Sector Update | 17 February 2017
Refer to our report
on Utilities, July 2016
A study of electricity demand pattern by POSOCO
Key highlights and our views
India is an evening peak load country; renewable energy (RE) is displacing coal
consumption but increasing the requirement of ancillary services, keeping the
overall requirements for other power generation capacities unchanged.
A more efficient management by DISCOMs will reduce their requirements for
base load PPAs. Base load capacities should not be more than 70-80% of the
peak load; DISCOMs currently secure PPAs for more than 100% of their peak
load. States have signed too many PPAs, in our view.
Per capita income is the key driver of electricity consumption. Odisha and
Chhattisgarh are exceptions.
Cities have higher per unit power purchase cost due to high variability in load.
Load pattern analysis is an important tool to set tariffs (rarely used in India).
Also, there is a need to introduce time-of-the-day tariff to smoothen load curve.
POSOCO, the agency that facilitates the transfer of power across India, recently put
out a study on India’s electricity demand pattern. Study of load patterns is an
important tool to optimize electricity sourcing and set tariffs. This is the first such
comprehensive study for India. We list below highlights from the report and our
views/implications from the same (if any).
India is evening peak country; RE cannot displace conventional generation
Electricity consumption does not happen at a constant rate in a typical day.
Consumption varies based on time-of-day, weather and profile of the consumer.
Electricity consumption in India peaks in the evening (5:00-9:00 PM), that is when
residential lightning and cooling consumption rises and commercial sector
consumption just starts to decline.
Solar energy is not available in the evening and wind energy is intermittent. Thus,
irrespective of the renewable energy (RE) capacity addition, India will still require
conventional generation capacity addition to meet its evening peak demand until
storage of energy becomes commercially viable.
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8 August 2016
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Sanjay Jain
(; +91 22 3982 5412
Dhruv Muchhal
(; +91 22 3027 8033

Exhibit 1: Typical all-India load curve
Source: POSOCO
States need to optimize their power sourcing, have signed enough PPAs
Base-load generation capacities should typically cover only 70-80% of the load
duration curve. A load duration curve is a graphical representation of number of
hours a particular level of load is required in a day. For instance, if the peak load is
100MW, but for 80% of time, the load is 60-70MW, the base-load generation
capacity should be 60-70MW. The rest of the capacity should be mid/peak
capacities (like gas/hydro). This helps in ensuring optimum PLFs and economics.
Exhibit 2: Load duration curve
Source: POSOCO
17 February 2017

Exhibit 3: India’s demand (load) duration curve
Source: POSOCO
Based on India’s FY16 load duration curve, the optimum base-load capacities should
not be more than ~115GW. This compares to coal/lignite/nuclear installed capacity
of ~150GW, suggesting significant over-supply.
A quick extrapolation of 115GW at 7% per year suggests base-load capacity
requirement of 150GW by FY20. The coal/lignite/nuclear capacity base is likely to
increase to ~225GW by FY20, based on projects already under construction. It
implies that the over-supply problem will continue to linger for long.
Income drives consumption; Odisha and Chhattisgarh are outliers
There is a good correlation between per capita income and electricity consumption.
Higher income drives demand for applications like air-conditioners, refrigerators and
water heaters, which drives demand for electricity. Delhi, Chandigarh, Maharashtra,
Tamil Nadu and Goa that have relatively high per capita income also show relatively
higher per capita electricity consumption. Odisha and Chhattisgarh are outliers.
Despite significantly low per capita income, the electricity consumption is similar to
that of the higher income regions. This is probably due to concentration of
electricity-intensive industries like aluminum and steel in the states.
17 February 2017

Exhibit 4: Per capita electricity consumption to per capita income (FY14)
Source: POSOCO
Split by regions, the western region had the highest per capita electricity
consumption at 1,393kWh. Consumption in the East was the lowest at 618kWh.
Exhibit 5: Per capita electricity consumption for different regions (FY15)
Sr. No.
All India
Per capita electricity consumption (in kWh)
Source: POSOCO
Consumer profile should drive power tariffs, reason cities have higher tariff
In cities, the residential and commercial categories are the major consumers of
electricity. Demand in residential, and to some extent commercial, is concentrated
for a few hours during the day, unlike industrial consumption that is more
consistent. Seasonal patterns like cooling demand in summers and lean night hours
in winters create wide gaps with respect to the average demand curve in cities.
For instance, Delhi’s average daily demand to peak demand ratio was ~80% as
compared to India average ratio of ~92% in FY16. A comparison of demand variation
(ratio of gap between maximum and minimum demand to maximum demand) for
Delhi and Maharashtra is another example. Delhi has higher demand variation than
Maharashtra, as Maharashtra has higher share of industrial consumers that have a
more stable consumption profile. Cities like Delhi have to depend on a higher share
of peaking power capacities or have their capacities run at lower PLFs. This drives
per unit cost higher with respect to other regions.
The point we are trying to make here is that because residential/commercial
customers have more volatile and variable load curve, the tariffs for them should be
generally higher than for industrial users that are more consistent. This is not the
case in India, where industrial consumers are asked to subsidize residential users.
We need a serious re-look at our tariff structure. We believe it is not the losses of
DISCOMs that are pulling down India’s electricity growth potential, but such
inverted tariff structure.
17 February 2017

Exhibit 6: Demand variation for Delhi and Maharashtra (FY16)
Demand variation = (Max demand – Min demand) / Max Demand
Source: POSOCO
Exhibit 7: Load characteristics by consumer type
Source: POSOCO
Pace of evening ramp-up varies; RE will increase ramp-up even further; can
thermal cope?
While electricity demand in India peaks in the evening, the pace of ramp-up to the
peak is significantly different. The eastern region has a significantly sharp ramp-up.
The northern region has a gradual ramp-up. The western and southern regions have
a sharp but smaller ramp-up. The ramp-up is different even across seasons. Winter
months have a sharper ramp-up.
A shaper ramp-up implies coal plants need to heat faster or that flexibility of hydro
generation is required. As the share of RE increases, which generates primarily in
the noon, the ramp-up required from coal plants will be even higher. Based on our
understanding, coal plants are not designed to significantly alter their generation
profile. If the share of RE has to increase, either dependence on hydro for peaking
load will increase or gas-based plants will have to be used (despite their higher cost).
17 February 2017

Exhibit 8: Load characteristic by region; evening peak ramp-up differs by region
Source: POSOCO
Exhibit 9: Load profile in summer
Exhibit 10: Load profile in winter
Source: POSOCO
Source: POSOCO
17 February 2017

17 February 2017

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