A taxonomy for modeling flexibility and a computationally efficient algorithm for dispatch in Smart Grids

The word flexibility is central to Smart Grid literature, but to this day a formal definition of flexibility is still pending. This paper present a taxonomy for modeling flexibility in Smart Grids, denoted Buckets, Batteries and Bakeries. We consider a direct control Virtual Power Plant (VPP), which is given the task of servicing a portfolio of flexible consumers by use of a fluctuating power supply. Based on the developed taxonomy we first prove that no causal optimal dispatch strategies exist for the considered problem. We then present two heuristic algorithms for solving the balancing task: Predictive Balancing and Agile Balancing. Predictive Balancing, is a traditional moving horizon algorithm, where power is dispatched based on perfect predictions of the power supply. Agile Balancing, on the other hand, is strictly non-predictive. It is, however, explicitly designed to exploit the heterogeneity of the flexible consumers. Simulation results show that in spite of being non-predictive Agile Balancing can actually out-perform Predictive Balancing even when Predictive Balancing has perfect prediction over a relatively long horizon. This is due to the flexibility-synergy-effects, which Agile Balancing generates. As a further advantage it is demonstrated, that Agile Balancing is extremely computationally efficient since it is based on sorting rather than linear programming.