A simulation-based optimization approach for sustainable energy supply chain transitions

Emissions are emitted throughout the energy supply chain. In demand sector, transitioning from fossil fuel-based vehicles to Electric Vehicles (EVs) is a key step. However, the growth of EVs will lead to higher emissions if, in supply sector, the energy sources rely mainly on Non-Renewable Energy Sources (NRES), forcing to transition to Renewable Energy Sources (RES). While managing these transitions, it is also important to make sure that energy requirements can still be fulfilled which is driven by population dynamics. Thus, this complex interdependence indicates the importance of using systemic framework, although most previous studies focusing on one of transitions which can lead to policy misalignment and unintended trade-offs of sustainability performance. This research proposes a System Dynamics model for assessing the impact of those three interplaying factors on sustainability performance including macroeconomic, job availability, and total emissions, followed by Response Surface Methodology (RSM) for performing efficient optimization. A case study from the United States is used. Through simulation and statistical analysis, 8 insightful propositions are generated. Subsequently, the metamodels based on second-order regression are developed, forming multi-objective non-linear programming revealing the optimal growth rates for sustainable transition which are very useful for helping the policy makers to make more informed decisions toward a sustainable energy system.