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Evaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems
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This study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stages of the system's lifetime are analyzed. The findings demonstrate that the equivalent-circuit-model-based non-linear optimization model outperforms the simpler linear model by delivering more accurate predictions of energy losses and system capabilities. This enhanced accuracy enables improved operational strategies, resulting in increased roundtrip efficiency and revenue, particularly in systems with batteries exhibiting high internal resistance, such as second-life batteries. However, to fully leverage the model's benefits, it is essential to identify the correct parameters.
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Multi-Objective Nonlinear Power Split Control For BESS With Real-Time Simulation Feedback
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