Modeling of battery energy storage systems (BESS) used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method.

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

Accurate load modeling is essential for power system stability analysis and control. This topic has regained interest, due to the high penetration of new types of loads and the increased availability of measurements in extended power grids. In this paper, an aggregated load model based on measurement data is formulated for dynamic simulations of large power systems. The proposed model employs variable-order transfer functions, enabling the accurate simulation of complex load dynamics. A complete methodology for the automatic derivation of the minimum-required model order is proposed with the model parameters calculated via a robust multi-signal identification procedure. For this purpose, the Vector Fitting method is introduced as a technique for measurement-based load modeling. Several simulations are performed using the NEPLAN software to investigate the accuracy and the generalization capabilities of the proposed model. The model performance is thoroughly compared with other conventional load models, using also measurements recorded on a laboratory-scale microgrid.

Narrowband power-line communication (PLC) over distribution networks is gaining the attention of researchers, as a communication medium in future power grids. This paper investigates the application of narrowband PLC in smart distribution grids, starting from a historical overview on the accomplished technological progress and continuing with a comparison of the advantages and drawbacks of PLC technology to other smart-grid communication solutions. The analysis shows that narrowband PLC applications are best suitable for medium-voltage (MV) networks, due to the vast and complex geographical extent. The channel and topological characteristics of MV distribution networks are examined for different operational states and configurations, since they are important for the optimal design and implementation of the PLC infrastructure. The analysis is also extended to an existing overhead MV distribution network with distributed-generation units. The obtained results and the proposed methodology are useful and comprehensive tools for the efficient implementation of PLC technology in future smart grids.