• Energy Research
• 7. Clean energy close
• 16. Peace & justice close
• Technical University of Berlin close

This paper introduces a model predictive control (MPC) strategy for the purpose of fuel-optimal operation of a range-extender hybrid vehicle. The modern navigation system nowadays can provide abundant road information. Using this information, the proposed controller solves a global optimization problem offline in order to determine a preset trajectory of the state of charge (SoC). The online MPC uses the resulting SoC trajectory as set-points for the terminal state in every moving horizon. Repeating this process, the optimal energy management along the trip to be traveled can thus be calculated. This proposed control strategy is implemented in the commercial vehicle simulation environment IPG CarMaker. From the first simulation results, the proposed strategy shows a promising fuel saving potential with real-time capability.

Although market-based environmental policy instruments feature prominently in economic theory and are widely employed, they often face public resistance. We argue that such resistance may be driven by moral responsibility, where citizens prefer to tackle the environmental problems that they have caused by themselves, rather than delegating the task to others by means of a market mechanism. Using a laboratory experiment that isolates moral responsibility from alternative explanations, we show that moral responsibility induces participants to take inefficient actions that reduce the earnings of the whole group of participants. We discuss the implications of this finding for the design and implementation of environmental policies.

AbstractThis paper presents an experimental assessment of a blended fatigue‐extreme controller for load control employing trailing edge flaps on a lab‐scale wind turbine. The controller blends between a repetitive model predictive controller that targets fatigue loads and a dedicated extreme load controller, which consists of a simple on‐off load control strategy. The Fatigue controller uses the flapwise blade root bending moments of the three blades as input sensors. The Extreme controller additionally uses on‐blade angle of attack and velocity measurements as well as acceleration measurements to detect extreme events and to allow for a fast reaction. The experiments are conducted on the Berlin Research Turbine within the large wind tunnel of the TU Berlin. In order to reproduce test cases with deterministic extreme wind conditions that follow industry standards, the wind tunnel was redesigned. The analyzed test cases are extreme direction change, extreme coherent gust, extreme operating gust and extreme coherent gust with direction change. The test cases are analyzed by on‐blade angle of attack and velocity measurements. The load control performance of the Blended controller is compared to the pure fatigue oriented and the pure extreme load controller. The Blended controller achieves a maximum flapwise blade root bending moment reduction of 23%, which is comparable to the reduction achieved by the Extreme controller.

This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D. {"references": ["Kunz, Friedrich et. al. (2017). Electricity, Heat and Gas Sector Data for Modeling the German System. DIW Data Documentation 92."]}

Exergy-based analyses are useful means for the evaluation and improvement of energy conversion systems. A life cycle assessment (LCA) is coupled with an exergetic analysis in an exergo-environmental analysis. An advanced exergo-environmental analysis quantifies the environmental impacts estimated in the LCA into avoidable/unavoidable parts and into endogenous/exogenous parts, depending on their source. This analysis reveals the potential for improvement of plant components/processes and the component interactions within a system. In this paper, the environmental performance of an advanced zero emission plant (AZEP) with CO2 capture is evaluated based on an advanced exergoenvironmental analysis. The plant uses oxy-fuel technology and incorporates an oxygen-separating mixed conducting membrane (MCM). To evaluate the operation of the system, a similar plant (reference plant) without CO2 capture is used. It has been found that the improvement potential of the AZEP concept is restricted by the relatively low avoidable environmental impact of exergy destruction of several plant components. Moreover, the endogenous environmental impacts are for the majority of the components significant, while the exogenous values are, generally, kept at low levels. Nevertheless, a closer inspection reveals that there are strong interactions among the components of the MCM reactor and the components constituting the CO2 compression unit. Such results are valuable, when the improvement of the environmental performance of the plant is targeted and they can only be obtained through advanced exergy-based methods.

Abstract In the context of the rural electrification challenge, sharing-based nanogrids represent an innovative approach for affordable and sustainable electricity. The peer-to-peer topology of these nanogrids is targeted at organic growth and thus requires real time monitoring and optimization. A data analysis tool has been developed, aiming at achieving an efficient power flow and real time optimization of generation, storage, load and distribution capacities. This paper elaborates the core algorithms of the network topology optimization and provides first results based on data from a pilot nanogrid in Bangladesh.

As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.

AbstractAn underactuated serial kinematics industrial handling robot using high torque direct drive motors combined with slip rings is presented. Pick and place tasks are performed through combining the underactuated motion with a null-space motion enabling the kinetic energy to be conserved within the system. This system is benchmarked against linear axis systems with belt and ball screw drives respectively and also against a conventional robot system regarding energy efficiency proving performance improvements.