ELECTRICAL INDIA | December 20166

contents Vol. 56 | No. 12 | December 2016

Publisher’s Letter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 04

Editorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

News . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Appointments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Product Avenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Index to Advertisers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

ARTICLES

DEPARTMENTS

How & Where Are We Going?– P K Chatterjee, Editor36

Power Generation– Basant Kumar42

Investment in Renewable Power– Vijay Singh Bisht62

Carbon Credits For Financing Renewable Projects– Nidhi M J, Shaikh Shamser Ali72

Future Perspective For Renewable Energy In India– Jay Thakar82

Techniques For Battery Testing In Railways– Dr Usha Surendra, et al102

Addressing Energy Needs & Environmental Challenges

– Chandrika Kulkarni

52

Analysis And Elimination Of Third Harmonics– Paresh Modha, Minesh Joshi110

Influence Of The Joint Design– Diego Cisilino118

Power Scenario Of Uttarakhand– Simmi Sharma 128

Power Scenario Of Chhattisgarh– Sandeep Banerjee130

Hydro Power Scenario In Tamilnadu– M P Singh132

Demand Estimation & Power Distribution– D Geethalakshmi, Fazlullah Syed138

Electricity Initiated Fire Hazard– Ritabrata Sanyal146

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Challenges & Solutions

Demand Estimation &Power Distribution

'Assured and quality electric supply' is one of the core

infrastructure elements of the smart city. The backbone to

drive this objective is to implement an efficient and

intelligent power distribution system. The trigger for an

efficient power distribution network begins with the demand assessment. The

methodology for arriving at the optimum demand

assessment and planning of power distribution network

for a smart city is deliberated in this article...

Government of India has a vision of

developing 100 smart cities across the

country aiming at higher economic

growth and improved quality of life.

Smart cities are considered to build a strong

and intelligent infrastructure with sustainable

environment. The core infrastructure elements

expected out of a smart city would include 24x7

water and power supply, robust transport

system, efficient water & waste management

system, reliable IT network, smart buildings,

state-of-the-art health care & education

facilities, e-Governance, safety & security, etc. It

is evident that to build these infrastructure

elements efficiently, electricity plays a vital role.

It would be impossible to build an efficient

infrastructure without reliable energy source.

To ensure 24x7 reliable and energy efficient

power to the smart city, it is imperative that the

electric services should be of best quality,

continuous and economic. Thus, the most

significant and crucial design goals would be

D GeethalakshmiAGM, ElectricalTATA Consulting Engineers Ltd.

Fazlullah SyedManager, ElectricalTATA Consulting Engineers Ltd.

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Smart City << Challenges & Solutions

optimal demand assessment followed by

establishment of an efficient power distribution

system. For building a new smart city, accurate

prediction of the demand is a challenging task

due to the diversified and varying pattern of the

load. This requires a holistic approach through a

diversified and optimized load model.

Demand Estimation Usually for a city development, historical and

statistical load pattern and recorded load data

will be used to estimate the maximum demand.

This normally includes monthly energy

billing data for domestic consumers and

hourly meter recordings of the maximum

demand values for bigger customers,

generally HT consumers.

Development of a new smart city which

aims at high reliability necessitates an

advanced model. Generally, the models

adopted till date in many countries are,

objective based models, identification based

models on spatial coverage, classification based

modeling approach. In a smart city, more

focused demand estimation is required in order

to meet the criteria of optimal, energy efficient

and economic demand estimation.

As per UN estimate, cities are responsible for

75% of global primary energy and contribute to

70% of global carbon emissions. The situation in

Indian cities is still severe, the energy demand is

rising each year, according to British Petroleum's

energy forecast, energy demand in India is

expected to increase by 132% by 2035 while the

growth in production will be about 112%. Thus,

to build a smart city, it is vital that low carbon

foot print and energy efficient power should be

considered as major factors.

To enhance the sustainability and energy

security of the city and to ensure that the city

remains an attractive destination for investment,

renewable energy becomes a significant

component of energy mix. It is crucial to

supplement energy produced by burning fossil

Figure 1: Stages of Demand Estimation

Figure 2: Typical Zonal Classification

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Challenges & Solutions

fuels with clean renewable energy.

In addition, demand should meet the

different types of load clusters with varying load

pattern. Electrical load varies with time, place

and climate. Therefore, a diversified demand

estimation using weighted arithmetic mean

model is considered. This model involves series of

strategic planning and scrupulous forecasting of

load demand. The various steps involved in this

model are depicted in Figure 1.

a. Phasing Plan and Zonal

Distribution of the CityBased on the city developmental plan and

with consideration of environmental and

sustainability objectives, zonal classification is

done. Generally in any smart city, different types

of zonal plot areas are envisaged. Few typical

classifications are: Residential, High Access

Corridor, City Center, Industrial, Knowledge & IT.,

Commercial, Recreation, Sports & Entertainment,

Utilities, Public Facility Zone, etc. A typical zonal

classification for a city is depicted in Figure 2.

b. Built-up Area EstimationThe optimized built-up area for each type of

land use and plot area is derived based on

resident population, floor space index norms,

urban and regional developmental plan

formulations and guidelines (UDPFI) and

National Building Code (NBC). The built-up area

considering growth plans & population density

of various clusters are used in the assessment of

the power demand and infrastructure planning

of electrical power system.

c. Identification of Electrical Loads for Each ClusterThe electric loads are influenced by various

factors i.e., application factor, area classification

factor, climatic factor and time factor. Considering

these factors, broad load classification are worked

out which are further framed into micro level

classifications. Various typical loads envisaged

are lighting & receptacles, air conditioning &

ventilation, workstations & servers, electronic

appliances, elevators & escalators, heaters, water

treatment loads, sewage treatment loads, etc.

d. Estimation of Individual

Watt/Sq.m for Each ClusterPrecise estimation of Watt/Sq.m of individual

loads is carried out considering the important

objective of load optimization. Being a smart

city, energy efficient, renewable energy source

and adoption of smart & advanced technologies

are the key factors which influence the load

estimation. Optimum estimation on Watt/load is

worked out considering the following energy

conservation aspects adopted in a smart city.

• All commercial (IT/office) buildings are LEED

certified

• Energy efficient lighting system for

commercial buildings, street lighting and

common area lighting

• Energy conservation measures in HVAC design

• Smart & Intelligent lighting controls for all

road lighting

• All motors employed in utility area (WTP, STP,

ETP, pumping stations, gas & fire stations, etc.)

and industries are energy efficient motors.

• Usage of Variable Frequency Drives (VFD) for

process motors

• Smart homes with smart metering system

• Integrated, smart and intelligent power

system automation for complete city

• Renewable energy source like solar for all

commercial and official buildings

In a typical city, major energy contributors

are residential sector, industries and commercial

buildings which require substantial consideration

of energy efficient measures.

Residential Zone: Various surveys

conducted across Indian cities reveal that

residential sector contributes around 25% of the

total power consumption. Major loads which

contribute to this includes lighting, kitchen

Figure 3: Energy Usage Pattern

Figure 4: Steps Involved in Estimation of Watt/Sq.m

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Smart City << Challenges & Solutions

appliances, air conditioners and space heating.

Typical energy usage pattern of residential loads

are depicted in Figure 3.

Due to the significant proportion of energy

consumption of residential sector, optimum

energy demand estimation should necessarily

take into consideration the various energy

conservation measures. In general, residential

energy pattern is influenced by variant factors

such as number of occupants, time of occupancy,

occupant behaviour and standard of appliances

used. There are various models used for

residential demand estimation such as statistical

model, top down approach, bottom up approach,

etc. The methodology adopted in this context is

bottom up engineering approach which is based

on end use energy model and this model has an

advantage of identifying potential energy

efficient measures. The major steps involved in

this approach is depicted in Figure 4.

Commercial Buildings: Predominant loads

envisaged in this zone are lighting (interior and

exterior), HVAC, workstations and datacenters.

Similar to residential, bottom up engineering

end use model is adopted where Watt/Sq.m is

arrived considering the energy wattage of

individual end use load with due consideration of

energy efficient measures.

As per ASHRAE standard, the lighting power

density using building area method for office

buildings is 0.9. However, in smart city,

considering energy efficient measures such as

efficient lighting, efficient lighting controls, the

value is further optimised to less than 0.9. In a

smart city, most of the official and IT buildings

are expected to be LEED certified. Therefore, as

per LEED norms, minimum of 3-5% renewable

energy source is considered while arriving at the

respective building Watt/Sq.m. While arriving at

Watt/Sq.m, diversity factor plays a significant

role as in commercial buildings, the power

consumption varies with occupancy of the

building and the work profile of the building.

Industrial Zone: Contribution of industrial

zone is equally substantial and more complex to

estimate the load. Due to varying industry types

with diversified and unique load pattern,

statistical energy data approach is considered in

this zone.

e. Watt/Sq.m for Mixed Type PlotIn a smart city, normally a mixed type of plot

is envisaged. In a mixed type of plot, identifying

the exact load model to be adopted is

complicated. For example, residential zone

comprises residences, commercial offices/retail,

leisure/hospitality, community facilities, local

public open space, roads & utilities. Standard

Watt/Sq.m model cannot be applied for this plot

type. Thus, average load is worked out for these

types of plots by applying a Weighted Arithmetic

Mean (WAM) model by determining the

percentage of different mix available. Table 1. Weighted Arithmetic Mean Model Of A Typical Residential Area

Residential Area (Mixed Plot)

Area under

consideration

% area

occupation

Watt/

Sq.m

Residences A1% W1

Commercial Offices A2% W2

Leisure/Hospitality A3% W3

Community Facilities A4% W4

Local Public Open

Space

A5% W5

Local Roads A6% W6

Utilities & ICT Devices A7% W7

Uniform Watt/Sq.m by means of Weighted

Arithmetic Mean (WAM) of the area cluster =

∑(A% x W x Y) /Y where Y is the total built up area

of residential area.

f. Maximum Demand

EstimationMaximum demand for each load cluster is

determined by applying hourly diversified factor

to the Watt/Sq.m arrived by means of WAM.

Hourly diversity factor varies with time,

application and climate.

Hourly Max Demand (MW) = Watt/Sq.m x

BUA (Built Up Area) x hourly group diversity factor.

Thus, the maximum demand of the city is

derived from the peak demand of the hourly

demand curve. A typical hourly energy demand

curve is shown in Figure 5.

Power DistributionThe distribution network is the most

extensive part of electrical power supply system,

therefore optimization plays a vital role in the

distribution system design. General design

criteria include:

Statutory rules applicable for particular

location

Power reliability and redundancy

Minimum T & D losses

Optimal selection of incoming power supply

voltage level

Optimal selection and close proximity

location of distribution equipment

Optimal sizing of main transformers and

other distribution transformers

Implementation of integrated and efficient

automation system for the complete power

distribution system

Quick fault isolation and restoration

Security and safety

To meet the aforementioned criteria and to

design an optimized power distribution system,

Figure 5: Typical Hourly Energy Demand Curve

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Challenges & Solutions

bottom up nodal methodology is adopted.

Figure 6 in next page indicates the different

stages involved in a bottom up nodal methodology.

Normally, the plot level feed points will be

allocated based on the kVA versus voltage level

identified by respective statutory norms. Based

on the above criteria, the total no. of LV and HV

customer feed points is arrived at for each voltage

level. Grouping of individual plot level feed nodes

(LV nodes and HV nodes) and formation of

distribution substation loops based on zoning

philosophy i.e., grouping within load clusters and

particular type of land like residential,

commercial, etc. is performed based on the

circuit loop kVA limitation imposed. Point to be

noted is, grouping should be within close

proximity and meeting the voltage drop limit

criteria. Further grouping of various load clusters

is done and this grouping will be based on the

limitation of HV equipment capacity.

In the design of main transformer and other

intermediate step down transformers, key point

to be noted are is ensuring continuity of power

supply till end customer. Thus, redundant or n+1

transformer design is normally considered. As

these transformers loading varies with time and

climatic factors, optimum loading factor and

diversity factor are chosen such that transformer

is neither overloaded during peak utilization nor

under loaded during off peak conditions and

transformer losses are kept minimal.

Continuity of supply is achieved by means

of Ring Main Units (RMU) looped to form a

ring network. Ring network is designed with

self-healing technology, which is achieved

through fault passage indicators and auto

sectionalisers for quick identification of fault

and restoration of supply.

Selection of incoming voltage level and other

intermediate voltages play a significant aspect in the

design which influences the distribution network

topology. Incoming voltage levels are normally

selected based on the total demand requirement,

and proximity of the source substations.

Power Distribution Automation

In order to achieve energy efficient power

supply system with high quality and reliability,

real time centralized control and monitoring is of

paramount importance. They are met by means of

integrated and intelligent power distribution

automation. Thus, following automation facilities

are provided for the power distribution of the city:

Main receiving substation automation

through IEC 61850 compatible SCADA system

Sub transmission substation automation by

means of micro SCADA with IEC 61850

compatibility

Distribution automation through fault

passage indicators, self healing system and

smart RTUs

LV distribution automation through smart

panels

Utility automation through intelligent MCCs

and smart panels

Home automation through Automated

Metering Infrastructure (AMI)

Smart lighting controls.

ConclusionDevelopment of Smart Cities is an ambitious

plan rolled out by the Indian Government with an

aim to improve the quality of life of people and

improve the growth of the nation.

To fulfill the smart city mission, the key

challenge for power providers/distributors would

be to create a balance between the supply and

demand of electric power. Robust demand

estimation techniques, efficient power distribution

system with energy efficient technologies are

essential for implementing an economical and

stable power supply system in smart cities.

Figure 6: Stages Involved in a Bottom up Nodal Methodology

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