POWER SYSTEM PLANNING AND LOAD FORECASTING

MODULE I-LOAD FORECASTING

LINSS T ALEXASSISTANT PROFESSORDEPARTMENT OF EEEMET’S SCHOOL OF ENGINEERING,MALA

LTA

Syllabus

Load forecasting

Electrical energy has to be generated whenever there is a demand for it. It is, therefore, imperative for the electric power utilities that the load on their systems should be estimated in advance. This estimation of load in advance is commonly known as load forecasting. It is necessary f or power system planning.

• Power system expansion planning starts with a forecast of anticipated future load requirements.

• The estimation of both demand and energy requirements is crucial to an effective system planning.

• Demand predictions are used for determining the generation capacity, transmission, and distribution system additions, etc.

• Load forecasts are also used to establish procurement policies for construction capital energy forecasts, which are needed to determine future fuel requirements. Thus, a good forecast, reflecting the present and future trends, is the key to all planning.

• The term forecast refers to projected load requirements determined using a systematic process of defining future loads in sufficient quantitative detail to permit important system expansion decisions to be made.

• Unfortunately, the consumer load is essentially uncontrollable although minor variations can be affected by frequency control and more drastically by load shedding.

• The variation in load does exhibit certain daily and yearly pattern repetitions and an analysis of these forms the basis of several load-prediction techniques.

• Electrical Load Forecasting is the estimation for future load by an industry or utility company. Load forecasting is vitally important for the electric industry in the deregulated economy.

• A large variety of mathematical methods have been developed for load forecasting. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development.

• Now a day, development in every sector is a heading at a very rapid pace and in the same pattern, the demand for power is also growing. While speaking about electrical power, it is important to understand that it has three main sectors i.e. generation, transmission and distribution.

• Electrical power generated by any source is then transmitted through transmission lines at different voltage level and then distributed to different categories of consumers later on.

• It is not as simple as described in few words but every stage is a complete independent system in itself. Effective load forecasts can help to improve and properly plan these three fields of power systems.

• 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.

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

• Over the past decade, many western nations have begun major structural reforms of their electricity markets.

• These reforms are aimed at breaking up traditional regional monopolies and replacing them with several generation and distribution utilities that bid to sell or buy electricity through a wholesale market.

• While the rules of how various wholesale markets operate differ, in each case it is hoped that the end result is a decline in the price of electricity to end users and a price that better reflects the actual costs involved.

• Load forecasting is however a difficult task. First, because the load series is complex and exhibits several levels of seasonality: the load at a given hour is dependent not only on the load at the previous hour, but also on the load at the same hour on the previous day, and on the load at the same hour on the day with the same denomination in the previous week.

• Secondly, there are many important exogenous variables that must be considered, especially weather-related variables.

• It is relatively easy to get forecast with about 10 % mean absolute error; however, the cost of error are so high that research could help to reduce it in a few percent points would be amply justified .

Types of forecasting

S h o rt term fo recas ts(o n e h o u r to a w eek )

M ed iu m fo recas ts(a m o n th u p to a year)

Lo n g term fo recas ts(o ver o n e year)

Load Forecasts

Long term load forecasting (LTLF): Applicable for system and long term network planning. Basically two approaches are available for this purpose.

(a) Peak Load Approach In this case, the simplest approach is to find the trend curve, which is obtained by plotting the past values of annual peaks against years of operation.

(b) Energy Approach • Another method is to forecast annual energy

sales to different classes of customers like residential, commercial, industrial, etc., which can then be converted to annual peak demand using the annual load factor.

• A detailed estimation of factors such as rate of house building, sale of electrical appliances, growth in industrial and commercial activities are required in this method.

2. Midterm Load Forecasting (MTLF): Applicable for quarterly, half yearly and yearly LF needs.

3. Short term Load Forecasting (STLF): Applicable for day ahead and week ahead LF needs.

Applications of STLF are mainly: • To drive the scheduling functions that decides the

most economic commitment of generation sources.

• To access the power system security based on the information available to the dispatchers to prepare the necessary corrective actions.

• To provide the system dispatcher with the latest weather predictions so that the system can be operated both economically and reliably

Need (purpose) of load forecasting1) For proper planning of power system.• To determine the potential need for additional new

generating facilities• To determine the location of units.• To determine the size of plants. • To determine the year in which they are required.• To determine that they should provide primary peaking

capacity or energy or both.• To determine whether they should be constructed and

owned by the Central Government or State Government or Electricity Boards or by some other autonomous corporations

2) For Proper Planning of Transmission and Distribution Facilities

• For planning the transmission and distribution facilities, the load forecasting is needed so that the right amount of power is available at the right place and at the right time.

• Wastage due to misplanning like purchase of equipment, which is not immediately required, can be avoided.

3) For Proper Power System Operation • Load forecast based on correct values of

demand will prevent overdesigning of conductor size, etc. as well as overloading of distribution transformers and feeders.

• Thus, they help to correct voltage, power factor, etc. and to reduce the losses in the distribution system.

4) For Proper FinancingThe load forecasts help the Boards to estimate the future expenditure, earnings, and returns and to schedule its financing program accordingly.

5) For Proper Manpower Development Accurate load forecasting annually reviewed

will come to the aid of the Boards in their personnel and technical manpower planning on a long-term basis.

Such a realistic forecast will reduce unnecessary expenditure and put the Boards finances on a sound and profitable footing.

6) For Proper Grid Formation • Interconnections between various state grids

are now becoming more and more common.• The expensive high-voltage interconnections

must be based on reliable load data, otherwise the generators connected to the grid may frequently fall out of step causing power to be shut down.

7) For Proper Electrical Sales• Proper planning and the execution of

electrical sales program are aided by proper load forecasting

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%.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

Factors for accurate forecasts

Weather influenceTime factorsCustomer classes

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

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

Customer Class• Electric utilities usually serve different types of

customers such as residential, commercial, and industrial

Forecasting techniques Qualitative Approaches to Forecasting Quantitative Approaches to Forecasting The Components of a Time Series Using Smoothing Methods in Forecasting Measures of Forecast Accuracy Using Trend Projection in Forecasting Using Regression Analysis in Forecasting An essential aspect of managing any organization is

planning for the future. Organizations employ forecasting techniques to

determine future inventory, costs , capacities, and interest rate changes.

Qualitative Approaches to ForecastingDelphi Approach• A panel of experts, each of whom is physically

separated from the others and is anonymous, is asked to respond to a sequential series of questionnaires.

• After each questionnaire, the responses are tabulated and the information and opinions of the entire group are made known to each of the other panel members so that they may revise their previous forecast response.

• The process continues until some degree of consensus is achieved.

Scenario Writing• Scenario writing consists of developing a

conceptual scenario of the future based on a well defined set of assumptions.

• After several different scenarios have been developed, the decision maker determines which is most likely to occur in the future and makes decisions accordingly.

Subjective or Interactive Approaches• These techniques are often used by committees

or panels seeking to develop new ideas or solve complex problems.

• They often involve "brainstorming sessions". • It is important in such sessions that any ideas or

opinions be permitted to be presented without regard to its relevancy and without fear of criticism.

Quantitative Approaches to Forecasting

• Quantitative methods are based on an analysis of historical data concerning one or more time series.

• A time series is a set of observations measured at successive points in time or over successive periods of time.

• If the historical data used are restricted to past values of the series that we are trying to forecast, the procedure is called a time series method.

• If the historical data used involve other time series that are believed to be related to the time series that we are trying to forecast, the procedure is called a causal method.

• Quantitative approaches are generally preferred.

Time Series Data

• Time Series Data is usually plotted on a graph to determine the various characteristics or components of the time series data.

• There are 4 Major Components : Trend, Cyclical, Seasonal, and Irregular Components.

• The trend component accounts for the gradual shifting of the time series over a long period of time.

• Any regular pattern of sequences of values above and below the trend line is attributable to the cyclical component of the series.

• The seasonal component of the series accounts for regular patterns of variability within certain time periods, such as over a year.

• The irregular component of the series is caused by short-term, unanticipated and non-recurring factors that affect the values of the time series. One cannot attempt to predict its impact on the time series in advance.

• In time series data we can learn the following Forecasting Approaches:

Smoothing MethodsTrend Projections• The time series is fairly stable and has no

significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series.

Three common smoothing methods are:• Moving average• Weighted moving average• Exponential smoothing

Moving Average MethodThe moving average method consists of computing

an average of the most recent n data values for the series and using this average for forecasting the value of the time series for the next period.

Weighted Moving Average MethodThe weighted moving average method consists of computing a

weighted average of the most recent n data values for the series and using this weighted average for forecasting the value of the time series for the next period.

The more recent observations are typically given more weight than older observations.

For convenience, the weights usually sum to 1. The regular moving average gives equal weight to past data values

when computing a forecast for the next period. The weighted moving average allows different weights to be allocated

to past data values. There is no excel command for computing this so you must do this

manually. You can either manually enter the formulas into excel and apply to all

periods or compute value by hand.

Exponential SmoothingUsing exponential smoothing, the forecast for

the next period is equal to the forecast for the current period plus a proportion (a) of the forecast error in the current period.

Using exponential smoothing, the forecast is calculated by:

Ft+1=a Yt + (1- a)Ft This is the same as Ft+1 = Ft + α (Yt – Ft)

a is the smoothing constant (a number between 0 and 1)

Ft is the forecast for period t Ft +1 is the forecast for period t+1 Yt is the actual data value for period t

Explanatory forecasting

• Explanatory models assume that variable to be forecasted exhibits an explanatory relationship with one or more independent variables.

GNP=f(monetary and fiscal policies,inflation,capital spending,imports,exports,error)

• Relationship is not exact.• There will always be changes in GNP that cannot

be accounted by the variables in the model and thus some part of GNP will remains unpredictable.

• Therefore it includes the error term on the right which represents random effects, beyond the variable in the model, that affect the GNP figures.

• Explanatory models can be applied to many systems-a national economy, a company’s market on a household.

• The purpose of explanatory model is to discover the form of relationship and using it to forecast future values of forecast variables.

• According to explanatory forecasting change in input will affect the output of the system in a predictable way, assuming the explanatory will not change.

Least-squares estimates• The method uses an operator that controls one

variable at a time.• An optimal starting point is determined using the

operator. • This method utilizes the autocorrelation function and

the partial autocorrelation function of the resulting differenced past load data in identifying a suboptimal model of the load dynamics.

• The weighting function, the tuning constants and the weighted sum of the squared residuals form a three-way decision variable in identifying an optimal model and the subsequent parameter estimates.

• Consider the parameter estimation problem involving the linear measurement equation:

• where Y is an n x 1 vector of observations, X is an n x p matrix of known coefficients (based on previous load data), β is a p x 1 vector of the unknown parameters and ε is an n x 1 vector of random errors.

• Results are more accurate when the errors are not Gaussian. β can be obtained by iterative methods (Mbamalu and El- Hawary 1992).

• Given an initial β , one can apply the Newton method.

Trend Analysis

• The trend extrapolation method uses the information of the past to forecast the load of the future.

• A simple example is shown in figure(2010), in which load is shown for the last 10 years and predicted to be 2906 MW in 2015.

• A curve fitting approach may be employed to find the load of the target year.

• This approach is simple to understand and inexpensive to implement.

• However, it implicitly assumes that the trends in various load driving parameters remain unchanged during the study period.

Regression Analysis• Regression Analysis is similar to trend analysis,

except the independent variable is not restricted to time.

• Instead of letting time represent our independent variable, we can forecast

• For this model, we would find the regression equation in the same manner in which we found the trend line except we would call the independent variable x, instead of t.

• Using the method of least squares, the formula for the regression line is:

Y = b0 + b1x. where: Y= dependent variable which depends on the value of x

b1 = slope of the regression line b0 = regression line projection for x= 0• The dependent variable Y can predict using the same

forecast function in Excel as used to forecast a trend line.

Peak Load forecasting• Annual peak load forecasts are important for

planning and, in particular, for securing adequate generation, transmission and distribution capacities.

• More accurate peak load forecasts improve decision making capabilities in capital expenditures and improve reliability of the system.

• Future peak load is not deterministic and it depends on several uncertain factors including weather conditions.

New approach

Box Jenkins time series method In time series analysis, the Box–Jenkins method, named after the

statisticians George Box and Gwilym Jenkins, applies autoregressive moving average ARMA or ARIMA models to find the best fit of a time-series model to past values of a time series.

The Box-Jenkins approach is one of the most widely used methodologies for the analysis of time-series data.

It is popular because of its generality; it can handle any series, stationary or not, with or without seasonal elements, and it has well-documented computer programs.

Although Box and Jenkins have been neither the originators nor the most important contributors in the field of Auto Regressive Moving Average(ARMA) models.

They have popularized these models and made them readily accessible to everyone, so much that ARMA models are sometimes referred to as Box-Jenkins models.

The basic steps in the Box-Jenkins methodology are:-(1) Differencing the series so as to achieve stationarity(2) Identification of a tentative model(3) Estimation of the model(4)Diagnostic checking (if the model is found

inadequate, we go back to step (2)(5) Using the model for forecasting and control.

1. Differencing to achieve stationarity: How do we conclude whether a time series is stationary or not?

We can do this by studying the graph of the correlogram of the series.

The correlogram of a stationary series drops off as k, the number of lags, becomes large, but this is not usually the case for a nonstationary series.

Thus the common procedure is to plot the correlogram of the given series , successive differences and so on, and look at the correlograms at each stage.

We keep differencing until the correlogram dampens

2. Once we have used the differencing procedure to get a stationary time series, we examine the correlogram to decide on the appropriate orders of the AR and MA components.

The correlogram of a MA process is zero after a point. That of an AR process declines geometrically. The

correlograms of ARMA processes show different patterns (but all dampen after a while).

Based on these, one arrives at a tentative ARMA model. This step involves more of a judgmental procedure than

the use of any clear-cut rules.

3. The next step is the estimation of the tentative ARMA model identified in step 2. We have discussed in the preceding section the estimation of ARMA models.

4. The next step is diagnostic checking to check the adequacy of the tentative model. We discussed in the preceding section the Q and Q* statistics commonly used in diagnostic checking.

5. The final step is forecasting

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