Load Research Techniques
1.0 Load Research
In utility operation, load research is an important function to understand, classify
and quantify behavior of customers. Load research is helps the management of utility
to take the effective decisions. Electrical utilities uses load research techniques
to study the electricity usage pattern of their customers. This study of pattern
could be based on either in total or by individual end-uses. Load research requires
knowledge of various disciplines such as statistics, marketing research, electrical
engineering and computers.
2.0 Importance of Load Research
Load research has a prime importance to the utility as it acts as a platform to
provide verifiable and accurate data for the decision making. This also improves
the value of regulatory liaison between utility and the regulatory commission. Although
data obtained through load research is useful for demand side management but does
not limit to this, it permits a utility to perform following functions:
Demand Side Management Enhanced Regulatory liaison Financial planning Calculation of unit price Transmission & Distribution upgrades Customer service improvement plans
^ TOP
3.0 Load Research and Regulation
Load research helps electric utilities to apply econometric and statistical techniques
in rate design and in the allocation of the costs of generation and T&D facilities
among the various classes of service. Therefore, obtaining accurate load data is
critical to form accurate cost. Regulatory commissions of the utility uses customer
load profile data. Such realistic data about customers is used for tariff rate appeals.
Load research is a win-win approach for all the stake holders as it benefits all
parties.
Utility: Based upon field data, receives proposals for DSM programs,
tariff changes and capital improvements.
Regulatory commission: can review the proposals
from the utility, and discuss the proposals with the knowledge and confidence that
these were developed in a systematic and scientific manner with accurate field data.
Customer: Customers benefited because of DSM programs, and tariff
changes.
Resources for load research can be grouped into four areas:
Data collection equipment Data translating/reformatting equipment Database storage and analysis systems Human resources
Data Collection System
Data collection equipment: A prime consideration in a load research
program is the equipment that collects the load profile data. For a load research
project, three pieces of equipment are installed for each customer: a watt-hour
meter, a pulse initiator, and a recorder. These could be combined into one multi-functional
unit. Subsidiary equipment for data retrieval, testing and maintenance must be considered.
Data translation equipment: Translation refers to the process of
transforming the field-recorded data into information that can be stored on the
utility’s central computer database.
Database storage and analysis systems: Minicomputers for translation
and mainframe computers for analysis are used. Translated load data stored on a
minicomputer, disk, or computer tape, are transferred to the utility’s mainframe
computer. In the mainframe, the load data are stored in a database where they may
be edited and analyzed. The mainframe also contains software to analyze load data
and may contain editing and report preparation software.
^ TOP
4.0 Load Research Techniques
4.1 Sampling Design Process
The sampling design process includes five steps that
are shown sequentially in figure below. These steps are closely interrelated and
relevant to all aspects of the load research.
4.1.1 Define the Target Population
Target population is the collection of elements or objects that possess the information
about which inferences are to be made. The target population should be defined in
terms of elements, sampling units, extent and time. An element is the object about
which or from which the information is desired. For example, meters readings from
the meters or from the old data. Sampling unit is unit containing the element, that
is available for selection at some stage of the sampling process. For example, in
utilities, households, SMEs, shops, commercial establishments could act as a sampling
unit. Extent refers to the geographic boundaries to which research is limited, this
could be some city or district or feeder. And time factor is the time period under
consideration such as winter, summer or monsoon.
4.1.2 Determining the sampling Frame
A sampling frame is a representation of the elements of the target population.
It consists of a list or set of directions for identifying the target population.
Example of sampling frame include connect load, type of industry, commercial establishment
or a residential colony.
4.1.3 Select a Sampling Technique
Selecting a sampling technique involves several decisions of a broader nature. During
load research one must decide whether to use a Bayesian or traditional sampling
approach, to sample with or without replacement, and to use non probability or probability
sampling. Bayesian approach is a selection method in which the elements are selected
sequentially. This approach explicitly incorporates prior information as well as
the costs and probabilities associated with making wrong decisions.
Non probability sampling relies on judgment of load research team whereas probability
samplings pre-specify every potential sample of given size that could be drawn from
the population. List of various non-probability and probability sampling techniques
are shown in the figure.
^ TOP
Simple Random Sampling:
Each element in the population has a known and equal probability of selection.
Each possible sample of a given size (n) has a known and equal probability of being
the sample actually selected. This implies that every element is selected independently
of every other element.
Systematic Sampling:
The sample is chosen by selecting a random starting point and then picking every
ith element in succession from the sampling frame. The sampling interval, i, is
determined by dividing the population size N by the sample size n and rounding to
the nearest integer. When the ordering of the elements is related to the characteristic
of interest, systematic sampling increases the representativeness of the sample.
If the ordering of the elements produces a cyclical pattern, systematic sampling
may decrease the representativeness of the sample. For example, there are 100,000
elements in the population and a sample of 1,000 is desired. In this case the sampling
interval, i, is 100. A random number between 1 and 100 is selected. If, for example,
this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523,
and so on.
Stratified Sampling:
A two-step process in which the population is partitioned into subpopulations,
or strata. The strata should be mutually exclusive and collectively exhaustive in
that every population element should be assigned to one and only one stratum and
no population elements should be omitted. Next, elements are selected from each
stratum by a random procedure, usually SRS. A major objective of stratified sampling
is to increase precision without increasing cost.
The elements within a stratum should be as homogeneous as possible, but the elements
in different strata should be as heterogeneous as possible. The stratification variables
should also be closely related to the characteristic of interest. Finally, the variables
should decrease the cost of the stratification process by being easy to measure
and apply.
Cluster Sampling:
The target population is first divided into mutually exclusive and collectively
exhaustive subpopulations, or clusters. Then a random sample of clusters is selected,
based on a probability sampling technique such as SRS. For each selected cluster,
either all the elements are included in the sample (one-stage) or a sample of elements
is drawn probabilistically (two-stage). Elements within a cluster should be as heterogeneous
as possible, but clusters themselves should be as homogeneous as possible. Ideally,
each cluster should be a small-scale representation of the population.
In probability proportionate to size sampling, the clusters are sampled with probability
proportional to size. In the second stage, the probability of selecting a sampling
unit in a selected cluster varies inversely with the size of the cluster.
4.1.4 Determine the sample size
Sample size refers to the number of elements to be included in the study.
Sample size is influenced by the average size of the samples in similar studies.
These sample size have determined based on experience and can serve as rough guidelines,
particularly when non probability techniques are used.
|
Steps
|
Based on Means
|
Based on Proportions
|
|
1.
Specify the level of
precision
|
Permissible difference, eg: ±5
|
Desired precision D = p -
p
|
|
2.
Specify the confidence level (CL)
|
CL, eg: 95%
|
CL, eg: 95%
|
|
3.
Determine the z value
associated with CL
|
Z value at confidence interval, eg: 1.96 at 95% CL
|
Z value at confidence interval, eg: 1.96 at 95% CL
|
|
4.
Determine the standard
deviation of the population
|
Estimate s, eg:
s = 55
|
Estimate p, eg:
p = 0.64
|
|
5.
Determine the sample
size using formula for the standard error
|
eg: n=552 x (1.96)2
/ 52 = 465
|
n = p(1-p)z2/D2
eg:
n = 0.64(1-0.64)(1.96)2/(0.05)2
|
|
6.
If the sample size
is represents 10% of the population, apply finite population correction
|
nc = nN/(N+n-1)
|
nc = nN/(N+n-1)
|
|
7.
If necessary, re-estimate
the confidence interval by employing s to estimate
s
|
nc = X ± zsx
|
nc = p ± zsp
|
|
8.
If precision is specified
in relative rather than absolute terms, then use these equations to determine the
sample size
|
D=Rm
n = C2z2/R2
|
D=Rp
n = Z2(1-p)/R2p
|
^ TOP
4.1.5 Execute the sampling process
Execution of the sampling process requires a detailed specifications of
how the sampling design decisions with respect to the population, sampling frame,
sampling unit, sampling techniques, and sample size to be implemented.
A preliminary questionnaire designed to identify various DSM projects (both pilot
and full scale regional level projects) can be sent to relevant authorities at the
state level who are willing to share the information. This can certainly help in
understanding the various aspects of the DSM as well as preliminary information
regarding the case studies. This information can also be used in the development
of a database of DSM initiatives undertaken by the utilities at the state level.
Based on the analysis of the Preliminary Questionnaire, a detailed questionnaire
can be developed specific to DSM measures undertaken by the Distribution Utilities
to identify the precise nature of measure, mode of implementation, impact on the
consumer as well as the Utility and key learning. This information can be used in
the development of detailed case studies and best pest practices which can further
set standards for different utilities looking forward to implement DSM initiatives.
This section provides a sample questionnaire that can help different stakeholders
of DSM programs seeking for preliminary information regarding the development and
implementation of various DSM initiatives. This questionnaire can also be suitably
modified by the stakeholders to collect specific information pertaining to the function
of the stakeholders’ organizations.
5.0 References
Debs A.S. 1988 Modern power system control and operation Irwin G.W., Montieth
W., Beattle W.C., Statistical electricity demand modeling from consumer billing
data Malhotra Naresh, Dash Satyabhushan, Marketting Research, An Applied orientation
|