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Why conventional distribution planning is under pressure
Conventional distribution system planning refers to the long-term process utilities use to decide when and where to build or reinforce feeders, transformers, substations, and related assets. The first stage of this process is known as System Assessment, which has historically relied on deterministic forecasts, peak-load assumptions, and predefined reliability criteria such as N-0 and N-1 planning rules [1]. Based on the deficiencies identified during the system assessment, planners typically initiate system expansion or upgrade projects. This process has traditionally ensured that the distribution system had enough capacity to serve expected demand growth while maintaining acceptable reliability and operating limits.
This planning methodology remains practical and transparent because deterministic criteria are simple to understand and implement. However, the realities facing distribution planners are changing faster than many traditional planning assumptions. Uncertainty in renewable generation and future load growth, influenced by changing customer behaviour, electric vehicle (EV) adoption, and increasing customer exports to the grid, is introducing new challenges for distribution system planners [2]. If utilities plan only around a small number of deterministic peak scenarios, they may over-reinforce assets for conditions that are rare, short-lived, or highly uncertain. For this reason, distribution system assessment requires more informative metrics than a simple pass/fail deficiency flag. Expansion projects should then be prioritized according to their system impact before being initiated.
From conventional to active distribution planning: challenges and opportunities
The main difference between conventional and active distribution planning lies in how the distribution system is represented and managed. Conventional planning assumes limited operational flexibility with largely unidirectional power flow from the upstream grid to customers and focuses on reinforcing substations, feeders, transformers, and other network assets to meet forecasted demand growth. In contrast, active distribution planning incorporates distributed energy resources, energy storage, electric vehicles, demand response, automation, communication infrastructure, and active network management as part of the planning solution [3].
The transition to active distribution planning creates both opportunities and challenges. Rather than relying solely on network reinforcement, active systems can be monitored, controlled, reconfigured, and operated more flexibly. However, this flexibility requires improved data, closer coordination between planning and operations, and detailed time-series analysis [4]. Deterministic methods are often not capable of embracing the aforementioned changes, creating the need for probabilistic approaches that explicitly account for uncertainty.
Probabilistic analysis provides a framework for incorporating uncertainty into distribution planning. Instead of treating load, renewable generation, and component states as fixed values, uncertain inputs are represented using probability distributions or probability mass functions. Methods such as Monte Carlo simulation, scenario analysis, and point-estimate techniques are then used to assess their impact on system performance [2], [4]. Also, when historical data are limited, possibilistic and hybrid probabilistic-possibilistic approaches can also be applied using fuzzy membership functions [5].
Probabilistic methods have been applied extensively to active distribution planning problems, including investment planning, asset expansion, operating strategies, hosting capacity assessment, demand response, electric vehicle integration, and energy storage planning [3], [4]. These studies demonstrate the value of uncertainty modelling in planning decisions. However, most focus on resource allocation, sizing, operation, or optimization rather than a more fundamental planning task: identifying overload deficiencies and determining which contingencies should be prioritized for system reinforcement.
From deterministic criteria to risk-based contingency assessment
Once overload deficiencies have been identified under uncertainty, planners must determine which contingencies contribute most to the risk and therefore deserve priority in reinforcement planning. This naturally leads to the problem of contingency ranking, where system conditions are evaluated not only by whether violations occur, but also by their likelihood and consequences.
Contingency ranking has a long history in transmission and composite generation-transmission systems. Performance indices are widely used to rank outages according to their severity [6]. Traditional indices combine post-contingency power-flow violations and voltage deviations, while more recent approaches incorporate outage probabilities to develop probabilistic ranking methods [7], [8]. Although effective, these techniques were developed primarily for meshed transmission networks, where power can redistribute through multiple paths and line-loading and voltage indices provide a natural measure of system stress.
Reliability assessment practice has increasingly moved beyond purely deterministic criteria. For example, NERC’s Long-Term Reliability Assessment supplements deterministic reserve-margin analysis with probabilistic metrics such as loss-of-load hours and expected unserved energy [9]. NERC further emphasizes evaluating the magnitude, frequency, duration, and timing of reliability shortfalls [9]. A similar concept appears in Literature by introducing system well-being framework, where deterministic criteria are embedded within a probabilistic assessment and system states are classified as healthy, marginal, or at-risk [10]. The framework was later extended to bulk electric systems using sequential Monte Carlo simulation, with the N-1 criterion serving as the deterministic performance requirement [11]. Together, these developments demonstrate that deterministic criteria alone cannot fully represent system uncertainty and that probabilistic assessment provides a practical means of quantifying both the likelihood and consequences of system deficiencies.
A critical gap remains in distribution contingency ranking
Distribution systems require a different approach to contingency ranking. Unlike transmission systems, most distribution networks are radial or weakly meshed. As a result, outage impacts depend on feeder topology, tie availability, transfer capability, receiver feeder constraints, transformer headroom, and the time-varying relationship between load and restoration options. A contingency may be tolerable for most hours yet become critical during a small number of peak-loading periods. Likewise, contingencies with similar peak overloads may have very different planning significance if their durations differ substantially.
This highlights the need for distribution-specific performance indices to support overload assessment and contingency prioritization under uncertainty. Such indices should account for overload magnitude, exposure duration, probability of occurrence, load at risk, network topology, and practical load-transfer limitations. By incorporating these factors, planners can distinguish between short-duration overloads and persistent high-risk deficiencies and select the most appropriate mitigation strategy, whether reinforcement, automation, load transfer, distributed energy resources, or operational monitoring.
References
[1] W. Wangdee, “Deterministic-based power grid planning enhancement using system well-being analysis,” Journal of Modern Power Systems and Clean Energy, vol. 6, no. 3, pp. 438–448, 2018.
[2] A. Rezaee Jordehi, “How to deal with uncertainties in electric power systems? A review,” Renewable and Sustainable Energy Reviews, vol. 96, pp. 145–155, 2018.
[3] A. Ehsan and Q. Yang, “State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review,” Applied Energy, vol. 239, pp. 1509–1523, 2019.
[4] G. Varathan and J. Belwin Edward, “A review of uncertainty management approaches for active distribution system planning,” Renewable and Sustainable Energy Reviews, vol. 205, 114808, 2024.
[5] M. Aien, M. Rashidinejad, and M. Fotuhi-Firuzabad, “On possibilistic and probabilistic uncertainty assessment of power flow problem: A review and a new approach,” Renewable and Sustainable Energy Reviews, vol. 37, pp. 883–895, 2014.
[6] A. M. Al-Shaalan, “Contingency selection and ranking for composite power system reliability evaluation,” Journal of King Saud University – Engineering Sciences, vol. 32, pp. 141–147, 2020.
[7] J. Venkateswaran, P. Manohar, Vinothini K., B. T. Monisha Shree, and R. Jayabarathi, “Contingency analysis of an IEEE 30 bus system,” 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, 2018.
[8] S. Y. Musa, M. A. Madaki, and J. Haruna, “Development and application of probabilistic performance index for ranking N-1 contingencies,” European Journal of Engineering and Technology Research, vol. 6, no. 5, pp. 63–69, 2021.
[9] North American Electric Reliability Corporation, 2025 Long-Term Reliability Assessment, January 2026.
[10] R. Billinton and R. Karki, “Application of Monte Carlo simulation to generating system well-being analysis,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1172–1177, 1999.
[11] W. Wangdee and R. Billinton, “Bulk electric system well-being analysis using sequential Monte Carlo simulation,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 188–193, 2006.
