Review

Aptamer-based lateral flow assays

  • Received: 15 January 2018 Accepted: 07 April 2018 Published: 23 April 2018
  • Lateral flow assays as point-of-care devices have attracted interest due to their advantages including low costs, easy operation by non-specialized users and low analyte volumes needed. These advantages make lateral flow assays to superior tools for the on-site detection of analytes with qualitative or semi-quantitative results within minutes. Aptamers are single-stranded nucleic acid oligomers with distinct conformational shapes that can bind their corresponding targets via molecular recognition. Due to their specific properties like an efficient chemical synthesis, a longer shelf life and easy introduction of modifications aptamers seem to be ideal biological recognition elements. Moreover, they enable the design of intelligent detection schemes not available with antibodies. This review focuses on lateral flow assays utilizing aptamers as an element for molecular recognition. In the first part of the review, a brief overview on lateral flow assays and aptamers, regarding their generation and properties, will be given. In the second part, a review of recently published literature on this topic will demonstrate the broad spectrum of possible applications and analytes, detectable with aptamer-based lateral flow assays.

    Citation: Torsten Schüling, Alina Eilers, Thomas Scheper, Johanna Walter. Aptamer-based lateral flow assays[J]. AIMS Bioengineering, 2018, 5(2): 78-102. doi: 10.3934/bioeng.2018.2.78

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  • Lateral flow assays as point-of-care devices have attracted interest due to their advantages including low costs, easy operation by non-specialized users and low analyte volumes needed. These advantages make lateral flow assays to superior tools for the on-site detection of analytes with qualitative or semi-quantitative results within minutes. Aptamers are single-stranded nucleic acid oligomers with distinct conformational shapes that can bind their corresponding targets via molecular recognition. Due to their specific properties like an efficient chemical synthesis, a longer shelf life and easy introduction of modifications aptamers seem to be ideal biological recognition elements. Moreover, they enable the design of intelligent detection schemes not available with antibodies. This review focuses on lateral flow assays utilizing aptamers as an element for molecular recognition. In the first part of the review, a brief overview on lateral flow assays and aptamers, regarding their generation and properties, will be given. In the second part, a review of recently published literature on this topic will demonstrate the broad spectrum of possible applications and analytes, detectable with aptamer-based lateral flow assays.


    In the present day, reliability evaluation of distribution networks is a topic of great concern. The requirement for continuous power supplies has continued to increases in competitive market scenarios with the reorganization of the network. In the report on Canadian customer service utility, 80% (approximately) of customer interruptions were due to the failure of any device in the distribution system [1,2]. Hence, enhancing the distribution network reliability in a cost-effective manner is the prime area of research for researchers.

    The most significant attributes for the reliability analysis are failure frequency, restoration time and switching time. Among the several ways to achieve reliability enhancement, incorporation of the automatic switch can provide faster restoration of service during unexpected failure events and can thus improve the reliability. As depicted in [2], a fully automatic network will reduce the interruption cost by 80%; however, it is not economically justifiable to install the remote-controlled switch (RCS) at all of the customer points. The installation and maintenance cost for a large number of RCSs would be extremely high. Therefore, an optimal number of RCSs is to be determined to minimize the investment cost for maximization of the reduction in cost for energy not served (ENS). Hence, major research works in this area are briefed in this section.

    Over the last few decades, the switch allocation issue has attracted researchers' attention and numerous studies have been conducted [3,4,5]. With new trends emerging in automation, RCSs are becoming increasingly important in reliability studies. In order to conduct the cost-worth assessments for reliability enhancement in distribution networks, the sequential Monte-Carlo simulation approach was used in [6]. This article show the calculations for various financial risk indices such as the volatility index, value at risk and conditional value at risk to quantify the risk. In [7], the malfunction probability of RCSs has been considered to extend the current reliability assessment procedure; the results showed that an RCS improves the system reliability. The non-dominated sorting genetics algorithm-II (NSGA-II) has been utilized in [8] for financial risk evaluation, as associated with RCS placement and tested on a 4-bus RBTS system.

    Mixed integer programming has been used in [9,10] to find the optimal location and numbers of RCS in a distribution network for reliability enhancement. A new bi-directional model has been proposed in [11] for the optimal placement of switches and protective devices in a distribution network using genetics algorithm (GA) with distributed generation (DG). In [12], a fuzzy multi-criteria decision-making algorithm has been used to allocate the RCS in a distribution network. In [13], mixed integer non-linear programming has been used to identify the optimal location and numbers of protective devices, including the load uncertainties, temporary and permanent failure rates and repair rates. A bi-directional model has been proposed in [14] for the optimized allocation of reclosers in a distribution network using a GA approach. In [15], a GA-based method was used for simultaneous allocation of the DGs and RCSs in a distribution network for power loss reduction and reliability improvement. The problem of optimal allocation of RCSs in a distribution network has been resolved using a differential search (DS) algorithm in [16]. In [17,18], a memetic optimization approach has been utilized for the multi-objective planning of a distribution network with switches and protective devices, respectively. An analytical hierarchical process decision making algorithm has been implemented in [19] for the allocation of RCSs in a distribution network. In [20] for reliability enhancement, Esmaeilian and Fadaeinedjad implemented a binary gravitational search algorithm for the reorganization of the system and capacitor placement in a distribution system. A new sample construction with a path relinking method has been applied for the switch allocation in [21] to achieve the reliability enhancement of distribution networks. In [22], the reliability evaluation of a distribution network was performed in consideration of the aging effect of the components and load growth. In [23], the optimal location of fuse cutouts was determined in consideration of the fuse failure probability by using a Markov model.

    Although an RCS can improve the system reliability in terms of service availability; however, it requires a huge investment as the installation cost of automatic switches is quite high. Therefore, considering the cost-effective allocation of RCSs in distribution network, this paper contributes the following to existing literature.

    1) This paper identifies the optimal location of the RCS by applying a greedy search algorithm in distribution networks in a cost-effective manner.

    2) It analyzes the impact of load growth on system and cost-worth reliability indices in the presence of RCSs.

    3) It analyzes the impact of fuse failure probability in the presence of an RCS on the system and cost-worth reliability indices.

    4) It analyzes the impact of feeder and transformer aging on the reliability parameters including RCS placement, for real-time analysis of the system.

    The outcomes of the proposed research extend the present reliability evaluation procedure to include all possible system contingencies. The organization of the paper is as follows. Section 2 provides brief details about RCSs and the reliability indices for the system. Sections 3 and 4 are dedicated to problem formulation and mathematical modeling, respectively. Section 5 presents the strategy used for finding the optimal location and numbers of RCSs. Section 6 presents the computational results. Finally, Section 7 concludes the article.

    In recent trends of the smart grid and modernization of existing distribution networks, the automatic switch is proved to be a source of revolution. It can improve the service availability at the consumer end as its switching time is very short. The RCS can be used as a sectionalizing switch (normally close) as well as tie switches (normally open). In a radial distribution network, normally closed automatic switches are used to isolate the faulty section from rest of the system. Therefore, the location of the automatic switch can improve the system reliability to a great extent.

    The main contributing reliability indices are failure rate (λ), repair time (r), switching time (s) and annual outage duration (U). The failure rate represents the failure occurrence frequency. The repair time represents the time needed to repair the faulty section. The switching time represents the time required to restore the supply to a healthy section by switching off the faulty part. Outage duration is considered on an annual basis and calculated via either multiplication of the failure rate with repair time or multiplication of the failure rate and switching time.

    Although the RCS does not have any considerable impact on the failure rate, the switching time, in the presence of an RCS is only 10 minutes; thus it will enhance the service availability by fast restoration of the supply. If a fault occurs downstream of the load point (LP) and no switch is available between them, then the LP will be affected by the repair time. In spite of this, if a normal switch is available between them, the switching time will be applicable. If the automatic switch is connected in place of a normal switch, the switching time would reduce by a great extent and fast service restoration could be achieved.

    With the help of the failure rate, repair/switching time data and details about the load and customers at each load point, the cost-worth reliability indices "ENS and expected interruption cost (ECOST)" and System Reliability Indices "system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI)" can be evaluated. The ENS is the reliability indices that is the focus of this work.

    If the failure rate is denoted as λ (failure/yr/km), the repair time as r (hours) and switching time as s (hours) then the annual outage duration U is given as

    $ {U}_{i} = \sum_{i = 1}^{n}\left({\lambda }_{i}\times {l}_{i}\right)\times {r}_{i}+\sum{i = 1}^{n}\left({\lambda }_{i}\times {l}_{i}\right)\times {s}_{i} $ (1)

    where, li represents the feeder length.

    The annual ENS is obtained as

    $ {ENS}_{i} = \sum_{i = 1}^{LP}{U}_{i}\times {load}_{i} $ (2)

    Other reliability indices are represented in Table 1 below.

    Table 1.  System and cost-worth reliability indices.
    S. No. Reliability Indices
    1. $ SAIFI=\frac{\sum_{i=1}^{n}{N}_{i}*{\lambda }_{i}}{\sum_{i=1}^{n}{N}_{t}} $ (3)
    2. $ SAIDI=\frac{\sum_{i=1}^{n}{N}_{i}*{U}_{i}}{\sum_{i=1}^{n}{N}_{i}} $ (4)
    3. $ CAIDI=\frac{SAIDI}{SAIFI} $ (5)
    4. $ ECOST=\sum\limits_{i=1}^{n}{\lambda }_{i}*{C}_{i}*{L}_{i} $ (6)

     | Show Table
    DownLoad: CSV

    In a distribution network, segments are branches of the network. In this structure, two branches may be connected through a switch in between them or they may be connected with an LP. An LP will experience an interruption under either of the following conditions:

    ● The segment connects the source and the LP.

    ● There is no fuse between the segment and the LP.

    After occurrence of the failure, the time required to restore the service can be the restoration time or switching time. The condition illustrated in Figure 1 can be used to determine the service restoration time.

    Figure 1.  Conditions for service restoration.

    The ENS depends on the load and annual outage duration. The annual outage duration depends on the failure frequency and restoration time or switching time. If the failure rate, length of sections, load at the LPs and switching time or repair time increases, then the ENS will also increase.

    The prime objective of this work was to identify the optimal location and number of RCSs in a distribution network to reduce the ENS. With the increased quantity of RCSs, the ENS may get reduced, but the associated costs would increase. Hence, the target is to reduce the cost of the ENS without any large increase in the RCS cost. To get more realistic results, the effect of fuse failure probability (FFP) and power equipment aging are also included for evaluation in the presence of the RCS.

    $ {G}_{i} = \sum\limits_{i = 1}^{n}{\left(ENS\right)}_{i}\times K\times {CPV}_{1}\left(7\right) $

    where, (ENS)i is the energy not served for the ith LP, K is the cost of per unit energy not supplied and CPV1 is the cumulative present value of the ENS cost. The cumulative present value method has been applied to evaluate the total cost and benefits during the economic life cycle of the equipment [16]. The proposed objective function considers the interest rate, inflation rate, load growth and economic lifetime of the equipment.

    CPV1 is calculated as:

    $ CP{V_1} = \frac{{1 - {{\left( {P{V_1}} \right)}^{EL}}}}{{1 - P{V_1}}} $ (8)

    where,

    $ P V_{1} = \frac{\left(1+\frac{R_{\mathrm{inf}}}{100}\right)\left(1+\frac{L G}{100}\right)}{\left(1+\frac{R_{\mathrm{int}}}{100}\right)} $ (9)

    Another objective function is proposed to reduce the cost of the RCSs. It is given as:

    $ {G_2} = \sum\limits_{j = 1}^{{N_s}} {{{\left( {{\text{Cost}}_{ins}^{RCS}} \right)}_j} + } \sum\limits_{j = 1}^{{N_s}} {{{\left( {{\text{Cost}}_{OM}^{RCS}} \right)}_j}} \times CP{V_2} $ (10)

    where, CPV2 and PV2 are respectively given as

    $ CP{V_2} = \frac{{1 - {{\left( {P{V_2}} \right)}^{EL}}}}{{1 - P{V_2}}} $ (11)
    $ P V_{2} = \frac{\left(1+\frac{R_{\mathrm{inf}}}{100}\right)}{\left(1+\frac{R_{\mathrm{int}}}{100}\right)} $ (12)

    In (11) and (12), the economic lifespan of the equipment is denoted by EL, Rint is the interest rate, Rinf is the inflation rate and LG represents the growth of the load.

    The accuracy of the failure rate model for the equipment in the power system determines the reliability of the results of the reliability assessment. The equipment failure rate is typically assumed to be constant, which prevents a real-time assessment of system reliability [22]. The bathtub curve, which is seen in Figure 2, can be used to understand and validate the relationship between the failure rate and life cycle. The full life cycle of the equipment has three stages, i.e., periods of infant mortality, stabilization and wear out. The initial/infant mortality period is not considered here and the failure rate will remain stable during the stabilization time as shown below in (13).

    $ {\lambda _1}(t) = {\lambda _C} $ (13)
    Figure 2.  Bathtub curve with failure rate and asset management.

    where, λc is the constant failure rate.

    The system becomes increasingly unreliable during the aging period as the failure rate changes over time. The Weibull Distribution function [1] is widely used to measure the rate of failure during the aging process, and it is given as

    $ {\lambda _2}(t) = {\lambda _C} + {\lambda _V} \times {\beta _2} \times {t^{{\beta _2} - 1}} $ (14)

    where, λv and β2 are the variable failure rate and aging coefficient, respectively.

    The majority of studies considered the constant failure rates of conventional power equipment. This type of failure rate model is considered as model-1 and the failure rate model considering the aging is termed as model-2 in this article. As discussed in [1], λC and λV are the constant and variable failure rates under various weather conditions. If the weather is considered as single-state weather, then λC can be considered equal to λV. The aging coefficient is β2, and the higher value of β2 reveals the fast-aging period of equipment. As this analysis has been done considering the single-state weather, we put λC = λV and use β2 = 1.5 in (14).

    The life cycle of the feeder and transformer is taken as 15 years. The failure rates of model-1 and model-2 are listed in Table 2.

    Table 2.  Failure rates of model-1 and model-2.
    Components Failure rate of Model 1 Failure rate of Model 2
    Feeder 0.065 0.443
    Transformer 0.015 0.102

     | Show Table
    DownLoad: CSV

    Until now, the network's reliability has been assessed while assuming constant load. However, given the current state of technology and its rapid growth, load is also continuously rising. This study uses a straightforward 1% annual load growth rate for each category of load (residential, government/institutional, commercial, and small users), and it uses a 15-year evaluation period to reflect the equipment's ageing. With 1% growth, the load after 15 years is evaluate as

    $ Loa{d^{15}} = {L_{\text{int} }} \times {\left( {1 + \% LG} \right)^t} $ (15)

    where, Lint is the initial load, t is the time of evaluation and %LG is the rate of load growth.

    The literature consists of a lot of models that evaluates the reliability including the relay's failure [23], but fuse modeling is different from relay modeling because the fuse does not have any assessment state when the current carrying element is up. Figure 3 represents the failure rate model of a fuse cutout.

    Figure 3.  Model for fuse element.

    In general, fuses are operated in a normal state. If a problem arises in the system, the fuse must operate by melting down in order to isolate the faulty portion [23]. However, occasionally fuses may stop working; in such a case, backup protection may be required to separate the faulty section. Fuse tripping can occasionally occur accidentally, de-energizing the system. In this case, the system goes straight from State (1) to State (3). Equation (16) calculates the failure rate for the system with FFP.

    $ {\lambda _{L{P_i}}} = {\lambda _{{f_{100\% }}}} + \sum\limits_{j = 1}^{Nt} {{\lambda _{{t_j}}}} + \left\{ (λfo)×(Pfo)+(λff)×(Pff)
    \right\} $
    (16)

    where

    $ {\lambda }_{{f}_{100\%}} $    represents the failure rate when a fuse operates with 100% probability,

    $ {\lambda }_{{t}_{j}} $    represents the failure due to the jth transformer,

    $ {\lambda }_{{f}_{o}} $    is the failure rate when the fuse operates,

    $ {P}_{{f}_{o}} $    is the probability that a fuse operates,

    $ {\lambda }_{ff} $    is the failure rate when a fuse fails to operate,

    $ {P}_{ff} $    is the probability that a fuse fails.

    The goal of this research is to reduce the ENS at the consumers end and achieve the optimal placement of RCSs by utilizing the greedy search algorithm. Although an alternative optimization method might be employed in this situation, the ability of this method to rapidly and accurately converge is the prime justification for its selection. A trade-off between the numbers and the cost of RCSs has resulted in improved reliability metrics. Figure 4 shows a flowchart for the greedy search optimization approach. The following steps should be taken:

    Pseudo-code

    Start

       Initialization: for i = 1: M

         Evaluate $ CP{V_1} = \frac{{1 - {{\left({P{V_1}} \right)}^{EL}}}}{{1 - P{V_1}}} $

         Evaluate $ P{V_1} = \frac{{\left({1 + \frac{{{R_{\inf }}}}{{100}}} \right)\left({1 + \frac{{LG}}{{100}}} \right)}}{{\left({1 + \frac{{{R_{{int} }}}}{{100}}} \right)}} $

       Computation: λLP, rLP, ULP;

        if i = M (no. of feeders in which an RCS is placed)

    Evaluate: $ {G_1} = \sum\limits_{i = 1}^n {{{\left({ENS} \right)}_i} \times K \times CP{V_1}} $ else i = i+1;

    Optimization: for j = 1: N;

       Evaluate $ P{V_2} = \frac{{\left({1 + \frac{{{R_{\inf }}}}{{100}}} \right)}}{{\left({1 + \frac{{{R_{{int} }}}}{{100}}} \right)}} $

       Evaluate $ CP{V_2} = \frac{{1 - {{\left({P{V_2}} \right)}^{EL}}}}{{1 - P{V_2}}} $

    compute: $ {G_2} = \sum\limits_{j = 1}^{{N_s}} {{{\left({{Cos} t_{ins}^{RCS}} \right)}_j} + } \sum\limits_{j = 1}^{{N_s}} {{{\left({{Cos} t_{OM}^{RCS}} \right)}_j}} \times CP{V_2} $

          if G1 > G2

          Display the optimal location of the RCS

          else

          Go for another location of RCS

         end

        end

       end

    end

    Figure 4.  Flowchart for the greedy search algorithm.

    The topology of the tested distribution network at bus 2 is shown in Figure 5. The investigated network is a component of the 6-bus RBTS system [22]. This network at bus 2 consists of four circuit breakers connected at the starting point of each feeder. There are four 11 kV feeders designated as F1, F2, F3 and F4, as well as 20 transformers, 14 sectionalizing switches, 20 fuses and 22 LPs that make up the network. A total of 1908 users are registered in the network as a whole. Tables 3 and 4 display the load statistics for various LPs and customers. It is evident that Feeders F1 and F4 have the highest loads relative to the other feeders, whereas Feeder F2 has the lowest loads (two consumers). Since both of the loads on Feeder F2 are major LPs and they do not require voltage transformation, they are both directly connected to the feeder. Reliability data for the system components is included in Table 5.

    Figure 5.  Distribution network at RBTS bus 2 [22].
    Table 3.  Peak load in % for each sector [22].
    Customer type Peak load (MW) Sector peak (%)
    Residential 7.25 36.25
    Small users 3.50 17.50
    Govt. & Inst. 5.55 27.75
    Commercial 3.70 18.50
    Total 20 100

     | Show Table
    DownLoad: CSV
    Table 4.  Loading data for LPs [22].
    Load points Load at various LPs, MW
    average peak
    1-3, 10, 11 0.535 0.8668
    12, 17-19 0.450 0.7291
    8 1.00 1.6279
    9 1.15 1.8721
    4, 5, 13, 14, 20, 21 0.566 0.9167
    6, 7, 15, 16, 22 0.454 0.7500
    Total 12.291 20.00

     | Show Table
    DownLoad: CSV
    Table 5.  System reliability data [1].
    Failure Rate
    (failure/yr/km)
    Repair time (h) Replacement time (h) Switching time (h)
    Feeder
    0.065 5 - 1
    Transformer
    0.015 200 5 1

     | Show Table
    DownLoad: CSV

    The distribution network connected at bus 5 of the RBTS also consists of four feeders (F1, F2, F3 and F4) at 11 kV each. The network consists of 26 transformers, 17 sectionalizing switches, 26 fuses and 26 LPs. The total number of consumers in the network was set to be 2858. Figure 6 shows the topology for the distribution network at bus 5.

    Figure 6.  Distribution network at RBTS bus 5 [23].

    In this analysis, RCSs are associated with installation and maintenance costs which have considered to be USD 4433.60 and USD 166.26 for each RCS [2]. The cost of the ENS is taken as USD 5/kWh and the economic lifetime is considered as 15 years. The rate of inflation is assumed to be 8% and the rate of interest is assumed to be 5%. The load growth is also assumed to be 5%. The service restoration time for an RCS is taken to be 10 minutes in this analysis [2].

    Table 6 shows the results for installation of RCSs in the bus 2 network, and Table 7 shows the results for bus 5. From Table 6, it can be ascertained that the presence of an RCS does not affect the SAIFI, but it does reduce the SAIDI and END significantly. With RCSs the SAIDI decreased by 16.67% and the ENS decreased by 10.38% relative to those for the network without an automatic switch. Figures 7 and 8 represent a comparative pictorial representation of RCS cost and savings in terms of the ENS for bus 2 and bus 5 respectively. The results show a significant improvement in the reliability of the network in consideration of the installation and maintenance cost of RCS.

    Table 6.  Analysis of RBTS bus-2 network with RCSs.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No. of RCSs Cost of RCSs (G2) (USD)
    No- RCS system 0.2482 0.6907 14,048.98 1,907,297.1 0 0
    RCS in F1 0.2482 0.6517 13,328.34 1,809,463.05 4 30,104.16
    RCS in F2 0.2482 0.6906 13,912.49 1,888,767.38 2 15,052.08
    RCS in F3 0.2482 0.6527 13,416.05 1,821,370.91 4 30,104.16
    RCS in F4 0.2482 0.6529 13,358.6 1,813,571.19 4 30,104.16
    RCS in F1 & F2 0.2482 0.6517 13,191.96 1,790,947.7 6 45,156.24
    RCS in F1 & F3 0.2482 0.6137 12,695.42 1,723,536.86 8 60,208.32
    RCS in F1 & F4 0.2482 0.6140 12,638.07 1,715,751.51 8 60,208.32
    RCS in F2 & F3 0.2482 0.6526 13,279.57 1,802,841.19 6 45,156.24
    RCS in F2 & F4 0.2482 0.6529 13,222.22 1,795,055.84 6 45,156.24
    RCS in F3 & F4 0.2482 0.6149 12,725.68 1,727,645 8 60,208.32
    RCS in F1 F2 F3 0.2482 0.6137 12,559.04 1,705,021.51 10 75,260.4
    RCS in F1 F2 F4 0.2482 0.6140 12,501.58 1,697,221.79 10 75,260.4
    RCS in F1 F3 F4 0.2482 0.5760 12,005.15 1,629,825.32 12 90,312.48
    RCS in F2 F3 F4 0.2482 0.6149 12,589.3 1,709,129.65 10 75,260.4
    System with RCS 0.2482 0.5760 11,868.66 1,611,295.6 14 1, 05,364.56

     | Show Table
    DownLoad: CSV
    Table 7.  Analysis of RBTS bus-5 network with RCSs.

    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No. of RCSs Cost of RCSs (G2) (USD)
    No-RCS system 0.2325 0.6262 13,234.89 1,796,776.37 0 0
    RCS in F1 0.2325 0.5911 12,565.14 1,705,850.55 4 30,104.16
    RCS in F2 0.2325 0.5893 12,674.42 1,720,686.26 5 37,630.2
    RCS in F3 0.2325 0.6155 12,722.65 1,727,233.95 4 30,104.16
    RCS in F4 0.2325 0.5971 12,667.38 1,719,730.5 4 30,104.16
    RCS in F1 & F2 0.2325 0.5543 12,004.56 1,629,746.18 9 67,734.36
    RCS in F1 & F3 0.2325 0.5804 12,052.79 1,636,293.87 8 60,208.32
    RCS in F1 & F4 0.2325 0.5621 11,997.63 1,628,804.68 8 60,208.32
    RCS in F2 & F3 0.2325 0.5786 12,162.07 1,651,129.58 9 67,734.36
    RCS in F2 & F4 0.2325 0.5602 12,106.8 1,643,626.13 9 67,734.36
    RCS in F3 & F4 0.2325 0.5864 12,155.14 1,650,188.08 8 60,208.32
    RCS in F1 F2 F3 0.2325 0.5436 11,492.22 1,560,189.5 13 97,838.52
    RCS in F1 F2 F4 0.2325 0.5252 11,437.05 1,552,700.31 13 97,838.52
    RCS in F1 F3 F4 0.2325 0.5513 11,485.28 1,559,248 12 90,312.48
    RCS in F2 F3 F4 0.2325 0.5495 11,594.56 1,574,083.71 13 97,838.52
    System with RCS 0.2325 0.5145 10,924.7 1,483,143.63 17 1, 27,942.68

     | Show Table
    DownLoad: CSV
    Figure 7.  RCS cost and ENS saving for bus 2.
    Figure 8.  RCS cost and ENS saving for bus 5.

    Further, placement of the RCSs in all feeders together reduces the SAIDI and ENS by 16.60% and 10.38%, respectively. In this case, 14 RCS were placed in the network; their cumulative present worth after 15 years of economic life was determined to be USD 1, 05,364.56 and reduction in the cost of the ENS was determined to be USD 2, 96,001.5.

    Further, the effect of FFP with and without an automatic switch was examined on bus-2 network; the results are included in Appendices A1, A2 and A3. The analysis was done for 10%, 20% and 30% FFP with the effect of load growth for 15 years. The results revealed that inclusion of 10% FFP with RCSs coincides with SAIDI and ENS reduction of 19.85% and 15.70%, respectively. In this case, 14 RCS were placed in the network and their cumulative present worth after 15 years of economic life determined to be USD 1, 05,364.56 and the reduction in the cost of the ENS was USD 2, 99,491.98. As a continuation of the FFP analysis, the effects of feeder aging and distribution transformer aging were also analyzed. The results are presented in Table 8 and Table 9 below. There were respective reductions of 19.85% and 18.64% in the SAIDI and ENS with aging of the feeder and distribution transformer when the RCSs were placed at sectionalizing switches. The reduction in the cost of the ENS with an automatic switch is USD 25, 09,541.73, while the cost of installation for 14 RCS was determined to be USD 1, 05,364.56. The installation and maintenance costs for RCSs were determined to be much lower than the reduction in the cost of the ENS. Therefore, it is economical to replace all sectionalizing switches with RCSs.

    Table 8.  Aging effect analysis for bus-2 RBTS network with no RCS, and given 10% FFP.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No. of RCSs Cost of RCSs (G2) (USD)
    Aging of transformer 0.4198 1.2099 23014.78 3,124,498.599 0 0
    Aging of feeder and transformer 1.9327 4.9471 99178.50 13,464,527.57 0 0

     | Show Table
    DownLoad: CSV
    Table 9.  Aging effect analysis for bus-2 RBTS network with RCSs, and given a 10% FFP.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No. of RCSs Cost of RCSs (G2) (USD)
    Aging of transformer 0.4198 1.0247 19581.51 2,658,397.142 14 105364.56
    Aging of feeder and transformer 1.9327 3.9647 80693.44 10,954,985.84 14 105364.56

     | Show Table
    DownLoad: CSV

    This work extends the existing reliability evaluation procedure by incorporating RCSs in to the distribution network and accounting for the FFP and components aging. This paper shows how o determine the optimal location and number of RCS to reduce the cost of the ENS and the installation and maintenance cost of RCSs by using a greedy search optimization approach. The proposed approach has been applied to a distribution network given RBTS bus 2 and bus 5.

    The results show that by installing 14 RCS in bus 2, ENS cost savings of USD 2, 96,001.5 can be achieved, which is much more than the installation and maintenance cost (USD 1, 05,364.56) of RCSs. Given the FFP and component aging, with 14 RCSs, the savings of USD 25, 09,541.73 has been demonstrated. Similarly, with the installation of 17 RCSs in bus 5, ENS cost savings of USD 3, 13,632.74 has been demonstrated while the installation and maintenance cost of RCSs were determined to be USD 1, 27,942.68.

    The results show a significant improvement in reliability of the network in the presence of RCSs.

    The authors declare that there is no conflict of interest regarding this study.

    Appendix A

    Table A1.  Analysis of RBTS bus-2 network with RCS and a 10% FFP.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No. of RCSs Cost of automatic switch (G2) (USD)
    Base case 0.2482 0.6907 14048.98 1,907,297.102 0 0
    Base case with FFP 0.2836 0.7261 14555.47 1,976,058.163 0 0
    RCS in F1 0.2836 0.6764 13637.61 1,851,449.388 4 30104.16
    RCS in F2 0.2836 0.7260 14403 1,955,359.461 2 15052.08
    RCS in F3 0.2836 0.6796 13785.52 1,871,530.434 4 30104.16
    RCS in F4 0.2836 0.6781 13683.11 1,857,625.962 4 30104.16
    RCS in F1 & F2 0.2836 0.6764 13485.14 1,830,750.686 6 45156.24
    RCS in F1 & F3 0.2836 0.6299 12867.67 1,746,921.659 8 60208.32
    RCS in F1 & F4 0.2836 0.6284 12765.25 1,733,017.187 8 60208.32
    RCS in F2 & F3 0.2836 0.6795 13633.69 1,850,917.916 6 45156.24
    RCS in F2 & F4 0.2836 0.6780 13530.64 1,836,927.259 6 45156.24
    RCS in F3 & F4 0.2836 0.6316 12913.27 1,753,112.596 8 60208.32
    RCS in F1 F2 F3 0.2836 0.6299 12715.2 1,726,222.956 10 75260.4
    RCS in F1 F2 F4 0.2836 0.5819 12612.78 1,712,318.484 10 75260.4
    RCS in F1 F3 F4 0.2836 0.6284 11995.41 1,628,503.821 12 90312.48
    RCS in F2 F3 F4 0.2836 0.6315 12760.7 1,732,399.529 10 75260.4
    Base case with RCS 0.2482 0.5760 11868.66 1,611,295.601 14 105364.56
    Base case with RCS under the effect of FFP 0.2836 0.5819 11842.95 1,607,805.119 14 105364.56

     | Show Table
    DownLoad: CSV
    Table A2.  Analysis of RBTS bus-2 network with RCS and a 20% FFP.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No of RCSs Cost of RCSs (G2)
    (USD)
    Base case 0.2482 0.6907 14048.98 1,907,297.102 0 0
    Base case with FFP 0.3190 0.7615 15194.21 2,062,774.38 0 0
    RCS in F1 0.3190 0.7011 14079.03 1,911,376.513 4 30104.16
    RCS in F2 0.3190 0.7614 15025.66 2,039,892.33 2 15052.08
    RCS in F3 0.3190 0.7065 14287.36 1,939,659.473 4 30104.16
    RCS in F4 0.3190 0.7032 14139.87 1,919,635.885 4 30104.16
    RCS in F1 & F2 0.3190 0.7011 13910.59 1,888,508.828 6 45156.24
    RCS in F1 & F3 0.3190 0.6461 13172.28 1,788,275.971 8 60208.32
    RCS in F1 & F4 0.3190 0.6429 13024.79 1,768,252.383 8 60208.32
    RCS in F2 & F3 0.3190 0.7064 14118.81 1,916,777.424 6 45156.24
    RCS in F2 & F4 0.3190 0.7032 13971.32 1,896,753.835 6 45156.24
    RCS in F3 & F4 0.3190 0.6482 13233.01 1,796,520.979 8 60208.32
    RCS in F1 F2 F3 0.3190 0.6460 13003.73 1,765,393.922 10 75260.4
    RCS in F1 F2 F4 0.3190 0.6428 12856.24 1,745,370.333 10 75260.4
    RCS in F1 F3 F4 0.3190 0.5878 12117.93 1,645,137.477 12 90312.48
    RCS in F2 F3 F4 0.3190 0.6482 13064.46 1,773,638.929 10 75260.4
    Base case with RCS 0.2482 0.5760 11868.66 1,611,295.601 14 105364.56
    Base case with RCS under the effect of FFP 0.3190 0.5878 11949.39 1,622,255.427 14 105364.56

     | Show Table
    DownLoad: CSV
    Table A3.  Analysis of RBTS bus-2 network with RCS and a 30% FFP.
    SAIFI SAIDI ENS (kWh) Cost of ENS (G1) (USD) No of RCSs Cost of RCSs (G2)
    (USD)
    Base case 0.2482 0.6907 14048.98 1,907,297.102 0 0
    Base case with FFP 0.3545 0.7969 15832.95 2,149,490.596 0 0
    RCS in F1 0.3545 0.7258 14520.55 1,971,318.003 4 30104.16
    RCS in F2 0.3545 0.7968 15648.32 2,124,425.199 2 15052.08
    RCS in F3 0.3545 0.7334 14789.19 2,007,788.513 4 30104.16
    RCS in F4 0.3545 0.7284 14596.62 1,981,645.808 4 30104.16
    RCS in F1 & F2 0.3545 0.7257 14335.92 1,946,252.606 6 45156.24
    RCS in F1 & F3 0.3545 0.6623 13476.79 1,829,615.92 8 60208.32
    RCS in F1 & F4 0.3545 0.6573 13284.22 1,803,473.215 8 60208.32
    RCS in F2 & F3 0.3545 0.7333 14604.56 1,982,723.117 6 45156.24
    RCS in F2 & F4 0.3545 0.7283 14412 1,956,580.411 6 45156.24
    RCS in F3 & F4 0.3545 0.6649 13552.86 1,839,943.725 8 60208.32
    RCS in F1 F2 F3 0.3545 0.6622 13292.16 1,804,550.524 10 75260.4
    RCS in F1 F2 F4 0.3545 0.6572 13099.7 1,778,422.182 10 75260.4
    RCS in F1 F3 F4 0.3545 0.5938 12240.46 1,661,771.132 12 90312.48
    RCS in F2 F3 F4 0.3545 0.6587 13255.23 1,799,537.445 10 75260.4
    Base case with RCS 0.2482 0.5760 11868.66 1,611,295.601 14 105364.56
    Base case with RCS under the effect of FFP 0.3545 0.5937 12055.83 1,636,705.736 14 105364.56

     | Show Table
    DownLoad: CSV
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