Sensitivity in Multi-Ensemble Scheduling

Future smart grid control demands delegation of liabilities to distributed, rather small energy resources in contrast to today's traditional large control power units. Distributed energy scheduling constitutes a complex task for optimization algorithms regarding the underlying high-dimensional, multimodal and nonlinear problem structure. For predictive scheduling with high penetration of renewable energy resources, agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good performance. On the other hand, such decoder-based methods are currently designed for single entities and not able to cope with ensembles of energy resources. Aggregating training sets sampled from individually modeled energy units results in folded distributions with unfavorable properties for training a decoder. Nevertheless, this happens to be a quite frequent use case, e. g. when a hotel, a small business, a school or similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take part in decentralized predictive scheduling. Recently, an extension to an established agent approach for scheduling individual single energy units has been proposed that is based on second level optimization. The agents' decision routine may be enhanced by a covariance matrix adaption evolution strategy that is hybridized with decoders. In this way, locally managed ensembles of energy units can be included. The applicability has already been demonstrated, but the effects of ensemble composition are so far unknown. Here, we give an widened view on the underlying power level distribution problem and extend the results by conducting a sensitivity analysis on the impact of ensemble size and penetration on communication overhead and residual error.