Power system planning involves arranging a scheme beforehand to adequately satisfy future load requirements. It determines new and upgraded generation, transmission, and distribution elements. Load forecasting is an important part of planning to estimate future loads. Short term forecasting is used for operations while long term forecasting informs infrastructure development decisions. Various statistical, artificial intelligence, and hybrid methods are used for load forecasting at different timescales, each with their own advantages and limitations regarding accuracy. Accurate load forecasting is essential for utility planning and operations.

1. MODULE III
Linss T

2. Syllabus

4. Power System Planning(Role of load
forecasting in power system planning)
• The word planning stems of the transitive verb to plan
meant as to arrange a method or scheme beforehand for
any work, enterprise, or proceeding.
• The aim here is to discuss the meanings of method or
scheme, beforehand and work, enterprise or proceeding
for a physical power system.
• In other words, we are going to discuss the power system
planning problem in terms of the issues involved from
various viewpoints; the methods to be used; the elements
to be affected the time horizon to be observed, etc.
• Power system planning issues may be looked at from
various viewpoints.

5. 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

6. – 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, 2025),
 What to select, in terms of the element
specifications (for instance, number of bundles and
conductor type).
– The loads should be adequately satisfied.

7. 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.

8. • Load forecasting has been an integral part in the efficient
planning, operation and maintenance of a power system.
• Short term load forecasting is necessary for the control and
scheduling operations of a power system and also acts as
inputs to the power analysis functions such as load flow
and contingency analysis.
• Owing to this importance, various methods have been
reported, that includes linear regression, exponential
smoothing, stochastic process, ARMA models, and data
mining models.
• Of late, artificial neural networks have been widely
employed for load forecasting.

9. • However, there exist large forecast errors using ANN when
there are rapid fluctuations in load and temperatures.
• In such cases, forecasting methods using fuzzy logic
approach have been employed
• Accurate models for electric power load forecasting are
essential to the operation and planning of a utility
company.
• Load forecasting helps an electric utility to make important
decisions including decisions on purchasing and generating
electric power, load switching, and infrastructure
development.

10. • Load forecasts are extremely important for energy
suppliers, ISOs, financial institutions, and other participants
in electric energy generation, transmission, distribution,
and markets.
• 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.

11. Comparison of electrical load
forecasting techniques
• Most of the forecasting methods use statistical techniques
or artificial intelligence algorithms such as regression,
neural networks, fuzzy logic, and expert systems.
• Two of the methods named trend analysis, end-use and
econometric approach are broadly used for medium- and
long-term forecasting.
• A variety of methods, which include the similar day
approach, various regression models, time series, neural
networks, statistical learning algorithms, fuzzy logic, and
expert systems, have been developed for short-term
forecasting.
• The method for short-term forecasting are similar day
approach, various regression models, time series, neural
networks, statistical learning algorithms, fuzzy logic, and
expert systems.

12. Similar day approach is based on searching
historical data of days of one, two or three years
having the similar characteristics to the day of
forecast.
 Regression is the one of most widely used
statistical techniques.
• For electric load forecasting, regression methods
are usually used to model the relationship of load
consumption and other factors such as weather, day
type, and customer class.
• There are several regression models for the next day
peak forecasting.
• Their models contain deterministic influences such
as holidays, random variables influences such as
average loads, and exogenous influences such as
weather.

13. Time series is a very popular approach for the electrical
load forecasting.
• Two important models of time series are ARMA and
ARIMA. ARMA and ARIMA use the time and load as the
only input parameters.
• Since load generally depends on the weather and time of
the day, ARIMAX is the most natural tool for load
forecasting among the classical time series models.
Artificial Neural Network , forecasting is based on the
pattern observed from the past event and estimates the
values for the future.
• ANN is well suited to forecasting for two reasons.
• First, it has been demonstrated that ANN are able to
approximate numerically any continuous function to be
desired accuracy. In this case the ANN is seen as
multivariate, nonlinear and nonparametric methods.

14. • Secondly, ANNs are date-driven methods, in the sense
that it is not necessary for the researcher to use
tentative models and then estimate their parameters.
• ANNs are able to automatically map the relationship
between input and output, they learn this relationship
and store this learning into their parameters.
Expert System is a computer program, which has the
ability to act as an expert.
• This means this computer program can reason, explain,
and have its knowledge base expanded as new
information becomes available to it. The load forecast
model is built using the knowledge about the load
forecast domain from an expert in the field.
• This knowledge is represented as facts and rules by
using the first predicate logic to represent the facts and
IF-THEN production rules. This representation is built in
what is called the knowledge base component of the
expert system.

15. • The search for solution or reasoning about the conclusion
drawn by the expert system is performed by the "Inference
Engine" component of the expert system.
• For any expert system it has to have the capability to trace its
reasoning if asked by the user. This facility is built through an
explanatory interface component.
 Fuzzy logic based on the usual Boolean logic which is used for
digital circuit design.
• In case of fuzzy logic, the input is related to the comparison
based on qualities.
• The advantage of fuzzy logic is that there is no need of
mathematical models for mapping between inputs and
outputs and also there is no need of precise or even noise free
inputs.
• Based on the general rules, properly designed fuzzy logic
systems are very strong for the electrical load forecasting

16. The methods for long- and medium-term forecasting are
trend analysis, end-use and econometric approach.
• The advantage of trend analysis is that it is quick, simple
and inexpensive to perform and does not require much
previous data.
• The basic idea of the end-use analysis is that the demand
for electricity depends what it use for (the end-use).
• The advantages of end-use analysis is that it identifies
exactly where electricity goes, how much is used for each
purpose, and the potential for additional conservation for
each end-use.
• The disadvantage of end-use analysis is that most end-use
models assume a constant relationship between electricity
and end-use (electricity per appliance, or electricity used
per dollar of industrial output).

17. • This might hold true over a few years, but over a 10-or 20-
year period, energy savings technology or energy prices
will undoubtedly change, and the relationships will not
remain constant.
• The advantages of econometrics are that it provides
detailed information on future levels of electricity
demand, why future electricity demand increases or
decreases, and how electricity demand is affected by
various factors.
• A disadvantage of econometric forecasting is that in order
for an econometric forecast to be accurate, the changes in
electricity demand caused by changes in the factors
influencing that demand must remain the same in the
forecast period as in the past.

18. • Modern load forecasting techniques, such as expert
systems, Artificial Neural Networks (ANN), fuzzy logic,
wavelets, have been developed recently, showing
encouraging results.
• Among them, ANN methods are particularly attractive, as
they have the ability to handle the nonlinear relationships
between load and the factors affecting it directly from
historical data.

19. Accuracy of Electrical load forecasting
• Accurate models for electric power load
forecasting are essential to the operation and
planning of a utility company.
• Load forecasting helps an electric utility to make
important decisions including decisions on
purchasing and generating electric power, load
switching, and infrastructure development.
• For a particular region, it is possible to predict
the next day load with an accuracy of
approximately 1-3%.

20. • However, it is impossible to predict the next year
peak load with the similar accuracy since accurate
long-term weather forecasts are not available.
• For the next year peak forecast, it is possible to
provide the probability distribution of the load
based on historical weather observations.
• It is also possible, according to the industry
practice, to predict the so-called weather
normalized load, which would take place for
average annual peak weather conditions or worse
than average peak weather conditions for a given
area.

21. • Weather normalized load is the load calculated for the
so-called normal weather conditions which are the
average of the weather characteristics for the peak
historical loads over a certain period of time.
• The duration of this period varies from one utility to
another.
• Load forecasting has always been important for
planning and operational decision conduct 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.

22. • Short-term load forecasting can help to estimate
load flows and to make decisions that can prevent
overloading.
• Timely implementations of such decisions lead to
the improvement of network reliability and to the
reduced occurrences of equipment failures and
blackouts.
• Load forecasting is also important for contract
evaluations and evaluations of various sophisticated
financial products on energy pricing offered by the
market.

23. • Most forecasting methods use statistical techniques
or artificial intelligence algorithms such as
regression, neural networks, fuzzy logic, and expert
systems.
• Two of the methods, so-called end-use and
econometric approach are broadly used for
medium- and long-term forecasting.
• A variety of methods, which include the so-called
similar day approach, various regression models,
time series, neural networks, statistical learning
algorithms, fuzzy logic, and expert systems, have
been developed for short-term forecasting.

24. • The development and improvements of appropriate
mathematical tools will lead to the development of
more accurate load forecasting techniques.
• The accuracy of load forecasting Load Forecasting
depends not only on the load forecasting
techniques, but also on the accuracy of forecasted
weather scenarios.
• Important Factors for Forecasts For short-term load
forecasting several factors should be considered,
such as time factors, weather data, and possible
customers’ classes.

25. • The medium- and long-term forecasts take into
account the historical load and weather data, the
number of customers in different categories, the
appliances in the area and their characteristics
including age, the economic and demographic data
and their forecasts, the appliance sales data, and
other factors.
• The time factors include the time of the year, the
day of the week, and the hour of the day

26. • There are important differences in load between
weekdays and weekends.
• The load on different weekdays also can behave
differently.
• For example, Mondays and Fridays being adjacent
to weekends, may have structurally different loads
than Tuesday through Thursday.
• This is particularly true during the summer time.

27. • Holidays are more difficult to forecast than non-
holidays because of their relative infrequent
occurrence.
• Weather conditions influence the load. In fact,
forecasted weather parameters are the most
important factors in short-term load forecasts.
• Various weather variables could be considered for
load forecasting.
• Temperature and humidity are the most commonly
used load predictors.

28. Factors for accurate forecasts
Weather influence
Time factors
Customer classes

29. 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

30. 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

31. Customer Class
• Electric utilities usually serve different types of
customers such as residential, commercial,
and industrial

32. Time-horizon effects on forecasting
methods
• The current and the future states of a power system are
called operation and planning, respectively.
• First it is foreseen that the predicted load in 10 years from
now, may be served provided that a new power plant is
built.
• The expert has to decide on its required capacity, type and
where the plant has to be connected to the network.
• Once decided properly, its constructing has to be started
ahead of time, so that the plant is available in 10 years
time.

33. • Second, suppose we are going to build a transmission line,
passing through a mountainous area.
• Once built, the line may be subject to severe lightning.
• Lightning is such a very fast phenomena that it affects the
system within nanoseconds.
• The designer should think of appropriate provisions on the
line, by proper modeling the system in these very fast
situations and performing enough studies, to make sure
that the line does not fail, if such lightning happens in
practice.
• This is a typical very short-term study of power systems.
• Provided sufficient generation and transmission facilities
are available for serving the loads, a power system decision
maker should perform a 1 week to 1 year study to decide,
in advance, on maintaining power system elements (power
plants, transmission lines, etc.).

35. • This type of study is strictly required since if the plants are
not maintained properly, they may fail in severe loading
conditions.
• Moreover, the decision maker should know which elements
are not available within the current year, so he or she can
base his or her next decisions only on available elements.
This type of study is called maintenance scheduling.
• Another term normally used is operational planning. The
operational phase starts from 1 week to minutes. These
types of studies may be generally classified as:-

36. • Hours to 1 week (for example, unit commitment),
• Several minutes to 1 h (for example, economic dispatch,
Optimal Power Flow (OPF)),
• Minutes (for example, Automatic Generation Control
(AGC)).
To discuss, briefly, the points mentioned above,
suppose from ten power plants of a system, in the coming
week, three are not available due to scheduled
maintenances .
• The decision maker should decide on using the available
plants for serving the predicted load for each hour of the
coming week.

38. • Moreover, he or she should decide on the generation level
of each plant, as the generation capacities of all plants may
be noticeably higher than the predicted load.
• This type of study is commonly referred to as unit
commitment.
• His or her decision may be based on some technical and/or
economical considerations. The final decision may be in the
form of
• Commit unit 1 (generation level: 100 MW), unit 3
(generation level: 150 MW) and unit 6 (generation level:
125 MW), to serve the predicted load of 375 MW at hour
27 of the week (1 week = 168 h).
• Commit unit 1 (generation level: 75 MW) and unit 3
(generation level: 120 MW), to serve the predicted load of
195 MW at hour 35 of the week

39. • A complete list for all hours of the week should be
generated.
• Once we come to the exact hour, the actual load may not
be equal to the predicted load. Suppose, for instance, that
the actual load at hour 27 to be 390 MW, instead of 375
MW.
• A further study has to be performed in that hour to allocate
the actual load of 390 MW among the available plants at
that hour (units 1, 3 and 6).
• This type of study may be based on some technical and/or
economical considerations and is commonly referred to as
economic dispatch or Optimal Power Flow (OPF).

40. • Coming to the faster time periods, the next step is to
automatically control the generation of the plants (for
instance units 1, 3 and 6) via telemetry signals to
required levels, to satisfy the load of 390 MW at hours
27. This task is normally referred to as Automatic
Generation Control (AGC) and should be performed,
periodically (say in minutes); as otherwise, the system
frequency may undesirably change.
• Further going towards the faster time periods, we come
to power system dynamics studies, in milliseconds to
seconds.

41. • In this time period, the effects of some components such as
the power plants excitation systems and governors may be
significant.
• Two typical examples are stability studies (for example,
small signal, large signal, voltage stability, etc.) and Sub-
Synchronous Resonance (SSR) phenomenon.
• The very far end of typical power system consists of the
very fast phenomenon of power system behaviors.

42. Pattern of the data and its effects on
individual forecasting methods