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Predictive Reliability Assessment of Distribution Grids with Residential Distributed Energy Resources

Arun Kumar Karngala, Chanan Singh, Le Xie

TL;DR

This paper tackles predictive reliability assessment of distribution grids with residential DERs by introducing a bottom-up probabilistic framework that models micro-level DER penetration using joint distributions for rooftop PV and energy storage. An adaptive Monte Carlo approach is employed to estimate not only mean reliability indices but the entire distribution of end-user outcomes, incorporating PV/ES component reliability. The method is demonstrated on a modified RBTS Bus 4 system across 16 joint PV--ES adoption scenarios, revealing substantial heterogeneity in customer-level reliability and highlighting cases where high, coordinated adoption yields dramatic improvements while uneven adoption can dampen benefits. The work provides utilities and regulators with a quantitative tool to forecast reliability under DER penetration pathways and to better capture the value of customer-level contributions and resilience, beyond conventional system-average indices.

Abstract

Distribution system end users are transforming from passive to active participants, marked by the push towards widespread adoption of edge-level Distributed Energy Resources (DERs). This paper addresses the challenges in distribution system planning arising from these dynamic changes. We introduce a bottom-up probabilistic approach that integrates these edge-level DERs into the reliability evaluation process. Our methodology leverages joint probability distributions to characterize and model the penetration of rooftop photovoltaic (PV) systems and energy storage across a distribution network at the individual residential level. Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level. To validate our approach, we applied this methodology to the RBTS test system across various adoption scenarios, effectively showcasing the capability of our proposed method in quantifying the variation in end-user reliability indices for each scenario within the distribution system.

Predictive Reliability Assessment of Distribution Grids with Residential Distributed Energy Resources

TL;DR

This paper tackles predictive reliability assessment of distribution grids with residential DERs by introducing a bottom-up probabilistic framework that models micro-level DER penetration using joint distributions for rooftop PV and energy storage. An adaptive Monte Carlo approach is employed to estimate not only mean reliability indices but the entire distribution of end-user outcomes, incorporating PV/ES component reliability. The method is demonstrated on a modified RBTS Bus 4 system across 16 joint PV--ES adoption scenarios, revealing substantial heterogeneity in customer-level reliability and highlighting cases where high, coordinated adoption yields dramatic improvements while uneven adoption can dampen benefits. The work provides utilities and regulators with a quantitative tool to forecast reliability under DER penetration pathways and to better capture the value of customer-level contributions and resilience, beyond conventional system-average indices.

Abstract

Distribution system end users are transforming from passive to active participants, marked by the push towards widespread adoption of edge-level Distributed Energy Resources (DERs). This paper addresses the challenges in distribution system planning arising from these dynamic changes. We introduce a bottom-up probabilistic approach that integrates these edge-level DERs into the reliability evaluation process. Our methodology leverages joint probability distributions to characterize and model the penetration of rooftop photovoltaic (PV) systems and energy storage across a distribution network at the individual residential level. Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level. To validate our approach, we applied this methodology to the RBTS test system across various adoption scenarios, effectively showcasing the capability of our proposed method in quantifying the variation in end-user reliability indices for each scenario within the distribution system.
Paper Structure (20 sections, 17 equations, 5 figures, 5 tables)

This paper contains 20 sections, 17 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivation and Scope of this Work
  4. Residential system Reliability evaluation
  5. Residential system model
  6. PV system model
  7. ES system model
  8. Residential system energy balance
  9. Residential system reliability metrics
  10. Probabilistic approach to system reliability assessment
  11. Modeling DER penetration
  12. Macro vs Micro Perspectives
  13. Quantitative Modeling of Micro-level Penetration
  14. Marginal Distributions for X and Y
  15. Joint adoption scenarios
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Case 1: no DER; Case 2: DER at {4,5,9,10}.
  • Figure 2: General-purpose residential system configuration.
  • Figure 3: Marginal adoption scenarios for X.
  • Figure 4: MCS flowchart.
  • Figure 5: Joint adoption densities (left) and AIF distributions (right) for selected PV--ES adoption scenarios. The top row corresponds to highly concentrated adoption of PV and ES ({HC, HC}). The bottom row corresponds to varied adoption of PV and ES ({V, V}). The red dashed line marks the baseline SAIFI (0.30 f/yr) and the black dashed line marks the estimated mean SAIFI for each scenario.