An Optimization Framework for Solving Integrated Planning and Scheduling Problems for Dense Energy Carriers

Abstract

Abstract Due to rapid population growth, the global demand of energy and water resources are accelerating. Simultaneously, there is a global trend to transition to renewable energy systems. Recently, researchers have begun to investigate how green dense energy carriers (DECs) can be apart of this transition. In this work, we present an optimization framework for solving large-scale supply chain problems and we apply it to explore the economic and environmental impacts of DECs. Specifically, we look at utilizing DECs to transport renewable energy produced in areas with high solar and wind potentials to regions with low renewable potential. To reduce the computational burden of the large-scale optimization problem, we have developed a greedy randomized adaptive search procedure (GRASP). The GRASP leverages the linear programming (LP) relaxation of the problem to generate feasible solutions. We have found that the GRASP is able to reduce the computational time by approximately two orders of magnitude as compared to a commercial grade mixed-integer linear programming (MILP) solver ran out-of-the-box on large-scale instances.