Load forecasting

Power System Planning and Reliability
Module-1: Load Forecasting

Divya M
Dept. of Electrical Engineering
FCRIT, Vashi

Power system planning
 Definition
 A process in which the aim is to decide on new as well as
upgrading existing system elements, to adequately satisfy the
loads for a foreseen future
 Elements can be:
• Generation facilities
• Substations
• Transmission lines and/or cables
• Capacitors/Reactors
• Etc.

PSPR Lecture-1 (Seifi & Sepasian)

Power system planning
 Decision should be
• Where to allocate the element (for instance, the sending and
receiving end of a line),
• When to install the element (for instance, 2020),
• What to select, in terms of the element speciﬁcations (for
instance, number of bundles and conductor type).
 The loads should be adequately satisﬁed.


Load forecasting
 The first crucial step for any planning study
 Forecasting refers to the prediction of the load behaviour for
the future
 Words such as, demand and consumption are also used instead
of electric load
 Energy (MWh, kWh) and power (MW,kW) are the two basic
parameters of a load.
 By load, we mean the power.
 Demand forecast
• To determine capacity of generation, transmission and
distribution required
 Energy forecast
• To determine the type of generation facilities required


Load curves
 Variations in load on a power station from time to time
• Daily load curves
• Monthly load curves
• Annual load curves
 Load curve gives:
• Variation of load during different time
• Total no. of units generated
• Maximum demand
• Average load on a power station
• Load factor

PSPR Lecture-1 (Pabla)

Daily load curve - example

PSPR Lecture-1 www.nationalgrid.com

Nature of loads
 Load characteristics: Max. demand
Demand factor
 Demand factor Connected load
Avg . demand
 Load factor Load factor
Max. demand

 Diversity factor Diversity factor
Sum of individual max . demands
Max. demand of power station
 Utilization factor Max. demand on power station
Utilisation factor
 Power factor Rated capacity of power station

• Higher the values of load factor and diversity factor, lower will be
the overall cost per unit generated.
• Higher the diversity factor of the loads, the fixed charges due to
capital investment will be reduced.


Types of loads
 Five broad categories:
 Domestic
• Demand factor: 70-100%
• Diversity factor: 1.2-1.3
• Load factor: 10-15%
 Commercial
• Diversity factor: 1.1-1.2
 Industrial
• Small-scale: 0-20 kW
• Medium-scale: 20-100 kW
• Large-scale: 100 kW and above
– Demand factor: 70-80%
– Load factor: 60-65%


Types of loads
 Agricultural
• Diversity factor: 1-1.5
 Other loads
• Street lights, bulk supplies, traction etc.
 Commercial and agricultural loads are characterized by
seasonal variations.
 Industrial loads are base loads and are little weather
dependent.


Numerical
 A power plant supplies the following loads with maximum demand
as below:
Type of load Max. demand (MW)
Industries 100
Domestic 15
Commercial 12
Agriculture 20
The maximum demand on the power station is 110 MW. The total units
generated in the year is 350 GWh.
Calculate:
• Yearly load factor
• Diversity factor


Electrical load growth
 Reasons for the growth of peak demand and energy usage within an
electric utility system:
• New customer additions
– Load will increase if more customers are buying the utility's product.
– New construction and a net population in-migration to the area will add
new customers and increase peak load.
• New uses of electricity
– Existing customers may add new appliances (replacing gas heaters with
electric) or replace existing equipment with improved devices that require
more power.
– With every customer buying more electricity, the peak load and annual
energy sales will most likely increase.

PSPR Lecture-2 (Willis)

Planning and electrical load growth

 Load growth caused by new customers who are locating in previously
vacant areas.
• Such growth leads to new construction and hence draws the planner's
attention.
 Changes in usage among existing customers
• Increase in per capita consumption is spread widely over areas with
existing facilities already in place, and the growth rate is slow.
• Difficult type of growth to accommodate, because the planner has
facilities in place that must be rearranged, reinforced, and upgraded.
This presents a very difficult planning problem.

PSPR Lecture-2 (Willis)

Factors affecting load forecasting
 Time factors such as:
• Hours of the day (day/night)
• Day of the week (week day/weekend)
• Time of the year (season)
 Weather conditions (temperature and humidity)
 Class of customers (residential, commercial, industrial, agricultural,
public, etc.)
 Special events (TV programmes, public holidays, etc.)
 Population
 Economic indicators (per capita income, Gross National Product
(GNP), Gross Domestic Product (GDP), etc.)
 Trends in using new technologies
 Electricity price


Forecasting methodology
 Forecasting: systematic procedure for quantitatively defining future
loads.
 Classification depending on the time period:
• Short term
• Intermediate
• Long term
 Forecast will imply an intermediate-range forecast
• Planning for the addition of new generation, transmission and
distribution facilities must begin 4-10 years in advance of the actual in-
service date.

PSPR Lecture-2 (Sullivan)

Forecasting techniques
 Three broad categories based on:
• Extrapolation
– Time series method
– Use historical data as the basis of estimating future outcomes.

• Correlation
– Econometric forecasting method
– identify the underlying factors that might influence the variable
that is being forecast.

• Combination of both


Extrapolation
 Based on curve fitting to previous data available.
 With the trend curve obtained from curve fitted load can be
forecasted at any future point.
 Simple method and reliable in some cases.
 Deterministic extrapolation:
• Errors in data available and errors in curve fitting are not accounted.
 Probabilistic extrapolation
• Accuracy of the forecast available is tested using statistical measures
such as mean and variance.


Extrapolation
 Standard analytical functions used in trend curve fitting are:
• Straight line: y a bx

• Parabola: y a bx cx 2

2 3
• s curve: y a bx cx dx

• Exponential: y ce dx

• Gompertz: y ln 1 (a ce dx )
 Best trend curve is obtained using regression analysis.
 Best estimate may be obtained using equation of the best trend
curve.


Correlation
 Relates system loads to various demographic and economic factors.
 Knowledge about the interrelationship between nature of load
growth and other measurable factors.
 Forecasting demographic and economic factors is a difficult task.

 No forecasting method is effective in all situations.
 Designer must have good judgment and experience to make a
forecasting method effective.


Impact of weather in load forecasting
 Weather causes variations in domestic load, public lighting,
commercial loads etc.
 Main weather variables that affect the power consumption are:
• Temperature
• Cloud cover
• Visibility
• precipitation
 First two factors affect the heating/cooling loads
 Others affect lighting loads


 Average temperature is the most significant weather dependent
factor that influences load variations.
 Temperature and load are not linearly related.
 Non-linearity is further complicated by the influence of
• Humidity
• Extended periods of extreme heat or cold spells
 In load forecast models proper temperature ranges and
representative average temperatures which cover all regions of the
area served by the electric utility should be selected.


 Cloud cover is measured in terms of:
• height of cloud cover
• Thickness
• Cloud amount
• Time of occurrence and duration before crossing over a population
area.
 Visibility measurements are made in terms of meters/kilometers
with fog indication.

 To determine impact of weather variables on load demand, it is
essential to analyze data concerning different weather variables
through the cross-section of area served by utility and calculate
weighted averages for incorporation in the modeling.


Energy forecasting
 To arrive at a total energy forecast, the forecasts for residential,
commercial and industrial customers are forecasted separately and
then combined.


Residential sales forecast
 Population method
• Residential energy requirements are dependent on:
– Residential customers
– Population per customer
– Per capita energy consumption
• To forecast these factors:
– Simple curve fitting
– Regression analysis

• Multiplying the three factors gives the forecast of residential sales.


Residential sales forecast
 Synthetic method
• Detailed look at each customer
• Major factors are:
– Saturation level of major appliances
– Average energy consumption per appliance
– Residential customers
• Forecast these factors using extrapolation.
• Multiplying the three factors gives the forecast of residential sales.


Commercial sales forecast
 Commercial establishments are service oriented.
 Growth patterns are related closely to growth patterns in residential
sales.
 Method 1:
• Extrapolate historical commercial sales which is frequently available.
 Method 2:
• Extrapolate the ratio of commercial to residential sales into the future.
• Multiply this forecast by residential sales forecast.


Industrial sales forecast
 Industrial sales are very closely tied to the overall economy.
 Economy is unpredictable over selected periods
 Method 1:
• Multiply forecasted production levels by forecasted energy
consumption per unit of production.
 Method 2:
• Multiply forecasted number of industrial workers by forecasted energy
consumption per worker.


Peak load forecasting
 Extrapolate historical demand data
• Weather conditions can be included
 Basic approach for weekly peak demand forecast is:
1. Determine seasonal weather load model.
2. Separate historical weather-sensitive and non-weather sensitive
components of weekly peak demand using weather load model.
3. Forecast mean and variance of non-weather-sensitive component of
demand.
4. Extrapolate weather load model and forecast mean and variance of
weather sensitive component.
5. Determine mean, variance and density function of total weekly
forecast.
6. Calculate density function of monthly/annual forecast.


Peak load forecasting
 Assume that the seasonal variations of the peak demand are primarily due
to weather.
 Otherwise, before step-3 can be undertaken, any additional seasonal
variation remaining after weather-sensitive variations must be removed
 To use the proposed forecasting method, a data base of at least 12 years is
recommended.
 To develop weather load models daily peaks and coincident weather
variable values are needed.


Weather load model
 Plot a scatter diagram of daily peaks versus an appropriate weather
variables.
• Dry-bulb temperature and humidity
• Using curve fitting three line segments can be defined in the example

w k s (T Ts ) if T Ts
k w (T Tw ) if T Tw
0 if Tw T Ts

Parameters of the model:
• Slopes: ks and kw
• Threshold temperatures: Ts
and Tw


Separating weather-sensitive and non-
weather sensitive components
 From the weather load model
• Weather-sensitive (WS) component of weekly peak load demand data is
calculated from the weekly peak coincident dry-bulb temperatures.
• Non-weather-sensitive (NWS) component of peak demand is obtained by
subtracting the first component from historical data.
• NWS component is used in step-3, of basic approach for weekly peak demand
forecast , to forecast the mean and variance of the NWS component of future
weekly peak demands.


Reactive load forecasting


Total forecast


Annual peak demand forecast


Monthly peak demand forecast


References
 “Electric Power System Planning: Issues, Algorithms and Solutions”,
Hossein Seifi and Mohammad Sadegh Sepasian, Springer-Verlag
Berlin Heidelberg, 2011.
 “Electrical Power Systems Planning”, A.S. Pabla, Macmillan India Ltd.,
1988.
 “Power System Planning”, R.L. Sullivan, McGraw-Hill International
 “Power Distribution Planning Reference Book”, H. Lee Willis, Marcel
Dekker Inc.

Weather sensitive load

w k w (T Tw )

kW kS

w k s (T Ts )
D0

TW TS
Temperature

Load forecasting

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