Load For Casting & Characteristics of Loads

8/2/2019 Load For Casting & Characteristics of Loads

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LOAD FORCASTING & CHARACTERISTICS OF

LOADS

Submitted to-

Mrs.Suman bhullar

From-

Amandeep Gill

ME-PSED

Roll-801041001


2/23

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


3/23

Characteristics of loads


4/23

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


5/23

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


6/23


7/23

Factors for accurate forecasts

Weather influence

Time factors

Customer classes


8/23

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


9/23

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


10/23

Customer Class

Electric utilities usually serve different

types of customers such as residential,

commercial, and industrial.


11/23

Short term forecasts

(one hour to a week)

Medium forecasts

(a month up to a year)

Long term forecasts

(over one year)

Load Forecasts


12/23

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


13/23

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.


14/23

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


16/23

Scatter Plot of the Actual Load

Vs the Model


17/23

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


19/23

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


20/23

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


21/23

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.


22/23

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


23/23

THANK YOU

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Load For Casting & Characteristics of Loads

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