teams N

tt

, (iii)

the new number of recovered failures N

RF

, and (iv)

the integrated number of renewable sources through

FROSLR N

NRS

. The addition of emergency lines and

technical teams allow to enhance the power avail-

ability rate (e.g., SG

2

needs a total required load of

4.25 kWh, where the number of new added renew-

able sources is 12, it permits to integrate 20 renewable

sources and 7 technical teams: N

Tech

=4 and N

Exp

=3.

Thanks to proposed reconﬁguration, we remark an in-

creasing in term of total availability rate from 85.29%

to 87.8%, besides a reduction in the term of waiting

time to 48%).

0 2 4

6

8 10 12

0

50

100

Simulation time(per Month)

Money cost in (10

3

$)

without SGREP

with SGREP

Figure 3: Financial gains due to SGREP simulator.

Moreover, The prediction of the number of technical

teams increases the number of ﬁxed failures. Fig-

ure 3 shows a comparative study in term of the ﬁ-

nancial cost before and after applying the reconﬁg-

uration. We identify a remarked increasing to more

than 135 ∗ 10

3

$ per year. Figure 4a represents the

cumulative waiting time of clients during the inter-

vention of technical teams, it reports a comparison

of the waiting times using SGCS-RB, FROSLR and

SGREP. Thereby, we supervise a remarked reduction

in the term of waiting time. Figure 4b compares the

growth in term of the total availability of energy dur-

ENASE 2020 - 15th International Conference on Evaluation of Novel Approaches to Software Engineering

388

2 4

6

8 10 12

20

40

60

80

Simulation time(per Month)

Total waiting time ρ(SG)(h)

cumulative waiting time

with SGCS-RB

with FROSLR

with SGREP

(a) Total waiting time reduction.

0 2 4

6

8 10 12

20

40

60

80

100

Simulation period (per Month)

Total availability σ(SG)(%)

Total availability performance evaluation

with SGCS-RB

with FROSLR

with SGREP

(b) Total availability enhancement.

0 2 4

6

8 10 12

0

50

100

150

Simulation time(per Month)

Renewable energy (kWh)

Emergency lines performance evaluation in Tunis

(c) Emergency lines number increasing.

Figure 4: Performance Evaluation due to Reconﬁguring Simulator SGREP.

ing failures recovery in SG

1

after applying different

contributions. Thus, the addition of the new num-

ber of emergency lines coming from added renewable

sources allows for increasing the recovery of failures.

Figure 4c shows the annual increase of use of renew-

able sources by integrating a new number of emer-

gency lines that allow to better enhance the recovery

of the power network. So that, the implemented sim-

ulator SGREP helps electricity markets to satisfy the

recovery and maintenance of recorded critical failures

and encourages the use of renewable sources.

Furthermore, SGREP permits to improve the per-

formed rates in term of power availability and total

waiting time thanks to implemented prediction model.

5 CONCLUSIONS

To guarantee the accurate integrated numbers of tech-

nical teams and emergency lines for best improve-

ment of failures recovery and maintenance in smart

grids, we have proposed a reconﬁguration approach

that permits to predict the integrated number of ex-

pert and technician teams, besides the addition of a

new number of emergency lines which allows increas-

ing the total availability of energy during interruptions

and outages of more than 2% with the reduction of

total waiting time without energy to the half. This

approach permits to reduce economic losses during

blackouts. In the future work, we aim to secure trans-

mitted data in the smart grid proposed architecture,

beside verifying formally the proposed strategies.

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