Optimizing Economic and Environmental Performances of Solar Power and Electric Vehicles: A MOMILP Application

International audienceThe EU has set targets for reducing its greenhouse gas emissions progressively up to 2050. Recognizing that the sectors of heat & electricity generation and transport are the world’s largest contributors to climate change, the deployment of clean energy and transportation technologies is widely stimulated. Unfortunately, better environmental performances often imply higher economic costs. This trade-off between two conflicting objectives calls for a multi-objective optimization (MOO) assessment, aiming to find the “best” possible solutions, i.e. the Pareto optimal solutions. Whereas the economic and ecological optimization of energy systems is extensively studied in literature, little research has been done on transportation systems. Furthermore, we argue that it is valuable to simultaneously optimize energy and transportation systems for two reasons. First, most entities (e.g. firms, areas, individuals) have needs regarding both energy and transportation. Second, when considered simultaneously, synergies between the energy and transportation systems can be exploited. This paper aims at filling this gap by performing a MOO on a Belgian case study, i.e. a SME having a certain need for electricity and traveling. The considered energy technologies are solar photovoltaics and grid electricity; for transport internal combustion engine vehicles, grid powered battery electric vehicles (BEVs), and solar powered BEVs are available. Aiming to obtain realistic results, the possible existence of scale economies is taken into account. The latter implies a mixed integer programming problem. Accordingly, this paper applies the exact algorithm described in: T. Vincent, et al.; Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case. Computers & Operations Research, 40(1)498-509, 2013. Additionally, the impact of policy measures on the Pareto front is visualized