The Impact of Distributed Generation in the Distribution Networks’ Voltage Profile and Energy Losses

Vasiliki Vita, Tareafa Alimardan, Lambros Ekonomou Department of Electrical and Electronic Engineering

City University London London, United Kingdom

e-mails: vasiliki.vita.1@city.ac.uk , tareafa.alimardan.1@city.ac.uk , lambros.ekonomou.1@city.ac.uk

Abstract — The worldwide increasing demand for electricity, coupled with governmental policy changes for “green” energy has led to significant interest in distributed generation (DG). Integrating DG into an electricity network, especially close to load centres, has many significant benefits but also brings with it many drawbacks such as voltage drop, and power losses. In this paper the impact of three different types of distributed generation (diesel generator, wind turbine and photovoltaic (PV)) on distribution networks’ voltage profile and power losses is studied. NEPLAN software and the extended Newton-Raphson method have been used in the analysis. The obtained results show that different types of DG influence differently the distribution network and that their precise location and size are vital in reducing power losses and improving the voltage stability.

Keywords — distributed generation; distribution networks; extended Newton-Raphson method; power losses; voltage profile.

I. INTRODUCTION An important development since the privatisation of the

electricity industry has been a shift towards, and the significant growth in distributed generation (DG) power plants which are connected to the electricity system near load centres. DG can be based on alternative energy sources, preferably on renewable energy such as wind and solar energy, however, non-renewable sources also are employed such as diesel generators, natural gas generators, fuel cells, etc. These are now larger in number than the traditional more massive, conventional power stations that are typically located closer to an energy source. As a result the configuration of the traditional electrical supply system has changed prominently over the last two decades when DG was reintroduced into the construction of the electricity network. The concept of DG, which can be traced back to the early 1900s, is based on small scale power plants that are connected to the electricity system close to customers. Although centralised large power stations located far away from load centres have several benefits in terms of efficiency, e.g., they only require a small number of staff to operate the station and the bulk of electricity can be transferred over long distances with small losses, they also have their drawbacks such as environmental concerns related

to emissions and the cost of expanding the electricity system because of the increased demand and concerns about fuel supply.

On the other hand DG has the potential to reduce emissions and increase the dependence on alternative energy sources, and hence participate in energy diversification. It also helps to deliver backup power during times of increased electricity demand, avoiding the investment in large power plants and transmission lines, having also as a result the reduction of the transmission and distribution power losses. Furthermore DG improves voltage profiles and the load factor, which minimizes the number of required voltage regulators, capacitors and their ratings and maintenance costs.

These benefits however are counterbalanced by impacts on the distribution network since the integration of DG in it is not straightforward. The integration of DG into the distribution network may result in higher active and reactive power losses because it is installed close to load centres. A higher level of short circuits is observed and voltage deviations appear since the penetration level of DG may cause either overvoltages or undervoltages [1]. Furthermore, some DG technologies, as in the case of photovoltaic (PV) and wind power, change their output power over time, having as a consequence, voltage fluctuations to occur which can be directly linked to the quality of the power delivered to the customers.

Different DG power plant types may have different impacts on distribution networks. For example the power injected into the distribution network from a photovoltaic system might have a different impact to that of a wind turbine. Therefore, the aim of this paper is to analyse the impact of the placement and sizing of different types of DG units in distribution networks with regard to power system losses and voltage profile. Three different types of DG, wind turbines, photovoltaics and diesel generators are installed on a radial distribution network and analysis is conducted using the NEPLAN (a software tool programmed to analyse, plan, simulate and optimise electricity networks) and the extended Newton-Raphson method. The obtained results show that different types of DG influence differently the distribution network and that their precise location and size are vital in reducing power losses and improving the voltage stability.

2015 IEEE European Modelling Symposium

978-1-5090-0206-1/15 $31.00 © 2015 IEEE

DOI 10.1109/EMS.2015.46

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2015 IEEE European Modelling Symposium

978-1-5090-0206-1/15 $31.00 © 2015 IEEE

DOI 10.1109/EMS.2015.46

260

II. RELATED WORK As it has previously mentioned the integration of DG in

the electricity system has proliferated notably as a result of privatisation of the electricity market, environmental alertness and technological advancement. During the last decade many researchers have focused on the challenges resulting from the unplanned installation of DG in the distribution networks. Distribution networks have inherent problems such as voltage drop, power losses, fault current levels associated to short circuits and problems resulting from the bidirectional power flow, in contrast to the unidirectional traditional power system flow from higher to lower voltages.

In [2] a genetic optimization algorithm has been used in order to find the optimal location and size of different DG units in a radial distribution system. Three constraints (voltage, active and reactive power losses and DG size) and the Newton-Raphson method have been used in an effort to reduce the total power losses and improve the voltage profile. In [3] an interesting method has presented based on a combined genetic algorithm and particle swarm optimisation for the optimal location and sizing of DG on radial distribution networks. The proposed method aimed at better voltage regulation and stabilisation and power loss reduction.

Following two different approaches Parizad et al. [4] tried to find the optimum location and size of DG by reducing losses and stabilising voltage. The first approach targeted real power losses through the development of an exact loss formula by finding the best location for the DG. In the second approach, a voltage stability index was used to site DG in the optimum place. Power flow has calculated by applying the forward-backward sweep method. Injeti and Kumar [5] have used fuzzy logic for finding the optimum placement of a single DG unit and proposed a new analytical expression for sizing the DG in radial networks. Their goal was to improve the voltage profile and minimize real and reactive power losses.

A relative simple analytical method for real power loss reduction, voltage profile improvement and substation capacity release based on voltage sensitivity index analysis has introduced in [6], while another analytical technique to calculate the optimum size and to allocate DG units in the optimum place has presented in [7] in where the authors found that minimum losses and a better voltage profile can be achieved with integrating one DG unit of optimum size and in an optimum location rather than integrating several DG units.

Reddy [8] aimed to reduce the total power losses and to improve the voltage profile of distribution systems with DG using the particle swarm optimisation technique. The Newton-Raphson method has been used in order to calculate power losses and voltages across the network. Similarly particle swarm optimisation has been used in [9] for the placement of DG in radial distribution systems reducing the active power losses and improving the voltage profile. In this work Power System Analysis Toolbox (PSAT), an open source MATLAB software package for analysis and design of electric power systems have been used for the power flow

calculation. In [10] the concept of evolutionary programming in particle swarm optimization process was introduced for obtaining the optimum size of DG in distribution networks. The authors achieved with this approach to obtain the optimum values faster.

Safigianni et al. [11] explored the impact of integrating different DG units on the system power flow, current, voltage profile, and short-circuit level. They conducted their analysis on a real distribution system in order to accurately diagnose the technical issues that can arise. The system’s behaviour was analysed using NEPLAN software. It was found that losses fluctuated depending on the load factor with the major impact observed on the voltage profile. Finally in [12] load flow and short circuit studies have been conducted to analyse the impact of photovoltaics on distribution systems. DigSilent power factory software has been used for the analysis. The authors found that the higher the penetration level the higher the positive impact of DG in terms of power losses and voltage profile.

III. SYSTEM CONFIGURATION

A. The distribution network The system under study is shown in Figure 1 and

constitutes a part of a distribution network.

Figure 1. Single line diagram of distribution network under study.

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This system is built from 29 bus bars and 27 lines. The bus bars and lines data are presented in Table I.

TABLE I. LINES DATA OF DISTRIBUTION NETWORK UNDER STUDY

Line No.

From bus

To bus

Length (km)

Line impedance Resistance (ohm/km)

Reactance (ohm/km)

1 2 3 9 0.0853 0.053 2 2 14 3 0.344 0.1742 3 2 23 4 0.8421 0.5421 4 7 19 3 0.9101 0.6201 5 4 25 6.2 1.6333 1.3411 6 3 4 8.2 0.581 0.19 7 4 5 6 0.9234 0.4514 8 5 6 5 0.7544 0.3277 9 6 7 3 0.9111 0.5233

10 7 8 3.4 0.9111 0.5233 11 8 9 5 1.3321 1.2222 12 9 10 2 0.5411 0.2111 13 10 11 4 0.4311 0.3992 14 11 12 2.5 0.7413 0.3413 15 12 13 6 0.9765 0.7325 16 14 15 3.5 0.2911 0.2 17 15 16 5.5 0.804 0.6265 18 16 17 3.5 0.1921 0.5455 19 17 18 5 0.1921 0.5455 20 19 20 4 0.4311 0.3992 21 20 21 4 0.5421 0.2222 22 21 22 5 0.9765 0.7325 23 23 24 3 0.7413 0.3413 24 25 26 4 0.9888 0.6211 25 26 27 7 0.1833 0.0432 26 27 28 8 1.6211 1.0922 27 28 29 5.5 0.58 0.52 Bus bars are referred to as nodes (N), and N2 is the main

distribution substation. One central station is feeding the system which consists of a network feeder (NETF) connected to the 33kV N1 and rated 1500MVA, and a synchronous generator of 50MVA, 44.5MW, 0.89 cos(phi). One transformer is used to step down the voltage from 33kV to 11kV with a capacity of (35MVA, 33/11kV).

The minimum and maximum voltage levels are 9.9kV and 12.1kV respectively for all nodes except N1. The system has been designed such that there are no overloaded lines. The system is lightly loaded by (4.375MW) and (2.76Mvar) connected to 21 nodes and of different power factors. The loads data of the network are presented in Table II.

B. Simulation Process For the analysis of the examined distribution network

under certain levels of DG penetration, NEPLAN software has been used. NEPLAN software is programmed to analyse, plan, optimise and simulate electricity systems [13]. It has a very friendly graphical user interface and it allows the user to make modifications to the network elements according to specific needs through its wide library.

Three different types of DGs (diesel generator, wind turbine and photovoltaic (PV)) and different sizes for each type have been connected on the examined distribution network in order to study their impact on voltage profile and power losses (active and reactive).

TABLE II. LOADS DATA OF DISTRIBUTION NETWORK UNDER STUDY

Load Location (bus bar)

Capacity (MW)

Capacity (Mvar) cos (phi)

L1 23 0.2 0.015 0.997 L2 24 0.01 0.02 0.447 L3 26 0.1 0.09 0.743 L4 27 0.36 0.1 0.964 L5 29 0.02 0.015 0.8 L6 3 0.3 0.04 0.991 L7 4 0.25 0.18 0.812 L8 5 0.5 0.27 0.88 L9 6 0.01 0.02 0.447 L10 8 0.05 0.03 0.857 L11 9 0.015 0.02 0.6 L12 10 0.02 0.01 0.894 L13 11 0.2 0.14 0.819 L14 13 0.1 0.15 0.555 L15 14 0.34 0.24 0.817 L16 15 0.25 0.31 0.628 L17 16 0.8 0.5 0.848 L18 17 0.33 0.16 0.9 L19 18 0.2 0.1 0.894 L20 20 0.08 0.2 0.371 L21 22 0.24 0.15 0.848

Total load 4.375 2.76

Firstly, the simulation of the examined distribution network is carried out without connecting any DG into the network. For each bas bur the voltage profile and for each line the active and reactive power losses are calculated.

Secondly several simulations are carried out connecting each time a different type and size of DG at specific locations recording each time for each bas bur the voltage profile and for each line the active and reactive power losses.

It must be mentioned that for the load flow analysis, the extended Newton-Raphson method has been used.

IV. SIMULATION RESULTS

A. Analysis of distribution network without DG Load flow analysis has been performed in the examined

radial distribution network. As it was expected significant voltage drops exist on some bus bars especially the ones that are located far away from the main substation. Voltage results presented as (U/kV) and lower voltage limits presented as percentage of the node voltage (u/%) for each bus bar are listed in Table III. As it can be seen lower voltage limits have been violated in 22 bus bars (values below 9.9kV). The total active power losses (Ploss) is 2.5943MW and the total reactive power losses (Qloss) is 1.2712 Mvar and are presented in Table IV for each line.

B. Analysis of distribution network with diesel generator DG Bus bars with low voltages have been chosen as

candidate locations to install diesel generators. Although there are many week points in the network, three locations were chosen to install three different diesel generators. At N10 was installed DG1-Diesel (0.2MW/0.2Mvar), at N13 was installed DG2-Diesel (3MW/3Mvar) and at N22 was installed DG3-Diesel (1MW/1Mvar).

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TABLE III. LOADS DATA OF DISTRIBUTION NETWORK UNDER STUDY

Bus bar name U (kV) u (%) Bus bar

name U (kV) u (%)

N2 10.87 98.82 N16 9.445 85.87 N3 10.432 94.84 N17 9.354 85.04 N4 8.2 74.55 N18 9.304 84.58 N5 6.089 55.36 N19 3.924 35.68 N6 5.07 46.09 N20 3.665 33.32 N7 4.282 38.92 N21 3.506 31.88 N8 3.771 34.28 N22 3.089 28.08 N9 2.564 23.31 N23 10.797 98.16

N10 2.426 22.05 N24 10.794 98.12 N11 2.14 19.46 N25 7.26 66 N12 2.044 18.58 N26 6.917 62.88 N13 1.0663 15.12 N27 6.842 62.2 N14 10.596 96.33 N28 6.787 61.7 N15 10.35 94.09 N29 6.772 61.57

TABLE IV. LOADS DATA OF DISTRIBUTION NETWORK UNDER STUDY

Element name

Ploss (MW)

Qloss (MVar)

Element name

Ploss (MW)

Qloss (MVar)

LINE1 0.1657 0.1001 LINE15 0.0134 0.0102 LINE2 0.0587 0.0287 LINE16 0.0404 0.0267 LINE3 0.0013 -0.0005 LINE17 0.1172 0.0898 LINE4 0.0288 0.0196 LINE18 0.0027 0.0067 LINE5 0.0549 0.044 LINE19 0.0006 0.0003 LINE6 0.9082 0.295 LINE20 0.0183 0.0169 LINE7 0.5834 0.2844 LINE21 0.0087 0.0035 LINE8 0.2113 0.0914 LINE22 0.0197 0.0148 LINE9 0.149 0.0855 LINE23 0 -0.001

LINE10 0.053 0.0304 LINE24 0.0215 0.0129 LINE11 0.0952 0.0873 LINE25 0.0041 0 LINE12 0.0136 0.0053 LINE26 0.0002 -0.0009 LINE13 0.0203 0.0189 LINE27 0 -0.0007 LINE14 0.0041 0.0019

Each diesel generator was installed at a time and load

flow analysis was performed. The obtained voltage profiles for all bus bars of the examined distribution network after the installation of DG1-diesel, DG2-diesel and DG3-diesel are presenting in Figure 2. In the same figure are also presented the voltage profiles for all bus bars after the installation of all three DG-diesel generators at the same time.

Active and reactive power losses have been calculated for all lines for each one of the four different cases. The total active and reactive power losses after the installation of DG1-diesel were 0.8985MW and 0.446Mvar respectively, while after the installation of DG2-diesel were 1.852MW and 0.9049Mvar. The installation of DG3-diesel had as a result the total active power losses to drop to 0.5689MW and the total reactive power to 0.3065Mvar. Finally, with all the diesel generators injecting power into the system, the total active and reactive power losses for all the lines were 0.4005MW and 0.2183Mvar respectively.

C. Analysis of distribution network with photovoltaic DG A PV system of three different sizes (1.2MVA/1Mvar,

3.5MVA/3Mvar and 4MVA/4.2Mvar) has connected at N5 in order to examine its impact on the distribution network. A load flow analysis has performed for each case. The obtained voltage profiles for all bus bars of the examined distribution network after the installation of DG1-PV, DG2-PV and DG3-PV are presenting in Figure 3. It is clear that the lower

voltage limits have been violated in more than half bus bars in all three cases.

(a) DG1-diesel generator is installed.

(b) DG2-diesel generator is installed.

(c) DG3-diesel generator is installed.

(d) All three DG-diesel generators are installed.

Figure 2. Voltage profiles of the examined distribution network with DG-diesel generators installed.

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(a) DG1-PV is installed.

(b) DG2-PV is installed.

(c) DG3-PV is installed.

Figure 3. Voltage profiles of the examined distribution network with DG-PV installed.

Active and reactive power losses have been calculated for all lines for each one of the three different cases. The total losses after the installation of DG1-PV were 1.2596MW and 0.6005Mvar respectively, after the installation of DG2-PV were 3.6813MW and 1.6411Mvar and after the installation of DG3-PV the total active and reactive power losses were 9.3136MW and 4.0626Mvar respectively.

D. Analysis of distribution network with wind turbine DG Three wind turbines were connected in the examined

distribution network in order to examine their impact on it. DG1-wind (1.8MW/1.895Mvar) has connected at N7, DG2-wind (2.54MW/2.674Mvar) has connected at N13 and DG3-wind (6MW/6.316Mvar) has connected at N15 and load flow analysis has performed for each case. The obtained voltage

profiles for all bus bars of the examined distribution network after the installation of DG1-wind, DG2-wind and DG3-wind are presenting in Figure 4. In the same figure are also presented the voltage profiles for all bus bars after the installation of all three DG-wind generators at the same time. It is clear that the lower voltage limits have been violated in more than half bus bars in all cases.

Active and reactive power losses have been calculated for all lines for each one of the four different cases. The total active and reactive power losses after the installation of DG1-wind were 0.9629MW and 0.4509Mvar respectively, after the installation of DG2-wind were 0.7614MW and 0.3384Mvar, after the installation of DG3-wind were 1.6135MW and 0.8014Mvar, while with all the wind turbines injecting power into the system, the total active and reactive power losses for all the lines were 0.6196MW and 0.3145Mvar respectively.

V. DISCUSSION Installing DG1-diesel at N10, the number of violated

lower voltage limits remained unchanged but voltage levels have been improved notably. Adding DG2-diesel at N13 resulted in voltage degradation which was a slightly better case than this without DG installed. Installation of DG3-diesel at N22 resulted in clear voltage support for all bus bars. A significant improvement in voltage levels was observed when all three DGs were installed in the system. At the same time the number of violated lower voltage limits was reduced by half. With regard to power losses, there was a significant reduction in total active and reactive losses after DG1-diesel was added, while there was an increase after DG2-diesel installation. It has also noted that the total power losses that resulted from installing DG3-diesel and all the generators at the same time were similar.

The installation of DG1-PV in the distribution network had as a result the improvement of voltage levels and the violation of lower voltage limits in 13 bus bars. Active and reactive power losses have decreased comparing to these obtained without the installation of DGs. The installation of DG2-PV had as a result the violation of lower voltage limits in 14 nodes, although voltage levels have improved significantly. Power losses increased comparing to these obtained without the installation of DGs, while lines 1, 6 and 7 became overloaded. By installing DG3-PV voltage levels degraded, in 15 bus bars lower voltage limits have been violated, total power losses have increased significantly while lines 1, 6, and 7 were overload.

The installation of each one of the three DG-wind generators in the distribution network had as a result the violation of lower voltage limits in 19 bus bars. As a final attempt to improve the voltage levels of the system, all three wind turbines were connected at the same time. On running a load flow analysis this resulted in a notable voltage support. As far concerning the power losses in the lines, it is clear that with the installation of wind turbines at the selected locations, losses were much less than compared to no wind penetration. The lowest active and reactive power losses were observed when all three wind turbines were installed at once.

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(a) DG1-wind generator is installed.

(b) DG2-wind generator is installed.

(c) DG3-wind generator is installed.

(d) All three DG-wind generators are installed.

Figure 4. Voltage profiles of the examined distribution network with DG-wind generators installed.

VI. CONCLUSIONS In this paper the impact of DG on a distribution network

has analysed. Different penetration levels of one non-renewable and two renewable types DG have examined and their impact on voltage profiles and power losses has studied. For the analysis NEPLAN software and the extended Newton-Raphson method have been used. The obtained results have shown that different types of DG influence differently the distribution network and that their precise location and size are vital in reducing power losses and improving the voltage stability.

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