• Energy Research
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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."]}

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.

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.

Electric mobility is beginning to enter East African cities. This paper aims to investigate what policy-level solutions and stakeholder constellations are established in the context of electric mobility (e-mobility) in Dar es Salaam, Kigali, Kisumu and Nairobi and in which ways they attempt to tackle the implementation of electric mobility solutions. The study employs two key methods including content analysis of policy and programmatic documents and interviews based on a purposive sampling approach with stakeholders involved in mobility transitions. The study findings point out that in spite of the growing number of policies (specifically in Rwanda and Kenya) and on-the-ground developments, a set of financial and technical barriers persists. These include high upfront investment costs in vehicles and infrastructure, as well as perceived lack of competitiveness with fossil fuel vehicles that constrain the uptake of e-mobility initiatives. The study further indicates that transport operators and their representative associations are less recognized as major players in the transition, far behind new e-mobility players (start-ups) and public authorities. This study concludes by identifying current gaps that need to be tackled by policymakers and stakeholders in order to implement inclusive electric mobility in East African cities, considering modalities that include transport providers and address their financial constraints.

AbstractOne variation of exergy analysis, specific fuel consumption (SFC) analysis, was modified according the advanced exergy analysis, where exergy destructions within each component were split into endogenous/exogenous and avoidable/unavoidable parts, and by combining the energy-savings effects of each component. The modified analysis approach can help locate not only the weak points at the component level but also certain bottlenecks from the topology viewpoint, which may indicate adding or deleting some components, or enhancing the thermodynamic interactions between different process or subsystems. The modified approach was then applied to a conventional coal-fired power plant. The detailed spatial distribution of SFC within the current system at different partial-load conditions were deeply discussed at both component and process levels. Further splitting of SFC and the energy-saving effects of each process are also obtained and discussed. The results show that combustion and heat-and-mass transfer processes have the largest SFC. Heat-and-mass transfer process and the vent process have the greatest avoidable SFCs. The closer the component to the final product, the larger its influence on the overall performance, and, thus, a small improvement to these components may lead to a large reduction in the overall fuel consumption. More effective energy-saving measures of coal-fired power plants should focus on the match of heat transfer at intermediate-and-low temperature level and the breakage of the isolation of heat transfer subsystems, especially enhancing the interaction between the air preheating process and feedwater preheating process.

For the detailed validation and verification of the capabilities of QBlade-Ocean in work package 2 (WP2) of FLOATECH, a detailed definition of the three considered models was recently presented in Deliverable 2.1 [1]. This document can be seen as a continuation of that work as the three aero-hydro-elastic models within QBlade-Ocean were thoroughly validated against experimental results and other state-of-the-art aero-servo-hydro-elastic simulation codes with complex load cases. As a first check, the models were validated in static and decay tests to assure that their natural frequencies and damping coefficients align. Secondly, regular and irregular wave only load cases were carried out to isolate the hydrodynamic response and quantify the hydrodynamic accuracy of QBlade-Ocean. Finally, combined irregular wave with turbulent wind load cases were analysed in order to obtain insight into the accuracy of QBlade- Ocean in complex load cases where aero-, hydro-, servo- and structural dynamics concurrently play a significant role. Of the simulated models, two include experimental validation: the upscaled 10 MW SOFTWIND experimental turbine mounted on a spar floater and the upscaled 5 MW OC5 experimental turbine mounted on a semi-submersible floater. Both models were additionally set up in the open-source software OpenFAST to, on the one hand, compare the performance of both software tools and on the other hand, as a preparatory step for a full-scale code-to-code comparison with the aim of uncertainty identification between the codes occurring later in WP2. The third model – the DTU 10MW Reference Wind Turbine mounted on the Hexafloat floater – is used for numerical validation against the established commercial code DeepLines Wind™ which was used during the design process of the floater by the company SAIPEM. The present document includes results of a selected set of load cases that demonstrate the capabilities of QBlade-Ocean compared to experimental results and the considered numerical codes. Links to the updated QBlade-Ocean models as well as the OpenFAST model are provided in the relevant sections of each model

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.