Field Study: Dynamic Pricing for GreenGrid's Renewable Power Sales
Overview
What this challenge is about.
Analyze renewable supply and day-ahead price data to build a dynamic pricing model for a simulated energy portfolio, then earn your verifiable certificate.
The scenario
GreenGrid is a renewable energy aggregator that pools the output of independently owned solar and wind farms and bids that combined supply into wholesale day-ahead and real-time electricity markets, where prices are set hourly and can briefly turn negative when supply outstrips demand. Aggregators in this space live or die on forecast accuracy and bid timing, because a single farm's output cannot be dispatched on command the way a gas plant can.
The Brief
What you'll do, and what you'll demonstrate.
How should GreenGrid set dynamic, hour-by-hour prices and volumes for its renewable output to maximize expected profit under weather and market uncertainty while keeping downside risk and regulatory limits within bounds?
Earning criteria — what you'll demonstrate
- Sample and characterize real wholesale electricity price and renewable generation time series from a public source
- Diagnose how weather-driven supply variability and price volatility interact in a renewable portfolio
- Formulate a dynamic pricing problem as a profit-maximization under uncertainty with explicit constraints
- Quantify downside risk using Value-at-Risk and tie a pricing rule to a stated risk tolerance
- Communicate quantitative findings as an evidence-backed business recommendation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Dynamic Pricing
Apply dynamic pricing to solve real industry problems and demonstrate production-level capability.
- Optimization
Apply optimization to solve real industry problems and demonstrate production-level capability.
- Risk Management
Identify, assess, and mitigate risks across business operations and projects.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Time Series Analysis
Apply time series analysis to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Energy Quantitative Analyst
Energy trading desks need analysts who can turn raw market and generation data into priced, risk-bounded bidding strategies. This challenge mirrors that daily work: sampling market data, modeling profit under uncertainty, and respecting hard regulatory and risk limits.
This challenge sharpens
- dynamic-pricing
- risk-management
- time-series-analysis
Power Markets Data Scientist
Renewable aggregators and utilities hire data scientists to forecast generation and prices and to optimize how supply is offered. The challenge builds exactly this pipeline, from cleaning public time series to deploying an optimization that drives commercial decisions.
This challenge sharpens
- python
- time-series-analysis
- optimization
Trading Risk Analyst
Risk teams validate that pricing and trading models keep losses within mandated limits. Here the student quantifies a 5 percent Value-at-Risk and proves the strategy stays within a stated tolerance, the core deliverable of a markets risk role.
This challenge sharpens
- risk-management
- optimization
- dynamic-pricing