Tariff Redesign in the Purpose of Increasing Market Business Quality in Distribution System

Tatjana Konji� #1, Senad Aganovi� *2, Edina Aganovi� Δ3

# Faculty of Electrical Engineering, University of Tuzla, Bosnia and Herzegovina 1 tatjana.konjic@untz.ba

* FERK Mostar, Bosnia and Herzegovina 2 s.aganovic@ferk.ba

Δ NOSBiH Sarajevo, Bosnia and Herzegovina 3 e.aganovic@nosbih.ba

Abstract - Main principle of a tariff system for electricity is

that customers should cover all costs appeared in the power system, but as realistic as it possible in accordance with a place and time of delivered energy. Defining real economical price of electricity for customers at the low voltage distribution level presents main problem due to a lack of data related to daily load diagram. Fuzzy logic and fuzzy c-means clustering was applied to development of a model for consumers’ classification. Obtained results could be used as an input for more realistic calculation of cost for electrical energy consumption of customers in household category. Additionally, the difference in calculation of electricity consumption cost based on the obtained values from proposed model and on current tariff system in Bosnia and Herzegovina is discussed.

I. INTRODUCTION According to EU Directives [1], member states should

provide a universal service for supplying electricity to customers from category household and small enterprises. EU Directive requires from regulatory authorities to create conditions that will enable power supply under specified quality and will provide reasonable, transparent and non-discriminatory tariffs. At the same time this task should not prevent the process of market opening. Regulatory authorities should develop plans to ensure universal service for the transition period as well as for the period after market opening.

One important task of regulatory bodies is to define elements that will protect customer in the process of market opening in accordance with EU Directives [1]. Universal service, in practice, is realized through role of default (DS) or reserve (SOLR) suppliers. Defining and control price of electricity delivered by DS or SOLR are responsibility of the regulatory bodies [2]. Regulatory bodies have to determine the cost caused by customers’ behavior in the network and to properly allocate them on the customer’s categories. Beside above mentioned, pricing of electricity consumption and power for each single customer is very important.

Tariff rates should reflect actual costs. Each consumer should pay only costs that he actually caused in power system by power network capacity engagement. Well set tariff design has a significant role in interactive relationship between customer and energy system. The ability to measure the electricity consumption and power of each customer in the system significantly affects on the creation of tariff design [3].

Unfortunately, in developing countries and countries in transitions, measurement of active power at low voltage (LV) exists only for consumers from group named ‘’larger consumers’’. Consumers from other groups at LV level usually do not have installed devices to measure active load.

In these countries, there are many problems in energy sector related to a question of ownerships and organizations of power utilities, preparation for electricity market-opening and small investments in power sector especially at distribution level. All above mentioned facts as well as a lack of information about customers’ behavior at LV level cause that no attention is paid on a problem of realistic cost calculation and transparent relationship between customers and system.

Unrealistic calculations of the cost mostly affect huge numbers of customers from household category. Therefore, there is an interest of regulatory authorities and customers that better tariff design be applied as soon as possible. In the case of lack of data about daily load diagram of each customer needed for the cost calculation, any model that can provide base for better calculation is acceptable.

Current tariff structure in Bosnia and Herzegovina (BH) has not been significantly changed more than 20 years. In time of many changes in energy sector as well as changes in consumers’ habits, tariff system should be adequately redesigned.

A developed model, presented in the paper, has an aim to enable clustering of customers in household category without information about actual load but using available data in utilities as well as an expert knowledge. The model is based on fuzzy logic and fuzzy clustering. Interesting discussion related to differences in electricity pricing based on current tariff design and on obtained values from proposed model is done.

II. ELECTRICITY MARKET AND DISTRIBUTION CAPACITY COST

Distribution system as a part of power system defines own capacities according to the needs of connected customers to the network. Most of the distribution costs have a fixed rate that is related to the required distribution capacity engagement and to the cost of measurements and readings (in a smaller

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2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia

978-1-61284-286-8/11/$26.00 ©2011 IEEE

amount). Variable part of the distribution costs is related to covering losses in distribution network [4].

Most of customers are connected to the distribution network. Under market-oriented power system operation, it could be a lot of suppliers and buyers but transmission and distribution network is only one. It is not cost-effective to construct and maintain two or more parallel distribution or transmission networks. Therefore, distribution network as well as transmission has a position of natural monopoly in the power system.

Under market conditions, a customer has an option to choose a supplier, and by this he has an option for free contracting of energy and power prices with the supplier. Obligation of distribution operator and supplier is a transparent relationship with each customer.

However, regulatory bodies have a role to protect customers through well established tariff design. Defining the distribution capacity cost is very important for each consumer at LV. The part of distribution cost in total costs of power system in BH is about 40% [5], [6]. Assignment of distribution capacity cost and their allocation on customer’s categories without knowledge about actual load profile results with error in calculation.

Operator of the distribution system must provide all needed distribution capacity for each consumer connected to the distribution network. Supplier of each customer is obliged to calculate the actual cost of distribution capacity usage. The distribution capacity cost and its allocation is possible to be expressed through two tariff elements: billing demand and/or consumption of active electrical energy.

Billing demand is determined for the certain period by one of the following ways: on the basis of peak load measuring if the end user has a measuring device, using a device to limit the power, on the basis of the load system analysis and the analyses of consumers’ categories and groups.

Applying one or both tariff elements to calculation of distribution capacity cost depends on a method used for actual load determination. However, only if there is a measurement of load (e.g. hourly or 15 min), it is possible to accurately calculate distribution capacity costs. Also, share of load from different consumer’s categories in total load at distribution level presents an important element for allocation of distribution capacity cost.

Generally, information about daily load diagram plays an important role in market-oriented power system operation. Beside daily load diagram can help in calculation and allocation of distribution capacity cost, it also could be used in the purpose of analyzing behavior of different consumers and consumers’ categories, estimating losses, planning process, load forecasting [7], etc. Monitoring of the load diagram is an important prerequisite for competitiveness, particularly in the retailed electricity market.

In Bosnia and Herzegovina, the customers at high, medium and partly at low voltage are obligated to have measurements of daily load diagram. Unfortunately, for the significantly large number of customers classified in different categories and groups at LV, there is no obligation to measure

peak load [5], [6]. Therefore, current calculation of distribution capacity cost based on two above-mentioned tariff elements for customers at LV level certainly contains an error in comparison with the calculation that would be based on actually consumed power.

Due to current structure of customers in BH, change in system load and system consumption is mainly dictated by the behavior of consumers in distribution network. More precisely, customers from household category have a dominant share in electricity consumption at distribution level as well as at system level (Table I). Therefore, it is important to pay attention to calculation of distribution capacity cost that will enable each consumer to pay only actual cost he causes by own behavior in the system.

TABLE I OVERVIEW OF ELECTRICITY CONSUMPTION IN BH

Category of consumers

Electricity consumption

MWh

Share in total consumption (%) Distribution

level System

level 110 kV 2118368 21,24 35 kV 4956727 6,32 4,98 10 kV 1223914 15,58 12,27

0,4kV

Households 4385453 55,83 43,97 Other consumption 1579248 21,10 15,83 Public lightning 169736 2,16 1,70

Total 0,4 kV 6134437 78,10 61,51 Total distribution 7855078 100,00 78,76 Total system 9973446 100,00

If a tariff system is well-designed the customer gets a signal how to effectively and economically use electricity. The signal should stimulate customers to correct the simultaneous engagement of installed electrical equipment. If customers from household category have a possibility to obtain a realistic calculation of expenses during the simultaneous engagement of electrical devices, the customer would have interest to engage devices cost-effectively. In this case, customers from household category would influence on significant reduction of total peak load. Reduction of the load would directly affect on distribution capacity engagement, which finally causes a decrease in the cost of their providing.

Calculation of the distribution capacity cost to the customers in Bosnia and Herzegovina depends on the method of determining billing demand value.

Calculation of the cost to the household category according to the tariff element billing demand is different in three power utilities existing in BH. Billing demand as a tariff element in not used for customers in the household category in the power utility Elektroprivreda BH in Sarajevo. The fixed billing demand of 1 kW for both categories of household groups [8], [9] is defined in the power utility of Croatian Community of Herceg-Bosna (Elektroprivreda HZ HB, Mostar). The fixed billing demand is also applied in the power utility of the Republic of Srpska (Elektroprivreda RS, Trebinje) with rates 3.3 kW for the consumers with single-tariff meter and 5.2 kW for the consumers with the two-tariff meter [10]. These fix established rates for billing demand do not reflect actual costs of customers in the household category.

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2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia

III. CONSUMERS CLASSIFICATION

In order to calculate the exact actual cost of the distribution network usage, it is necessary have load diagram for each consumer at distribution level. Unfortunately, customers at the low voltage level mostly do not have load measurement and they will not have it soon. In the purpose of more realistic calculation of distribution capacity costs for the customers of household category the model based on fuzzy logic is proposed in the work.

A. Proposed model Proposed model is divided in two parts. The first part of the

model is used to calculate hourly peak load while the second one defines consumers’ groups.

The first part of the model is based on Mamdani fuzzy inference system (FIS) [11] that allows usage of expert knowledge. Expert knowledge is used to set membership functions and rules of Mamdani FIS. Input data in the model is electricity consumption from billing files, type of building (apartment, cottage, others), duration of stay in the building (periodically, after working hours, all day) and type of heating (electrical, others) for a group of consumers from the household category. Output in the proposed Mandami FIS is load duration (short, medium, long).

A typical fuzzy rule in Mamdani fuzzy inference system with two inputs is:

If x1 is A and x2 is B Then y is C, where A, B and C are fuzzy sets. Each fuzzy set is associated with a linguistic term such as short, medium, approximately zero, positive small, etc.

Inputs in proposed Mamdani FIS are: type of heating (x1) and duration of stay in the building (x2) while the output of the system (y) is time of load duration TU. One of the rules in the proposed FIS for apartment could be written as:

If type of heating is electrical and duration of stay in the building is all day

Then time of load duration is long.

Membership functions of inputs and output in the Mamdani FIS for apartment is shown in Fig.1.

On the same way, the Mamdani FIS was developed for other two types of building (cottage and others).

Obtained time of load duration for each customer in household category is used to calculate individual peak load, Pms, on an hourly base. Analyzing diagram of load duration, shown in Fig. 2, it is possible to set acceptable relationship between the load duration curve surface in the period of one year and the surface of characteristic triangle Pms0TU as

Up

Egms Tk

WP

∗∗

=2

where Pms is hourly peak load, WEg is total energy in one year, TU is time of load duration and kp is correction factor [12].

Fig. 1. Membership functions of inputs and output in Mamdani fuzzy

inference systems for apartment

Fig. 2. Load duration curve and characteristic triangle Pms0TU

Second part of the proposed model provides clustering of consumers in the household category based on energy consumption obtained from billing files in the utilities and the calculated peak load. In that purpose, fuzzy c-means clustering [11] was chosen. Fuzzy partition of the data is made in fuzzy clustering. Membership function μik can be any value between 0 and 1. For a given data set Z, in fuzzy c-means clustering the cost function J tried to be minimized:

2ik

mikJ �z −=� �

= =

n

1k

c

1iμ

where μik is membership function of kth data point in the ith cluster, ik �z − is a Euclidian distance between the ith cluster center and the kth data set, and m is weighting parameter ranged [ )∞= ,1m [13].

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2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia

Fuzzy c-means clustering has an option to pre-define number of clusters. In the proposed model three clusters in the household category is defined. The model gives a typical peak load for each cluster Pms1, Pms2 and Pms3. The obtained centers of pre-defined three clusters and their boundaries are shown in Fig.3.

Fig. 3. Centers of the pre-defined clusters

B. Analysis of results The proposed model enables calculation of the individually

daily load as well as determination of the representative clusters in the household category.

The first part of the proposed model was tested (validated) on a group of customers that had a measurement of daily load diagram. The group approximately reflects a structure of customers in the household category.

Comparison of the obtained values Pms in the model with really achieved one obtained in measurement campaign was done on the sample of customers. The average deviations are less than �<7%.

This percentage is higher than percentages obtained in the case of usage models for load curve estimation based on actual measurement of daily load diagram [7]. It was expected due to input data base was relatively small (contains information about up to 500 consumers) and due to approximations made in calculation.

The second part of the proposed model, define boundaries of three clusters inside the household category and center of the clusters (Table II). Reason of choosing only three clusters lies in the fact that results should be used for tariff redesign and higher number of clusters is not acceptable due to tariff simplicity.

TABLE II PROPOSED CLUSTER BOUNDARIES

Proposed boundaries Pms and WEg for each of three customer clusters

Clusters Pms WEg kW kW kWh kWh I - cluster 0 4 0 3000 II - cluster 4 8 3000 7000 III - cluster > 8 > 7000

To point out advantage of the proposed model, two different approaches to calculation of consumed energy consumption are presented. One calculation was done according to the current tariff rates over the tariff element active energy. Another calculation was done according to obtained peak load from the proposed model.

To illustrate the difference in these two ways of calculation, four customers are selected; customers A1 and A2, B1 and B2 shown in the Fig. 3. Customers named A1 and A2, whose total annual electricity consumption is approximately the same, WEgA�3300 kWh, have achieved a different hourly peak load PA1=3,22 kW and PA2=4,86 kW. Calculations summarized in Table III indicate that consumer A1 should pay less 24 KM while consumer A2 should pay extra 44 KM for the cost in comparison with current tariff design. It is also similar in the case of consumers B1 and B2, whose total annual consumption is about WEgB �4900 kWh with different hourly peak load PB1=5,04 kW and PB2=6,87 kW. Calculations indicate also that consumer B1 should pay less 20 KM while consumer B2 should pay extra 55 KM for the cost in comparison with current tariff design.

Obtained results are very understandable. Even if consumers A1 and A2 has almost the same energy consumption as well as consumers B1 and B2, realized peak load of consumers A1 and B1 is lower then realized peak load of consumers A2 and B2, respectively. It means that consumers A1 and B1 engage less distribution capacity then consumers A2 and B2. Therefore, the payment for the distribution capacity cost of consumers A1 and B1 should be smaller than for the consumers A2 and B2. These calculations justified the possibility of use the proposed model for improvement of current tariff design in Bosnia and Herzegovina.

TABLE III DIFFERENCES IN CALCULATION

Cus

tom

er c

ode

WEg Pms

Tot

al c

ost -

curr

ent t

ariff

Tot

al c

ost

- m

odel

Diff

eren

ce

(mod

el-c

urre

nt)

%

[kWh] [kW] [KM] [KM] [KM] A1 3386 3,2 423 399 -24 -5,57 A2 3322 4,9 415 459 44 10,53 B1 4.917 5,0 592 572 -20 -3,34 B2 4858 6,8 586 640 55 9,33

IV. APPLICATION OF THE MODEL RESULTS The model results can be well used to determine the

changes of engaged power. Correction of hired capacities is also possible. In the model, three customer clusters in household category were defined. In general, the number of clusters in the category could be greater than proposed, but due to simplicity of further tariff design [12] it is not acceptable. A larger number of clusters would complicate tariff design, and it would be incomprehensible for customers.

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2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia

The possibility to get more accurate calculation of the distribution capacity costs represents the quality of electricity supply. Consumer (buyer) is charged by the cost of using the distribution capacity in a transparent manner by correctly identified elements.

The model is based on the available databases in the utilities. The type and amount of data that needs to be involved in calculation of the value TU and Pms, can certainly be improved.

Improving the current database with more information about consumers' characteristics and providing peak load measurements of consumers with different behavior in the household category it is possible to develop model that will decrease errors in above presented results. However, the aim of the paper is to show a possibility of relatively quickly and simple improvement of current distribution costs calculation for a large number of consumers from the household category in the case of lack of data available in the utilities.

The proposed methodology could be used to develop similar models for consumers from other categories that have no obligation to measure peak load.

Application of the model includes continuous monitoring of energy consumption, updating customer characteristics and type of buildings. Based on the known information about customers’ characteristics, the customer could be allocated to one of the defined group of customers. The control of the belongings consumers to the group should be checked periodically (e.g. six months).

Before applying the proposed model for tariff design, it is necessary to do cost analysis for the household category and to make cost allocations to defined customer groups. The unit cost per 1 kW of peak load should be determined in the cost analyze and allocation. Tariff rates for the billing demand are not necessary to be differentiated by a season or period of a day, because seasons or day period has no significant effect on the deviation of the distribution capacity cost. According to the General Conditions for Electricity Supply [14], the power limiter in measurement set for consumers from household category is required. Installation of a limiter can provide additional verification of the model results accuracy.

Calculated values of individual peak loads and typical peak loads of consumer groups (clusters) beside the tariff redesign can be used for: • load forecasting, • estimation of simultaneous load, • control demand overflow according to energy agreement, • assessment of load share of the category household in

simultaneous peak load of the distribution system, • allocation of the distribution capacity costs on the groups

of customers in household category, • designing a tariff system, • estimation of the load diagram, • load planning, • planning of distribution capacities, • planning of needed production capacities.

V. CONCLUSION Fulfilling the obligations of equality and transparency for

the customers is one of the tasks of the electricity market. A prerequisite for correct calculation and billing consumed electricity of each customer connected to the power network is existence of information about actual consumer’s behavior. The actual image of network load will be available only if there is a real-time measurement of load (power).

Power is a value that defines distribution network capacity. Customers connected to LV network present a significant part of consumers in distribution network and their participation in total energy consumption as well as in peak power of the system is high. Each consumer has to pay only costs that he caused in power system in accordance to his way of energy usage.

The fact is that a large number of customers connected to the distribution network do not have power measurement and they will not have it in the near future. Until the conditions of power measurement for each customer would not be fulfilled, final electricity pricing will contain an error. Existence of the error in the electricity bill delivered to customers is not acceptable in market-oriented power system operation. It can cause negative customers’ feedback to the network. Changes in tariff design are not fast dynamic processes, and a negative customer impact to the network can have long-term consequences.

The proposed model presents just one of the possible solutions for utilities in transition period when measurement campaign for monitoring and collecting data about actual load has not yet been organized. Organizing measurement campaign is a time-consuming process and requests certain financial investments. In the meantime, calculation of distribution capacity cost in accordance with current tariff system allows charging a large numbers of consumers from household category with an incorrect electricity bill. The worst fact is that customers with smaller engaged power and smaller energy consumption subsidies customers with higher engaged power and higher energy consumption. In general, it means that customers with lower standard of living subsidies customers with higher living standard that is not acceptable at all.

Nowadays, in time of more and more market oriented operation of power system, regulatory authorities as well as consumers requires more realistic calculation of consumed energy. Therefore application of proposed model, even if it gives very rough values of peak load for consumers from household category, is better than waiting on collection of real-time data.

REFERENCES [1] Directive 2009/72/EC of the European Parliament and of the Council

of 13 July 2009, concerning common rules for the internal market in electricity and repealing Directive 3003/54/EC

[2] Technical Assistance to the Energy Regulation System of Bosnia and Herzegovina, ENREG-Emerging Markets Group, Assessment of the impact of opening electricity markets to regulatory procedures, Sarajevo, February 2008.

[3] R. Zarumba, K. Mc. Dermott, C. Peterson, Tariff methodology, Pierce Atwood/USAID, Seminar Tuzla 6-10 September 2004.

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[4] H. Pozar, Power and energy in power systems – 2nd Edition, Informator Zagreb, 1985.

[5] Regulatory commission for electricity in Federation Bosnia and Herzegovina-FERK Mostar, Rule on Tariff Methodology and Tariff Proceedings, (Official Gazzette F BiH, No 45/05).

[6] Regulatory Commission for Energy of the Republic of Serbian-RERS Trebinje Rules on tariff methodology and tariff proceedings, ("Official

Gazette of the Republic of Serbian" No. 61/05). [7] T. Konjic, “Using Fuzzy Inference System to Demand Forecasting in

Electric Power Distribution System”, Ph.D. dissertation, University of Tuzla, October 2003.

[8] Regulatory commission for electricity in Federation Bosnia and Herzegovina-FERK Mostar, Decision on tariff for sales of electricity to no-eligible (tariff) customers of JP Bosna i Hercegovina Sarajevo, December 2007.

[9] Regulatory commission for electricity in Federation Bosnia and Herzegovina-FERK Mostar, Decision on tariff for sales of electricity to no-eligible (tariff) customers of JP HZ HB Mostar, juny 2010.

[10] Regulatory Commission for Energy of the Republic of Serbian-RERS

Trebinje , website. Available: http://www.rees.ba/ ("Official Gazette of the Republic of Serbian" No. 61/05).

[11] T.J. Roos, Fuzzy logic with engineering aplications, 2nd Edition, John Wiley&Sons Ltd, WILEY, England 2004.

[12] S. Aganovic: “Fuzzy logic applied on consumer clustering in the purpose of tariff improvement”, Master work, University of Tuzla, June 2010.

[13] J. Bazdek, Pattern recognition with fuzzy objective function algorithm, Plenium, New Yourk, 1981.

[14] Regulatory commission for electricity in Federation Bosnia and Herzegovina, FERK Mostar website. Available: http://www.ferk.ba/

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2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia