Distributed hybrid constraint handling in large scale virtual power plants

On local minima in distributed energy scheduling

“…Also in the continuous case outliers indicating results worse than with the coarse grained model (upper triangle) are present. This result also reproduces preliminary results from [10].…”

“…As a unit model we used a co-generation model that has already served in several studies and projects for evaluation [34], [35], [10], [36]. This model comprises a micro CHP bundled with a thermal buffer store.…”

“…Apart from finding a combination of schedules whose sum resembles a given target power profile best, further objectives are usually integrated due to the manyobjective nature of energy scheduling. In the following, we consider predictive scheduling, where the goal is to select exactly one schedule x i for each energy unit U i from a search space of feasible schedules with respect to a future planning horizon, such that a global objective (e. g. resembling a target power profile) is optimized by the sum of individual contributions [10]. A basic formulation of the scheduling problem is given by…”

“…To this end, a support vector data description (SVDD) model and a decoder approach have been introduced [9], allowing for a continuous representation of flexibility combined with a targeted unconstrained search based on this surrogate model. Although the usage of the decoder largely helps to identify solutions that better use the units' flexibility [10], there has been evidence that the amount of local minima increases as well. With the general predisposition of heuristic algorithms to local minima [11] a more in-depth analysis of the relation of flexibility modelling and optimization performance is necessary: The combination of a search space representation and the heuristic using this search space has to be chosen carefully [12].…”

Conjoint Dynamic Aggregation and Scheduling Methods for Dynamic Virtual Power Plants

“…This way, economically or ecologically rooted soft constraints can be taken into account as secondary optimization goals while preserving privacy and autonomy of the participating units. Thus we employ the self-organizing heuristic COHDA as described in [19], [18] for this task as follows.…”