Abderrahim Ait-Alla

Weather conditions have a significant impact on the installation of offshore wind turbines. The rules for installation set clear limits. These limits are usually based on estimations of various experts and not on real assumptions and measurements on-site. When wind speeds and wave heights are too high, work cannot be carried out, and this leads to delays and additional costs. Therefore, we have carried out a measurement campaign during the installation of rotor blades to investigate to which extent the limits can be adjusted by using a tuned mass damper. The results from the measurement campaign—specifically empirically derived significant wave height limits—are used in a discrete event simulation. This study simulates delays resulting from weather conditions. Based on this, the total installation costs are considered. The results of the measurement campaign show that a safe installation with the use of a damper is possible at wave heights of up to 1.6 m. With the discrete event simulation, it is possible to prove that 17.9% can be saved for the costs of the installation vessel. In addition, the wind farm could be erected 32 days faster. Thus, it can be stated that the use of a tuned mass damper simplifies the installation from a technical point of view and is economical.

Besides managing accruing data in production systems, Digital Twins provide additional services like the simulation or control of production systems. The Digital Twin and its real-world physical counterpart need to be interconnected using sensors and actuators to enable such services. This article proposes a simulation-based method to design and evaluate such an interface. Applying concepts from the area of software-in-the-loop, it proposes the use of a so-called Physical Twin: a simulation model which mimics the abilities of the physical system but allows simulation-based experiments to optimise the interface. After presenting the general approach, the article provides an application example of the proposed procedure. A Digital and a Physical Twin are implemented for an application scenario and connected using a simple TCP/IP interface. By varying the number of sensors as well as considering component breakdowns in the Physical Twin, this setup allows evaluating different configurations in terms of production performance. The evaluation confirms that configurations with more sensors result in higher production performance of the system. However, a saturation of the performance gain is also observable when exceeding a certain number of sensors. Therefore, determining the optimal configuration is vital to optimise the interface from a financial point of view.

Offshore wind turbines are exposed to harsh maritime conditions. This makes the planning of the maintenance processes more challenging and therefore cost-intensive compared to onshore wind turbines. To deal with these challenges, it is important to investigate the impact of different maintenance strategies, e.g. reactive and predictive maintenance on the turbines’ performance. In this context, the availability of turbines, the energy production and the overall maintenance costs are considered. This paper addresses the impact of different maintenance strategies by presenting a parametric multi-agent-based discrete-event simulation. Based on this simulation, we study the variation of failure detection on the performance of the turbines. The results of the simulations show that through a better predictability of the maintenance measures, both, the downtime of wind turbines and the failure consequences can be reduced. Saving potentials lie primarily in the reduction of the material costs and lower production losses.

In the recent decades, the introduction of a sustainable and green energy infrastructure, and, by this, the reduction of emissions caused by fossil energy generation, has been focused on by industry-oriented nations worldwide. Among the technologies of renewable energy generation, wind energy has the highest deployment rate, due to the high wind resource availability and the high technology maturity reached mainly by the onshore installation of wind turbines. However, the planning and the installation of offshore wind farms are a challenging task, because of harsh weather conditions and limited resource availability. Due to the current practice of decentralised information acquisition by the supply chain partners, we investigate the impact of sharing information on the installation process of offshore wind farms by means of a simulation model. Therefore, relevant information items will be identified in order to improve the installation process.

This paper presents a mathematical model for robust production planning. The model helps fashion apparel suppliers in making decisions concerning allocation of production orders to different production plants characterized by different lead times and production costs, and in proper time scheduling and sequencing of these production orders. The model aims at optimizing these decisions concerning objectives of minimal production costs and minimal tardiness. It considers several factors such as the stochastic nature of customer demand, differences in production and transport costs and transport times between production plants in different regions. Finally, the model is applied to a case study. The results of numerical computations are presented. The implications of the model results on different fashion related product types and delivery strategies, as well as the model’s limitations and potentials for expansion, are discussed. Results indicate that the production planning model using conditional value at risk (CVaR) as the risk measure performs robustly and provides flexibility in decision analysis between different scenarios.