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Geospatial Technology: A Tool to estimate revenue loss from JJ Clusters in Delhi and implementation of Delhi Govt. Social Welfare Schemes

Authors Name: Anil Kumar Rai+, Pooja Yadav+ & Varun Prakash* +Amity institute of Geoinformatics and remote sensing

* Sr Manager, BSES Rajdhani Power Ltd +Amity University, Sector-125 Noida, Uttar Pradesh -201313, India

*Address: BSES Rajdhani Power Limited Nehru Place, New Delhi-110019, India

Abstract:

Efficient decision making regarding revenue recovery

from the slum area has been the issue of utmost

concern in metropolitan cities. The first and the

foremost thing is that the methodology identifies the

pockets, where the revenue recovery has become an

issue for a power supply company. Then to lower the

revenue deficit from this pocket with the help of GIS, to

integrate it with a model that estimates the degree of

accessibility and capability of that area which includes

calculating the number of DTs (Distribution

Transformer) and substations feeding the area, further

calculating the power fed to this area and the amount

of revenue has been calculate from it, we undertook

this project. The areas under consideration during the

project are those that are notified under slums

improvement and clearance area act of 1956. The

project focusses on use of geospatial technology to find

out the details of consumers and estimate revenue,

outstanding to the utility. This paper will analyze the

characteristics of distribution network in slum areas.

We will discuss the problem related to minimizing the

gap between the consumer and the company in terms

of revenue. The result of the case study in the BRPL

division of Delhi that covers under BSES will help us in

policy making and come up with a special scheme that

will benefit both the provider as well as the consumer

of that area.

About the Author:

Mr. Anil Kumar Rai

Holds a Bachelor’s degree in Geography from St.

Edmunds College, Shillong and currently pursuing a

Master Degree in GIS and Remote Sensing at Amity

University, Noida Uttar Pradesh and seeking a

rewarding career in GIS.

E mail ID: anilr2512@gmail.com

Contact: +919716118438

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Introduction

The project intends to map JJ clusters (jhuggur Jhopri) identified by MCD (Municipal Cooperation of Delhi) and

use Geospatial tools to find the customers falling in these area. Project focuses on the power supply in JJ

clusters in the area of BRPL. Digitization of these JJ clusters has been done with the assistance of ArcGIS

software and is able to recognize near-by substations with reference to SAP Code, which supports in

demarcating the clusters getting power supply from different distribution transformers and Feeder Tracing

through ArcFM. These clusters face great challenge in terms of revenue. Government provide certain schemes

which provide aid to those people who are affiliated to these clusters so that the consumers can pay deficit

amount of revenue, which helps the government to come up with the new schemes. So, both the government

and the consumers can be benefitted.

This study is meant to improve the understanding the networking of electricity power distribution towards

effective power distribution. Most importantly, the BSES Rajdhani is expected to keep track of the electrical

facilities (poles, circuits, power lines and transformers) involved in the distribution of energy to the end-users.

With ArcGIS, variety of information can be better organized on a computer system linking the database to an

output map. GIS can effectively be used to manage and monitor information on the distribution of electricity

to end-users including information describing their spatial and non-spatial attributes such as geographical

location and electricity use.

Study Area

BSES provides power supply to West Delhi and South Delhi that comes under BRPL and have 19 Sub-Division.

There are about 351 JJ Cluster in this division which have been identified by Govt. of Delhi.

Fig: 1 – BRPL Division (Study Area)

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Feeder Tracing

Electric trace tasks allow you to check how current is flowing through a network of connected features

(substation, DT, poles etc.). You can place edge flags in ArcFM to define where the trace begins and ends

enable or disable features. You can define the upstream or downstream protective devices you want to find

and indicate phase configurations for tracing electric distribution throughout the network. With these options,

you can strategically tailor a variety of custom analysis tasks to specific implementations.

Electric Distribution Trace

The Electric Distribution trace finds all features connected to the same feeder system, by phase. If you change

the status of a switch or breaker, an Electric Distribution trace will return all features affected by the status

change. The trace operation executes as follows:

Find all circuit sources that can reach the flagged edge on one or more phases. If no sources are

located, the trace returns nothing.

If one or more sources are located, Arc FM traces from both ends of the flagged edge to each source.

The trace filters the results according to the value selected in the "Phase of conductors and devices" setting in

the Arc FM Electric Trace Options. The resulting features in addition to the flagged edge are returned as the

trace results.

Single Line Diagram (SLD)

The map displays the geographic location of all the facilities in the Lajpat Nagar area (Fig: 2). The entities

identified included the Low Tension Electric Poles, High Tension Electric Poles; high and low tension cables,

Distribution substation transformer, power transformers, feeders and Injection Substation. Figure shows a

more detailed map of the electricity distribution facilities connected to the end consumers. Instead of

representing each of three phases with a separate line or terminal, only one conductor is represented. It is a

form of block diagram graphically depicting the paths for power flows between entities of the system.

Elements on the diagram do not represent the physical size or location of the electrical equipment.

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EHV (EXTRA HIGH VOLTAGE) SLD

Fig: 2- Lajpat Nagar Grid Sub-Station SLD

The term grid usually refers to a network, and should not be taken to imply a particular physical layout or

breadth. Grid may also be used to refer to an entire continent’s electrical network, a regional transmission

network.

A grid is an inter-connector network for delivering electricity from supplier to consumer. It consists of

generating stations that produce electrical power, high voltage transmission line that carry power from distant

source to demand center.

Feeders are conductors which connect the consumer with the substation. The distribution feeder circuit

conductors leave the substation from a circuit breaker via underground cables or overhead cable, which shows

in single line

Methodology

Data Acquisition

The data acquisition began with the physical phenomenon to be measured. The first step in the methodology

of this work was the collection of the required data from the study area. In this case, there were two types of

GIS data involved which included: spatial and non-spatial data.

Spatial Data

The spatial data used for the research study include the following:

GPS Data: GPS Collection points on the available BRPL Division facilities -Poles, Transformers, etc. are

acquired from the BSES and they have used a Hand Held GPS.

INCOMING

FEEDER

BUS

BAR

POWER

TRANSFORMER

11 KV

FEEDER

S

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Non-Spatial Data

The non-spatial data includes the following information which was acquired from the BSES:

Consumer Details: Assembly Name, Address of the Cluster, the Line Transmission Pole/Pillar to which

a Cluster is connected

Electrical Network Details: Single line diagrams with cable sizes (HT, LT), lengths, distribution

substation transformers, poles and low voltage networks i.e.-LT Cables.

For this study, BSES was the major source of Data both Spatial and Non-spatial.

JJ Cluster Mapping & Revenue Loss Estimation

The exploding population and the new industrial economy brought with it the slums in Delhi. What should we

call mechanical slums-rows and rows of crowded worker houses in the shadow of factory, parks, in an around

Yamuna bank, near the side of flyovers/bridges, which can be seen entering the city and new within the city.

Socio-economic conditions of JJ clusters are becoming worst day by day. People who are living in JJ clusters do

not have sufficient resources or enough income to meet the basic needs. For them to pay the electricity bill is

a hard job. BSES provides electricity to these clusters. They are unable to pay proper revenue on time.

Government planning to provides certain schemes to these clusters so that they can pay some amount of

revenue. So for these purpose Municipal Corporation of Delhi have assigned 351 JJ Cluster in Delhi.

Identifying the attributes of JJ clusters from the web link that are assigned by the municipal

corporation of Delhi. (new JJ code, ward no. assembly name)

Identifying different distribution transformers (DT) near the JJ clusters providing power through

feeders to the consumers.

Determining pole number from OH feeders which provide power to the consumer (no. of houses)

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Web link provided by MCD

Overlap with Google Image

Digitization

Cluster detail (attribute

such as assembly

name, ward no., JJ code

etc)

JJ cluster mapping delhi

Intersect JJ cluster with nearby data and

DT

Buffer zone around cluster

DT and poles feeding in JJ cluster

CA no. with status live or disconnected

Study consumer outstanding Bill and late

payment charges

List of consumers with details for whome

scheme to be applicable

Calculation of outstanding amount payable

to BSES

Proposed revenue gap and revenue

recovery from consumer

Wave off late payment

charges

Encourage consumer

Arc GIS Spatial Analysis Tools

Energy Audit Module & Customer

Information System

Delhi GOVT. proposed

scheme

Flowchart: Estimation of outstanding revenue in JJ Clusters using GIS

The image shows the JJ clusters through online web link.

Fig: 3- JJ clusters

Data were formally entered into their respective tables, created in Arc Catalog, in the Arc Map environment.

After the data entry process, the shapefile was saved in Arc Map in a folder for easy recovery, geo spatial

analysis and queries carried out on them. The data stored in the database forms the information base. Once

the layers were digitized, the non-spatial data were then added as attributes to the digitized features. This

attribute table was linked to the spatial themes containing geographic information. The database created

included location and descriptive information for all the different components of the system.

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Fig: 4- Pole number from OH feeder

Result & Discussion

After identifying the pocket i.e. JJ Cluster and knowing Station which are feeding power to these areas. With

the help of ArcGIS we have digitize these pockets and tried to figure out substations (SAP code), DT, pole

number etc. the figure shows the JJ clusters with their attributes.

JJ clusters in BRPL division of BSES

Figure shows the different attributes of JJ clusters.

Fig:5- Attribute table (JJ Clusters)

A proper database management system which intergrates with GIS can helped us to find how the consumers

are connected through the transformers, feeders, poles, circuit breakers through periodic updating and

monitoring GIS mapping of electrical network and consumer database to improve loss management, possibly

better consumer relationship and come up with new schemes and policies which can be implemented for

better management and efficient use of energy.

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Fig: 6-Distribution of JJ Cluster

Conclusion

GIS could be a valuable tool not only for mapping facilities but for improved decision-making, better asset

infrastructure and above all identifying different electrical assets in the network.

The study has been focus mainly on identifying the pocket were the revenue recovery has been a major issue.

In the study area, 351 JJ clusters were mapped as per the information shared by DUSIB (Delhi Urban Shelter

Improvement Board) of BRPL divisions. The consumers are classified mainly into two categories i.e. live

consumer and disconnected consumer in JJ cluster, So as to know the number of consumers and also the

amount of revenue receive in this area, as shown in the table (list of consumers).

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Total no. of live and disconnected consumers

S.NO. TYPE OF CUSTOMER IN JJ CLUSTER

NO OF CASES Amount(In Cr) (Fig may be changed due to security reason)

1. Live 12952 2.92

2. Disconnected 5496 2.37

No. of live and disconnected consumers

Conclusively, it has been observed that GIS applications have not reached the optimum exploration/level and

there is still room for further exploration and extension in the field (Power Sector) as far as utility service

delivery like in the case of electricity distribution is concerned.

The following recommendations are advised for efficient electricity distribution networks:

More efforts must be done to bring out refined and scientific approaches such as Geographic

Information Systems into the management of electricity distribution network in an around JJ Cluster.

Policy making should be done with ground based truthing by knowing needs and requirement of the people.

Monitoring of electricity distribution.

References

1. ESRI GIS for Smart Electric Grid [Online]. Available:

(http://www.esri.com/industries/electric/business/smartgrid.ht ml)

2. Power companies of India [Online]. Available: (http://en.org/Category:Power_companies_of_india).

3. USAID - The Smart Grid Vision for India’s Power Sector, March 2010.

4. Telvent Smart Grid Solution Suite [Online]. Available:

(http://www.telvent.com/en/business_areas/smart_grid/solution s_overview/smart_grid/).

5. RenewGrid – How the Smart Grid is in step with GIS, by Andrew Zetlan, August 2010.

6. Geospatial Modeling of electrical Distribution Network [Online].Available:

(http://geospatialworld.net/Paper/Technology/ArticleView.aspx?aid=30522)

No. of customers in JJ cluster

Live Consumer

Disconnected

consumer