Optimizing Renewable Energy Resources through Climate Resilience Modeling for Sustainable Energy-Aware Networked Systems

Integrating climate resilience modeling into renewables optimization is essential for the stability and efficiency of energy-aware networked systems. This paper presents an initial dynamic framework for improving sustainability by utilizing historical climate data, machine-learning forecasts, and the climate-informed energy management view of energy. By forecasting climate variability and accommodating renewable energy generation, this method reduces inefficiencies, provides carbon-offsetting, and allows the grid to be more reliable. We demonstrate an adaptable optimization scheme that utilizes climate forecasts and can inform the tactical distribution of energy. Through the legitmization of climate variability into planning for infrastructure and allocating energy resources accordingly, we contribute to more resilient and sustainable energy-aware networks which exhibit operational resilience against climate-related disruptions. The framework we propose is particularly relevant for smart cities and ecosystems deploying IoT technologies that require adaptable, reliable sources of renewable energy.

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