Wastewater treatment plants and power generation constitute inseparable parts of present society. So the growth of wastewater treatment plants is accompanied by an increase in the energy consumption, and a sustainable development implies the use of renewable energy sources on a large scale in the power generation. A case study of the synergy between wastewater treatment plants and photovoltaic systems, aiming to improve the energetic, environmental and economic impacts, is presented. Based on data acquisition, the energy consumption analysis of wastewater treatment plant reveals that the highest demand is during April, and the lowest is during November. The placement of photovoltaic modules is designed to maximize the use of free space on the technological area of wastewater treatment plant in order to obtain a power output as high as possible. The peak consumption of wastewater treatment plant occurs in April, however the peak production of the photovoltaic is in July, so electrochemical batteries can partly compensate for this mismatch. The impact of the photovoltaic system connectivity on power grid is assessed by means of the matching-index method and the storage battery significantly improves this parameter. Carbon credit and energy payback time are used to assess the environmental impact. The results prove that the photovoltaic system mitigates 12,118 tons of carbon and, respectively, the embedded energy is compensated by production in 8 ½ years. The economic impact of the photovoltaic system is analyzed by the levelized cost of energy, and the results show that the price of energy from the photovoltaic source is below the current market price of energy.

For critical infrastructures, technological developments regarding real-time data transmission and processing improve the system’s operability and reliability. However, vulnerabilities are introduced in the case of implementing new remote access methods or where redundancy is low. At the national level, most critical infrastructures are connected, and, therefore, achieving a level of security and resilience is based on identifying a multitude of risks. In this respect, the reduction of risk to acceptable levels directly affects the quality of citizens’ lives and decreases losses in the industry. This study starts from the threats to power systems, namely cyberattacks, which are much more dangerous, although less visible, to operators, and almost invisible to the public or the media. From this point of view, it was proved that the most vulnerable parts of the power system were human–machine interfaces, electrical equipment, Surveillance, Control, and Data Acquisition (SCADA) systems. This paper’s main achievements include the simulation of cyberattacks on existing electrical equipment from a petrochemical plant (case study), which consists of modifying the remote data transmitted by the SCADA system. Two locations were submitted to simulated cyberattacks that were considered critical for the overall plant operation. Furthermore, the changes that occur following each fault resulting from the cyberattack and the influence of the electrical parameter changes upon the process flow were analyzed. Furthermore, by using Electrical Power System Analysis Software—ETAP—the changes that occur following each fault due to the cyberattack and the influence of the electrical parameter changes upon the process flow were analyzed. By considering the two malfunction events, the resilience assessment of the system was analyzed. In the second case, only partial resilience action, up to 40%, restored the operability of the industrial power plant.

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month.

Standby power consumption in household electrical and electronic equipment remains a persistent source of energy waste worldwide. Despite regulatory measures and ongoing technological developments, a considerable amount of electricity is still consumed by devices in standby or “off-mode”, resulting in higher utility costs and carbon emissions. This review synthesizes the latest research to clarify the scale of standby energy consumption, discusses relevant policies and regulations, and explores intelligent technologies and behavioral strategies that minimize energy consumption. Starting from the theoretical analysis and modeling of equipment consumption in standby mode to the implementation of intelligent systems to reduce it, the paper highlights heuristic optimization methods, smart grid integration, and occupant-centered interventions, all of which demonstrate tangible energy savings. This research was carried out in close connection with current policies regarding energy consumption and sustainable development, respectively, with the implementation of new technologies. Thus, in accordance with the latest European directives, the intelligent systems used have reduced the energy consumption of some common household appliances by 26.68 kWh. Additionally, knowledge gaps, particularly regarding user behavior, data granularity, and the integration of advanced analytics that limit the efficacy of current solutions, are identified. Recommendations for future research, emphasizing the importance of harmonized policies, precise data measurement, and artificial-intelligence-driven approaches for further reducing standby loads, are finally presented.

The pandemic period was caused by COVID-19 and it has been an unprecedented event in the last 100 years of human history. Regarding universities, major changes have occurred both in the online method of education as well as in the patterns of their electrical consumption, respective of both students’ and teachers’ residential electrical consumption. The focus of this research is to conduct and assess a comparative analysis of universities’ electrical consumption during the pre- and pandemic periods. Polynomial regression is used to model the electrical consumption of four Romanian universities during the period 2019–2021. Also, this study proposes a method for predicting the electrical consumption of universities in three months of 2021, compared to that of the same months in 2020. The data analysis shows that the electrical consumption had decreased between 20.6% and 36.29% in the pandemic period of 2020 compared to that of 2019. Additionally, this study evaluates the electrical consumption of universities due to their use of computers, which represents an important percentage of the total consumption; this was between 11.28% and 60.5% in the pre-pandemic year 2019, but this was substantially reduced in 2020, to be between 57.13% and 77.27%. Based on the data that has been provided by students and teachers, the calculated values show that the electrical residential consumption increased by about 20 kWh per month and per computer unit during the pandemic.

Over the last decade, online data acquisition and processing of power systems’ parameters led to significant improvements in power systems’ operability, control and reliability