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Field Teams Coordination for Earthquake-Damaged Distribution System Energization

İlker Işık, Ebru Aydin Gol

TL;DR

This work develops an $MDP$-based framework to coordinate multiple field teams for energizing an earthquake-damaged distribution system, explicitly modeling team mobility and travel times to minimize expected energization time. It extends prior methods by incorporating probabilistic health information via fragility curves tied to PGA, yielding a realistic restoration policy that respects electrical and topological constraints. To enable practical use, the authors introduce multiple performance optimizations, including deterministic-transition consolidation, action elimination, and symmetry reductions, along with a partitioning approach to scale to larger networks. The PowerRAFT tool embodies these ideas and demonstrates substantial reductions in model size and computation time, enabling timely decision support in post-disaster scenarios. Overall, the approach offers a principled and scalable path toward real-time, field-oriented restoration planning that leverages probabilistic health information and mobility constraints.

Abstract

The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.

Field Teams Coordination for Earthquake-Damaged Distribution System Energization

TL;DR

This work develops an -based framework to coordinate multiple field teams for energizing an earthquake-damaged distribution system, explicitly modeling team mobility and travel times to minimize expected energization time. It extends prior methods by incorporating probabilistic health information via fragility curves tied to PGA, yielding a realistic restoration policy that respects electrical and topological constraints. To enable practical use, the authors introduce multiple performance optimizations, including deterministic-transition consolidation, action elimination, and symmetry reductions, along with a partitioning approach to scale to larger networks. The PowerRAFT tool embodies these ideas and demonstrates substantial reductions in model size and computation time, enabling timely decision support in post-disaster scenarios. Overall, the approach offers a principled and scalable path toward real-time, field-oriented restoration planning that leverages probabilistic health information and mobility constraints.

Abstract

The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.
Paper Structure (38 sections, 3 theorems, 41 equations, 11 figures, 5 tables)

This paper contains 38 sections, 3 theorems, 41 equations, 11 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Preliminaries and Notation
  3. Markov Decision Process
  4. Restoration Process
  5. Probability of Failure ($P_f$) Values
  6. Distribution System
  7. Field Teams Mobility
  8. Problem Formulation
  9. Proposed MDP Model
  10. States
  11. Distribution System State
  12. Field Teams State
  13. Action Set
  14. Energizable Buses
  15. Feasible Actions for the Field Teams
  16. ...and 23 more sections

Key Result

Proposition 4.1

Let $M = (S, A, p, c)$ be the original MDP with a cost formulation as in def:c and value function $V_n^{\pi}$, and let $M' = (S', A', p', c_n)$ with $\mathbb{V}_n^{\pi}$ be the modified one as given in Definition def:modified_mdp. Then for any policy $\pi$ of $M$, its projection $\pi'$ on $M'$ (i.e.

Figures (11)

  • Figure 1: Sample distribution system
  • Figure 2: Sample configuration
  • Figure 3: MDP state $\left(\textbf{s} , [(1, 0), (3, 1)] \right)$
  • Figure 4: MDP state $\left(\mathbf{s'} , [(2, 0), (3, 0)] \right)$
  • Figure 5: The distribution systems used in the experiments: WSCC 9-bus (A), 12-bus (B), real-life 17-bus (C).
  • ...and 6 more figures

Theorems & Definitions (12)

  • Definition 2.1
  • Example 2.2.1
  • Example 3.1.1
  • Example 3.2.1
  • Example 3.3.1
  • Definition 4.1
  • Proposition 4.1
  • Corollary 4.1
  • Proposition 4.2
  • proof
  • ...and 2 more