## 1 Introduction
However, rather than being used for regulatory adjustments or strategic planning, the results are merely accompanied by a statement indicating whether reliability has improved or worsened [6; 7]. In neighboring countries, some regulatory methodologies set to take effect in 2026 will link _SAIDI_ to the calculation of approved revenues for distribution system operators [8]. Other methodologies use _SAIDI_ as a parameter for assessing investments in network maintenance and development [9]. _SAIDI_ has been a fundamental reliability indicator for decades, widely applied in power system analysis to evaluate supply continuity and guide network improvements. Its application extends across various aspects of distribution system performance improvement, from assessing routine operational reliability to supporting long-term planning decisions. Over time, different methodologies have been developed to enhance _SAIDI_ values, adapting to changes in system design, load dynamics, and the increasing integration of automation. _SAIDI_ is frequently used as a key performance indicator in research analyzing various aspects of distribution network reliability. Recently, it has been applied in studies that examined the impact of renewable portfolio standards on grid reliability [10] and explored fault elimination techniques that do not require opening the faulty line [11]. Improvements in _SAIDI_ values have also been observed in studies examining fault restoration times in substations, where the implementation of remote-controlled equipment significantly enhanced outage restoration processes [12]. Additionally, network operation analyses have identified several technical measures for _SAIDI_ improvement, including upgrading communication infrastructure, accelerating fault isolation with fault detection, isolation, and restoration modules, optimizing switching point placement, increasing network redundancy, and strengthening resilience to external disturbances [13]. A method for assessing and improving the reliability of power distribution systems has been proposed in [14], where the evaluation process begins with a Monte Carlo simulation to determine system reliability based on _SAIFI_ and _SAIDI_ indices, followed by the development of a novel risk priority index. Primary substation failures can significantly increase _SAIDI_ due to their widespread impact. In [15], a _SAIDI_-optimized service restoration algorithm for meshed MV grids has been proposed to address this issue. In [16], machine learning regression methods have been applied to predict _SAIDI_ and _SAIFI_ based on technical, financial, and customer data collected from Swedish DSOs between 2010 and 2021. _Energy Not Supplied (ENS)_ is one of the adequacy indices used in distribution system evaluation [4]. While _SAIDI_ measures the duration of supply interruptions, _ENS_ quantifies the total amount of lost electrical energy, providing a more comprehensive assessment of outage impact. _ENS_ is often used alongside cost analyses to evaluate the economic consequences of unserved energy. In recent reliability assessments, _Expected Energy Not Supplied (EENS)_ has been used in studies analyzing the optimal placement of sectionalizers in medium-voltage (MV) distribution networks [17]. It has also been applied in developing a resilience indicator, _Transmission Lines' Contribution to Energy Not Supplied (TLENS)_, quantifying the impact of specific transmission line failures [18]. Moreover, _ENS_ has been one of the key indicators in a study evaluating the effects of protection system performance and miscoordination in reliability assessment [19]. Further research has considered integrating distributed energy sources to improve reliability indicators, with _ENS and Average Energy Not Supplied (AENS)_ included as key metrics [20]. Additionally, _AENS_ has been used alongside other indicators to assess the impact of smart grid functionalities on system reliability [21].