Accurate Energy Predictions for Optimal Power Flow Problems

Abstract This paper investigates how prediction accuracy in renewable energy generation influences the performance of Optimal Power Flow (OPF) in green energy systems. We propose a two-level optimization framework in which the outer level models the OPF problem, while the inner level captures the machine learning--based forecasting task. Because prediction accuracy is inherently multifaceted, we evaluate its impact using several metrics, including root mean squared error, weighted mean squared error (WMSE), and mean bias error. Our formulation incorporates cost components associated with solar curtailment and carbon emissions. The contributions of this work are threefold: (i) we analyze the sensitivity of OPF solutions to prediction errors and derive theoretical bounds on the resulting optimality gap; (ii) we quantify reserve requirements under varying levels of prediction accuracy; and (iii) we present simulations based on real-world data to illustrate system behavior across different accuracy regimes. For example, in a representative case study based on real-world data, increasing forecast RMSE from roughly 200 MW to 480 MW leads to a substantial increase in composite system cost, from a few thousand dollars to several tens of thousands of dollars, highlighting the strong sensitivity of system performance to prediction accuracy. Overall, our results provide both theoretical and empirical guidance on the level of prediction accuracy needed for cost-effective and reliable renewable-integrated energy operation.