Predictive Optimization of Hybrid Energy Systems with Temperature Dependency
arxiv(2023)
摘要
Hybrid Energy Systems (HES), amalgamating renewable sources, energy storage,
and conventional generation, have emerged as a responsive resource for
providing valuable grid services. Subsequently, modeling and analysis of HES
has become critical, and the quality of grid services hedges on it. Currently,
most HES models are temperature-agnostic. However, the temperature-dependent
factors can significantly impact HES performance, necessitating advanced
modeling and optimization techniques. With the inclusion of
temperature-dependent models, the challenges and complexity of solving
optimization problem increases. In this paper, the electro-thermal modeling of
HES is discussed. Based on this model, a nonlinear predictive optimization
framework is formulated. A simplified model is developed to address the
challenges associated with solving nonlinear problems. Further, projection and
homotopy approaches are proposed. In the homotopy method, the NLP is solved by
incrementally changing the C-rating of the battery. Simulation-based analysis
of the algorithms highlights the effects of different battery ratings, ambient
temperatures, and energy price variations. Finally, comparative assessments
with a temperature-agnostic approach illustrates the effectiveness of
electro-thermal methods in optimizing HES.
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