Energy Regulator Supply Restoration Time

Abstract

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.

Time-Dependent Unavailability Exploration of Interconnected Urban Power Grid and Communication Network

“…• HV/MV transformer (110/22 kV) labeled T1 is determined according to [26] [18,[35][36][37][38][39][40], which generalized LV under 11 kV power line cables from various sources in the literature.…”

“…Catering to vast models, it encompasses both qualitative and quantitative examination instruments. Despite a wealth of research, significant discrepancies in the interpretation of KPI persist across several publications [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. With an intent to address and rectify these inconsistencies, in paper [26], the authors propose a time-dependent reliability analysis tailored for a real critical energy infrastructure use case, which consists of interconnected urban electrical and communication network reliability assessment of highly reliable elements, which leverages exact reliability quantification of highly reliable systems.…”

Urban and rural medium voltage networks reliability assessment

“…Based on the methods in MCS, a random variable (generated by a random generator) is assigned to an inverse cummulative distribution function to convert fault rates and mean repair time (see Table 4) 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 Raleigh [13]. To have an accurate estimation of network performance, a lot of factors need to be considered especially related to customer load and fault rates of components.…”