New rolling horizon optimization approaches to balance short-term and long-term decisions: An application to energy planning

The planning of complex systems such as energy systems calls for multiple and recurrent operational decisions depending on the present situation as well as future trends. Such decisions can be optimized with rolling-horizon approaches where most immediate decisions are fixed, based on current previsions, while next decisions are made at further optimization steps with updated information. In this paper, focus on cases where long-term decisions have to be balanced with detailed short-term decisions to insure operational realism. On such problems, standard rolling horizon approaches are hard to solve due to the substantial increase of the temporal dimension. To overstep this issue, new approaches to balance short and long-term decisions. Two modelling approaches, based on aggregated time steps, are proposed and tested on an energy production problem where energy can be stored seasonally. Approaches are compared to benchmarks approaches (myopic and a posteriori optimization), and a sensitivity analysis is performed. Both approaches are promising and correspond to different compromises between the model complexity, computation times and solution quality.