## 1 Introduction
The survey data are used to estimate the interruption cost in CAD/interruption for each customer sector for a range of interruption durations. This value is then normalized by the sector load demand to obtain the demand-normalized cost (DNC) in CAD/kW. The DNCs of a customer sector calculated for the selected outage durations form a customer damage function (CDF) [21] for that customer sector. The CDFs of all the different customer sectors are aggregated to obtain the composite customer damage function (CCDF) [1], which can then be used in alternate HLI expansion plans to determine the corresponding outage costs. The cost of interruption during the system peak becomes the deciding factor in determining the capacity reserve needed above the peak demand [22]. The interruption costs are therefore normalized by the peak demand in VBRI assessment at the HL-I level. The CDFs thus obtained are not suitable for VBRI assessment of transmission or distribution systems and facilities. Transmission planning usually follows HL-I expansion planning and makes use of deterministic reliability methods, such as the North American Reliability Corporation's N-1 planning criteria [23]. The rapid growth in renewable/intermittent generation and the expected growth of electric vehicles will significantly increase uncertainties in line loadings and net demands at the bulk load points. There are considerable efforts and activities [24] among electric utilities and research institutions to adopt probabilistic reliability methods that recognize these uncertainties in HL-II system planning and decision-making. An appropriate VBRI must be conducted to determine HL-II reliability criteria that yield minimum societal costs. The increased variability and uncertainty in the bulk electric systems require additional support resources and emerging smart technologies to operate the system in a reliable manner. For example, various types of energy storage systems, voltage support devices, and smart-grid technologies to acquire and process relevant system data for operational management of power supply and demand will be expected to be deployed throughout the network. A VBRI assessment at the HL-II level will be necessary to justify the cost and worth of deploying these new technologies into the bulk system network. Also, the increase in frequency of extreme weather events in past decades due to climate change [25] as well as other extreme events like cyber-physical attacks and seismic events have impacted the grid facilities due to their exposure to these events [26] causing catastrophic losses [27; 28]. The cost and worth of hardening the grid infrastructure against such adverse events requires an appropriate HL-II VBRI assessment. There has been limited work on VBRI at the transmission system level. A VBRI approach is illustrated in [29; 30] to make expansion planning decisions on a composite generation and transmission system. Ref. [31] presents a methodology to determine the optimum probabilistic reliability criterion for grid expansion planning to meet a specified level of reliability at the lowest cost. A VBRI approach is introduced in [32] for determining optimal reliability criteria considering the load bus reserve rate in a long-term load forecast for optimal transmission systems expansion planning with minimal costs for society. An application of a value-based reliability model is presented in [33] for transmission capital projects in a real transmission system with feasible transmission reinforcement alternatives that provide reliable electricity at the lowest possible rate. Ref. [34] proposes a VBRI approach to determine line upgrades to alleviate transmission congestion and maintain the reliability of a bulk system integrated with large-scale wind power. Ref. [35] describes the reliability worth scheme for multiple dispatch scenarios using binary disjunctive techniques and screening strategies. A multi-stage planning of transmission system to minimize overall societal cost is proposed in [36] based on the metaheuristic Ant Colony Optimization.