Distributed optimization for the exploitation of multi-energy flexibility under uncertainty in city districts
Diekerhof, Michael; Monti, Antonello (Thesis advisor); Moser, Albert (Thesis advisor)
1. Auflage. - Aachen : E.ON Energy Research Center, RWTH Aachen University (2018)
Book, Dissertation / PhD Thesis
In: E.ON Energy Research Center 63. Ausgabe : ACS, Automation of complex power systems
Page(s)/Article-Nr.: 1 Online-Ressource (xxvi, 222 Seiten) : Illustrationen, Diagramme
Dissertation, RWTH Aachen University, 2018
This dissertation centers on distributed optimization for the achievement of the electrical balance of demand and supply of an aggregator on a local distribution level. The distributed optimization techniques perform almost equally to central benchmark algorithms but, compared to a non-optimized scenario, achieve a significant flexibility exploitation. In case of heterogeneous customer and aggregator objectives, the results prove that there is a relevant trade-off. Both the customers and the aggregator can still though achieve a metric improvement. Uncertainty affects the aggregator´s portfolio balancing. Thus, this dissertation investigates the potential of robust optimization with model predictive control for a robust portfolio. The results demonstrate a reduction of the adjustments that could otherwise lead to significant imbalances for the balance responsible party and simultaneously minimize the total costs of the system. Furthermore, this dissertation describes a portfolio optimization of an aggregator as a two-stage stochastic optimization. The computational effort of stochastic optimization is only worthwhile for specific days in the year. A large spread and probability distribution of the first stage day-ahead prices as well as low presence of flexibility characterize these days. Additionally, this dissertation provides a novel customer-centric approach for flexibility exploitation of electro-thermal heating systems. The approximate differentiation method, an inverse simulation technique, turns the customer´s thermal comfort specifications into technical power profile values that are most relevant for the flexibility user.