The accuracy of wind power forecasts is a crit- ical determinant of operational costs in electricity markets that employ Deterministic Market Clearing (DMC). This paper presents a comprehensive sensitivity analysis of a two-stage DMC framework—comprising a Day-Ahead (DA) scheduling stage and a Real-Time (RT) recourse stage—on a 6-bus power system with three conventional generators and two wind farms. Four distinct forecast cases, ranging from a perfect forecast to significant over- and under-predictions, are evaluated using Linear Programming implemented in GAMS. Numerical results demonstrate that total system costs range from $1,200 (perfect forecast) to $1,700 (severe over-forecast), revealing a 41.67% cost penalty for large forecast errors. The analysis of Nodal Marginal Prices (LMPs) and real- time generator adjustments provides insight into the economic signals generated under each forecast scenario, quantifying how forecast bias propagates into commitment decisions and recourse actions.

This paper presented a detailed sensitivity analysis of Deter- ministic Market Clearing under varying wind power forecasts on a 6-bus system. Four forecast scenarios—covering perfect prediction, moderate over-forecast, severe over-forecast, and under-forecast—were solved to optimality as two-stage Linear Programs implemented in GAMS. The principal conclusions are: 1)Forecast accuracy is the primary cost driver. Total costs range from $1,200 (perfect forecast) to $1,700 (se- vere over-forecast), a 41.67% spread attributable solely to forecast error. 2)Over-forecasting is more costly per unit of error than under-forecasting when upward ramp resources are scarce, as the system must activate progressively more expensive flexible units. 3)Ramp capacity, not generation capacity, is the bind- ing real-time resource. The zero-ramp generator i1 contributes no flexibility, concentrating the balancing burden on i2 and i3. 4)LMPs degenerate under under-forecast conditions, producing zero prices that fail to signal the true scarcity of committed resources—a structural limitation of the deterministic framework. These findings provide a quantitative motivation for invest- ing in improved wind forecasting and for considering the adoption of forecast ensemble methods or robust optimization frameworks that can hedge against forecast errors at the day- ahead stage, particularly in power systems with high wind penetration.

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