Planning of Renewable DGs for Distribution Network Considering Load Model: A Multi-Objective Approach

Tanushree Bhattacharjee Department of Electrical Engineering, Bengal Engineering and Science

University, Shibpur, Howrah, India

Partha Kayal Department of Electrical Engineering, Future Institute of Engineering and

Management, Kolkata, India

Chandan Kumar ChandaDepartment of Electrical Engineering, Bengal Engineering and Science

University, Shibpur, Howrah, India

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Introduction Distributed generation(DG)promises to supply eco-friendly,

reliable and cost effective electricity to the customer. It includes renewable technologies (photovoltaic, biomass and

wind power) and high efficiency non-renewable power (fuel cell, internal combustion engine, gas turbine and micro turbine).

MNRE report, Govt. of India has set a target to produce 9,000 MW wind, 1,780 MW biomass and 50 MW solar power with grid interaction in the 11th plan (2011-2017).

Indiscriminant application of individual DG may cause higher distribution power losses and poor voltage stability of the network.

So, appropriate location, type and size selection of renewable DGs in the distribution network is a very crucial aspect of energy system planning.

This paper presents a multi-objective particle swarm optimization (MOPSO) based approach to allocate renewable DGs optimally in a 28-bus radial distribution network employing trade-off between multiple objectives.

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Literature Survey• Wang et al (2004) [3] have formulated an expression which only

chooses proper location keeping size of DG constant.• Acharya et al (2006) [4] have select appropriate location of DG in

distribution network based on loss sensitivity of buses and appropriate size of DG was evaluated based on exact loss formula.

• Singh et al (2009) [8] have presented multi-objective genetic algorithm approach for DG sitting and sizing.

• In Zangeneh et al (2009) [10], multi-objective genetic algorithm is applied to produce optimal planning schemes by taking into account cost and grant functions as objectives.

• In Arya et al (2012) [6], voltage stability criterion has been used to find DG location and differential evaluation optimization technique is used to determine DG capacity considering objective of power loss minimization.

• In Guo et al (2012) [9], optimal generation dispatch by renewable energy has been calculated using weighted sum multi-objective particle swarm optimization technique.

• Kathod et al (2013) [5] has present evolutionary programming based optimization technique to place renewable DGs optimally .

• In Injeti et al (2013) [7] a simulated annealing optimization technique has been utilized for optimal placement and sizing of DGs.

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OBJECTIVE FUNCTIONS

The researchers choose objective functions from different technical, economical and environmental aspect and they are not empirical. While some authors have given concern on economy for DG selection [13, 14], others emphasized on power loss minimization [3, 4] or both economical benefit and power loss minimization [6]. Optimal placement and sizing of DG based on system security and reliability are also reported in the literature [15, 16]. Some authors have discussed about location and size selection of DG on view point of voltage stability improvement [7, 9].

This paper studied with objective of benefit to cost ratio maximization, voltage stability enhancement and network security improvement satisfying power and voltage constraint criteria.

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Benefit to cost ratio BCR indicates the economical benefit that can be realized with respect

to cost for implementation of the project.

I

Icij and OMCij are investment cost; and operating and maintenance cost of type-j renewable DG at bus-i

ni is the number of DG unit that can be connected at bus-i.

li is location variable at bus-i

P is the power generated by type-j DG at bus-I

N is the number of buses in the network

is investment, operating and maintenance cost

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Cumulative Present Value (CPV) of future cost includes interest rate, inflation rate, and economic life of equipment .It can be determined as follows:-

PV is the present value of cost.

IF and IR are the percentage of inflation rate and interest rate

Nyr is the number of year in planning horizon

is the power loss reduction due to allocation of renewable DGs

Chr is the cost of electricity

,2

{( * * ) }* *8760*N

renwDG renwDG ij i i renwDG hrj typei

Benefit P n l Ploss C CPV

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Voltage stability indexVSI is a very fast and effective tool and widely used by the power engineers for off-line measurement of voltage stability condition of buses

Where ri and xi are resistance and reactance of line connected between bus-i and bus-i+1 PD,i +1 and QD,i +1 are active and reactive load at bus-i+1 respectively Vi is the voltage magnitude at bus-iVoltage stability condition of the whole network can be realized as

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Network security index To get idea about network security index we need to know

line loading of the network.

Line Loading (LL) is the MVA flow through the line with respect to maximum MVA capacity of that line

network security is represented as

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LOAD MODELConsideration of voltage dependent load model is more relevant

than constant power load model for analysis of a system as loads of the buses in distribution network depend on their respective bus voltages.So practical voltage dependent load model i.e. residential, industrial and commercial used have been adopted for investigation.

PD,i, , QD,i and Vi are active load, reactive load and voltage magnitude at bus-i;

P0.i , Q0,i and V0,i are nominal active load, reactive load and voltage magnitude at bus-i;

α and are active and reactive power exponents

, 0,0,

* iD i i

i

VP P

V

, 0,0,

* iD i i

i

VQ Q

V

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Load type

(1) Constant power 0 0

(2) Residential 0.92 4.04

(3) Commercial 1.51 3.40

(4) Industrial 0.18 6.0

Table 1: Values of exponents used for different types of load

Active and reactive power exponents are different for different types of load [15, 18] and shown in Table 1

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PROBLEM FORMULATIONThe problem of this study is to generate potential solution with

maximization of minimization of VSI renwDG and minimization of

Overall objective can be formulated as

renwDGBCR renwDGNSI

Equality constraint:

, ,2 2

* * 0N N

SS renwDG ij i i D i Lj typei i

PG P n l P P

,2

0N

SS D i Li

QG Q Q

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Inequality constraints:

, ,min , , ,max* * *renwDG ij i renwDG ij i renwDG ij iP n P n P n

,min ,maxi i iV V V

maxij ijS S

1. Generation limit constraint at bus-i

2. Bus voltage tolerance constraint at Bus-i

3. Line capacity constraint of line connecting bus-i and bus-j

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Multi-objective Particle Swarm Optimization (MOPSO)

Particle swarm optimization (PSO):

PSO is basically a population-based search procedure in which individual particle adjusts its position according to its own experience, and the experience of fittest neighboring particle.

• The first one is best solution it has achieved so far which is called pbest..

• Second one is the best solution in the whole swarm and it is called gbest.

• In each flight the particles modify their position and velocity and try to converge in more promising region of solution.

Non-dominated sorting and crowding distance concepts are incorporated into PSO and extended it to handle multi-objective optimization problems.12/04/2023

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Multi-objective Particle Swarm Optimization (MOPSO)

In MOPSO, the whole population is sorted into various non-domination front levels based on non-domination criteria.

A solution is said to be non-dominated if it is impossible to improve one component of the solution without worsening the value of at least one other component of the solution.

After complete of set iteration, MOPSO generate a set of non-dominated solutions.

Fuzzy rule have the capability to select the optimal non-dominated solution in better way.

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Overall membership value of any non-dominated solution k is

1

1 1

obj

obj

Nki

k iNM

ki

k i

objN

M is number of non-dominated solution

is the number of objective functions

A linear fuzzy membership function is chosen with and corresponds to unacceptable and satisfactory value of objective f

maxif

minif

Objective function in MOPSO can be defined as:max

maxmax min

max min

min

0

1

i i

i ii i i i

i i

i i

if f f

f fif f f f

f f

if f f

is the membership value of objective

i thi

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Simulation algorithm of multi-objective problem : 1. Initialize number of particle in MOPSO and dimension of each particle. Se-

lect dimension of particle with location variable, type variable and size variable (number of installed renewable DG units at load bus). Generate ini-tial position and velocity of each particle randomly within specified range. 2. Run power flow algorithm and calculate the values of objective functions. 3. Do non-dominated sorting based on domination of objectives for different particles.4. Sort particles in ascending order according to their occupancy of front levels and calculate crowding distance of each particle.5. Select Pbest of particle as the best position it has occupied so far.6. Eliminate solutions that are not satisfying constrain criteria.7. Choose Gbest randomly from top 5% of non-dominated list. 8. Select maximum size of archive equal to number of particle.9. For iteration number one, store all particles in an archive. Otherwise compare the front level and crowding distance of current particles with pre-vious particle stored in archive. Particles with better front level and crowd-ing distances are stored by replacing previous particles.10. Update position and velocity of each particle.11. Repeat step 2 to 10 up to maximum iteration.12. Select particle with highest fuzzy membership value from archive as op-timal non-dominated solution. 13. Display value of each objective function and variables of particle for op-timal non-dominated solution. Show the values of location variable, size variable and type variable correspond to best non-dominated solution.12/04/2023

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Simulation results Simulation is carried out on 11kV, 28-bus rural Indian distribution

network. Simulation is done in MATLAB 7.10 software and Newton-Raphson

power flow algorithm is used. Base values are taken for the network as 1MVA and 11 kV . For planning horizon of 10 years, IF, IR and Chr are taken 5%, 3%

and Rs 5.8/kW-hr . Required system data and maximum capacity of lines are collected

from [13, 20].

Single line diagram of 28-bus Indian distribution network

DG technology

Com-mercial

size (kW)

Plant factor (%)

Invest-ment cost

(Rs/kW)

Operating and maintenance cost

(Rs/kW-year)

(1) Solar photo-voltaic (PV) 20 25

350,000 4,818

(2) Wind turbine 125 2075,000 11,826

(3) Biomass 200 60100,000 15,330

Technical and economical data of wind , biomass and solar based renewable DGs

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Case of constant power load

Solution generated by MOPSO compromising three conflicting objectives

Location , type and size of renewable DGs in the network for best trade-off between objective functions

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Comparative graphical representation of voltage magnitudes for different buses with and without renewable DGs in the network

Comparison of substation (S/S) power consumption, total power loss, VSI and NSI of DG installed network with before DG installation case

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Case of voltage dependent load

Solution generated by MOPSO by trade-off between three conflicting objectives

Location, type and size of renewable DGs in the network for best trade-off between objective functions

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Comparative graphical representation of voltage magnitudes for different buses with and without renewable DGs in the network

Comparison of substation (S/S) power consumption, total power loss, VSF and NSI of DG installed network with before DG installation case

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Conclusion The article presents a novel multi-objective formulation of re-

newable DG planning for distribution network incorporating load model.

A set of non-dominated solution is generated by trade-off be-tween three objective functions viz. benefit to cost ratio, voltage stability index and network security index.

In this study satisfactory economical benefit, voltage stability improvement and network security enhancement is obtained for best non-dominated solution.

This investigations show the importance of load models on choice of DGs.

It has shown that proper allocation of renewable based DGs have potential to improve voltage profile at distribution buses.

Considerable reductions in consumption of grid power and net-work power losses are also obtained.

The method can be used to allocate renewable DGs in any radial distribution system and find automatically the best solution.

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THANK YOU FOR YOUR KIND ATTENTION

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