Rapid growth in data centers and transportation electrification is creating uncertainty in long-term electricity demand, challenging traditional resource planning approaches. This report explores how deterministic and adaptive planning methods perform across a wide range of stylized load scenarios that vary in magnitude, timing, location, and flexibility, offering a structured framework for exploring how different load uncertainty dimensions may shape planning outcomes. Deterministic planning optimizes for individual futures, while adaptive planning identifies flexible strategies that hedge against a wide range of futures. Using a long-term capacity expansion model, the study compares resulting portfolios, system costs, and reliability, finding that adaptive planning more effectively manages risk across scenarios, improves reliability, and reduces expected costs relative to deterministic approaches. The findings highlight which dimensions of load uncertainty (i.e., magnitude, timing, location, and flexibility) most strongly influence planning outcomes and how electric companies can incorporate adaptive planning methods and insights to complement existing processes.