The EU power market system has successfully maintained a centralized governance structure ensuring stable electricity supply and affordable prices for over two decades. However, the ongoing energy transition towards carbon neutrality has exposed critical governance limitations, leading to challenges in community projects implementation. Given that Heating and Cooling (H&C) accounts for more than 50% of the EU’s energy consumption, community H&C initiatives can drive local energy transitions and support renewable integration. This study analyzes the best practices from European community energy initiatives, supplemented by insights from the Energy Leap project. By employing a comparative analysis approach, the study proposes a technically sound and regulatory feasible governance model, alongside a robust ecosystem support framework. The proposed framework introduces new roles and new forms of partnerships between communities—private entities and consumers—taking advantage of the benefits offered by the operation of Energy Communities (ECs), enhancing community engagement and regulatory adaptability. These insights offer practical guidance and contribute to effective policymaking in support of the EU’s energy transition objectives.

The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values.

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects.

Seismic risk assessment in bridge networks is of great interest in reducing earthquake adverse effects in structures and the society. Among existing methods for risk and loss assessment, Hazus methodology is widely accepted and used. The method includes several steps and calculations, being thus rather complex and time-consuming, especially if several bridges with diverse characteristics are to be examined. The present study investigates the potential of using soft computing techniques for determining the seismic hazard and expected losses as an alternative to the Hazus methodology. In particular, a number of representative datasets have been developed on the basis of Hazus and a number of methods have been formulated and tested regarding their capacity to effectively simulate the process. The methods that are examined range from regression curve-fitting to artificial neural networks of different size and characteristics. Alternative statistical tests have been used to evaluate the developed model capabilities. Evaluation results indicate that appropriately designed ANN methods can attain high accuracy and stable results among different datasets (training, testing, and validation). The analysis indicates the potential of using similar techniques to simulate seismic risk assessment based on actual data from earthquake events that have taken place in the past.

Concreting in hot weather conditions is a major challenge in construction projects with a dominant effect on concrete quality. As such, identifying the most favorable time for concreting within hot weather periods can be of valuable importance to minimize the risks associated with declined concrete performance or process failure. This work aims to provide a decision model for identifying the most favorable concreting deployment time in construction operations, under hot weather conditions (mainly referring to ambient temperatures above 27°C and high evaporation rates), based on short-term meteorological forecasts. The study develops a semi-empirical model for describing the potential adverse outcomes in concrete performance due to hot weather conditions and provides a cost assessment of the countermeasures for offsetting high evaporation rate and strength degradation, in addition to the cost of potential delay in order to obtain better concreting conditions. This model has been tested with meteorological data from two Mediterranean cities. The results indicate that, based on ambient and concrete temperature, wind speed, and relative humidity input values, the model can provide reasonable and practical decision assistance in scheduling concreting operations in a stochastically optimal manner.

Recent technological advancements in distributed sensing, pervasive computing, context-awareness, machine learning and Digital Twins (DTs) allow the built environment to cope with upcoming challenges in a better way than before and achieve comfort and well-being in buildings. This paper takes a unique approach by not conducting a systematic and exhaustive review, that would require enormous effort to uncover intricate interdependencies among various subtopics. Instead, it proposes a framework leveraging Artificial Intelligence and Machine Learning (AI/ML) techniques to extract valuable insights from the existing literature. Adopting the Digital Twin high-level architecture as its foundation, the paper introduces a clustering approach to scrutinize Indoor Environmental Quality, Energy Efficiency, and Occupant Comfort—key facets influencing indoor building performance. This innovative methodology aims to provide a more nuanced understanding of the relationships within these critical aspects by harnessing the capabilities of AI/ML techniques and the conceptual framework of Digital Twin architecture.

The main objective of this research is to evaluate how the transport sector affects the satisfaction of citizens. The model developed aims both at assessing the satisfaction of citizens and using it as a tool to measure the change in citizens’ satisfaction resulting from new mobility practices or policies. The developed scenarios are based on the principles of sustainability and the action plans concern: better accessibility conditions for alternative means of transport; improving travel safety; reducing air pollution, greenhouse gas emissions and energy consumption; increasing efficiency and effectiveness in the movement of people and goods; and enhancing the attractiveness and quality of the urban environment. The results reveal that it is necessary for local decision makers to take further measures to increase the overall satisfaction of citizens with the aim of prosperity and happiness of citizens within their city, and the proposed model can support the decision-making process. Utilizing the developed system dynamics model, it is possible to make simulations with new data and at the same time to evaluate the change they bring to the individual sectors and to the overall satisfaction of the citizens.

The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed to identify both the number of stations to be deployed and their respective locations that minimize the total cost by utilizing Genetic Algorithms. This is implemented by combining these components into a linear objective function aiming to minimize the overall cost of deploying the charging network and maximize service quality to users by minimizing the average travel distance between demand spots and stations. Several numerical and practical considerations have been analyzed to provide an in-depth study and a deeper understanding of the model's capabilities. The optimization is done through commercial software that is appropriately parametrized to adjust to the specific problem. The model is simple yet effective in solving a variety of problem structures, optimization goals and constraints. Further, the quality of the solution seems to be marginally affected by the shape and size of the problem area, as well as the number of demand spots, and this may be considered one of the strengths of the algorithm. The model responds expectedly to variations in the charging demand levels and can effectively run at different levels of grid discretization.

The arrangement of temporary facilities within a construction site is essential for successfully undertaking a project, as it enhances productivity and ensures both safety and environmental protection.The Construction Site Layout Planning (CSLP) problem is a challenging discrete combinatorial optimization problem involving multiple objectives and has been tackled using various methods, from linear programming to heuristic and meta-heuristic techniques.Evolutionary algorithms have patently been preferred for solving the CSLP problem due to their ability to provide efficient (near-optimal) solutions in reasonable computational time.The present work aims to comparatively evaluate the effectiveness of five well-known evolutionary algorithms in terms of these performance indicators based on a number of case studies of different structure and characteristics.The model implementation is structured in an Excel environment to facilitate the problem setting and calculations while the optimization algorithms have been implemented in the Matlab software.The examined case studies include simple, single-objective formulations (i.e., minimizing the total traveling distances among facilities) and multi-objective formulations that consider, in addition, preferences or constrains in facility setting to account for operational, safety, and environmental considerations.The evaluation results indicate that all methods perform reasonably well from a practical point of view, however, those based on harmony search, simulated annealing, and particle swarm optimization appear to be more flexible in attaining better solution quality and lower computational time.

Market penetration of electric vehicles is nowadays gaining considerable momentum along with the move towards increasingly distributed clean and renewable electricity sources. The penetration rate varies among countries due to several factors, including the social and technical readiness of the community to adopt and use this technology. In addition, the increasing complexity of power grids, growing demand, and environmental and energy sustainability concerns intensify the need for energy management solutions and energy demand reduction strategies. Hence, integration strategies for energy efficiency in the building and transport sector are increasingly important. The present study considers key parameters leading to Electric Vehicle adoption, utilizing background data from countries where Electric Vehicles have already been introduced and adopted in everyday living. The study also presents a case study of an energy management scheme in Greece, where the penetration rate is still low. Based on the above, an optimization algorithm is proposed where buildings, photovoltaic plants, storage systems, and Electric Vehicles (utilization of Vehicle to Grid technology) can efficiently meet the energy requirements and peak-hour energy demand in both economic and sustainability terms. The study proposes a hybrid approach based on the Analytic Hierarchy Process methodology and Genetic algorithms, aiming to foster the diffusion of the Vehicle to Grid concept to support building energy demand.

Electric Vehicles offer one of the most efficient solutions towards the direction of providing sustainable transportation systems.However, a broader market uptake of Electric Vehicle--based mobility is still missing.The lack of sufficient infrastructure (Electric Vehicle charging stations) in combination with the lack of information about their availability appears as a major limitation, leading to low user acceptance.Additional, technology based, assistance services provided to Electric Vehicle users is a key solution to unlock the full potential of their utilization.This paper presents a multi-factor dynamic optimization model using multi-criteria analysis to select the best alternatives for Electric Vehicle charging within a smart grid with the goal of supporting a larger uptake of Electric Vehicle -based mobility.The application provides assistance to the Electric Vehicle drivers through functionalities of energy price, cost and travel time of the electric vehicle to the charging station, the specifications of vehicles and stations, the status of the charging stations as well as the user's preferences.The proposed model is developed by incorporating PROMETHEE II and Analytic Hierarchy Process methodologies to provide the best charging solutions after considering all possible options for each Electric Vehicle user.The multi-criteria analysis algorithm is not only limited to comparing alternative charging options at a specific time but also looks at several starting times of charging.A simulated case study is implemented to examine the functionality of the proposed model.From the results, it is evident that by applying the findings of this work entrepreneurial community and industry can develop new services that will improve user satisfaction, electromobility, urban mobility, and sustainability of cities.At the same time, academia, leveraging the methodology and factors that influence the choice of charging station, can conduct further research on digital innovations that will contribute to the consolidation of e-mobility ensuring the sustainability of cities, while accelerating digital transformation in the transport sector.

A key aspect of flexibility is demand response (DR), where consumers adjust their energy consumption through pricing signals and financial incentives, reducing peak demand and increasing usage during low-demand periods. This enhances the utilization of renewable energy and stabilizes the electricity grid. The integration of Internet of Things (IoT) and Digital Twins (DT) technology in DR transforms energy flexibility into a valuable distributed energy resource asset. This paper presents a demand response framework that lowers the operating costs, and efficiently handles the stochasticity of the energy demand and generation.

Hub-and-Spoke (H & S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H & S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain.

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector's energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.

The aim of this study is to investigate the effectiveness of multi-objective optimization in solving the time-cost trade-off problem at different project scales.For this purpose, the NSGA-II algorithm was used, with the analysis to extend from small-scale problems (18 activities) to large-scale ones (up to 4,608 activities).In order to check the effectiveness of the multi-objective optimization algorithm, a single-objective formulation for cost minimization at specific project durations and the corresponding GA algorithm was also developed (both the GA and NSGA-II algorithms were developed in the Visual Basic environment).Finally, the same problems were solved by a general-use commercial software that employs genetic algorithms as a means for optimization.The case studies that were analysed have resulted from a benchmark 18-activity network from the literature.This basic network was repetitively applied in serial and parallel forms to develop larger networks for which the optimal solutions can be determined based on the corresponding solutions of the basic network.In this regard, it is feasible to realistically assess the performance of the methods under analysis.The comparison between the NSGA-II and the GA algorithms indicates that the latter performs better in all cases (in a general perspective, the NSGA-II results in deviations from 50% to 100% higher than those of the simple GA).This is expected as the solution space is larger in the first case and includes the whole allowable project duration range, while the simple GA searches at a specific project duration every time.On the other hand, the single-objective GA needs to be repetitively run at several project duration levels in order to develop the Pareto front.The employment of the Time-cost trade-off optimization at different project sizes

The description of the functionality of a smart grid’s architectural concept, analyzing different Smart Grid (SG) scenarios without disrupting the smooth operation of the individual processes, is a major challenge. The field of smart energy grids has been increasing in complexity since there are many stakeholder entities with diverse roles. Electric Vehicles (EVs) can transform the stress on the energy grid into an opportunity to act as a flexible asset. Smart charging through an external control system can have benefits for the energy sector, both in grid management and environmental terms. A suitable model for analyzing and visualizing smart grid use cases in a technology-neutral manner is required. This paper presents a flexible architecture for the potential implementation of electromobility as a distributed storage asset for the grid’s capacity optimization by applying the Use Case and Smart Grid Architecture Model (SGAM) methodologies. The use case scenario of booking a charge session through a mobile application, as part of the TwinERGY Horizon 2020 project, is deployed to structure the SGAM framework layers and investigate the applicability of the SGAM framework in the integration of electromobility as a distributed storage asset into electricity grids with the objective of enhanced flexibility and decarbonization.

E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders’ needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.