Electric Vehicle Charging Optimization Data with 2 Million Monte Carlo Scenarios

Multiple files support heuristic optimization of electric vehicle charging to minimize overloading in low voltage grids. The data includes lognormally sampled hourly scenarios across 11 charging locations and normally distributed household electricity use scenarios from agent-based modeling. The dataset was created by Sajjad Haider of Technische Universität Dresden to support a related research article.

Use Cases

• Training surrogate models for grid behavior based on the attached machine learning algorithm weights and codes.
• Benchmarking optimization algorithms for reducing voltage drops, cable overloading, and total line losses.
• Analyzing the impact of varying EV penetration (0 to 100 in increments of 20) on low voltage grid performance.
• Conducting stochastic risk assessment using the 2 million Monte Carlo simulation scenarios based on actual probabilities.

Strengths

• Includes a large-scale Monte Carlo simulation with 2 million scenarios designed to incorporate most possible situations.
• Covers a parameter sweep of electric vehicle numbers from 0 to 100 in increments of 20.
• Provides benchmark grid parameters and intermediate outputs defining worst-case scenarios for validation.
• Contains results from three different optimization approaches for comparative analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Technische Universität Dresden

Collection Method

Data generated from simulations in DigSilent Powerfactory software using lognormal and normal sampling, agent-based modeling, and Monte Carlo methods.