MPC and perfect foresight

The default version of WHAT-IF assumes "perfect foresight", but it is possible to activate the "MPC" module to avoid it.

Perfect foresight is a common approach used in sectorial planning models, where the system is optimized over the whole planning period with assumed perfect knowledge of the future. This means that optimization models with perfect foresight anticipate future conditions, such as droughts, and adjust, for instance, by selecting crops with lower water requirements or storing additional water. In actual operation, water planners and managers do not have perfect foresight, and are limited by the availability and skill of existing forecasting systems.

While the assumption of perfect foresight is common to many long-term planning models, and recognized as a limitation, few studies have analyzed its effect.

This paper describes how a Model Predictive Control (MPC) framework is integrated to WHAT-IF to over-come this assumption by iteratively running the perfect foresight model with forecasts.

Model predictive control simulates how infrastructure can be operated in practice by repetitively answering the question "What are the decisions to take now considering the information we have about the current system state and the prediction of the future?"

In fact, this is how the Colorado reservoirs are operated. Every month, the "24-Month study" predicts the expected behavior of the water system for the next two years to draw the operation rules for the cur-rent month.

This is particularly relevant for the water system as hydrologic parameters are characterized by a strong variability and uncertainty. It is also relevant for renewable energy production, as well as any uncertain socio-economic parameter such as population growth, demand changes, and technological development.