28-02-2013, 12:08 PM
ENERGY LOSSES ESTIMATION IN POWER DISTRIBUTION SYSTEMS
ENERGY LOSSES.docx (Size: 622 KB / Downloads: 131)
ABSTRACT:
Estimating technical losses is fundamental to the planning and economics of electric power networks. This paper surveys the evolution of the ideas behind energy loss estimation and focuses on the development of the concepts of the loss factor and equivalent hours. The paper next identifies difficulties in using maximum demands and the loss factor to estimate energy losses. Based on this analysis, this study proposes an alternative loss estimation approach that relies on the “loss coefficient” as the fundamental parameter for describing load variations in loss estimation. A large load-curve data bank from Brazilian utilities is used to characterize load-curve parameters and provide perspective on the old and new concepts. Practical applications put the proposed ideas into perspective, showing how the use of average demands and loss coefficient can help to make better cable choices, increase accuracy in loss estimation for distribution transformers, and enhance the quality of information in loss estimation analysis.
INTRODUCTION :
Everyday, everywhere, energy losses must be estimated in power systems for engineering and economic purposes. These are the so called “technical losses.” The usual to estimate these losses is to run a load flow algorithm. Such a procedure requires detailed information about the network, such as cable impedances, cable lengths, and load curves at each load point. However, this information is often unavailable in planning studies or in incipient distribution systems. In these situations, the prevailing approach to evaluating losses is to estimate them at the highest demand (using some loss estimation method) and to apply the loss factor to predict the energy losses; the loss factor is a parameter that meant to represent the relationship between the losses at maximum load and the total energy losses or a given network.
There are difficulties in using the maximum demand and the loss factor to estimate energy losses. For instance, there is no direct relationship between the maximum demand and energy losses—indeed, the estimation of energy losses based on information about maximum demand is an empirical procedure that should be adjusted for each system. Additionally, the maximum demand for a given system is an uncertain variable and is usually measured at a lower precision than the energy consumption ; therefore, using the maximum demand to estimate energy loss should be avoided whenever possible .Previous papers have already proposed alternatives to the loss factor.
A CENTURY OF LOSS ESTIMATION:
[6] Technical losses have been discussed since the beginning of pre related to energy losses was published in 1881, when Lord Kelvin expressed the principle of economical conduction. He formulated what has sometimes been called Kelvin’s law of power transmission, which deals with the optimal conductor size and its relation to the cost of losses
Discussions:
As we noted before, energy losses have been taken into account in design and economic analysis since the beginning of power distribution. At that time, power flow methods and digital computers were not available to aid those analyses. Metering technology was also still evolving, and, for billing purposes, the maximum demand was recorded in meters .The load behavior was more difficult to store, and because the maximum demand was already in use in circuit design, the first approaches to computing energy losses were derived from this information. The loss factor (or equivalent hour) was an approach well suited to those purposes. No precise information was available, and energy loss analysis seemed to be a for in design cost analysis because energy loss costs were relatively small.
Random Nature of the Maximum Demand:
Supposing that the maximum demand is perfectly defined and measured for a given load, there are still some open questions .What is its usefulness in system analysis? Is it a representative statistic of a load? Fig. 3 shows the maximum demand histogram of a low budget residential customer over 43 days measured at a demand interval of 15 min. The customer was selected randomly from a Brazilian distribution company. Wide daily variation is evident…
The maximum demand is a random variable, and using a deterministic value may be a risk. However, many studies in distribution apply a load flow method to static loads, computing ,for instance, the maximum voltage drop. The probability of such a voltage drop may be negligible, and maybe the system should not be designed to meet such criteria power systems planning, including optimal system expansion, optimal configuration of radial feeders and optimal capacitor allocation. The maximum coincident demand is usually adopted in these analyses. How different would these results be if they utilized probabilistic load scenarios?
Are these solutions really Many advanced methods based on the state of the art in optimization techniques are being developed to aid decisions in optimal? Statistical indexes are already used to classify system performance in terms of power quality. Given the disturbances and the effects of using only the highest magnitude of a measurement, some indexes adopt, for instance, the 95% value of the distribution. Distribution planning criteria could also benefit from a similar approach.
Economic Choice of Cables:
In order to choose the right cable, the constant and variable costs of each available cable option need to be analyzed. A traditional visual analysis of the economic range for each cable is shown in Fig. 8. In this figure, curves were drawn using the relationship between the load factor and the loss factor(with values of 60% and 46%, respectively), and losses were computed from the maximum demands .Fig. 9 shows the economic cable curves redrawn in a simple perform in which the only fixed parameter is the CV. Instead of using the power, the average current (amps) is plotted on the horizontal axis. The values inside the parentheses on the horizontal axis are the maximum expected currents at 95% probability—the value of two , assuming that the LC is normally
CONCLUSION:
This paper discussed technical energy loss estimation in power distribution systems. The main point of the paper is to estimate energy losses using information about the mean and variation components of the load curve, by the use of the LSC. The EHL provides a link between the average loss and the time necessary to achieve the equivalent loss at the average load. Previous papers have already presented loss estimation methods based on similar concepts [1]–[5]. This paper extends these ideas and argues for a change of paradigm in loss estimation; instead of presenting a specific method to compute energy losses, it advocates the development of procedures that use load curve information and aggregated statistics as the basis for designing any loss estimation methods. The proposed LSC and EHL can replace the loss factor and the equivalent hours currently adopted in los estimation studies. The main point is that information about the energy consumption is more meaningful and usually more reliable than the maximum demand in loss estimation. If, for any reason, information about losses during the peak loads is necessary it can be obtained from the energy loss distribution through statistical methods. This paper also argues for the use of the CV as a measure of load variability.