Impacts of Service Quality Regulation on Distribution Automation Investment

Mohammad Sadegh Modarresi Electrical Engineering Department Sharif University of Technology

Tehran, Iran ms_modarresi@ee.sharif.edu

Mahmud Fotuhi-Firuzabad Electrical Engineering Department Sharif University of Technology

Tehran, Iran Fotuhi@sharif.edu

Hosein Mohammadnezhad-Shourkaei

Electrical Engineering Department Sharif University of Technology

Tehran, Iran h_m_shourkaei@ee.sharif.edu

Abstract— This paper investigates the effects of service quality regulation on investing in distribution automation. Among different regulatory instruments for quality regulation, the Reward-Penalty Scheme (RPS) has been selected to evaluate the effect of monetary incentive on behavior of Electric Distribution Utility (EDU) for service reliability. By implementing different automation strategies, the value of enhancing reliability indices and consequently the value of rewards and penalties are calculated for each strategy. For achieving to more realistic outcome, ten years horizon is considered. Furthermore, in the calculation of reliability indices, the time varying failure rate of components is taken into account. The proposed approach is applied to the Swedish Urban Reliability Test System to show the effectiveness of the proposed technique. This paper can be useful for decision makers in EDU to select the best automation strategy to maximize the utility profit in the presence of service quality regulation.

Keywords- Distribution automation, Electric distribution utility, Reward-penalty scheme.

I. INTRODUCTION Most customer interruptions are directly in relation to the

distribution network. Data on network failure statistics show that distribution system failures constitute approximately 80 percent of total customer’s interruptions. In spite of this fact, distribution systems have received less attention than generation and transmission systems. This can clearly be seen by having a simple review over the publication of the last decades. However, as business attention changes from consumers to customers, focuses are moving toward distribution in recent years [1]. Nowadays, the regulators examine the incentive-based mechanism to induce the EDUs to improve the system reliability.

The basic concept which the regulator controls the EDUs has been switched from cost-based regulation to a more competitive regulation method known as Performance-Based

Regulation or Performance-Based Rates (PBR) [2]. Both theory and empirical evidences show that when a regulator chooses to regulate prices using price or revenue caps, it causes a decrease in company’s incentives for delivering efficient levels of service quality. Consequently, price cap or revenue cap regulation must be supported by service quality regulation [3]. The reward-penalty scheme is the most used tool in quality regulation to penalize the undesirable performed EDUs and reward the desirable performed EDUs.

In a profit based EDU, the effect of the newcomer factor, reward/penalty, should be consider on the profit. The utility can examine the maintenance or capital projects to improve the reliability or decide to leave the upgrading of the network to be fined. The decision-making process about the network either upgrading itself or accepting penalties are evaluated in this paper. Distribution automation has been recognized as a way to use appropriate technology intelligently to enhance the reliability at lower system-operation cost [1]. Therefore, the effect of RPS on different automation schemes has been studied to show which automation scheme should be taken. This approach can also be applied to the other projects related to reliability improvement as well.

In this paper, Swedish Urban Reliability Test System (SURTS) is used as the test network and different effective automation schemes are implemented in that system. The RPS is applied within a time horizon of ten years.[3] Reliability indices associated with each scheme and different financial outcomes are met as results. For the sake of facing practical issues, the time varying failure rates for both cables and transformers are applied. In addition, the maintenance, which is a typical procedure in practical distribution systems, is conducted on both cables and transformers in a fixed time interval.

II. PERFORMANCE BASED REGULATION As a result of privatization of grid ownership in recent

years, and due to monopoly nature of distribution systems, the need for service quality regulation has been increased. Traditionally, cost-based regulation used to be applied to the

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distribution networks, but in recent years a more competitive way of regulation known as PBR is going to become much more popular in effective quality improvement of distribution networks. This quality may include improvement of system reliability, power quality, customer services, etc [4].

Service Quality Regulation (SQR) has been defined to regulate the quality of service. One of the most important regulatory instruments used in SQR is RPS. For applying this scheme, some system level reliability indices are chosen which cover customer preferences. For instance, when both frequency and duration of interruptions are important for our purpose, commonly “System Average Interruption Frequency Index” (SAIFI) and “System Average Interruption Duration Index” (SAIDI) are in use. [5] After choosing appropriate indices, a performance standard should be defined, which it can be current value of indices of the network or the ideal value of the indices or etc. This selection depends mainly on the regulator policy. So, the quality, which is better or worse than the performance standard, will be rewarded or penalized, respectively, as illustrated by the dashed line in Fig. 1[3].

Figure 1. Linear and capped the incentive schemes.

The ramp of this figure is called incentive rate (IR). It should reflect the customer valuation of higher or lower level of quality and these valuations are best obtained through customer surveys [3] in which they are mainly depend on the customer interruption cost and willingness to pay. When the value of interrupted energy and value of interrupted power are pre-defined for different customers, IR can be formulated for SAIFI and SAIDI as follows:

��

���n

iiiiSAIDI VENPIR

1 (1)

��

���n

iiiiSAIFI VPNPIR

1 (2)

Where;

n is the number of customer categories,

Pi is the annual average load of customers in the ith category in (kW),

Ni is the number of customers in the ith category,

VEi is the value of energy not supplied for customers in the ith category in ($/kWh),

VPi is the value of interrupted power for customers in the ith category, ($/kW),

As shown in Fig. 1, in some countries, there is a linear relation between quality and rewards, but some regulatory authorities define upper and lower bound for it. Actually, in this way, rewards and penalties are capped. These upper and lower bonds are different among different countries and different reliability indices. For instance, in Netherlands, the change in the revenue is capped at ±5% of total annual revenue [3].

Ideally, when financial incentive accurately reflects the customer costs and benefits for different quality of supply, there is no need for putting these boundaries [3]. However, in practice, this might make some problems for the EDU. For instance, there is a risk of penalizing the company to a point that will be financially unbearable for it.

III. DISTRIBUTION AUTOMATION

Considering Smart Grid as today’s popular research field in power systems, Distribution Automation (DA) is the verb that empowers the smart grid to become really smart [6]. DA can be divided into three levels, substation, feeder and customer premises automation [1]. Among them, Feeder Automation (FA) is known as an attractive remedial action in enhancing distribution system reliability to the desired level.

Two typical types of failures can occur on distribution network; permanent failures and temporary failures. In cable-based networks, which are the case here, almost all of the interruptions are due to component damage and are permanent. Since recloser is installed to remove the effect of temporary failures, any reclosing strategy cannot be used. However, a compact kind of switchgear called Metal Clad Switchgear (MCS) is being used in cable-based network [7]. Metal-clad switchgear assemblies are used for indoor and outdoor installation with a voltage range of 2.4 kV to 15 kV, with continuous current rating [8]. Its function is similar to the conventional breakers and can operate in the case of extreme faults. When MCS senses lack of voltage on the source side, it will automatically open and let the possible loop restoration.

Among all levels of automation, there is a simple but effective way of automation called “Loop Restoration Scheme” (LRS). Some distribution feeders have a normally open switch supported by a breaker at their tie point. This switch can be remotely or manually ordered to restore the customers, which are not directly affected by probable fault. In the automated system, by coordinating the timers of metal-clad breakers and normally open tie switches, reliability indices of the whole feeder improve dramatically. In networks which consist of both overhead and cable lines, natural locations for line reclosers are downstream of load centers, at the interface between underground cables and overhead lines [9], but in the cable based networks, the best location of line recloser is defined either by experts of the system or by modeling and comparing all possible locations. In some cases, modeling of the system helps the experts to decide about their strategy in using them. A

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typical cable-based distribution feeder is shown in Fig. 2 which is the base of modeling in this paper.

When a fault occurs in a typical distribution system, a process begins which is shortly described here. First, the customers announce the dispatcher about their problem and their condition, then the dispatcher contacts troubleshooter, until this stage it takes at least 5-10 minutes. After that, the troubleshooter must travel to the faulted location, the travel time is not predefined practically but it usually takes about half an hour in urban areas. In the next stage, troubleshooter must perform the required switching actions to restore some affected users. Finally, the troubleshooter repairs the faulted component and restores the remaining customers.

Figure 2. Typical cable based feeder with a tie switch.

For instance, assume that a fault occurs on section 2 without loop restoration availability, substation circuit breaker CB1 first opens and cutoff all the customers in load points 1 to 3 and it locks out afterwards (because almost all the faults are permanent and there is no need for reclosing strategy). The customers on those load point remain powerless until the faulted area is repaired which requires a meaningful time that last for few hours in most cases. At this point, if another feeder is available for LR, this time will decreases to “switching time” by the troubleshooter, which takes about one hour for the customers in load points 2 and 3. This switching time is still a considerable time, especially for industrial customers.

To describe the impact of automation on reliability indices of the typical distribution network, a metal clad Section Breaker (SB) is used instead of section switch on 2A in Fig. 2. The operation condition of SB in presence of different fault conditions expressed as follows. It should be noted that implementing more than one SB along a cable-based distribution feeder is not common in practice.

� When a fault occurs on the section 1, CB1 opens and locks out, then the normally closed section breaker SB on 2A (SB2A) feels the lack of voltage, also the normally open breaker on tie switch scenes voltage loss on its SB2A side. Timers of both switches begin to operate. The control system designed in a way that the time delay at SB2A expires first, and then SB2A opens and locks out. The time delay of tie switch expires next, and tie switch closes and restores the load points 2 and 3. This takes about 3 minutes, which is a

considerable improvement in contrast to the switching time.

� When a fault occurs on section 2, SB2A opens and locks out. The load point 1, does not feel this fault and its electricity supply remains uninterrupted. The tie switch senses voltage loss on its SB2A side, after expiration of its time delay, it closes and senses fault current, then trips and locks out. At this point troubleshooter should manually open 3A, close the breaker of tie switch, and restore the customers in load points 2 and 3.

� When a fault occurs on section 3, a procedure similar to the previous case will follow. At this time, troubleshooters open the required section switches (4A and 5A), then they will close SB2A and tripped tie breaker and restore the customers in load points 2 and 3.

� When a fault occurs on transformer T1, CB1 opens and locks out. SB2A and the tie switch feels the lack of voltage and their timers begins to count. The timer of SB2A expires first and it opens and locks out, then the time delay of the tie switch expire and it closes and restore the load points 2 and 3. Load point 1 remains out of service until the repairing or switching of the transformer.

� When a fault occurs on the transformer T2, SB2A first opens and locks out, the tie switch senses voltage loss on its SB2A side and its timer start counting, after expiration of its time delay, it closes and senses fault current, then trips and locks out. Then the troubleshooter opens section switches 3A and 4A and close breaker of the tie switch. Consequently load point 3 remains out of service for this switching period, load point 2 is out for repair time or switching time of the transformer, and load point 1 is not affected by this fault.

� When a fault occurs on transformer T3, a similar procedure to the last case will follow. This time the troubleshooter will open the required section switches 5A and switches of tie switch and close SB2A and restore the customers in load point 2.

IV. IMPACTS OF RPS ON DISTRIBUTION AUTOMATION For studying the impact of RPS on distribution automation,

first some indices for applying regulation must be selected. After choosing the desired indices, which are SAIFI and SAIDI here, for each year these indices are calculated using (3) and (4):

��

�

ll

lllj

j N

NSAIFI

,� (3)

��

�

ll

lllj

j N

NUSAIDI

,

(4)

375

Where λj,l ,Uj,l and Nl are respectively the failure rate, annual outage time and the number of customers for load point l, and j is the year of study. Nl has been assumed to be fixed during the period of study. In practice, λl varies among years and usually follows a Weibull distribution. It is increasing during the time, but has been assumed to be constant during each year as shown in Fig. 3.

Figure 3. Time varying failure rate.

For the purpose of designing a RPS based on SAIFI and SAIDI, (1) and (2) are in use for calculating incentive rates. Since the caps value for reward and penalty are related to EDU revenue, first, Total Revenue (TR) of the EDU must be calculated as follows:

��

��n

ii

it TariffETR

1 (5)

Where itE is the energy consumption in MWh for customer

category ith during the tth year, and Tariffi is tariff per MWh by customer category ith. Then cap values for target indices can be estimated as:

TRCapSAIFI �� %� (6)

TRCapSAIDI �� %� (7)

Where value of σ and ε must be defined by regulator and depends on its policy. Usually, the cap values do not exceed the ten percent of company revenue.

It has been assumed that the whole amount of energy supplied in the test network is incomparable to the energy not supplied during applying different automation schemes. So, the amount of energy supplied is assumed to be fixed in all schemes. This assumption leads to a fixed amount of “total annual revenue”, which means that, instead of maximizing revenue in our objective function, the cost should be minimized.

The system cost consists of investment cost and operation cost. In the presence of RPS, annual penalties that an EDU must pay are included in the operation cost. It should be noted that the reward is considered as a penalty with negative sign in the objective function. So, the objective is to minimize both investment cost which depends on the level of automation applied to the system, and the penalties. Other operation costs, which are approximately fixed, are not included in the

optimization procedure. In regulation studies, when a time frames of “ß-α+1” years is in use, our objective function can be defined as:

k

jkj

kj

kj

kj

kCost

SAIFIRSAIFIP

SAIDIRSAIDIPCostMin

�

�

�

�

�

�� �

�

�

� )()(

)()()( (8)

Which α is the start year of regulation, ß is the end year of the study and k is the number of automation schemes in the study.

)(SAIDIPkj , )(SAIDIRk

j , )(SAIFIPkj and )(SAIFIRk

j are the penalty and reward due to SAIDI and SAIFI in the jth year for scheme kth, respectively. costk is the investment cost of applying the automation scheme kth.

V. CASE STUDY In this case study, SURTS, shown in Fig. 4 is used for

the analysis. The system has ten identical loops with approximately 1100 customers and 10 km cable feeder. There is no overhead line in this distribution test system. Four different customer categories represented, residential, commercial, industrial and governmental [10]. The customer composition at different load points, load data, line length and reliability data for components are specified in [10]. In [10], the failure rate of underground cables and transformers are assumed to be constant during the study period. This is not a good assumption for a long term reliability study, especially in applying regulation policies which are validated at least for 3 to 10 years. In addition, practically, there are several environmental issues that lead to increasing failure rate of each component, such as water-treeing, which is the predominant cause of failures in underground polymeric cables, diffusing structure of generally unconnected and Swiss cheese-like holes in the polymer [11].

Figure 4. System layout of the Swedish urban reliability test system

(SURTS).

Failure rate of substation transformers on the first year of study are extracted from [10]. In this paper, α and ß of (8), which they define the first and the last year of study, are fixed to 5 and 14 respectively. Based on [12], maintenance for cables is conducted each 10 years. In this paper, the same interval is used for transformers. For the transformers, it has been assumed that the maintenance brings the failure rate back to the second year of transformer age but it is not the same for the

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cable. For cables, data on failure rates before and after the maintenance, and repair times are extracted from [12]. Also, the stair wise failure rate of transformers has been calculated from Weibull distributions at the beginning of each year with αw=5.8 and βw=3 as follows:

w

w

w

wt

t�

�

���

1

)(

��� (8)

Failure rate, repair time and switching time of components, used in this paper at the beginning of our study, which is the 5th year, are summarized in Table I.

Fig. 5 clearly shows one of the 10 feeders illustrated above in different manner. As it is desire to be investigated whether or not DA implementation is economical, first the system under study must be known. Based on [10], there are some fairly important loads connected to buses 2 and 6. Although it is definitely depends on experts of the system to decide; it was opted for taking care about these two loads more than the others in our automation case study. There reason of this decision is existence of a fairly big commercial load at bus 2 and a big industrial load at bus 6.

In Table II, network tariff, value of power and value of energy for different customer types are tabulated based on [13, 14]. The parameters of RPS are given in Table III. These parameters are calculated based on data in Table II. In this paper, the value of SAIFI and SAIDI calculated in [10] are used as performance standards in RPS.

Figure 5. Schematic of one chosen feeder of the system.

Five different automation schemes are introduced as following:

1) Simple system without LR. 2) Simple system with LR. 3) Automation applied to feeder A with LR. 4) Automation applied to feeder B with LR 5) Automation applied to both feeders A and B with LR.

A summary of the schemes and the results are tabulated in

Table IV. Investment costs for scheme 2 is extracted from [6] and due to restricted access to data investment costs of schemes

3-5 are estimated. Variations of SAIDI and SAIFI for different scheme during ten years study are shown in Figs. 6 and 7. The reduction of SAIDI and SAIFI in year 10 is due to performing maintenance on cables and transformers.

By looking at Figs. 6 and 7, it may be thought that scheme 5 is the best scheme among these five. However, by looking back at Table IV, it will be found out that choosing the answer is not that simple. If the cost is the main concern, EDU will choose the 3rd scheme which has the minimum cost. But, one point has been missed here. By considering the second scheme of automation in Table IV and Figs. 6 and 7, it can be seen that remarkable improvement in reliability indices and reduction in penalty have occurred with lowest investment cost. So, depending on companies’ financial capabilities, the answer is different.

TABLE I. RELIABILITY DATA OF COMPONENTS

Component λ5*(f/year) r or rp

**(min) Sw**(min) Cable 0.14 87 60

Transformer 0.388 150 60 * λ5 means the value of λ in the 5th year which is the first year of study. ** rp is replacement time, Sw is switching time

TABLE II. NETWORK TARIFF, VALUE OF POWER AND VALUE OF ENERGY (PER CUSTOMER TYPE)

Cus. Type Tariff per MWh ($/MWh)

Value of Power ($/kW)

Value of Energy ($/kWh)

Residential 55.9 0.18 0.35 Industrial 25.5 1.1 4.1

Commercial 25.6 3.5 12.1 Pub. sectors 25.6 6.9 6.9

TABLE III. THE PARAMETERS IN THE INVESTED REWARD-PENALTY SCHEME

Parameters SAIDI SAIFI Incentive Rate 13.5 4.4

PS*** 0.232 0.210 Capped values****(k$) 32.5 19.5

* SAIDI and SAIFI are in hr/year and failure/year respectively

** Incentive rates are in k$/hr and k$/failure *** PS is performance standard used as ideal case

**** 10% of TR for SAIDI and 3% of TR for SAIFI, TR is approximately 650 k$ in each year.

TABLE IV. SUMMERY OF VARIOUS SCHEMES

Scheme SB location LR Inv. Cost(k$) Penalty(k$) Total

Cost(k$) 1 - No - 687 687 2 - Yes 13.7 556.1 569.8 3 4A Yes 70 476 546 4 2B Yes 70 538.2 608.2 5 4A & 2B Yes 125 445.1 570.1

In most cases, the company desire is the highest decrease in the penalties at the lowest investment; In addition, some distribution company’s policy of conservatism affects this decision. Sometimes, they do not trust new technologies, and they prefer using conventional facilities to enhance their system

377

performance. Even occasionally for some reasons, the company is trying to serve their industrial customers at a higher level of reliability, then it compare 4th and 5th schemes. Therefore, theoretically, third scheme is the best but practically the answer may switch to the other schemes due to the company’s policy.

0

2

4

6

8

10

12

5 6 7 8 9 10 11 12 13 14Year

SAID

I (hr

/yr)

Scheme 1 Scheme 2 Scheme 3Scheme 4 Scheme 5

Figure 6. Comparison of SAIDI among different cases

0

1

2

3

4

5

6

5 6 7 8 9 10 11 12 13 14Year

SAIF

I (f/y

r)

Scheme 1 Scheme 2 Scheme 3Scheme 4 Scheme 5

Figure 7. Comparison of SAIFI among different cases.

VI. CONCLUSION As far as electric distribution utilities are tend to maximize

their profit, due to monopoly nature of them, service quality deterioration is the main concern. So, regulators tend to focus more on performance based regulation. Like all regulatory policies, PBR uses some tools for its purpose. The reward penalty scheme, which is one of the regulatory tools for regulating quality, is applied in this paper. This paper investigated the impact of RPS on applying different automation schemes. The results presented indicate that top manger in the electric utility should consider the RPS in their decision making for investing on network automation. A trade-off between the automation cost and the value of penalty reduction due to reliability improvement should be considered.

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[3] E. Fumagalli, L. Lo Schiavo, F. Delestre, “Service Quality Regulation in Electricity Distribution and Retail”, Springer-Verlag Berlin Heidelberg 2007.

[4] R. Chen, S. Sabri, “The Benefits of Implementing Distribution Automation and System Monitoring in the Open Electricity Marke”, Canadian Conference on Electrical and Computer Engineering, 2001, Toronto.

[5] R. Billinton, R.N. Allen, “Reliability evaluation of power systems”, New York, US (2nd Edition, Plenum) 1996.

[6] R. Kimura, B. Flynn, “Justifying Distribution Automation”, 20th International Conference on Electricity Distribution, Prague, 8-11 June 2009, CIRED 2009.

[7] J.N. Green, R.Wilson, “Control and Automation of Electrical Power Distribution Systems”, Taylor & Francis Group, 2007.

[8] E. Sarviel, “Construction Estimating Reference Data”, Craftsman Book Company, 1993.

[9] H. Lagland, K. Kauhaniemi, T. Hakola and J. Rintamaki, “Impact of Quality Improvement Incentives on Automation Investment in The Finish New Regulation Model”, 20th International Conference on Electricity Distribution, Prague, 8-11 June 2009, CIRED 2009

[10] M. J. Ueda, O. Engblom, and K. Alvehag, “Representative test system for Swedish distribution networks,” 20th International Conference on Electricity Distribution, Prague, 8-11 June 2009, CIRED 2009.

[11] http://www.utilx.com/power/cablecure.aspx [12] L. Bertling, R. Allan, and R. Eriksson, “A Reliability-Centered Asset

Maintenance Method for Assessing the Impact of Maintenance in Power Distribution Systems,” IEEE Transactions on Power Systems, vol. 20, No. 1, February 2005.

[13] K. Alvehag, L. Soder, Financial risk assessment for distribution operators regulated by quality regulation, PMAPS 2010.

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