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.

Buildings play an important role in the energy consumption of a household. There are different types of buildings and different standards, which are for each of them. Hence, the decentralized energy system has different configurations for each building standards and buildings built up according to each standards and have necessity to be controlled in a different approach. Using a case study of four different standards—Sonnenhaus, KfW55, Passive house and WSchVO95 of single family houses (SFH) of same geometry and boundary conditions the control constraints are showcased. The houses are selected such that high renewable energy self-production, low energy demand house, low net energy house and an old 1995 constructed house are compared. The differences in the system design, their control strategy and how it affects the system sizing or renewable fraction is explained in this paper. The same SFH according to different standards is simulated with TRNSYS and the energy system (including solar thermal collectors, PV, gas boiler, fuel cell CHP, thermal storage and electrical storage) for each house is optimized and compared. Thus, the paper showcases the importance of the building, not only geometry but also building physics and energy efficiency. Finally, the necessity for intelligent control system for a complicated building system with multiple energy source is justified and the requirements of such control systems are enlisted.

The sensing characteristics of toluene gas are monitored by fabricating ZnO nanorod structures. ZnO nanostructured sensor materials are produced on a Zn film via an ultrasonic process in a 0.01 M aqueous solution of C₆H₁₂N₄ and Zn(NO₃)₂∙6H₂O. The response of the sensors subjected to heat treatment in oxygen and nitrogen atmospheres is improved by 20% and 10%, respectively. The improvement is considered to be correlated with the increase in grain size. The relationship between the heat treatment and sensing characteristics is evaluated.