The continuous growth of renewable energy sources and combined heat-power generation plants in public infrastructures allow them a certain degree of independence from the public power grid. Nevertheless, during the day, the power demand fluctuates, creating peak loads that cannot be fulfilled by the generated power and must be covered by the public power grid. Peak loads could be diminished by saving energy produced during low power demand periods and returning it during the high demand periods. The use of electric vehicles is expected to keep increasing in the next years and the use of vehicle-to-grid technology allows electric and hybrid cars to return stored energy to the power grid. An algorithm to evaluate the vehicle-to-grid technology as a solution for peak reduction, also called peak shaving, for public infrastructure was developed. The purpose of the algorithm is to predict the optimal charging and discharging schedule of the battery from electric vehicles in a parking space so that the peak load demands are met without sacrificing the driving demands of the electric vehicle (EV) users. This takes into consideration factors like the state of charge of the batteries and the mobility needs of the vehicle user. The algorithm takes the driving demands and mobility needs as constraints, and schedules the charging and discharging of the EVs during their stay in a parking place in order to reduce the peak load demands by supplying stored electrical power from EV batteries back to the grid. In order to validate the algorithm, three test case scenarios representing different parking patterns were generated using random and statistical distributed parameters. It is concluded that vehicle-to-grid technology can be used to reduce peak demands, where the number of EVs and their stay hours in a parking place represent the most critical parameters for the effectivity of the peak reduction. This implies that in the future, public infrastructure could profit better from renewable energy sources by adding loading stations for EVs that are vehicle-to-grid capable.

Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.

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