Resource Planning Analyst – Long-Range Energy Modeling

Xcel Energy Inc; Minneapolis, Northern (MN, KY); Hybrid; USD 85,000 - 159,000; Full time

Benefits offered by this job

Annual Incentive Program

Medical/Pharmacy Plan

Pension

401(k) plan

Job summary

Xcel Energy Inc. is seeking a Resource Planning Analyst at multiple levels to develop and maintain long-range models for generation planning. The role involves quantitative analysis, cost forecasting, and risk assessment of various generation options.

Successful candidates will join a collaborative team and contribute to regulatory filings and strategic planning. A strong background in engineering, economics, or operations research is preferred, with experience in large-scale optimization tools.

Qualifications

• Bachelor's degree in engineering, finance, economics, operations research, mathematics or business.
• Two years of energy industry experience or equivalent experience with large optimization/forecasting models.

Responsibilities

• Develop/run/maintain long range resource planning models.
• Produce quantitative analyses for executives and regulatory filings.
• Implement Monte Carlo simulations to quantify cost risk of generation options.
• Coordinate with Forecasting, Risk Management, Trading, Gas and Coal Supply, Transmission Operations and other teams.
• Ensure expansion plans and production cost models reflect current fuel costs and operating costs.

Education

Bachelor's degree in engineering / finance / economics / operations research / mathematics / business

Tools

EnCompass

PLEXOS

Aurora

EGEAS

Job description

Xcel Energy Inc. is seeking a Resource Planning Analyst at multiple levels to develop and maintain long-range models for generation planning. The role involves quantitative analysis, cost forecasting, and risk assessment of various generation options.

Successful candidates will join a collaborative team and contribute to regulatory filings and strategic planning. A strong background in engineering, economics, or operations research is preferred, with experience in large-scale optimization tools.