Possibilities to Improve Reliability of Distribution

Possibilities to Improve Reliability of Distribution Network by Intended Island Operation Jussi Antikainen, Sami Repo, Pekka Verho, Pertti Järventausta Tampere University of Technology, P.O. Box 692, FI-33101 Tampere, Finland

e-mail [email protected]

Abstract— This paper discusses the possibilities of improving reliability of distribution networks. The focus of this paper is to study the reliability impacts of intended island operation in distribution systems. In this study, traditional development actions on networks are explored to enable comparison of the effects of intended island operation. Intended island operation is executed by means of distributed generation units (DG) located in medium voltage (MV) or in low voltage (LV) side of the network. The impacts of intended island operation are examined in case studies using reliability analysis software by observing changes in distribution reliability indices and in outage costs. The case studies are examined in a test network, which is based on a real network consisting of one rural feeder. Different placement of DG unit and changing environmental aspects are considered. Network reliability analysis methods are discussed in this paper in general level. Index Terms— Distributed generation, distributed energy resources, network reliability, optimal location, reliability based network analysis

planned carefully in any case. Therefore, it is very important to take island operation systematically into considerations when designing a network. Island operation requires, for instance additional control equipment and new communication methods. Load shedding can be necessary as well. In this paper we will not specify these technical issues involved with islanding. Also, the reliability of the DG unit itself is not under consideration. The main target is to quantify the effects of island operation on the reliability of the distribution network from the network owner’s point of view. The first part of this paper contains the general discussion of network reliability analysis. Information of this study and used initial data are presented too. In the second part of the paper the results and considerations of the simulations are given.

II. NETWORK RELIABILITY ANALYSIS I. INTRODUCTION Dependency on reliable power supply is an emergent trend [1]. In consequence of that trend, the development of reliability and availability of electricity networks has received more attention year by year. Intended islanding has been regarded as to be one of the possibilities to rise to the challenge [2,3]. Island operation in distribution networks can be based on local DG units or backup power units, like mobile stand-by power units. The idea of intended islanding is that DG unit supplies power to the loads during a grid failure in the upstream network. This will improve reliability by reducing the outage time of customers in the islanded part of the network. In case of abnormal operation, DG units can also be used as a part of power restoration arrangements by backup connections. This could offer a possibility of restoring power supply to more customers than in a case without DG. These considerations are studied and widely published in many earlier studies [4]. The results of earlier studies are quite positive on the part of island operation. Intended island operation requires some new approaches to planning and operating distribution networks. In addition, intended island operation must be

A. Reliability Major problems in a reliable power supply are consequences of faults on one or several network components. A failure in one network component, like a part of overhead line or a cable, is able to prevent power supply to a large area of distribution network. Customers in that area will suffer an outage whose duration depends on possibilities to arrange backup power to the downstream network from the faulted part. If there is no possibility to restore power supply, the outage time is the same as the repair time. If there is a possibility to isolate the faulted component and restore power supply, the outage time will be shorter. Distribution network reliability is related to fault frequency and outage time. Reliability can be defined numerically by reliability indices [5]. These indices give trends for trouble spots on the network and they make it possible to compare different ways to evolve the supply system. Simplifying the task, there are three main ways of improving reliability of the supply system: reducing the average fault frequency, the outage time and size of the fault-affected zone. However, this is a complex task because these three main ways include many possibilities for reaching the desired effect. The optimal

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solution is found when reliability of the network is maximised and the costs of outages and investments in a given consideration period are minimised. The average failure frequency can be reduced by using components with lower failure rates, e.g. replacing overhead lines with underground cables, improving the maintenance program and using network reclosers. Using distribution automation in networks (e.g. remote controlled disconnectors), supply restoration equipments (e.g. temporary cables and mobile generation units) or personnel training, the average interruption duration will be reduced. These actions to improve reliability are partially overlapping. At the same time, these actions can also be divided into preventive and corrective actions. Preventive actions reduce the probability that a potential problem will occur. These actions may also reduce the severity of a problem if one should occur. Corrective actions eliminate the cause of an existing undesirable situation in order to prevent reoccurrence.

B. Evaluation of outage costs The evaluation of outage costs is the way to estimate the quality of power supply along with reliability indices. The exact evaluation needs a lot of detailed information from the entire network, such as network topology, used components, components environment and condition as well as information about the customers. The fault isolation and power supply restoration are made on the grounds of the network topology. The information of used components, like conductor type, length, age and environment, weigh heavily on failure frequency. The type and placement of disconnectors affect the outage time. The customer details give information about needed power and energy. Based on these, it is possible to calculate the probability of outages and outage times in different parts of the network. This makes calculating outage costs possible. The outage cost evaluation is based on the value of nondistributed energy. Non-distributed energy will incur direct and indirect damage to customers. This damage is highly

Residential Agriculture Industry Public Commercial

Unexpected outage A B [€/kW] [€/kWh] 0.36 4.29 (0.07) (0.61) 0.45 9.38 (0.54) (4.90) 3.52 24.45 (2.60) (8.70) 1.89 15.08 (0.65) (3.40) 2.65 29.89 (1.90) (11.00)

dependent on several factors such as customer type, the actual load demand at the time of the outage and the time of day when the outage occurs. The outage types as long fault interruptions, planned maintenance outages or outages from auto-reclosing (high speed AR or delayed AR) affect the outage costs as well. The outage costs can be modelled with the outage cost parameters (more details in references [6-8]. Table 1 shows the parameters for each customer group and for different outages. The data is based on a Finnish questionnaire study conducted in 2005 [6] where different customers are divided into five groups: residential, agricultural, industry, public and commercial. In parentheses (Table 1) are the older parameters, which are based on earlier Finnish studies from the 70s, 80s and 90s [7]. The expected annual outage costs (C) caused by a fault in the zone under study is defined by using following equation 1. A zone refers to a part of feeder, which can be isolated by one or more switches from the rest of the feeder.

C = ∑∑ λzone ( Ai + Bit j ) Pij nij

(1)

j ∈ J i∈ I

where J is a set of load points to which a fault in the zone causes interruption, I is a set of customer groups, λzone is the sum of the individual network component failures per year in the zone, Ai is the constant outage cost parameter of customer group i [€/kW], Bi is time dependent outage cost parameter of customer group i [€/kWh] tj is expected outage duration of load point j in case of fault in the zone [h], nij is the number of customers of group i at load point j, Pij is average power of customer group i at load point j [kW]. Our economy is increasingly based on continuous power supply. The outage cost parameters (Table 1) have grown

Planned outage A B [€/kW] [€/kWh] 0.19 2.21 (0.03) (0.30) 0.23 4.80 (0.18) (1.60) 1.38 11.47 (0.80) (3.80) 1.33 7.35 (0.23) (1.50) 0.22 22.82 (0.80) (7.20)

High speed AR A [€/kW] 0.11 (0.03) 0.20 (0.25) 2.19 (1.10) 1.49 (0.23) 1.31 (0.95)

Delayed AR B [€/kWh] 0.48 (0.09) 0.62 (2.90) 2.87 (2.90) 2.34 (0.73) 2.44 (2.10)

Table 1: Interruption cost parameters for different customer groups based on questionnaire studies in 2005 and in 1993 (1993 results in parentheses).

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outstandingly in twelve years. We can assume that the outage cost parameters are in a trend of growing. This motivates network owners to evolve new approaches to improve the reliability of distribution networks. However, network investments in improving reliability should be related to the benefits customers enjoy from quality power supply. By applying reliability analysis and the outage costs as a part of network planning, the quality and reliability can be taken into consideration when optimising total costs and technical solutions. In this task the consideration period, the internal rate of interest and the growth of load have to be considered. These factors are always case-specific and have affect on the final results of the life-time costs.

III. DESCRIPTION OF THE RESEARCH The idea of this research is to find out how the intended island operation affects distribution network reliability. Intended island operation is based on the possibility of continuing power supply in a specific part of the network during an outage. The power demand should be related to the power generation in the island. This depends on the size of the island, quantity and type of the customers, point of time and the used power generation technology and the generation equipment among others. Power control and ability to start power generation in a de-energized network are important requirements as well as protection aspects. In this paper we do not consider these. We assume that these technical issues are solved. The effects of intended island operation are examined in case studies, which are presented later. The effects are calculated by using reliability-based network analysis software, developed by Tampere University of Technology. In the developed reliability based network analysis failure rates are based on the “partial failure rates” due to certain failure causes. Partial failure rates are, in turn, dependent on one or more weight factors. For example, the partial failure rate due to wind and/or snow for overhead lines is dependent on the surroundings of the line (forest, field or alongside a road) and on the neutral earthing method of the overhead line feeder. The reliability analysis software determines the outage time in different parts of the network by taking into account that some parts of the network can be restored in a few minutes by using remote controlled disconnectors, but some other parts must be restored manually, which will take some tens of minutes. In a fairly small part of the network the outage time is the same as the real repair time. In the reliability model several different switching times are applied and each outage time depends on how the faulty component, load point and remote controlled and manually operated disconnectors are situated. The reliability analysis results are the expected number and duration of outages at each load point in the network as well as the overall reliability indices (SAIFI, SAIDI, CAIDI and MAIFI). The load point specific information can further be

used as an input for outage cost modeling. More details are presented in references [8-10]. In this study the power generation is modelled for the power source with 100 % reliability. This means the secure power generation satisfies the power demand in all cases with complete availability. This could distort the simulation results but it is possible to reduce inaccuracy with simple modelling. The generation unit does not operate in parallel with the supply system. Thus, the generation unit switches on to the network after a 6-minute delay in case of an outage. The study network is based on a real network with real network data. The network data contains information about customers (group and average power) and network components (type, length, location) for example. The study network consists of one radial rural feeder including 54 distribution substations fed by a primary 110/20 kV substation (100 % reliability). The total line length of existing (overhead line) network is 71 km. The peak power is 1050 kW. The feeder has 4 remote controlled backup connections, 8 other remote controlled disconnectors and several manual operated disconnectors. The switching delay for a remote controlled disconnector is 6 minutes and for a manual operated

Figure 1: The test network used for analysis. Cross-lines mean remote controlled disconnectors (one line means that the disconnector is closed, two lines mean that the disconnector is open). BC is a backup connection, SA is the substation, L1-L6 are studied network nodes.

disconnector 60 minutes. The test network used is shown in Figure 1. In rural networks, conductors could be located in different environments. This affects the probability of an outage. The annual outage frequencies are different between conductors in a forest or in a field. It is easy to understand that the failure frequency in a forest will be higher than the failure frequency in a field. The failure frequency of a conductor which is located alongside a road could be lower than the frequency in the forest but more than the frequency in the field. Therefore, the conductor placement is another way of reducing outages. In the analysing software used, this is noticed by defining the outage frequency parameters for the main conductor types in different environments. The used outage frequencies are presented in Table 2. In case of a permanent outage, the repair time depends on

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the conductor type and the fault location. The type and location of the conductor also affects the maintenance costs. The data used for the repair times and the maintenance costs Location

Overhead line in forest by road in field Covered conductor line in forest by road in field Cable

Permanent faults [per 100 km,a]

Scheduled maintenance [per 100 km,a]

7.0 2.3 1.0

2.0 2.0 2.0

For assessing the effects of DG unit locations on the distribution system reliability, 6 different locations are examined. These locations, from L1 to L6, are shown in Temporary faults

Repair time

[per 100 km,a] High speed AR Delayed AR

[min/fault]

Maintenance costs [€/km,a]

180 50.0 37.0 5.0

20.0 12.0 1.0

260 215 170 180

4.0 1.3 1.0

2.0 2.0 2.0

10.0 10.0 10.0

4.0 4.0 4.0

0.5

2.0

-

-

200 170 140 240 80

Table 2: Used outage frequencies, repair times and maintenance costs.

are presented in Table 2. In this study, we also consider optional network solutions to improve reliability of the test network. These solutions are based on network development actions such as building new backup connections with different types of conductors, removing existing backup connections or building remote controlled disconnector stations. Removing backup connections reduces the length of the network and thus the annual outage frequency and volumes will be diminished. This can affect the reliability of the test network by upgrading it, but it can worsen the reliability on a larger scale, e.g. adjacent feeders. If the existing backup connection is removed, the sufficiency of the stand-by power has to be ensured. One way of ensuring the sufficient stand-by power during outages is to use distributed generation, e.g. mobile DG units. In this study, the study period is 20 years. The internal rate of interest used is 5 %, and growth of load is 1 % per year.

IV. THE RESULTS OF THE CASE STUDIES A. Intended island operation in medium voltage network

Figure 2: Variation of SAIDI in different cases. Basic signifies the original network without any changes.

Figure 1. The network automation, in locations L1, L3 and L6, are examined in point of comparison. The results of SAIDI are shown in Figure 2. The results show that the variation of reliability is strongly sensitive to the location of DG. According to the results, the best location for DG is the point L5, which causes the biggest improvement in reliability measured by SAIDI. SAIDI reduced from 217 (min/a) to 104 (min/a). Based on the results, DG or automation could be very beneficial from a reliability viewpoint. This depends on the existing backup connections and the focus of the loads. Therefore, there is no exact definition for the best location of DG and it is always case specific. It is also important to notice that reliable power supply for some customers is more significant than for others. Examples of these uppermost customers are hospitals, military services and other important society services. This affects the definition of the best location of DG as well. The results show that the network automation (remote controlled disconnector) reduced the system average outage time more than the DG in L1 or L3. At location points L1 and L3, the simulation results between DG and automation are almost the same, but it has to be noticed that the DG has abnormal 100 % reliability in power supply. This distorts the simulation results. In consequence, the network automation in the L1 and L3 can be preferable solution than DG to improve the reliability of the test network. This depends for instance on characteristics and technology of DG and the used protection methods. Automation or DG at a branch line has no great effect on the reliability of the entire test network. Even then, it can have a strong effect on an individual customer of the branch. DG at the location point L6 has almost the same effect on reliability as DG located in point L3. The small difference is explained by outages, which occur at the branch between points L3 and L6. If DG is located at L6 and a fault occurs at the branch, it is possible to continue power supply to customers in downstream network from the fault location after fault

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isolation time in intended island operation of the branch. If DG is located at L3, this is not possible. The load of the branch (180 kW) is quite small compared to the entire test network (1050 kW). Thus, if the load (and number of customers) of the branch is higher, the positive effect of DG at L6 on SAIDI is stronger. The total costs have to be considered to define the economical effects of DG or automation. The total costs during the planning horizon are defined by the sum of outage costs, maintenance costs, investment costs and failure repair costs to the network owner in the period of 20 years. The failure repair costs used for one failure was 1600 €. Maintenance and operation costs of DG are not included in the results. The cost estimate of investments with DG was 100 k€. The investment cost of remote controlled disconnectors in locations L1 and L6 was 16.5 k€, in location L3 the investment cost was 40.4 k€. The difference in the investment costs is based on the required number of new remote controlled disconnectors. The results are shown in Figure 3.

Figure 3: Variation of the total costs in planning horizon.

The results show that the investment costs are higher than savings in the total cost except for locations L4 and L5. The cost difference between cases Basic and DG located in L5 is 17 k€. From the economic point of view, DG solutions are not very advantageous. However, an interesting question lies in definition who makes the investment decisions and takes responsibility for them.

These constructions are overhead line (OHL in Figure 4) mainly in the forest, overhead line mainly alongside a road, covered conductor (CC in Figure 4) and cable. The length of the backup connection is varied as well. The results are shown in Figure 4. Note that SAIDI (217 min/a) in the case Basic is not printed on Figure 4. All considered network solutions have positive effects on the test network from the reliability point of view. The results show that the new backup connection reduces the average outage duration. This depends strongly on the length and the type of the backup connection. Cabling presents the biggest improvements in reliability, except for DG. When building the backup connection by overhead line selectively alongside a road, the failure frequency decreases in comparison to the overhead line mainly in the forest. An overhead line mainly alongside a road has the same effect on reliability than a covered conductor when using the selected relative ratio of forest, road and field. The relative ratios used are also presented in Figure 4. The results of the total costs are shown in Figure 5. The following investment costs have been used: overhead line 19.6 k€/km, CC 30.9 k€/km, cable 43.6 k€/km. The investment costs make many of these solutions quite unbeneficial, especially cable-based solutions. DG solution and the new backup connection constructed using overhead line or CC with 3 km in length, reduces the total costs. It is noteworthy that the results of the simulations indicate that an overhead line alongside a road can be a more cost-effective solution than a CC-based solution. For the future, it is reasonable to consider the same case with higher outage cost parameters and failure frequencies. This is because our society is growingly based on a highly

B. Comparison of DG and a new backup connection in connection point L5 DG located at L5 has the biggest influence on the reliability of the test network. Therefore, an exhaustive research is performed in L5. This includes comparison of DG and a new backup connection built in different constructions.

Figure 4: Variation of SAIDI in different cases. Gross-line is SAIDI in case of DG located at L5 (104 min/a).

Figure 5: Variations of the total costs in different cases. Gross-lines mean costs in cases Basic (dashed line) and DG at L5 (solid line).

Figure 6: Variations of the total costs in altered cases. Gross-lines mean costs in cases Basic (dashed line) and DG at L5 (solid line).

reliable power supply and the holding times of investments are very long. In this simulation the outage cost parameters are doubled and the used failure frequencies are increased by 50 %. The results are shown in Figure 6. The variations of outage costs and the failure frequencies alter the solution acceptability from the economical point of view. According to the results, the total costs decrease substantially with DG solution or with a new backup connection. Also, cabling has become more advantageous than in earlier cases. For example, savings made in the outage costs are now even bigger than the investment costs of a cable-based backup connection over 5 km long. In the cases of basic parameters and changed parameters the relative variation of SAIDI (-52 %) has not changed but the relative variation of the total costs have. The results are shown in Table 3.

SAIDI [min/a] Relative variation The total costs [k€] Relative variation

Basic parameters Changed parameters Basic DG L5 Basic DG L5 217 104 306 147 - 52 % - 52 % 730 713 - 2,3 %

1380 1162 - 15,8 %

Table 3: Relative variations of the total costs and SAIDI.

DG has become more advantageous in an altered environment. In the cases of basic parameters and changed parameters, DG solution gives the best results from the economical and the reliability point of view, except in the case of a 3 km long overhead line alongside a road.

C. Replacement of an existing backup connection by DG The examined test network has 4 backup connections, which are used to ensure reserve power supply during failures in the upstream network. This means reserve power supply to the examined test network and also reserve power supply to adjacent feeders throughout the test network. Table 4 represents the results of simulations where all 4 existing backup connections are removed and replaced by means of DG in locations L3 and L5. The simulations are carried out on two levels, on the test network level and on the level of the primary substation that supplies power to the test network and to the adjacent feeders.

reliable enough power supply unit with sufficient generation capacity. On the level of primary substation, the results are different. DG located in L3 led to a situation where the total costs and SAIDI increased. In a situation where DG is located in L5 SAIDI decreases but the total costs increase slightly. One reason for the decrease of SAIDI is that the length of the entire network is reduced. Another reason is that DG in L5 has such significant influence on the reliability of the test network. However, the total costs increased, which means worsened reliability to the adjacent feeders. According to the results on the level of primary substation, it is not possible to remove all existing backup connections. The simulation results give information about the possibility of replacing some of those 4 backup connections. DG located in L5 and one backup connection connected to L3 leads to a situation where SAIDI reduces 15.2 % and the total cost reduces 0.8 %. So, it may even be possible to make savings in investment costs when renovating a network by using DG-based solutions instead of rebuilding all backup connections.

D. Power supply of a low-loaded branch The test network has a low-loaded branch, which supplies power to one single low voltage network. Customers of this branch are mainly summer cottages. The following data has been used for the branch in this simulation: number of customers 12, peak power 24.4 kW, the length of branch 2 km, the investment cost of DG 10 k€. The average failure frequency of the entire test network was 3.9 failures per year. This consists of failures which occur partially at the branch and partially at other parts of the test network. The branch is shown in Figure 7. Single low voltage network Low-loaded branch

Figure 7: A low-loaded branch.

DG L3

DG L5

Test Test Substation Substation network network Total costs 10.4 % 3.1 % -2.4 % 0.3 % SAIDI -10.8 % 0.8 % -52.0 % -12.3 % Table 4: Relative variations of the total costs and SAIDI in the case of the replacement of an existing backup connection.

On the test network level, the results indicate that removing the existing backup connections could be possible. This is based on the variation of SAIDI. Secondly, the total costs decrease if DG is located in L5. Based on these, it could be possible to remove the existing backup connections if L5 has a

If the low-loaded branch is removed or de-energized and power supply to the low voltage network is arranged by means of DG, the customers of the low voltage network will not suffer outages that occur in the medium voltage network. In that case, reliability of power supply in the low voltage network depends on the characteristics of DG. And vice versa, reducing the length and number of branches in the medium voltage network diminishes the possibility of outages. So, reliability of the test network will increase. This affects a large group of customers. In that case, removal of the branch decreases the average

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failure frequency by 0.1 failures per year. SAIDI reduces by 5.7 minutes per year and the total costs reduce by 13.6 k€ when the investment cost to DG has been taken into account. The results are shown in Table 5.

Investment [k€] Total costs [k€/20a] SAIFI [1/a] SAIDI [min/a]

Basic

DG

CC

Cable

0.0 729.7 3.9 217.3

10.0 716.1 3.8 211.7

61.8 781.6 3.8 214.3

87.1 799.8 3.8 211.8

Table 5: Relative variations of the total costs and SAIDI.

Different constructions of the branch are explored to enable comparison of the effects of DG. Using CC or cable instead of overhead line improves the reliability of the test network with higher total costs.

V. CONCLUSION In this paper the possibilities of intended island operation to improve the reliability of distribution network are examined. Intended island operation effects are examined by observing changes in distribution reliability indices. Rationality of the effects is also evaluated in perspective of incurred costs or achieved savings in planning horizon. Effects are examined by reliability-based software in a test network, which is based on a real distribution network. In this study, traditional development actions on a network such as building new backup connections in different constructions and remote controlled disconnector stations are explored to enable comparison of the effects of intended island operation. According to this study, intended island operation can increase the reliability of the distribution network and reduce long-term costs of the network. The number and quality of the effects are strongly dependent on the placement of the production unit and on the reliability of power supply. According to calculations, the number of positive effects grows when failure frequency and outage costs increase. The effects of intended island operation on distribution network reliability are higher than examined alternatives except cabling the backup connections. From an economical point of view DG, is not always the best solution. Based on the study results, it can be possible to replace some of the network’s backup connections by means of DG, but it is important to notice the effects that removing backup connections can cause on a larger scale. The study results demonstrate that electrification of a single low voltage network by means of DG in island operation can be an advantageous solution.

[3]

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VI. REFERENCES [1]

[2]

Commission of the European communities, “The Green paper – A European strategy for sustainable, competitive and secure energy,” Brussels, 2006. Y. Sun, M.H.J. Bollen and G. Ault, “Improving distribution system reliability by means of distributed generation,” International conference on electricity distribution, Vienna, Austria, 21-24 May 2007.

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