Micro-energy Systems (MESs); Modeling and Optimal Operating Based on NSGA-II

Abstract

In this paper, an optimization tool for Micro-energy systems (MESs) is designed and implemented in a modular and scalable architecture. A flexible modeling tool suitable for optimizing a microgrid setup under diverse conditions is developed. In addition to modeling of the small-scale generation or storage unit, where the modeling tool reflects the trade between connected entities as well as behavior such as load shifting and curtailment. It is then applied to a case study set in a village in North Rhine-Westphalia which is due to be destroyed by lignite mining. The case study furthermore indicates the potential of a decentralized energy system, especially as the investment costs of the applied technologies further decrease, and 'energy quality' factors, such as CO 2 emissions are becoming increasingly more relevant. Finally, the objective of the model will be to meet the demand of all connected entities at a minimum cost. Moreover, with these functionalities at a small solving time, the modeling tool should meet the criteria of being scalable, flexible in regard to the form and quality of input data and as user-friendly as possible. At the last, a multi-objective optimization has been executed based on the non-dominated sorting genetic algorithm-II (NSGA-II).

Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

“…Classical FCS-MPC usually deals only with the inner (power or current) control. An outer control loop to generate parts of the references 1 , is therefore further needed, design of which requires a rough linearization approximating process, applying only for a very narrow range near the operating points, but hardly considers the undesirable disturbances and model deviations. The above issues motivate numerous research.…”