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A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy

  • Mandhir Kumar Verma

    Mandhir Kumar Verma received B. Tech. degree in Electrical Engineering from Kurukshetra University, Haryana, India in 1999, M. E. degree in Electronics Instrumentation & Control engineering from Thapar University, Patiala, Punjab India in 2005 and pursuing Ph. D. degree in Power System Engineering from Indian Institute of Technology (ISM), Dhanbad, India. His area of interest are distribution system planning & expansion, AI tools and is also involved in teaching various electrical engineering subjects.

     ORCID logo EMAIL logo     , Vinod Kumar Yadav

    Vinod Kumar Yadav received B.Tech. degree in Electrical Engineering from Institute of Engineering and Technology (IET), Bareilly, India in 2003, M. Tech. degree in power system engineering from National Institute of Technology (NIT), Jamshedpur, India in 2005 and Ph. D. degree in power system Engineering from Indian Institute of Technology (IIT), Roorkee, India in 2011. Since 2011, he is associated with various technical universities and involved in teaching electrical engineering. Currently he is Associate Professor of Electrical Engineering in Delhi Technological University (previously Delhi College of Engineering), Delhi, India. His research interests include optimization of renewable energy systems, power system policy and restructuring, distributed generation and smart grid.

         , Vivekananda Mukherjee

    Vivekananda Mukherjee received his graduation in Electrical Engineering and post-graduation in Power System from B.E. College, Shibpur, Howrah, India and B.E. College (Deemed University), Shibpur, Howrah, India, respectively. He received his Ph.D. degree from NIT, Durgapur, India. Presently, he is an associate professor in the department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India. His research interest is application of soft computing intelligence to various fields of power systems. DR Mukherjee is a member of The Institution of Engineers (India).

         and Santosh Ghosh

    Santosh Ghosh received B.E. in Electrical Engineering from The Institution of Engineers (India) in 2007 and M. Tech. in energy system from Indian Institute of Technology (IIT) Roorkee, India in 2009. He also received post graduate diploma in business management from Symbiosis Institute of Management Science, Pune, India in 2011. Since 2009, he is associated with Kirloskar Brothers Limited, Pune, India, and working in corporate R&D department. His responsibilities include design of electrical machines and systems for renewable and nuclear energy system. Currently, he is pursuing his Ph.D. degree from Indian Institute of Technology (ISM), Dhanbad, India.
Published/Copyright: August 13, 2019
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International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 20 Issue 4

Abstract

Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

Keywords: distribution network expansion planning; data envelopment analysis; multi-criteria data envelopment analysis; Shannon’s entropy analysis

About the authors

Mandhir Kumar Verma

Mandhir Kumar Verma received B. Tech. degree in Electrical Engineering from Kurukshetra University, Haryana, India in 1999, M. E. degree in Electronics Instrumentation & Control engineering from Thapar University, Patiala, Punjab India in 2005 and pursuing Ph. D. degree in Power System Engineering from Indian Institute of Technology (ISM), Dhanbad, India. His area of interest are distribution system planning & expansion, AI tools and is also involved in teaching various electrical engineering subjects.

Vinod Kumar Yadav

Vinod Kumar Yadav received B.Tech. degree in Electrical Engineering from Institute of Engineering and Technology (IET), Bareilly, India in 2003, M. Tech. degree in power system engineering from National Institute of Technology (NIT), Jamshedpur, India in 2005 and Ph. D. degree in power system Engineering from Indian Institute of Technology (IIT), Roorkee, India in 2011. Since 2011, he is associated with various technical universities and involved in teaching electrical engineering. Currently he is Associate Professor of Electrical Engineering in Delhi Technological University (previously Delhi College of Engineering), Delhi, India. His research interests include optimization of renewable energy systems, power system policy and restructuring, distributed generation and smart grid.

Vivekananda Mukherjee

Vivekananda Mukherjee received his graduation in Electrical Engineering and post-graduation in Power System from B.E. College, Shibpur, Howrah, India and B.E. College (Deemed University), Shibpur, Howrah, India, respectively. He received his Ph.D. degree from NIT, Durgapur, India. Presently, he is an associate professor in the department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India. His research interest is application of soft computing intelligence to various fields of power systems. DR Mukherjee is a member of The Institution of Engineers (India).

Santosh Ghosh

Santosh Ghosh received B.E. in Electrical Engineering from The Institution of Engineers (India) in 2007 and M. Tech. in energy system from Indian Institute of Technology (IIT) Roorkee, India in 2009. He also received post graduate diploma in business management from Symbiosis Institute of Management Science, Pune, India in 2011. Since 2009, he is associated with Kirloskar Brothers Limited, Pune, India, and working in corporate R&D department. His responsibilities include design of electrical machines and systems for renewable and nuclear energy system. Currently, he is pursuing his Ph.D. degree from Indian Institute of Technology (ISM), Dhanbad, India.

Appendix

A
Table 6:

Feasible paths of new node-34 with existing system and impedances of new purposed lines.

S.No. From Bus (D) To Bus No. of feasible Paths Distance (Km) R (p.u.) X (p.u.)
1 34 7 01 0.80 0.10240 0.057280
2 34 8 01 0.75 0.09600 0.053700
3 34 9 02 0.70 0.08960 0.050120
4 34 10 02 0.66 0.08448 0.047256
5 34 11 01 0.64 0.08192 0.045824
6 34 12 02 0.62 0.07936 0.044392
7 34 13 02 0.63 0.08064 0.045108
8 34 14 02 0.64 0.08192 0.045824
9 34 15 01 0.68 0.08704 0.048688
10 34 16 01 0.72 0.09216 0.051552
11 34 17 02 0.78 0.09984 0.055848
12 34 18 02 0.84 0.10752 0.060144
13 34 19 01 0.64 0.08192 0.045824
14 34 20 01 0.55 0.07040 0.039380
15 34 21 01 0.445 0.05696 0.031862
16 34 22 02 0.35 0.0448 0.02506
Table 7:

Feasible paths of new node-35 with existing system and impedances of new purposed lines.

S.No. From Bus (D) To Bus No. of feasible Paths Distance (Km) R (p.u.) X (p.u.)
1 35 23 6 0.88 0.11264 0.063008
2 35 24 6 0.78 0.09984 0.055848
3 35 25 6 0.68 0.08704 0.048688
4 35 26 4 0.62 0.07936 0.044392
5 35 27 5 0.56 0.07168 0.040096
6 35 28 3 0.51 0.06528 0.036516
7 35 29 2 0.47 0.06016 0.033652
8 35 30 2 0.43 0.05504 0.030788
9 35 31 2 0.42 0.05376 0.030072
10 35 32 2 0.43 0.05504 0.030788
11 35 33 2 0.475 0.0608 0.03401

NOMENCLATURE

Abbreviations
ADD

Additive model of DEA

AHP

Analytical hierarchy process

BCC

Banker, Charnes and Cooper model of DEA

BTL

Branch thermal limit

CCR

Charnes, Cooper and Rhodes model of DEA

CES

Comprehensive efficiency score

DEA

Data envelopment analysis

DMUs

Decision making units

DNEP

Distribution network expansion planning

MCDEA

Multi-criteria data envelopment analysis

NOP

Normally open points

SEA

Shannon’s entropy analysis

Sets and Indices
c

Index of configuration (1 to NC)

i

Index of system buses (1 to NB)

k

feasible configuration

M

Number of total branches in the given network

p , q

Rows and column of admittance matrix [Y]

n

Previous number of nodes

n n

New number of nodes

n n s

Total number of substation nodes

n i t r , n i , m a x t r

Number (and maximum number) of transformers installed at ith substation

S s s n

Set of substation nodes

S k

Set of feasible configurations

S e x b

Set of existing branches

S n b

Set of new branches

S N O P

Set of NOP switches

Pdi,c/Qdi,c

Active and reactive power demand

Pi,c/Qi,c

Active and reactive power imported by local distribution companies via substations

P b , f , s s , Q b , f , s s

Active and reactive power flow in branch b of the network feeder f at substation ss

S S i k

Substation connections in configuration k

t m

Highest number of transformers connected for any load center

t m p

Maximum number of permissible transformers in a potential path

V i + 1

Upper bound of voltage magnitude at ith bus

V i

Lower bound of voltage magnitude at ith bus

V i m i n / V i m a x

Minimum/Maximum voltage at ith bus

V i , c , V j , c

Voltage at ith bus (and jth bus) of cth configuration

V i j

Voltage between ith and jth bus

x i j

Input i of DMUs

Parameters
G m n

Conductance of element in admittance matrix of mth row and nth column (mho)

L L I

Line loading index

N o d e 34 , N o d e 35

Number of configurations to connect Node 34 and 35 with the existing network

R j

Resistance of branch j

R E S

Relative efficiency score of DMUs

S 0

Entropy constant

V S I W N

Voltage stability index for whole network

[ Y i j ] p × q

Admittance between node ij

Variables
B n

Number of busses to which connections are feasible

c v i j

Construction variable in branch ij

C T E

Total expansion cost

C i n s

Total installation cost

C o p

Total operation cost

C m a i n

Total maintenance cost

C p r c

Poor reliability cost

C S S

Substation cost

C D C

Distribution cable cost

C T F

Transformers cost

C N L P

New load points cost

I f L o w , I f U p

Lower and upper bound of current magnitudes at feeder section f

I j , I j , m a x

Current (and maximum current capacity) in jth branch

O V i j k

Operational variable for branch ij in configuration k

S b m a x

Maximum load flow in branch b

P T L

Total power loss

y r j

Output r of DMUs

δ i , c

Voltage angle at ith bus of cth configuration

θ p q

Angle of bus admittance matrix element

θ

Voltage angle at particular node

δ i m i n / δ i m a x

Minimum/Maximum voltage angle at ith bus

δ i , c , δ j , c

Voltage angle at ith bus (and jth bus) of cth configuration

v r

Weight assigned to the output r

u i

Weight assigned to the input i

ε o

Duel variables (to categorize standards for incompetent units)

s i − / s r +

Input/output slacks

γ

overall scale constraint

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Received: 2019-02-08
Revised: 2019-06-14
Accepted: 2019-07-01
Published Online: 2019-08-13

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijeeps-2019-0043
Keywords for this article
distribution network expansion planning; data envelopment analysis; multi-criteria data envelopment analysis; Shannon’s entropy analysis

Articles in the same Issue

  1. Research Articles
  2. A Real Time Price Based Demand-Response Algorithm for Smart Grids
  4. Enhanced Model Predictive Current Control Based on Runge–Kutta Approximation for a Voltage Source Inverter
  5. A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy
  6. Digital Metering of Electrical Power Components Using Adaptive Non-Uniform Discrete Short Time Fourier Transform
  7. Algorithm for Determining the State of Impregnated Paper Insulation of High-Voltage Cables
  8. Experimental and Estimation of Flashover Voltage of Outdoor High Voltage Insulators with Silica Filler Based on Grey Wolf Optimizer
  9. Investigation of the Temperature Effect on the Electrical Parameters of a Photovoltaic Module at Ouargla City
  10. Selection of an Efficient Linear State Estimator for Unified Real Time Dynamic State Estimation in Indian Smart Grid
  11. Impact of Harmonics on Power Transformer Losses and Capacity Using Open DSS
  12. Voltage Control Methods in the MV Grid with a Large Share of PV
  13. PV-Battery Hybrid System with Less AH Capacity for Standalone DC Loads
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