Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system’s buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid’s household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs’ batteries and the EVs’ charging delay time.

The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs’ charging by residential RES is presented.

Integrating electric vehicles (EVs), residential PVs, and energy storage systems (ESSs) into low-voltage distribution networks poses significant challenges, particularly regarding grid capacity and efficiency. This study addresses these challenges by developing distributed energy management systems for the EVs and the ESSs, and sizing approaches for the PVs and the ESSs, by enabling multi-agent systems and fuzzy cognitive maps. The optimization framework simultaneously considers six key objective functions which studied separately or in limited combinations: (i) ESS installation cost, (ii) low-voltage network operation parameters, (iii) active power losses, (iv) EV charging times, (v) total energy costs, and (vi) excess energy from PV systems. Using environmental conditions and energy purchase costs to define two regions, Heraklion, Greece, and Berlin, Germany, under varying EV integration rates, the proposed methodology enables the IEEE European Low Voltage feeder as a case study and Euclidean distance is used to identify optimal solutions from the Pareto frontiers. Results show that the integration of the distributed ESS contributes to a significant reduction in network’s energy consumption, while reduced solar irradiance in Berlin leads to different cost and operational patterns. Heraklion generally achieves better cost efficiency due to higher solar irradiance. Additionally, network operational parameters are significantly improved, and energy costs for residences are reduced up to 39%.

With the increasing integration of Distributed Energy Resources (DERs), effective methods for flexibility quantification and evaluation become necessary to ensure grid stability and reliability. This review systematically explores two complementary approaches to flexibility quantification in medium voltage (MV) and low voltage (LV) networks: metric-based (MB) and region-based (RB) methodologies. MB methods provide concise evaluations of system-wide flexibility, facilitating benchmarking and operational insights. However, they often fail to capture the spatiotemporal complexities and interdependencies inherent in modern grids. RB methods, on the other hand, leverage Feasible Operating Regions (FOR) to map flexibility spatially and temporally but are hindered by low observability characterizing distributions systems and limited uncertainty modelling. Compared to previous studies that analyze MB and RB approaches separately, this review uniquely synthesizes both methodologies and proposes their combination as a more comprehensive tool for dynamic flexibility management in modern distribution networks. By enhancing FOR with flexibility metrics, the approach bridges gaps in uncertainty and time modelling, addressing key challenges posed by the increased penetration of DERs. This integration offers a practical and scalable solution for system operators to achieve a more comprehensive and dynamic assessment of flexibility, paving the way for enhanced grid stability and operational efficiency.

The flour milling industry—a vital component of global food production—is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.