Energy Regulator Supply Restoration Time

: In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconﬁguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconﬁguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconﬁguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reﬂected the applicability of reconﬁguration and the replace/repair time of network component. Theoretically, the network reconﬁguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.


Introduction
The reliability performance of distribution networks incorporates all possible contingencies associated with all power components in the network, including distribution feeders and protection systems. Reliability performance of the network is mostly related to maintaining the power supply to the customer. Apart from maintaining the voltage level within permissible limits and minimising the feeder losses, network reconfiguration is able to maintain an adequate level of reliability set by the energy regulator [1,2]. In addition, the network operation must adhere to the P2/6 Engineering Recommendation [3] that suggests transfer capacity from alternative sources by certain maximum times based on class of group demands.
A typical UK suburban distribution network was considered in the analysis (see Figure 1). The radial type of power distribution network delivers power from the main branch to sub-branches, then splitting out from the sub-branches again. This appears to be the cheapest, but least reliable network configuration. Tables 1 and 2 present the parameters of UK suburban network.

Mean Fault Rates and Mean Time to Repair (MTTR)
Mean fault rates and MTTR are the two basic inputs required for system reliability assessments. In the literature, the reported values of these two input data vary in wide ranges (based on the characteristics and location of network, types and features of power components, as well as their Figure 1. Typical distribution network configuration supplying suburban residential load [22][23][24][25][26][27]. Mean fault rates and MTTR are the two basic inputs required for system reliability assessments. In the literature, the reported values of these two input data vary in wide ranges (based on the characteristics and location of network, types and features of power components, as well as their operating conditions). Table 3 presents the statistics of mean fault rates and mean repair times obtained from two main sources: UK-related values reported in [33] and from other sources [34][35][36][37][38][39][40][41].

Fault Types
The classification of customer interruption into short interruption (SI) and long interruption (LI) is impossible without, for instance, modelling the applied protection systems. One simple way to make a clear distinction between short and long supply interruptions of customers is by defining a uniform distribution and linking it to the system reliability assessment procedure. For that purpose, past recordings collected from 14 UK DNOs between 2005 and 2009 [42] were analysed, in which 54% of supply interruption events were caused by temporary faults (i.e., SI), and 46% were due to permanent faults (i.e., LI).

Guaranteed Standard of Performance
The energy regulator has specified certain requirements for the duration and the number of interruptions in order to protect domestic (i.e., residential) and non-domestic customers (i.e., customers without special contract or agreement with the DNOs regarding LI) from excessive LI events. References depicted in [1] and [28] refer to the main UK statutory instrument, specifying the permissible supply restoration times for up to 5000 customers and more than 5000 customers, respectively. This is illustrated in Table 4 (normal system operating conditions), along with the corresponding compensations that DNOs pay directly to the customers (and not to the regulator), if the supply is not restored within the specified time [1] and [28].

Reliability Methodologies
Probabilistic reliability assessment procedures seem to suit the analysis of system reliability performance, particularly in terms of their ability to model stochastic and inherently unpredictable variations of input parameters and data (e.g., fault rates and repair times) with their assumed probability distributions. The approaches of the probabilistic reliability assessment model provide a wide range of variations of practically all input parameters and data in one or a few simulation/calculation setups, without repeating the calculation after an input data is modified.
Although the probabilistic reliability assessment procedures are more difficult to implement (particularly in complex large-scale systems), they provide accurate and detailed outputs. The most frequently used probabilistic reliability assessment approach is the Monte Carlo simulation (MCS) [43][44][45][46][47]. Aside from network modelling, conventional MCS analysis requires statistical information on fault rates and MTTR of faulted power components as input data. Network models and fault rates of power components are used to establish customers experiencing interruptions (and the frequency), whereas MTTR of faulted components and network protection, reconfiguration, switching and alternative supply functionalities are used to estimate the duration of corresponding supply interruptions. The outputs of MCS analysis are reliability indices that reflect probability distributions with the corresponding mean values.

Monte Carlo Simulation (MCS) Procedures
In any power system reliability procedures, MTTR is used to define the restoration times of network components that directly have an impact on the duration of interruption. In some cases, where network automation is unavailable (network reconfiguration) or in the absence of regulatory supply requirements (in some nations) on distribution networks, it is indeed realistic to use MTTR values. Nevertheless, in a country that applies regulatory supply requirements, the function of MTTR as input data may result in significant overestimation of reliability performance. Thus, DNOs should consider a new method to assess the duration of interruption by correlating with regulatory supply requirement time. Accordingly, this section presents a new methodology (see Figure 2) of assessing duration of interruption realistically, based on GSP-UK restoration times. In any power system reliability procedures, MTTR is used to define the restoration times of network components that directly have an impact on the duration of interruption. In some cases, where network automation is unavailable (network reconfiguration) or in the absence of regulatory supply requirements (in some nations) on distribution networks, it is indeed realistic to use MTTR values. Nevertheless, in a country that applies regulatory supply requirements, the function of MTTR as input data may result in significant overestimation of reliability performance. Thus, DNOs should consider a new method to assess the duration of interruption by correlating with regulatory supply requirement time. Accordingly, this section presents a new methodology (see Figure 2) of assessing duration of interruption realistically, based on GSP-UK restoration times.
Based on the methods in MCS, a random variable (generated by a random generator) is assigned to an inverse cumulative distribution function to convert fault rates and MTTR (see Table 3) into system states, time to fail (TTF) and time to repair (TTR). The system states of the network component can be modelled with a series of distribution functions: Exponential, Weibull and Rayleigh. The parameters of distribution function are available in [48][49][50] Exponential: TTF/TTR = , Based on the methods in MCS, a random variable (generated by a random generator) is assigned to an inverse cumulative distribution function to convert fault rates and MTTR (see Table 3) into system states, time to fail (TTF) and time to repair (TTR). The system states of the network component can be modelled with a series of distribution functions: Exponential, Weibull and Rayleigh. The parameters of distribution function are available in [48][49][50] Exponential : Rayleigh : , only components on the main feeder (carrying a high current that may affect many customers) are selected to replace MTTR values with TTR values (option 2). To compare the practicality of option 2 with complete network automation, option 1, network reconfiguration, was generated. In option 1, the network component fault/interruption time adheres to the exact values of MTTR, while the customer restoration time is shorted by the GSP-UK duration limit via network reconfiguration. In other word, customers experience outages through the normal path of electrical supply and the duration of outage experienced by the same customer is shortened by rerouting the electrical supply through the network reconfiguration until the faulty component is repaired/replaced.

Considered Scenarios
In Table 5, scenario SC-1 is a base case that quantifies the benefits of network reconfiguration and repair/replace network component with TTS value. Scenario SC-2 represents the existing network reconfigurations and functionalities (option 1) in accordance with GSP requirements. This means that the network should have switching functionalities to transfer to an alternative supply and for reconfiguration, since, otherwise, many customers would face excessively long supply interruptions (determined by MTTR network components). Next, scenario SC-3 (option 2) has the same purpose in scenario SC-2, but without any transfer to an alternative supply and reconfiguration, as it only substitutes the MTTR of each power component into TTS in accordance with GSP. Scenario SC-3 determines the variance between network reconfiguration and the replacement time of MTTR in adherence to GSP. The purpose of scenario SC-4 is to list the benefits of minimising time window of fault via network reconfiguration. Finally, scenario SC-5 embeds "smart grid", wherein automatic remote-controlled switching may be implemented in future for a suburban distribution network.

Discussion
The results of scenarios SC-1, SC-2A/2B, and SC-3A/3B suggest that network reconfiguration and repair/replace with TTS can successfully reduce long supply interruptions. Figure 3d illustrates that the MCS outputs displayed a greater reduction in hours, from 68.4914 to 13.7321/18.1494, for scenarios SC-1 and SC-2B/3B, respectively.
In scenarios SC-2A/2B and SC-3A/3B, although the methods (options 1 and 2) of restoration supply differed, both scenarios shared almost similar values. In detail, Figure 3d shows that the line graph of scenario SC-2B is up to 175.5 h, while that for scenario SC-3B is up to 190.5 h. This signifies that for scenario SC-2B, two separate durations of interruptions occurred, and they overlapped with the reconfiguration duration time causing the tail of scenario SC-2B to be smaller than scenario SC-3B.
Between  Figure 4d were almost identical.
In Figure 3a, the MCS mean value of SAIFI scenario SC-2B was slightly lower than SC-3B because in scenario SC-2B (see Figure 5), the frequency of interruptions was lower than that in scenario SC-3B (see Figure 6). In Figure 5, customers only experienced single interruption, while double interruptions are shown in Figure 6. Thus, scenario SC-3B exhibited higher values of average duration of interruption than those recorded for scenario SC-2B. Figures 5 and 6 portray the tail graphs of scenarios SC-2B and SC-3B for better understanding. In Figures 5 and 6, the same customers experienced LIs with varied average duration of interruption. In Figure 5, no second duration of interruption was noted, while in Figure 6, the customer experienced a second interruption within a 3 h duration. Thus, as displayed in Figure 6, the duration of interruption was 21 h, which is longer than that in Figure 5, 18 h.
As for scenario SC-5, when "smart grid" automatic switching was applied to the network reconfiguration, the CAIDI values (i.e., average duration of LIs) increased after all faults were addressed within 18 h, to turn into Sis, due to less than 3 min of automatic switching. In detail, the shorter duration of LI no longer contributes to the average values, causing the average values of CAIDI of scenario SC-5 to be higher. This also indicates that automatic switching reduced the number of LIs but increased the average duration of interruptions and the number of SIs.

Discussion
The results of scenarios SC-1, SC-2A/2B, and SC-3A/3B suggest that network reconfiguration and repair/replace with TTS can successfully reduce long supply interruptions. Figure 2d illustrates that the MCS outputs displayed a greater reduction in hours, from 68.4914 to 13.7321/18.1494, for scenarios SC-1 and SC-2B/3B, respectively.
In scenarios SC-2A/2B and SC-3A/3B, although the methods (options 1 and 2) of restoration supply differed, both scenarios shared almost similar values. In detail, Figure 2d shows that the line graph of scenario SC-2B is up to 175.5 h, while that for scenario SC-3B is up to 190.5 h. This signifies that for scenario SC-2B, two separate durations of interruptions occurred, and they overlapped with the reconfiguration duration time causing the tail of scenario SC-2B to be smaller than scenario SC-3B.
In Figure 3a, the MCS mean value of SAIFI scenario SC-2B was slightly lower than SC-3B because in scenario SC-2B (see Figure 5), the frequency of interruptions was lower than that in scenario SC-3B (see Figure 6). In Figure 5, customers only experienced single interruption, while double interruptions are shown in Figure 6. Thus, scenario SC-3B exhibited higher values of average duration of interruption than those recorded for scenario SC-2B.
Between scenarios SC-2A and SC-2B, or SC-3A and SC-3B, huge variances were noted in the values based on Figure 3d (CAIDI index). This is because the repair time in scenario SC-2B/3B was always exactly 18 h, while in scenario SC-2A/3A, although the repair time window was up to 18 h, it was not always exactly 18 h. This led the values of CAIDI in Figure 3d for scenario SC-2A/3A to be lower than scenario SC-2B/3B. As long as the duration of interruption is within the permissible limit  Figure 3d were almost identical. Figures 5 and 6 portray the tail graphs of scenarios SC-2B and SC-3B for better understanding. In Figures 5 and 6, the same customers experienced LIs with varied average duration of interruption. In Figure 5, no second duration of interruption was noted, while in Figure 6, the customer experienced a second interruption within a 3 h duration. Thus, as displayed in Figure 6, the duration of interruption was 21 h, which is longer than that in Figure 5, 18 h.  As for scenario SC-5, when "smart grid" automatic switching was applied to the network reconfiguration, the CAIDI values (i.e., average duration of LIs) increased after all faults were addressed within 18 h, to turn into Sis, due to less than 3 min of automatic switching. In detail, the shorter duration of LI no longer contributes to the average values, causing the average values of  As for scenario SC-5, when "smart grid" automatic switching was applied to the network reconfiguration, the CAIDI values (i.e., average duration of LIs) increased after all faults were addressed within 18 h, to turn into Sis, due to less than 3 min of automatic switching. In detail, the shorter duration of LI no longer contributes to the average values, causing the average values of CAIDI of scenario SC-5 to be higher. This also indicates that automatic switching reduced the number

Conclusions
This paper presents the reliability performance under various reconfigurations and replacement repair times based on regulator supply requirements. Each presented scenario has its own pros and cons. It is possible and realistic to change the mode of operation from a power loss-based to a regulator reliability-based network reconfiguration or repair/replace network component by adhering to GSP requirements on the existing network, so as to meet the target set by the energy regulator. In option 1 (network reconfiguration), the selection of restoration time (either 3 min, or 3 or 18 h) was unrestricted by human activity and weather, as DNOs may operate switches/breakers manually or automatically, rerouting the electrical supply. As for option 2 (repair/replace network component with TTS value), it is practical to completely clear the fault within 18 h, but optional (either feasible or otherwise) for 3 h or below 3 h. The 3 h replacement/repairing of network component depends on the definition, by including or excluding travelling time, locating fault area, weather condition, and others. Hence, several scenarios bring about extra flexibility to DNOs. DNOs may choose the most appropriate methods/options or scenario in accordance with their operation conditions and the requirements of the network system so as to meet their own reliability target, as well as the target fixed by the energy regulator.