Summary of the various methods

Load profiling (LP) is the study of the electricity consumption habits of groups or categories of consumers, to estimate the amount of power they use each hour and produce load profiles, which can be calculated using various techniques. This is not a new development resulting from market liberalisation, but a tool that has long been used for system planning and for regulatory purposes; liberalisation has however changed its role and value. LP is a “second best” choice compared with hourly metering of all consumption but, in the medium term, it is needed to help develop competition in the power market and especially in the provision of supply, as it allows all end users—even those not yet equipped with an hourly meter— to choose their supplier. The techniques, methods, and forecasting models used depend on a number of technical factors related to costs, but also on economic policy issues and the context in which these are applied.

Techniques for building profiles

Consumers are categorised according to common characteristics (such as geographical location and economic consumption category), and each group is given a profile. Various approaches can be used to define load profiles: these vary in complexity, accuracy, and naturally cost (because of the information needed and the time and cost involved in gathering it). The basic criteria in choosing an LP method are accuracy and sophistication of the estimates it produces, and the cost of producing or improving these estimates. The choice of the “best” method is subject to various limitations, such as the availability of the necessary data and other information, technology that makes gathering these easier and the time needed. The more complex and accurate methods also cost more, and the trade-off between cost and accuracy is vital in choosing the method. The choice cannot therefore be the same for any system, but depends on the nature and limitations of the system where it will be applied.

These methods are briefly outlined below and can be grouped in three main categories:

q System Residual Profiling;

q Load Research Sample;

q Deemed Profiling.

The first group contains methods that calculate profiles based on the system’s load curve: System Load Shape (SLS) and Net System Load

Dynamic Load Profile, True Dynamic Load Profile), which use data for the day in question to determine load profiles, and static statistical methods (Static Load Profile), which are based on analysis of historical data, mixed methods (static but corrected with current data—Adjusted Load Profile), and methods based on economic, econometric or statistical models, which estimate profiles by looking at the current trends in certain variables that affect electricity use (Proxy Day Load Profile). The third group contains all the methods used to build load profiles for consumption classes whose use is predictable (for example street lighting and traffic lights). These are called Non-Metered Load Profiles, and can be calculated using an engineering approach, calculating the hourly pattern of this consumption using data from other areas or by combining different approaches. The various methods are outlined below.

System Load Shape and Net System Load Shape

These two methods, which are very similar, use a single curve to describe consumption by all consumers not equipped with an hourly meter. SLS uses the system’s load curve, while NSLS uses the load curve excluding use by consumers with hourly meters and adjusted to allow for leakage. The main disadvantages of these two methods, which must be balanced against their extreme simplicity and almost zero cost, are their highly generic nature, which limits their ability to reflect true consumption since they do not distinguish between users. The NSLS method (on which the LP by area used in Norway is based) reduces the problem, albeit minimally, by introducing greater homogeneity.

Static Load Profiling and Adjusted Static Load Profiling

This method produces approximate load curves for “typical days” and user categories, based on historical averages for electricity use; these are used to calculate static consumption profiles. This method is also a simple way to calculate and manage profiles, is easily understood by market players, and produces highly accurate estimates. However, two of these advantages have attendant disadvantages. Because of its simplicity, the method risks overlooking factors that may have considerable impact on consumption; and accuracy has its cost in the price paid to gather the information and the financial resources needed to implement the method (including research on consumption, sample selection, and the time needed to gather historical data on consumption, which is at least 24 months). Adjusted Static Load Profiling has the special feature that it allows “typical” profiles to be corrected based on figures for certain specific variables that affect the day for which use needs to be estimated (the target day).

Proxy Day Load Profiling

This method uses models and theoretical hypotheses regarding the impact of certain variables on electricity consumption. To forecast use on the target day, it uses consumption figures for a sample of consumers during a day that has features in common (for example, the same air temperature). This retains the simplicity of the static method but takes into account the impact of various factors on consumption, producing even more sophisticated forecasts. However, it also introduces a “model”—a set of theoretical hypotheses on how consumption should be forecast—and therefore also risks connected to errors in calculating the theoretical model. It is also less readily understandable—and thus less readily accepted—by market players. Like the static method, but perhaps more so, it involves high research costs and long implementation timescales, in the absence of historical data (at least 12 months before the first day on which consumption is forecast).

True Dynamic Load Profiling and Lagged Dynamic Load Profiling

“True” dynamic load profiling is defined as the daily analysis of a sample of consumers drawn from those whose consumption is measured on an hourly basis, in order to produce load profiles that reflect the true current use by consumers not metered on an hourly basis on a given target day. Dynamic profiles offer sophisticated and prompt forecasts that reflect the impact of factors on consumption. It does not use models, which reduces the risk of errors and makes it easier for market players to understand. Applying the dynamic profile method does not require a long series of historical data gathered over months or years—one of the main limitations in building static profiles—but it is the most costly forecasting method because all consumers in the sample need remote meters that not only record data for hourly use but transmit them so that they can be gathered and analysed daily. Lagged Dynamic Load Profiling differs in the frequency with which data is gathered and analysed, and usually operates alongside other methods for settlement operations.

Non-Metered Load Profiling

Consumption due to street lighting, traffic lights and other use that does not involve hourly meters may be measured per hour using “engineering” methods. Typically, the total amount and duration of this use is known, so building profiles for different times of the day that take into account variations in certain parameters, such as sunset and sunrise times, is fairly straightforward.

Load Profiling by Area and Load Profiling by Category

The methods described above may give rise to various LP models depending on which are used and how they are applied to a given electrical system. The two main LP models applied in highly liberalised European sectors are LP by area and LP by category, the first in Norway and the second in the UK.

Load Profiling by Area

This belongs in the NSLS method category. However, the load curve used as a basis for calculation is not that for the whole system but for a given area considered a subsystem in its own right. Curves are therefore not determined in advance, but on the basis of figures for hourly energy input into the system (area). Electricity use for each area is subtracted from this figure to give an Adjusted Area Load Profile (AALP) and from this in turn, leaks are subtracted (as a percentage of AAL or as a forecast figure). The figure thus obtained is no less than total consumption, hour by hour, in the area in question, which must then be applied to consumers without hourly meters (for example, based on consumption). The most critical aspect of this process is determining the amount to be attributed to each consumer, since the grid operator’s unbalanced positions relating to dispatch and supply are set based on this amount and the figure for leakage. The main advantages of this method are its simplicity, low cost (because limited information and technological input are needed), and rapid implementation, which allows the market to be opened up to all end users more quickly than when hourly meters are used.

Load Profiling by Category

This subdivides consumers into categories according to the typical features of their use. It then applies a consumption profile to each category and

based on this classification, allocates electricity consumption, net of consumption measured with hourly meters, among consumers not thus equipped. The underlying principle is to group consumers into homogeneous categories believed to use electricity at similar times. This approach also distinguishes between the various methods of forecasting profiles for each category (static, dynamic, and engineering-based) through which each category is assigned a coefficient from which total use during each hour is calculated. Since this involves profiles calculated in advance, this does not guarantee that the total forecast use matches total use by non-metered users on an hourly basis, and consumption above or below forecast needs to be distributed among individual consumers. The imbalance between actual and forecast consumption by end users leads to uncertainty regarding the use that suppliers will experience hour by hour. This method can produce accurate forecasts and allows the difference between the consumption habits of different groups of users to be taken into account, but it is more complex and generally more expensive than LP by area, partly because it requires mechanisms to correct errors in forecasts.