MODULE III

Linss T Alex

Syllabus

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.

DefinitionA 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 beGeneration facilitiesSubstationsTransmission lines and/or cablesCapacitors/Reactors

– Decision should beWhere 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.

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.

• 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.

• 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.

• 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.

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.

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.

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.

• 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.

• 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

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).

• 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.

• 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.

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

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.

• 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.).

• 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:-

• 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.

• 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

• 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).

• 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.

• 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.

Pattern of the data and its effects on individual forecasting methods

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