Deterministic modeling is limited by its assumption of predictable outcomes including prices, costs, and end-user loads – a process that ignores forecast uncertainty. A key limitation of this approach is it produces a single forecast based on fixed inputs, which means it fails to capture the range of potential outcomes. This can lead to inaccurate results – particularly when used for long-term analysis such as multi-year forecasts and integrated resource planning. While sensitivity analysis can provide useful insights, it does not adequately reflect the interactions among multiple sources of uncertainty or long-term market volatility.

Historical experience demonstrates the consequences of this limitation, including unpredictable economic growth, unexpected customer demand, fluctuating fuel prices, technological changes, power supply uncertainty, and evolving environmental and climate policies, along with technology disruptions and innovations. The uncertainty associated with data centers demand has profound implications for generation planning, creating major investment risks and threatening grid reliability. The issue is compounded by the uncertainty in the evolution of artificial intelligence, which renders traditional long-term forecasting and capacity planning models obsolete.

In contrast, stochastic modeling explicitly incorporates market uncertainty and generates a range of outcomes with associated likelihoods using solid data-driven techniques. This approach provides more realistic forecasts for long-term planning and, critically, supports more reliable resource addition and retirement decisions. Most importantly, it provides very useful risk metrics and insights and enables critical risk management decisions.

Deterministic modeling may be a good start but it is inadequate for making good and informed decisions.