Optimization of Distributed Energy Systems Through a LinearProgramming Framework

Abstract

Transitioning to sustainable energy systems requires new tools to face new challenges in energy security, climate change mitigation and socio-economic equity. The research at hand extends the foundation established by Rabe et al. (2022) to optimize distributed energy systems based on linear programming. In this work, we extend the model with scenario-based policy analysis, group-based economic fairness, and carbon penalties. Scenario analysis assesses the effects of dynamic policy changes like stricter renewable energy mandates and carbon pricing, on the energy mix and costs. Economic fairness requires energy resources to be distributed equitably, while carbon penalties internalize the environmental costs of carbon-intensive processes, thus incentivizing decarbonization. The results reveal a best-suited energy mix, achieving renewable energy targets with minimum costs, while effectively performing under changing policy constraints to provide a reliable grid. This framework aims to synthesize the myriad of insights from policy design literature on energy, allowing policymakers and other stakeholders to develop actionable roadmaps in support of sustainable energy transition.