Load Forecasting and economic load dispatch

Dr. R. Senthil Kumar
Deputy Director
NPTI(ER), Durgapur
LOAD FORECASTING

Objectives of load forecasting
•To know the peak load of system
•Energy requirement in day, month and year
•To know the load duration curve
•Supply side management
•To estimate the proper investment requirement
•Demand side management

Load Demand
The load demand of an area depends upon –
1.Its terrain
2.Its population
3.Their living standards
4.Its present and future development plans
5.Cost of power

Load characteristics
Basic definition –
a. Demand Factor = maximum demand /
connected load of a consumer
b. Load Factor - Ratio of average demand to maximum
demand.
c. Utilization factor - Ratio of maximum demand to the
rated capacity of the system.

Load characteristics
d. Diversity factor -Ratio of sum of maximum power
demands to the maximum demand of the system.
Types of load -
1. Domestic – demand factor- 70-100%
diversity factor- 1.2 to 1.3
load factor- 10 to 15%.

Types of Load
2. Commercial - demand factor-90-100%
diversity factor-1.1 to 1.2
load factor-25 to 30%
3. Industrial - demand factor-70-80%
4. Municipal - demand factor-100%
diversity factor-1.0

Types of Load
5. Agriculture load - demand factor-90-100%
diversity factor-1 to1.5

System Power Factor
Measurement of angular difference between voltage and
current.
Pf varies from 0 to 1.
Pf =0, when phase angle is 90.
Pf=1, when phase angle is 0.

Power factor of various equipment
Fluorescent lamps 0.6-0.8
Fans 0.5-0.8
Induction motors 0.55-0.85
Refrigerator 0.65
Tube light 0.5-0.9
Textile 0.65-0.75
Cement 0.8-0.85

System load diversity
• Evaluate the diversity of the load in different states or regions
to plan for peak power demand.
Ex-Himachal Pradesh and Punjab.
• Important to determine system load sensitivity with respect to
variation of system voltage and frequency.

Feeder load characteristics
Types of load composition of feeder-
1.Domestic
2.Commercial
3.Industrial
4.Agriculture
• Percentage load of each category in the total demand
depends upon-
1.Time of year.
2.Time of day.
3.Geographical location.
4.Socio-economic conditions.
5.Diversity factors.

System load
System load may be divided into –
1.Motive load
2.Heating load
3.Lighting load
4.System losses.
• At transmission level load profile in an almost predictable
situation.

Connected Loads
• Industrial loads-
a. Cottage industries - very small electrical motors ranging from
¼ to ½ kW average power.
b. Small scale industries-ranging from sugar mill 0.5 kW per
tonne of sugarcane to arc furnace 350 per tonne steel.

Connected Loads
• Street lighting - determined separately for each town.
IS-1944 gives code of practice for the design of public
lighting.
• Water supply - load estimated from the water
requirement.
Rural areas - 50 litres/day/person
Semi-rural areas - 70 litres/day/person
For urban area - 90 litres/day/person
Metropolitan cities - 120 litres/day/person

• Irrigation - load depends on the
1. Number of waterings required for crop
2. Area under the crops
3. Nature of soil
4. Depth of water level.
Connected Loads

Ground water
level
HP for 20 L/s
discharge
HP for 40 L/s
discharge
0-2 3 5
2-5 3.5 5-7.5
5-10 5 7.5
10-15 7.5 10
20-30 15-20 25
Connected Loads

• Dirty loads or non-linear loads - known for producing
harmonics, voltage fluctuation and large variation in the
demand for reactive power.
• Computers, TVs, large motors, railway traction and other
heavy loads.
• Remedial measures are-
1. Underground feeder circuits
2. Supply high voltage and provide parallel transformers.
3. Provide capacitors
Connected Loads

Load Forecasting
Prediction
 Statement or guess, Beyond control
 Ex-Rain or bad weather
Forecasting
 What will happen if certain trends/events continue
 Causes of Events Can be controlled
Forecasting in Electricity Industry
 tremendous importance due to large investments
involved
 medium range forecasting
 real time forecasting
 long term forecasting
 conservative estimates vs. optimistic estimates

Importance of Load Forecasting
Forecasting gives magnitude and location of loads
Accurate model helps in
1)Economic size of plant and apparatus at
correct time and place.
2)Generation authorities plan their water
and fuel requirements and the
generator allocation schedules.

Importance of Load Forecasting
3)Load forecasting helps an electric utility to make important
decisions including decisions on purchasing and generating
electric power, load switching, and infrastructure
development.
4)Load forecasts are extremely
important for energy suppliers, ISOs,
financial institutions, and other
participants in electric energy
generation, transmission, distribution, and
markets.

Types of Load Forecasting
Load forecasts can be divided into three categories:
 short-term forecasts
(usually from one hour to one week)
 medium forecasts
(which are usually from a week to a year)
 long-term forecasts
(longer than a year)
The forecasts for different time horizons are important for different
operations within a utility company. The Natures f these forecasts
are different as well.

De-regulation and Forecasting
Load forecasting has always been important for planning and
operational decision conducted by utility companies.
However, with the deregulation of the energy industries, load
forecasting is even more important.
 With supply and demand fluctuating and the changes of
weather conditions and energy prices increasing by a factor
of ten or more during peak situations, load forecasting is vitally
important for utilities.

Concepts
 Time series (based on historical datas)
 Sampling frequency (nyquist frequency)
 Sampling theory
 Trend analysis
 Correlation Theory

Times Series
• Typical power system load curves can be represented as
Y=T×C×S×I Y=T+C+S+I
multiplicative model additive model
Where
T = long term trend
C= cyclical trend (mainly over many years)
S = seasonal trend (1 year cycle)
I = irregularr movements(noise)*
*In part due to temperature effects

Regression/Trend Analysis
 Trend Analysis
Study of behaviour of a time series or a process in the past and
its mathematical modelling so that future behaviour can be
extrapolated from it.
 Gives nature of relationships between the variables, where
as correlation analysis measures the degree of relationship
between the variables.
Approaches to Trend Analysis
1.Fitting continuous mathematical functions through actual
data
2.Fitting of a sequence on discontinuous lines or curves to data
(short term forecasting)

Trend analysis
 Typical regression curves used in power system forecasting
Linear y=A+Bx
Exponential y=A(1+B)x
Power y=AxB
Polynomial y=A+Bx+Cx2
Method of Least squares
 Can be used either to fit a straight line trend or a parabolic
Trend

Correlation Theory
It is the measure of how one variable is related to another.
Methods
1. Scatter Diagram
2. Karl Pearson’s coefficient of Correlation
3. Spearman’s Rank correlation coefficient
4. Method of least squares
 Coeeficient of correlation is also called “goodness of fit”
 Karl pearson’s coeeficient of correlation
∑(X-Xav). (Y-Yav)
r= -------------------------
(∑(X-Xav
)2.
∑(Y-Yav)2
)1/2

Summary of Fundamental steps
1)Collection of data (reliability)
2)Draw a graph
3)Construct a long-term trend
4)Sesoanl index if it exists…deasonalize the data
5)Adjust data for the trend

Factors in Power System Loading
Econometric
Single factor modeling
Forecast of system peak
Capacity forecast model
Strategic forecasting
Spatial load forecasting

ECONOMETRIC
Certain factors under econometric are
• Business and economic cycle (cyclic Variation)
• Growth of GNP (Long term variation)
• Growth in population (Long term trend)
Most of these factors effect the long term trend and
not effect normal model based on past history.

Single Factor Modeling
Single factor modeling is based
on a model that assumes one
dominant factor, determines
the model outcome.

Defects in Single factor modeling
• It is too general
• Uneven distribution may Bias the forecasting values
as the respective values of one parameter may be
less or more in some other parameter.
• As the rate of growth of different sectors is not
same hence it is not comprehensive

System Peak
It is given by the formula :-
Annual System Peak = Energy Requirement
8760 x Load Factor
The load factor here is plotted against time series to
have a trend graph.

It is observed that load factors change a lot with
changes in economic activities although the
relationship is not linear.
For any year system peak is given as : -
P1 = K + dR1 + cC1 + f I1
The value can be determined by method of least
squares

Where
P = Monthly or Yearly peak
R = Monthly or Yearly domestic sales
I = Monthly or Yearly Industrial sales
C = Monthly or Yearly Commercial sales
K = an additional Constant

• Relation of GDP to energy consumption is an
important indicator.
• The elasticity of consumption with respect to GDp
for India in 1980 – 1992 was 1.61
This implies that increase in GDP of 1 % will
increase 1.61 % of electricity consumption.

Capacity forecast Model
As the forecast for electrical energy is on national
level, in this the national projections is converted to
regional peaks.
From this the regional capacity requirements are
made, removing the current generation and
planned capacity addition there.
Finally addition of planned retirement of units gives
the net new capacity to be added.

kWh Sales Forecast
Regional kWh
Generation
kWh Peak
Net Peak Demand
Required Capacity
Capacity to be
Planned
System Losses Regional
Distribution
Load Factor
Demand Side
Management
Reserve Margin
Retirement of
Generating Units
Imports
Planned
additions

Strategic Forecasting
It involves thorough examination of factors effecting
future growth.
It requires following : -
• Consumers current demand
• Consumers potential demand
• Competitiveness in market
• Availability of alternatives

In industry : -
Strategic management combines
• Econometric
• Technological detail
Strategic Management provides : -
• Future Assessment
• They provide planners for shaping up the future.

Spatial Forecasting
This method breaks down to geographically and
consumer oriented forecasting.
A land use map can be converted to electric load by
using kW per acre of load curves on land use class
basis.

Increase in Specific
Consumption
Increase in No. of
Connections
Electricity
Demand Growth
Diversity Load Factor
Transmission and
Sub- Transmission
Consumers
(-)
(Extrapolation)

Forecasting for growth in regions : -
• Inner city – Increase in Specific Consumption
• Agriculture –
1. Projections of land irrigation
2. Prospective agricultural consumers
3. Availability of land water
• Industrial –
1. Diversification of business
2. New consumers
3. Change in production process

Technological forecasting
It consists of two parts : -
1. The Delphi Technique
2. Scenario Technique

Delphi Technique
• A product of RAND Corporation
• Uses experts in the field.
It has three main features: -
1. Anonymity
2. Questions raised to all members in panel
3. Panel director receives information and edits it to
infer from it.

Scenario Technique
• Method to view future in Quantitative form
As follows : -
1. Possible sequence is developed using narrative
description.
2. Events sequentially recorded
3. All possible outcomes investigated

HARMONICS
Harmonic
currents are
generated by the
use of :
Rectifiers
 Inverters
Thyristor controlled
variable speed drives
Induction furnaces
Arc furnaces
Fluorescent lamps
TVs
UPS & Computers etc.

Why Harmonics
These current result
due to the fact that
the device either has
an impedance which
varies during each
half cycle of applied
emf or it generate a
back emf of non
sinusoidal shape.
Result - Distortion of
the Wave shape .
As per IS:325-1996
limit is 5% at any
instant in supply
voltage up to 11 KV.

Harmonic Analysis
Harmonic analysis can also be carried out on the
network itself with aid of a HARMONIC ANALYZER .
The waveform is sampled and the analyzer sweps
through the range of frequencies and tunes out
various frequencies.
Harmonic penetration studies s/w are also
available.
Total Harmonic Distortion (THD) is expressed as a
%age of fundamental voltage by the expression:
THD(%) = 100 * SQRT[(V2
2
+ V3
2
+ V4
2
+ ... + Vn
2
)] / Vt
Where V2 = Fundamental Frequency voltage component.
Vn = nth harmonic voltage component

3rd Harmonic
The 3rd harmonics is the
most prevalent and is
generally of highest
magnitude . Delta/Star
connected Xmers are
excellent natural filters
for the 3rd harmonics .

In a balanced 3ph system, the triple
harmonics (3rd, 6th ,9th etc) have the
same instantaneous magnitude in each
ph. When triple harmonics appear in a
low-voltage system, they can overload
the neutral conductor. Theses also
circulate in the delta windings of the DT.
The 5th,7th,11th &13th harmonics can
cause resonance in capacitor banks
and are a source of noise in
communication circuits.

Effect of Harmonics on Power
Distribution Networks
Overloading of power factor correction
capacitors due to tuning for a particular frequency.
Resonance between capacitances and Xmer
reactances resulting in excessive voltages or currents.
Interference with telephone circuits and
broadcasting due to zero sequence harmonic currents.
Malfunctioning of control equipment as a result
of distorted waveform affecting firing position of
thyristor circuits.

Overheating of rotating MCs and overheating of
conductors as the skin effect increases with the
frequency increase.
Overloading of the Delta connected windings of
Xmers on account of either excessive 3rd harmonics.
Overloading of neutral conductor of low
voltage distribution system where large 1ph
electronic loads are connected.
Corruption of data in computers.
Metering errors in Electro-mechanical rotating
meters.

Flow of Harmonic Currents
All the non –linear loads
dram non-sinusoidal
currents, causing distortion
in the voltage waveform,
not only within the
individual plant or
equipment, but also in the
network supplying power to
it, i.e. in Xmers, Lines, CBs,
Motors etc and this effect is
to be limited to safeguard
the operations of the power
equipments in the N/w.

LIMITS ON HARMONICS AT
COMMON COUPLING IN
VARIOUS COUNTIRES
Country
System
Voltage
level (KV)
Total
Harmonic
voltage
distortion %
(THD)
Individual
Harmonic
voltage
distortion %
(THD)
Odd
Even
Australia 33 5.0 4.0 2.0
110 1.5 1.0 0.5
Sweden 36-72 4 - -
Finland 110 1.5 1.0 1.0
U.K. 33-66 3.0 2.0 1.0

Maximum harmonics observed in
India in case of some consumers

Measurement of Harmonics
Microprocessor based electronic
meters can measure ( individual as
well as total % THD ) are being
installed at feeders from the sub-
station, and for industrial,
commercial or other consumers with
predominant non linear load.
The consumers should be levied a
penalty for any harmonics abovee
stipulated limits.
Modern electronic equipment have
a low power factor (average 0.65
lagging) mainly due to harmonics

Filters
A series- tuned harmonic filter
consists of a capacitor bank
with a reactor (inductor) in series
with it. The series combination
provides a low impedance path
for a specific harmonic
component, there by minimizing
harmonic voltage distortion
problems.
The filter is tuned slightly below
the harmonic frequency of
concern.

Harmonics in India
The massive electrification program undertaken by
the Indian Railways at the rate of 800-1000 route kms
per year. The modern thyristor controlled locomotives
add to the harmonics.
Increasingly week supply system, on account of the
Demand –Supply miss match, due to the rapid growth
of demand.
Increased thrust on energy conservation, leading
to the rapid proliferation of several electronic energy
conservation devices such as electronic fan
regulators, electronic chokes for tube light etc.

Power System Economic Operation
• Power system loads are cyclical.
• Therefore the installed generation capacity is
usually much greater than the current load.
• This means that there are typically many ways we
could meet the current load.
• Since different states have different mixes of
generation, we will consider how generally to
minimize the variable operating costs given an
arbitrary, specified portfolio of generators.
70

Thermal versus Other Generation
 The main types of generating units are thermal and hydro, with
wind and solar rapidly growing.
 For hydro the fuel (water) is free but there may be many
constraints on operation:
– fixed amounts of water available,
– reservoir levels must be managed and coordinated,
– downstream flow rates for fish and navigation.
 Hydro optimization is typically longer term (many months or
years).
 We will concentrate on dispatchable thermal units, looking at
short-term optimization:
 Non-dispatchable wind and solar can be incorporated by
subtracting from load.
71

Generator types
 Traditionally utilities have had three broad groups of generators:
– “Baseload” units: large coal/nuclear; almost always on at max.
– “Midload,” ‘intermediate,” or “cycling” units: smaller coal or gas that
cycle on/off daily or weekly.
– “Peaker” units: combustion turbines used only for several hours. during
periods of high demand
72

Block Diagram of Thermal Unit
•To optimize generation costs we need to develop
cost relationships between net power out and
operating costs.
•Between 2-10% of power is used within the
generating plant; this is known as the auxiliary 73

Thermal generator Cost Curves
 Thermal generator costs are typically represented by one or
other of the following four curves
– input/output (I/O) curve
– fuel-cost curve
– heat-rate curve
– incremental cost curve
 For reference
- 1 Btu (British thermal unit) = 1054 J
- 1 MBtu = 1x106
Btu
- 1 MBtu = 0.29 MWh
74

I/O Curve
 The IO curve plots fuel input (in MBtu/hr) versus net MW
output.
75

Fuel-cost Curve
 The fuel-cost curve is the I/O curve multiplied by fuel cost.
 A typical cost for coal is $ 1.70/MBtu.
76

Heat-rate Curve
• Plots the average number of MBtu/hr of fuel input needed per
MW of output.
• Heat-rate curve is the I/O curve divided by MW.
Best heat-rate for most efficient coal
units is around 9.0
77

Incremental (Marginal) cost Curve
 Plots the incremental $/MWh as a function of MW.
 Found by differentiating the cost curve.
78

Load Forecasting and economic load dispatch

Load Forecasting and economic load dispatch

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