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Hybrid Energy Systems (HES), amalgamating renewable sources, energy storage,and conventional generation, have emerged as a responsive resource forproviding valuable grid services. Subsequently, modeling and analysis of HEShas become critical, and the quality of grid services hedges on it. Currently,most HES models are temperature-agnostic. However, the temperature-dependentfactors can significantly impact HES performance, necessitating advancedmodeling and optimization techniques. With the inclusion oftemperature-dependent models, the challenges and complexity of solvingoptimization problem increases. In this paper, the electro-thermal modeling ofHES is discussed. Based on this model, a nonlinear predictive optimizationframework is formulated. A simplified model is developed to address thechallenges associated with solving nonlinear problems. Further, projection andhomotopy approaches are proposed. In the homotopy method, the NLP is solved byincrementally changing the C-rating of the battery. Simulation-based analysisof the algorithms highlights the effects of different battery ratings, ambienttemperatures, and energy price variations. Finally, comparative assessmentswith a temperature-agnostic approach illustrates the effectiveness ofelectro-thermal methods in optimizing HES.

Predictive Optimization of Hybrid Energy Systems with Temperature Dependency