load for casting & characteristics of loads
TRANSCRIPT
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LOAD FORCASTING & CHARACTERISTICS OF
LOADS
Submitted to-
Mrs.Suman bhullar
From-
Amandeep Gill
ME-PSED
Roll-801041001
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Classification of loads
Load means demand or energy
Demand is the time rate of energy
Loads are classified as given below
Resenditial load
Commercial load
Industrial load
Other loads
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Characteristics of loads
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Forecast
Forecast refers to projected load requirments
determined using a systematic process of
defining future loads in sufficient quantitative
detail to permit system expansion decission
Planning in advance for gestation period
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Forcasting methodology
Extrapolation:-it involve fitting trend curves to basic historical
data adjusted to reflect the growth trend itself
- Deterministic- Probabilistic
- stochastic
Correlation:-
it relate system loads to various DEMOGRAPHIC
& ECONOMIC factors
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Factors for accurate forecasts
Weather influence
Time factors
Customer classes
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Weather Influence
Electric load has an obvious correlation to
weather. The most important variables
responsible in load changes are:
Dry and wet bulb temperature Dew point
Humidity
Wind Speed / Wind Direction
Sky Cover
Sunshine
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Time factors
In the forecasting model, we should also
consider time factors such as:
The day of the week
The hour of the day
Holidays
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Customer Class
Electric utilities usually serve different
types of customers such as residential,
commercial, and industrial.
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Short term forecasts
(one hour to a week)
Medium forecasts
(a month up to a year)
Long term forecasts
(over one year)
Load Forecasts
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Load Forecasting Categories
Short-term load forecasting
One hour ~ One week
Control and schedule power systemin everyday operations
Medium-term and Long-term loadforecasting
One week ~ longer than one year
Determine capacity of generation,transmission, distribution systems,type of facilities required intransmission ex ansion lannin
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Long Term Forecasting
The focus of this project was to forecast the
annual peak demand for distribution
substations and feeders.
Annual peak load is the value most important
toarea planning, since peak load most strongly
impacts capacity requirements.
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Model Description
The proposed method models electric power
demand for close geographic areas, load pockets
during the summer period. The model takes into
account:
Weather parameters (temperature, humidity, sky
cover, wind speed, and sunshine).
Day of the week and an hour during the day.
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model
A multiplicative model of the following
form was developed
L(t)=L(d(t),h(t))f(w(t))+R(t)
where:
L(d(t),h(t)) is the daily and hourly component
L(t) is the original load
f(w(t)) is the weather factor
R(t) is the random error
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Scatter Plot of the Actual Load
Vs the Model
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Weather Normalized Load Profiles
Weather Normalized Load Profiles
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours
MW
Sun Mon Tue Wed Thu Fri Sat
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Actual Load Profiles
Actual Load Profiles
0
50
100
150
200
250
300
350
400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours
MW
Sun Mon Tue Wed Thu Fri Sat
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Correlation Between the Actual Load
and the Model
Correlation between the Actual Load and the Model
0.955
0.96
0.965
0.97
0.975
0.98
0.985
0.99
1 2 3 4 5 6 7 8 9 10
Iteration
Correlation
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R-square Between the Actual
Load and the Model
Regression Output : R2
(defined as the proportion of variance of the response that is predictable
from the regressor variables)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
Iteration
R2
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Short Term Forecasting
We need STLF for problem like
1. unit commitment
2. economic dispatch
The regression model used is
,
,,0
i
titiw Xf t
whereXi,t-are non-linear functions of the appropriate
weather parameters.
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If minor error occur inve direction then
immediately start peaking unit which are inefficient
& costly If error occur in +ve direction then there is excessive
generation in hot reserve
A temp. difference of 2 degree can vary total load by
1 percent
Acurracy around 1 percent is desirable
So there is need of reliable weather forcast
Random factors
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THANK YOU