The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings’ energy efficiency and management, as well as improving the electricity grid’s reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers’ comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort.

The labor market is characterized by imbalances and dysfunctions that can be addressed through a study of the management and costing of workers’ payroll. Although there is an ongoing conflict of interest between employees and employers, it can be combated because the employment relationship is a mixture of conflicting and consensual elements (mixed-motive). This paper is about flexible forms of employment, which corporations can take advantage of in a constantly evolving environment. At the same time, reference is made to the institution of telecommuting that improves remote worker efficiency, minimizes labor costs, and opens the labor market frontiers. It is necessary to create new strategies aimed at improving the efficiency of the workforce, managing labor costs, and studying the sustainability of each activity of the business unit. Lastly, the constantly evolving technological environment and the constant development of the skills of the workforce require the incorporation of ongoing management and efficiency studies to achieve the maximum possible objectives at the lowest possible and most flexible cost and risk.

Remote distributed generation control and demand response is nowadays a basic need, especially for the Distributed Network Operators (DNOs). The integration of Renewable Energy Sources (RES) with stochastic production characteristics into the grid in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the ef-forts of the DNOs to keep the grid’s operation within safe limits and prevent cascading black-outs while staying in compliance with the SAIDI and SAIFI indices during maintenance and re-pair operations. Taking also under consideration the aging of the existing grid infrastructure, making it year by year more prone to failure, it is yet of great significance for the DNOs to have access to real-time feedback from grid’s infrastructure, fast, with low-cost upgrade interventions, easily deployed on the field and with fast response potential, in order to be able to perform real-time grid management (RTGM). In this article, we describe the architecture, development and deployment of a hybrid real-time control system of distributed generation (DG) units, which has the potential to be applied easily to DNO’s substations, RES plants and consumers (i.e. electric vehicles’ charging stations), that combines a low-cost minimum intervention into the electricity grid and its components and eliminates all communication interoperability problems between the different vendors’ installed electrical equipment.

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command.

Τhe building automation control is a crucial factor for improving buildings energy efficiency and management, as well as improving the electricity grid's reliability indices. This paper presents the methodology, describes the necessary technology requirements and presents the decentralized building automation novel algorithm for Efficient demand side management in a building. All these are applied in an experimental university microgrid and the results are presented in terms of energy saving in kWh, money in € and working hours.