The energy sector is where mathematical optimization truly shines. The problems are complex, the data is plentiful, and the stakes are high. Here are two real-world examples showing how operations research transforms how utilities make decisions.
When I talk to energy companies about optimization, I often see skepticism. "We've been running grids for decades. What can math tell us that experience can't?" The answer becomes clear when you see the numbers.
Use Case 1: Unit Commitment
Every day, grid operators face the same question: which power plants should be running tomorrow, and at what output? This is the unit commitment problem, and it's fiendishly complex.
Each generator has different startup costs, minimum operating levels, ramp rates, and fuel costs. Demand varies by hour. Wind and solar output is uncertain. Transmission constraints limit what can flow where. And the decisions made in hour 1 constrain what's possible in hour 24.
Traditional approaches rely on heuristics and operator experience. Mathematical optimization—specifically mixed-integer programming—can find solutions that are provably close to optimal. For a medium-sized utility, the savings from better unit commitment decisions can run into millions of euros annually.
Use Case 2: Energy Trading
Energy markets operate on multiple timescales: day-ahead, intraday, balancing. Each market has different prices, different rules, and different risks. Traders must decide when to buy, when to sell, and how much flexibility to keep in reserve.
Stochastic optimization models can incorporate price uncertainty, generation uncertainty, and demand uncertainty into a coherent decision framework. Instead of trading based on intuition, traders can see the expected value of different strategies under thousands of scenarios.
One trading company I worked with improved their gross margin by 15% after implementing an optimization-based trading system. Not by taking more risk—by making better decisions under the same risk constraints.
Why Energy Is Perfect for Optimization
Energy systems have characteristics that make them ideal for mathematical optimization: clear objective functions (minimize cost, maximize profit), well-defined constraints (physical laws, market rules), and abundant data (SCADA systems, market prices, weather forecasts).
The energy transition is only making these problems more important. As grids integrate more renewables, the complexity of operational decisions increases. The companies that master optimization will have significant advantages over those that don't.
The math is ready. The software is ready. The question is whether energy companies are ready to embrace it.
Written by
Jonasz Staszek