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GP-EE-1MWU0U7ElectricalReady

Climate-Resilient Distribution-Network Planning Under Compound Hazards

A completed electrical engineering study comparing ten distribution-network resilience strategies across extreme heat, flood, wind, wildfire and compound events.

Climate-Resilient Distribution-Network Planning Under Compound Hazards project visual
GP-EE-1MWU0U7 · Electrical
  • Python 3.12
  • NumPy
  • Pandas
  • Matplotlib

Software compatibility

Python 3.12 or later

The retained release and clean Linux container run use Python 3.12. No paid power-system software or confidential utility dataset is required to reproduce the comparison.

Project definition

Problem statement

Distribution networks face heat, flood, wind and wildfire hazards that can occur together or in sequence. Measures that help one hazard can leave another exposure unchanged or make restoration harder.

The engineering problem is to compare protection, continuity and recovery strategies without turning literature-informed screening positions into utility measurements or investment approval.

Project objectives

  • Compare hardening, elevation, undergrounding, feeder automation, vegetation management, distributed resources, mobile supply, monitoring, spares and critical-load restoration.
  • Represent heat, flood, wind, wildfire and compound hazard sequences.
  • Separate asset protection, service continuity, restoration speed, operational flexibility, cost, equity and evidence maturity.
  • Test balanced, protection, recovery and affordability planning priorities.
  • Propagate evidence uncertainty with a fixed random seed.
  • Retain hard gates, validation criteria, FMEA cells and assurance requirements alongside weighted scores.

Project structure

Project components

01

Strategy register

Declares engineering, operational, cost, equity and evidence positions for ten resilience strategies.

02

Hazard and context model

Crosses five hazards with urban, coastal, forest-interface and rural planning contexts.

03

Decision model

Compares balanced, asset-protection, service-recovery and affordability priorities using visible weights.

04

Uncertainty model

Runs 20,000 fixed-seed samples per strategy and retains intervals and threshold probabilities.

05

Assurance model

Retains validation criteria, workflow FMEA cells, hazard gates and a strategy-to-requirement crosswalk.

Methodology

Project workflow

  1. 01
    Define the planning boundary

    State hazard horizon, asset scope, critical services, operational limits and decision purpose.

  2. 02
    Build compound scenarios

    Keep concurrent and sequential hazards, access constraints and cross-infrastructure dependencies visible.

  3. 03
    Screen strategies

    Apply hazard applicability and hard gates before comparing weighted evidence.

  4. 04
    Test priorities and uncertainty

    Compare four planning priorities and fixed-seed variation in the declared evidence positions.

  5. 05
    Plan utility validation

    Convert open gaps into feeder studies, fragility work, field inspection, restoration exercises and current compliance review.

Demonstration scenario

Under balanced weights, ring, mesh, sectionalising and automation leads the retained screening at 78.22, followed closely by critical-load restoration planning, monitoring and distributed resources. The leading strategy changes with the planning priority, showing why resilience requires a portfolio rather than one universal measure.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, Pandas, Matplotlib for subject analysis, simulation, and results.
Engineering model
Python and NumPy implement the strategy, hazard, context, gate and uncertainty calculations.
Data analysis
Pandas retains all 800 cases, summaries, scenarios, FMEA, assurance and sensitivity results.
Figures
Matplotlib generates twelve labelled planning, uncertainty and resilience-boundary figures.
Reproducibility
A fixed seed, retained configuration, automated tests and Docker reproduce the study.
Documentation
The report covers compound hazards, network impacts, adaptation measures, Indian context, methodology, results, limitations and further work.

Testing

Evaluation

Evaluation measures

  • Heat, flood, wind, wildfire and compound-event applicability
  • Service continuity, restoration speed and operational flexibility
  • Critical-load and equity position
  • Balanced, protection, recovery and affordability rankings
  • Uncertainty intervals and sensitivity to twelve evidence dimensions
  • Workflow FMEA priorities, hard gates and assurance completeness
  • Exact reproduction of 800 retained cases in a clean Linux container

Project boundaries

  • Inputs are literature-informed screening positions and sensitivity cases, not measurements from one utility network.
  • Scores are not outage forecasts, fragility curves, switching instructions or investment approvals.
  • The project contains no real feeder topology, customer information or critical-infrastructure operating data.
  • Costs are relative screening positions and not supplier quotations or project estimates.
  • A utility decision requires local hazard layers, asset condition, power-flow and protection studies, customer consequence analysis, current regulation and qualified engineering approval.

Included

  1. 01Complete reproducible Python source code
  2. 02Ten distribution-network resilience strategies and five hazard classes
  3. 03800 deterministic strategy, hazard, context and evidence cases
  4. 04Four planning scenarios with 20,000 uncertainty samples per strategy
  5. 05180 workflow FMEA cells and 150 assurance crosswalk cells
  6. 06Complete CSV, JSON and analytical figure outputs
  7. 0795-page project documentation in PDF and editable Word formats
  8. 0816-page setup and usage guide in PDF and editable Word formats
  9. 0955 annotated references and one sourced literature figure
  10. 10Automated tests, accessibility audits, repository validation and Docker verification

Project record

No information is collected on this page.

Permanent project ID
GP-EE-1MWU0U7
Catalogued
21 Aug 2026
Completed
03 Sept 2026
Verified
03 Sept 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.