Trends and Directions for Energy Saving in Electric Networks

Increasing the efficiency of existing distribution and consumption equates to making additional power available at lower cost. Such efficiencies reduce the need for constructing new generation plants and associated transmission facilities. Smart Grid can provide the communications and monitoring necessary to manage and optimize distributed and renewable energy resources and to maximize the environmental and economic benefits.


Introduction
The existing grids are one-way systems for the delivery of electricity without the selfhealing, monitoring and diagnostic capabilities essential to meet demand growth and new security challenges facing us today.
Increasing the efficiency of existing distribution and consumption equates to making additional power available at lower cost. Such efficiencies reduce the need for constructing new generation plants and associated transmission facilities. Smart Grid can provide the communications and monitoring necessary to manage and optimize distributed and renewable energy resources and to maximize the environmental and economic benefits.
The term "smart grid" is hyperbole that seems to imply a future when the grid runs itself absent human intervention. The smart grid concept in many ways suggests that utility companies, executives, regulators and elected officials at all levels of government will indeed face a brutal "pass/fail" future with regard to electric service, a driving force of the U.S. world-leading economy (IEA, 2001).
Intelligent distribution systems are an inevitable reality for utilities as they replace aging infrastructure, deal with capacity constraints and strive to meet the demands of an increasingly sophisticated end-use customer. The benefits of a real-time, single-platform smart distribution network are clear.
The business case must take into account the cost-effectiveness, operational improvements and return on investment of specific initiatives and must consider community-wide benefits. A proactive incremental implementation of smart distribution systems can have a dramatic impact on system improvements and customer satisfaction. A proactive review of smart grid strategy is vital: the utility leadership landscape will reward those who move early.  (Breuer et al., 2007) Thus, in developing countries, the main task is to provide local power supply. Emerging countries have a dramatic growth of power demand. During the transition, the newly industrialized countries need energy automation, life time extension of the system components, such as transformers and substations. Higher investments in distribution systems are essential as well. At the same time, the demand for a high reliability of power supply, high power quality and, last but not least, clean energy increase in these countries. In spite of all the different requirements one challenge remains the same for all: sustainability of power supply must be provided.

Introduction of Higher
Taking into account these aspects, the energy saving has become a major problem in the worldwide. Numerous studies have indicated that reduction of the power/energy losses in the electric networks is much easier than the increase of generating capacities, and energy efficiency represents the cheapest resource of all. The worldwide experience shows that in utilities with high network loss level, 1 $ expended for loss reduction saves 10 -15 $ to the utility (Raessar et al., 2007).
But, in evaluation of the energy losses from the electric distribution systems is necessary to know the loads from nodes of the system. Because, in distribution system, except the usual measurements from substations, the feeders and the loads are not monitored, there is few information about the network state. In this situation a modern technique, based on fuzzy set model, it can provide a good operating solution. The core of this technique is the fuzzy correlation model (Cârţină et al., 2003). The combination of the fuzzy approach with the system expert leads to an efficient and robust tool.

Minimization of the power/energy losses
Nowadays, power/energy saving has become a major problem in the worldwide. Numerous studies have indicated that reduction of power/energy losses in the electric networks is much easier than the increase of generating capacities, and energy efficiency represents the cheapest resource of all.
Energy losses throughout the world's electric distribution networks vary from country to country between 3.7% and 26.7% of the electricity use, which implies that there is a large potential for improvement. The distribution networks in most countries in the world were significantly expanded during the late 1960s and early 1970s, with different nominal voltages. For example, in distribution networks from Romania there are three levels of voltage: 6, 10, and 20 kV. The 6 kV level is the first who was developed and the availability of this in urban centres and other areas of concentrated demand for power is still quite high. Perspective to maintain the level of 6 kV is full of difficulties because the networks are very old, some distributors are loaded close to maximum capacity and energy losses are very high. The electric equipments installed in these networks now approach the end of their useful life and need to be replaced. But after replacing, the lifetimes of primary components are long and the networks built today will still be in use after several decades. The same problems in electric distribution networks are occurring during past years all over the world. The 20 kV level appeared later and covered the rest of urban and rural distribution areas. The 10 kV level included still very small areas of urban networks (Grigoraş et al., 2010c(Grigoraş et al., , 2010d. Thus, in the Figs. 2 and 3, the location by components of energy losses in the electric networks of a Distribution Company from Romania is presented. From Fig. 2 it can observe that a major part of the energy losses of a distribution system are the energy losses in the 6 kV distribution networks. It should be noted that energy losses in the 6 kV networks have about the same percentage as the 20 kV networks (1.25 % vs. ≈ 1 %), Fig 2, even if their total length is much smaller (report lengths, respectively the number of transformers is about 1 to 3). Another issues relates to the energy losses from the 6 kV cables that are very high compared with those on the 20 kV cables, and from the iron of the power transformers.
In the power transformers, the energy losses fall into two components: no-load losses or iron losses (constant, resulting from energizing the iron core; this phenomenon occurs 24 hours per day, 7 days per week, over the lifetime of the transformer, 30 years in average) and load losses (variable, arising when providing power to a user, from the resistance of the coils when the transformer is in use, and for eddy currents due to stray flux) (Eiken, 2007;European Commission, 1999;Grigoraş et al., 2010a).
www.intechopen.com The variable losses depend on the effective operating load to the transformer. The energy consumed in meeting these losses is dissipated in the form of heat, which is not available for the consumers to use.
No-load loss (iron loss) is the power consumed to sustain the magnetic field in the transformer's steel core. Iron loss occurs whenever the transformer is energized; iron loss does not vary with load. These losses are caused by two factors: hysteresis and eddy current losses.
Load loss (copper loss) is the power loss in the primary and secondary windings of a transformer due to the resistance of the windings. Copper loss varies with the square of the load current. The maximum efficiency of the transformer occurs at a condition when constant loss is equal to variable loss. For distribution transformers, the core loss is 15% to 20% of full load copper loss. Hence, the maximum efficiency of the distribution transformers www.intechopen.com occurs at a loading between 40% -60%. For power transformers, the core loss is 25% to 30% of full load copper loss. Hence, the maximum efficiency of the power transformers occurs at a loading between 60% -80%. The efficiency of the transformers not only depends on the design, but also, on the effective operating load.
A policy for the reduction of losses can contain short and long term actions, (Grigoraş et al., 2010a;Raesaar et al., 2007). The some short term measures are following:


Identification of the weakest areas in distribution network and improve them;  Reduction the length of the distribution feeders by relocation of distribution substation/installations of additional transformers, and so on.
The long term measures may relate to:  Mapping of complete distribution feeders clearly depicting the various parameters such as nominal voltage, the length, installed transformation capacity, the number of the transformation points, the circuit type (underground, aerial, mixed), load being served etc.  Replacement of the 6 kV or 10 kV voltage level with 20 kV voltage level;  Replacement of the old power transformers with the efficient transformers;  Compilation of data regarding existing loads, operations conditions, forecast of expected loads etc.
For further development of plans of energy loss reduction and for determination of the implementation priorities of different measures and investment projects, an analysis of the nature and reasons of losses in the system and in its different parts must be done.
From these measures, we will refer only to replacement of the voltage of 6 kV level to 20 kV and the old power transformers with the efficient transformers.
The replacement of the voltage of 6 kV level to 20 kV can be done in order to improve reliability and to minimize power losses in electrical distribution networks. On the other hand, most of the electric distribution infrastructure in urban areas is underground, so if excavation work is done to lay new distribution feeders, it makes much more economic sense to deploy 20 kV distribution lines that have about three times the capacity of 6 kV lines. Other solution that can be applied to minimize the power losses, correlated with the above is the use of efficient transformers. The distribution power transformer is the most important single piece of electrical equipment installed in electrical distribution networks with a large impact on the network's overall cost, efficiency and reliability. Selection and acquisition of distribution transformers which are optimized for a particular distribution network, the utility's investment strategy, the network's maintenance policies and local service and loading conditions will provide definite benefits (improved financial and technical performance) for both utilities and their customers (Amoiralis et al., 2007) For most electric distribution networks in Europe consist of aged network assets that have reached the end of their original amortized life. Fig. 4 shows a typical asset age profile of such assets and suggests that if original replacement times were to be exercised the majority of gear would have to be replaced in a short interval (Northcote-Green & Speiermann, 2010).
Thus, for an electric utility (Distribution Company) that has numerous distribution transformers in its network, there is an opportunity to install high efficient distribution transformers that have less total energy losses than less efficient transformers, so they pollute the environment less.

Energy performance standards for power transformers
Worldwide there are programs on Minimum Energy Performance Standard (MEPS) for to reduce energy losses associated with transformer operation in the electricity distribution system. Since the original MEPS levels were specified there has been significant development in transformer efficiency standards and requirements in other countries including the USA, European Union, Canada, Japan, China, Mexico and India. Thus, in Fig.  5 it presents a comparison of the requirements of international standards in terms of performance transformer oil at a loading of 50% (Ellis, 2003).
HD 428 standard imposed by European Union specific levels of energy losses in the transformer core for three different classes: A', B' and C' (C' having the lowest level of energy loss and A' the highest level). Also energy losses in the windings for three categories: A, B and C (C being the lowest level of losses and type A has the highest level of losses) (Ellis, 2003;European Commission, 1999). Some states have used the category of transformers the most efficient C-C' as a necessity while others use transformers less efficient by category B-B'. C-C' category present iron and copper losses of low values compared with other types of categories, presented in Table 1  There are a number of factors that will enable the achievement of higher efficiencies and support the increase in the current minimum efficiency performance standards levels (Blackburn, 2007):  Better use of traditional materials to achieve loss reduction and improvement of efficiency;  Better computer-aided design of transformers to reduce losses and improve efficiency;  Use of low loss core materials such as amorphous metals;  New lower loss core configuration designs such as the "Hexaformer";  Improved operational applications of transformers to optimize energy efficiency in operation;  Consideration of total life cost of transformers: purchase cost plus operational energy losses;  The effect of increasing harmonic levels from non-linear loads in increasing losses and reducing efficiency;  Increased transformer life resulting from lower operating temperature with more efficient transformers.
The savings brought about by loss reduction not just about the monetary value of the energy saved: the released capacity of the system can serve to delay a costly expansion and reduce ageing of the components.
In the past there was little concern for lowering losses in transformers. This was mainly due to the fact that when compared to motors and other electrical devices, transformers were considered to be very efficient.
Thus, low loss transformers can be called"efficient transformers". Operating losses are less causing less heat generation and effecting longer life. One of the prime components of losses is the no-load loss which can be drastically reduced by better design and using superior grades of electrical steels. The other components of losses are the load loss. Load loss can be reduced by using thicker conductors. With use of superior grades of electrical steels and thicker conductors for the windings, the losses of transformers may be brought down to minimum.
The conventional transformer is made up a silicon alloyed iron (Grain oriented) core. The iron loss of any transformer depends on the type of core used in the transformers. However, the latest technology is to use amorphous material for the core. The expected reduction in energy loss over conventional (Si Fe core) transformers is roughly around 70%, which is quite significant. Electrical distribution transformers made with amorphous metal cores (high efficiency transformers) provide an excellent opportunity to conserve energy right from the installation. Though these transformers are costlier than conventional iron core transformers, the overall benefit towards energy savings will compensate for the higher initial investment.
It must be underline if now for us the objective is replacements of old transformers by efficient transformers, (EU, Fig. 6), in Japan the objective the passing to high efficient transformers (Amorphous).
Thus, the technical solutions exist to reduce transformer losses. Energy-efficiency can be improved with better transformer design (selecting better, lower-core-loss steels; reducing flux density in a specific core by increasing the core size; increasing conductor cross-section www.intechopen.com to reduce current density; good balancing between the relative quantities of iron and copper in the core and coils; and so on.), or by the adoption of amorphous iron transformers worldwide (distribution transformers built with amorphous cores can reduce no-load losses by more than 70% compared to the best conventional designs).  (Eiken, 2007;European Commission, 1999)

Fuzzy modeling
In the last few years, research in the area of the optimal operation and planning of the electric networks is in expansion. Many papers and reports about new models have been published in the technical literature, due mostly to the improvement of the computer power availability, new optimization algorithms, and greater uncertainty level introduced by the power sector deregulation.
A considerable part of the information is uncertain, i.e. it is vague, fuzzy, and even ambiguous. Uncertainty of the information in distribution planning, as example, is caused by errors in measurements as well as inevitable errors in estimation of future forecasts. Furthermore, since most of the data used for the planning tasks are not based on the direct measurements, the degree of information uncertainty may be quite high. From the descriptive viewpoint, all the initial information may be categorized into the following several classes (Neimane, 2001): The basic idea of FT is to model and to be able to calculate with uncertainty. Mathematical models and algorithms in distribution systems aim to be as close to reality as possible. The required human observations, descriptions, and abstractions during the modeling process are always a source of imprecision, Fig. 7. Fig. 7. Mathematical models for imprecision (Steitz et al., 1993) While the two sources of imprecision have long since led to suitable mathematical models, the last one came in our mind only a few decades ago, although we use it instinctively in our everyday life, e.g.: The reliability of this component is very high. Most of linguistic descriptions such as Small, Medium or High are in nature fuzzy. These vague descriptions are as well part of modeling process and the algorithm. The system analyzer has to differ between classes, e.g., when classifying system operation states according to certain operational aspects (Steitz et al., 1993;Cârţină et al. 2003).
Uncertainty in fuzzy logic is a measure of nonspecifically that is characterized by possibility distributions. This is, somewhat similar to the use of probability distributions, which characterize uncertainty in probability theory. Linguistic terms, used in our daily conversation, can be easily captured by fuzzy sets, for computer implementations. A fuzzy set is a set containing elements that have varying degrees of membership in the set. Elements of fuzzy set are mapped to a universe of a membership function. The uncertain of the load level, the length of the feeders or loading of the power transformers and so on will be represented as fuzzy numbers, with membership functions over the real domain . A fuzzy number can have different forms but, generally, this is represented as trapezoidal or triangular fuzzy number, Figs. 8 and 9.
In the case of triangular and trapezoidal representations, a fuzzy number Ã is usually represented by its breaking points (Cârţină et al., 2003).
x 3 =n In particular case of the triangular fuzzy number representation, m = n, Fig. 8, and from (11) and (12) For defuzzification process, the most used method is the center of gravity (CG) method. According to this method, the crisp value is calculated with relation:

Fuzzy modeling in determination of the energy losses
In electrical distribution networks, except the usual measurements from stations, there is few information about the state of network. The loads are not usually monitored. As a result, there is at any moment a generalized uncertainty about the power demand conditions and therefore about the network loading, voltage level and power losses. The effects of the load uncertainties will propagate to calculation results, affecting the state estimation and the optimal solutions of the various problems concerning the operation control and development planning.
Therefore, the fuzzy approach may reflect better the real behavior of a distribution network under various loading conditions. For modeling of the loads, two primary fuzzy variables are considered: the loading factor K L (%) and power factor cos, so that the representation of the active and reactive powers result from relations: L n K PS c o s , Q P t a n 100 www.intechopen.com where S n is the nominal power of the distribution transformer from the distribution substations.
Thus, the hourly loading factor of a particular distribution transformer can be employed to approximate the nodal load. And, because the most utilities have not historical records of feeders, it is proposed to use linguistic terms, usually used by dispatchers, to describe the uncertain hourly loading factor. These linguistic terms are defined in function by the loading of the transformers at the peak load. Each loading level represented by a linguistic variable is described by a fuzzy variable and its associated membership function.
The loading factor K L and the power factor cos were divided into five linguistic categories with4 the trapezoidal membership function, Table 2 ( Cârţină et al., 2003;Grigoraş et al., 2010b).
The fuzzy models used in this case for the loading factor and power factor correspond to urban residential loads. Also, active power and power factor must be correlated as it is shown in Fig. 10 (Cârţină et al., 2003).
Linguistic Categories where: ΔP Cable -the power losses at the peak load in the cable; ΔP Tr Co -the cooper losses at the peak load in the transformers; ΔP Tr Ir -the iron losses in the transformers; LF -loss factor.
The values of the ΔP Cable , ΔP Tr Co , and ΔP Tr Ir are calculated as fuzzy variables using the modeling presented above.
Determination of the loss factor (LF) can be done for each distribution feeder, using the following formulae (Albert&Mihailescu, 1998;Grigoraş et al., 2010d): where: W P -active power measured during a period T (usually a year), (kWh); W Q -reactive power measured during a period T (usually a year), (kVAr); S max -peak load of the distribution feeder, (kVA); T max -peak load hours.

Case study 4.1 Technical analysis
In this paragraph it's presented as example a strategy for energy saving based on the replacement of the 6 kV voltage level with 20 kV voltage level, in correlation with the extent of using efficient transformers. Thus, it considered an urban distribution network with 8 electric stations (110/20/6 kV), which supplies 102 distribution feeders (52 feeders by 6 kV and 50 feeders by 20 kV). The characteristics of this urban distribution network are presented in the Tables 3 and 4.
An analysis of the information from the Table 3 indicates that the length of the distribution networks for two voltage levels is about the same, but the sections between 150 and 185 mm 2 predominates at the 20 kV. For the 6 kV level the length of the sections less than 150 www.intechopen.com mm 2 is close to that of sections between 150 and 185 mm 2 . Regarding the number of transformers, Table 4, it can observed that the average installed power (S i ) of a transformer at the 6 and 20 kV voltage levels is about the same (510 vs. 550 kVA) for a ratio of about 2 to 3. More than eighty percent of the transformers have an installed power above 400 kVA.  Table 6 the crisp annual energy losses, as function of the linguistic loading level, for the urban feeders by 6 kV which leave from an electric station (electric station no. I), were presented.     Fig. 16. The annually total energy losses/voltage levels From the analysis of the results it can be seen that by implementing this strategy, a reduction in losses (which translates into energy savings) of about 9420 MWh /year (4.5% from total energy that entering in the 6 kV network) was obtained. Total energy losses (old and new networks by 20 kV) in the whole analyzed network decrease from 5.8 to 1.63 %, as can be seen in Fig. 16. In this figure, the energy losses for every voltage level and whole distribution network were calculated in percents from the total energy that entering in the every voltage level, respectively from the circulating total energy in network.

Economic analysis
For economic analysis of the strategy for energy saving, the payback time method can be used. This method is quite simple. At today's commodity prices (low loss magnetic steel 2 500 -3 000 euro/tonne, copper 6 000 -7 000 euro/tonne) the indicative transformer price for AC' class 100 kVA typical distribution transformer is around 3 000 euro, 400 kVA is around 7 000 euro and 1 000 kVA around 12 000 euro. The price/rating characteristics can be roughly described as (Eaton Corporation, 2005): x in 10 0n S CC S     where: C i -is cost of transformer "i" C 0 -is cost of transformer "0" S in -is rated power of transformer "i" S 0n -is rated power of transformer with the nominal power by 100 kVA; x -exponent (cost factor).
The x factor is about 0.4 to 0.5. For more efficient units this factor has a tendency to increase up to 0.6 or even higher.
Also, the price for one km of electric cable with section of 150 mm 2 was considered 4700 euro/km, and for a section of 185 mm 2 , the price is 5900 euro/km.
In Table 10, the payback times of investment, in the case of the urban distribution network with 8 electric stations (110/20/6 kV) considered in the above paragraph, are presented.
The payback times of investment vary different from one to another distribution feeder in function by the loading level, power installed and the length. In Fig. 17, the variation of the payback time of investment in function of energy savings is shown.

Conclusions
Power/energy losses have a considerable effect on the process of transport and distribution of electrical energy and thus the strategies for saving energy are a concern to electrical companies in the country and abroad. In this chapter, a strategy for energy saving based on the minimization of the power/energy losses in electric networks, especially by replacement of the 6 kV voltage level with 20 kV voltage level in correlation with using efficient transformers, is presented.
This strategy can lead to increased capacity of electric distribution lines (by switching from 6 kV to 20 kV), to increase network reliability and minimize energy losses (the annually energy saving is about 9400 MWh, 2.67% from the circulating total energy in network). In terms of the environmental impact, the strategy can have a control and management of energy use not entailing the use of supplementary resources.