An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function’s fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively.
Lithium Sulfur (Li-S) batteries are a promising energy storage technology with very high theoretical limits in terms of specific capacity and specific energy. However, these batteries suffer from high self-discharge rates, associated with a low coulombic efficiency due to the polysulfide shuttle mechanism. A better understanding of the self-discharge characteristics and suppression of the self-discharge is of great interest for most applications. Hence, a continuous self-discharge current measurement method is applied to evaluate the self-discharge behavior of a Li-S battery, based on a corrected reference open-circuit voltage. The result is a continuous self-discharge current measurement method, that investigates the self-discharge in the upper plateau of a Li-S battery at 10 °C and 25 °C. This self-discharge current displays a plateau and extended balancing times directly before this plateau and is validated by a discrete self-discharge current measurement method at 10 °C and 25 °C. Furthermore, the activation energy is continuously calculated for the upper plateau and compared to a discrete reference measurement.
Model-based comparison of hybrid propulsion systems for railway diesel multiple unitsIn order to reduce operating costs, railway vehicle operators need to find technical solutions to improve the efficiency of railway diesel multiple units on non-electrified railway routes. This can be achieved by hybridization of diesel multiple unit propulsion systems with electrical energy storage systems to enable brake energy recuperation. After highlighting the state of the art of hybrid railway vehicles and electrical energy storage systems, a simulation model of a generic diesel multiple unit in a 3-car formation is developed and equipped with three types of hybrid power transmissions. Simulations on realistic service profiles with different driving strategies show the potential for fuel consumption reduction for the different transmission types. On a suburban service profile a 3car diesel multiple unit is able to achieve simulated fuel savings of up to 24.1 % and up to 18.9 % on a regional service profile.
Lithium sulfur batteries have a promisingly high theoretical specific energy density of about 2600 Wh/kg and an expected practical specific energy density of about 500-600 Wh/kg. Therefore, it is a highly promising future energy storage technology for electric vehicles. Beside these advantages, this technology shows a low cell capacity at high discharge currents. Due to the capacity recovery effect, up to 20% of the total cell capacity becomes available again with some rest time. This study shows a newly-developed capacity recovery model for lithium sulfur batteries. Due to the long rest periods of electric vehicles, this effect has an important influence on the usable cell capacity and depth of discharge in lithium sulfur batteries.
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