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The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption.

Due to the growing use of Green Energy Sources (GESs), the activities of mapping, monitoring, measurement, and detection of various GESs have become crucial. Assessing and measuring GESs are very complex since different environmental conditions occur. This importance is even greater when researchers face a shortage of measuring instruments and tools in many parts of the world. GES assessment is a challenging task that requires accurate and continuous measurement methods. Currently, traditional methods are very time-consuming and require spending money and human sources. So, the use of accurate and fast measurement methods and tools assessing measuring GESs potential are seriously recommended, which can greatly help the growth of the use of GESs, especially to cover and focus large areas. Satellite remote sensing is used to observe the environment in many fields and new and fast applications. Satellites remote sensing technologies and techniques for GESs assessing are fast, accurate, and can help to reduce costs and decision-making risks of GESs converters installations projects and provide suitable products to the public end-users. These could also be used to identify regions of interest for energy converter installations and to accurately identify new areas with interesting potentials. In this case, researchers can dramatically reduce the possibility of significant error in assessment methods. There is a lack of in-situ measuring tools mainly due to their high economic costs in the interested areas; an accurate analysis was carried out to assess the GESs energy potential. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome in-situ limitations. Actually, to use and develop it as much as possible, a correct interdisciplinary understanding is needed. Satellite remote sensing technology for identifying suitable areas for GESs power plants could be a powerful tool that is constantly increasing in its new and fast applications but requires good planning to apply it in various GESs converters installations projects. In this article, a comprehensive review on wind, wave, biomass, geothermal sources assessment using Sentinel-1 (S-1) Synthetic Aperture Radar (SAR) satellite estimation has been summarized along with the different techniques available to measure GESs using satellites. In the paper, several of the successful estimation techniques were introduced in each section and compared for the understanding of limitations and strengths of different methods of GESs availability evaluation.

The delivery of electricity employing an electromagnetic field that extends across an intervening region is called a wireless power transfer (WPT). This approach paves the way for electric vehicles (EVs) to use newly available options to reduce their environmental impact. This article is a review that examines the WPT technology for use in electric vehicle applications from both the technical aspect and the environmental impact. This review will attempt to accomplish the following objectives: (1) describe the present state of the technology behind the development and application of a WPT across the transportation industry; (2) substantiate the actual implementation of WPT EV systems; and (3) estimate the functioning of the autonomous system, as well as detect the potential stumbling blocks and openings for enhancement. The most recent advancements and implementation in compensating topologies, power electronics converters, and control techniques are dissected and debated scientifically to improve the system’s performance. To evaluate the performance from a sustainable perspective, energy, environmental, and economic factors are utilized, and at the same time, policy drivers and health and safety problems are researched.

Electric vehicles could be a significant aid in lowering greenhouse gas emissions. Even though extensive study has been done on the features and traits of electric vehicles and the nature of their charging infrastructure, network modeling for electric vehicle manufacturing has been limited and unchanging. The necessity of wireless electric vehicle charging, based on magnetic resonance coupling, drove the primary aims for this review work. Herein, we examined the basic theoretical framework for wireless power transmission systems for EV charging and performed a software-in-the-loop analysis, in addition to carrying out a performance analysis of an EV charging system based on magnetic resonance. This study also covered power pad designs and created workable remedies for the following issues: (i) how power pad positioning affected the function of wireless charging systems and (ii) how to develop strategies to keep power efficiency at its highest level. Moreover, safety features of wireless charging systems, owing to interruption from foreign objects and/or living objects, were analyzed, and solutions were proposed to ensure such systems would operate as safely and optimally as possible.

Increasing and inexorable urbanization calls for the involvement of all the stakeholders [...]

To meet the exponentially growing demand for clean and green energy, the solar photovoltaic (PV) system’s importance is increasing day by day, for which PV modeling is considered to be one of the most important work in the current state-of-the-art methods. To effectively model a PV system, accurate PV parameter estimation is of the utmost importance. In line with this, although the values of some of the parameters are provided in the manufacturer’s datasheet, the values of unknown parameters, such as shunt resistance, series resistance, the diode ideality factor, photo-generated current and diode saturation current, are not provided. To estimate these values a lot of algorithms are already reported in the literature. After careful observation of all the reported algorithms, a few best-reported algorithms are identified and their performances are compared with respect to accuracy, convergence issues, computational complexity and thermal stability. All kind of algorithms, such as numerical, analytical and evolutionary algorithms, are considered in this study, and only the best reported algorithms are considered for the comparison.

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

In the present study, an Enhanced SSA (ESSA) has been proposed where the parameter of the SSA technique, which balances the exploration and exploitation phases, has been modified. Additionally, the variable scaling factor is engaged to regulate the salp’s position during the search procedure to minimize the random movement of salps. To demonstrate the effectiveness of the enhanced SSA (ESSA), a set of multimodal test functions are engaged. The statistical outcomes demonstrate that ESSA profits from local optima evasion and quick convergence speed, which aids the proposed ESSA algorithm to outclass the standard SSA and other recent algorithms. The fair analysis displays that ESSA delivers very promising results and outclass current methods. Next, frequency control of power systems is executed by designing a Combined Fuzzy PID (CFPID) controller with the projected ESSA method. Next, a Partially Distributed CFPID (PD-CFPID) controller is designed for a distributed power system (DPS). It is shown that the ESSA method outclasses the SSA method in engineering problems. It is also noted that the ESSA-based PD-CFPID scheme has become more operative in monitoring the frequency than similar structured centralized fuzzy PID (CFPID) as well as PID controller. Finally, the outcomes of the PD-CFPID controller are equated with CFPID and PID for various uncertain situations to validate the benefit of the proposed control approach.

In recent years, induction heating applications assisted by electronic power control have been very appealing. For melting applications, induction heating is widely used as it seems to be appropriate and provides higher efficiency, zero pollutants, non-contamination of material, etc. in comparison with conventional heating. The conventional variable frequency control scheme is not sufficient for melting applications because of its high switching loss, low efficiency, and lower heat rate. A superlative control technique is required to control the output power smoothly, for a high heating rate with minimum power loss, and to lower the number of components. In this paper, a capacitorless self-resonating bifilar coil is proposed for induction surface melting applications. The performance of the system in terms of modular losses, heat rate, and efficiency is analyzed for various power methods such as pulse duty cycle control, phase shift control, pulse density modulation control, and asymmetric duty cycle control. An experimental validation is performed for the 1 kW prototype, and the heating rate, efficiency, and modular losses are calculated. The control technique is digitally validated using a PIC16F877A microcontroller with 30 kHz switching frequency. The temperature distribution is analyzed using a FLIR thermal imager. Among the tested methods, pulse density modulation-based control provides smooth and varied power control from 0% to 100% with minimum modular losses. The efficiency of the system is 89% at a rated output power and is greater than 85% for pulse density modulation control with a fast heating rate.

Geographic Information Systems (GIS) and Remote Sensing (RS) techniques are of great interest for the renewable energy field [...]

In the current scenario, power electronic device-based induction heating (IH) technologies are widely employed in domestic cooking, industrial melting and medical applications. These IH applications are designed using different converter topologies, modulation and control techniques. This review article mainly focuses on the modelling of half-bridge series resonant inverter, electrical and thermal model of IH load. This review also analyses the performance of the converter topologies based on the power conversion stages, switching frequency, power rating, power density, control range, modulation techniques, load handling capacity and efficiency. Moreover, this paper provides insight into the future of IH application, with respect to the adaptation of wide band-gap power semiconductor materials, multi-output topologies, variable-frequency control schemes with minimum losses and filters designed to improve source-side power factor. With the identified research gap in the literature, an attempt has also been made to develop a new hybrid modulation technique, to achieve a wide range of power control with high efficiency. A 100 W full-bridge inverter prototype is realised both in simulation and hardware, with various modulation schemes using a PIC16F877A microcontroller. The results are compared with existing techniques and the comparisons reveal that the proposed scheme is highly viable and effective for the rendered applications.

The main aim of this paper was to classify and to analyze the expeditious resource assessment procedure to help energy planners and system designers dealing with tides and tidal currents. Depending on the geographical features of the site to be evaluated, this paper reported the easiest methods to adopt for later working plans, crucial for preliminary considerations but to be supported by in situ measurements and by a more complex and detailed modelling. While tide trends are predictable by using Laplace equations and Fourier series, tidal currents velocities prediction is not easy, requiring suitable methods or hydraulic applications. Natural and artificial sites were analyzed and the best method for each type of them was presented. The latter together highlighting the minimum set of required information was discussed and provided as a toolkit for assessing tides and tidal current energy potential.

In recent years, induction heating (IH) applications aided by electronic power control have gained significance. Particularly, for cooking applications, an appropriate control technique is required to feed power from a single source to multiple loads with minimum switching losses. Additionally, when multiple loads are used, it requires independent control and operation for each of the loads. The main idea of this work is to develop a single-stage AC-AC converter topology to feed power to multiple loads independently with a single source, with a reduced number of switching devices and with minimum switching losses. The proposed topology uses a frequency bifurcation concept to feed power to multiple loads by placing the transmitting coil and work coil at a distance of 3 cm. The source is resonated at a 25 kHz switching frequency, with the designed bifurcated frequencies of 20 kHz and 33 kHz. The resonant capacitors are appropriately chosen to operate at those frequencies. For real-time applications, simultaneous and independent power control are inevitable in multi load-fed IH applications. This is achieved through a pulse density modulation scheme with minimum switching losses. The simulation of the proposed system is performed in MATLAB/Simulink, and also the 1 kW system is validated using a PIC16F877A microcontroller. The real-time thermal variation in the load is also recorded using a FLIR thermal imager. The experimental and simulation results are observed, and the obtained efficiency of the system is plotted for various duty cycles of pulse density modulation control.

The purpose of this investigation is to propose a way for acquiring the foremost window allocation scheme to have the best trade-off among energy, environmental, and comfort criteria in a building. An advanced decision-making tool, named the technique for order preference by similarity to ideal solution (TOPSIS), is utilized to find the best building amongst different alternatives for having windows on the building façades. Three conditions, namely two parallel, two perpendicular, and three façades, considered as A, B, and C types, respectively, are investigated. For each type, four possible orientations are studied. Heating, cooling, and lighting energy demands in addition to carbon dioxide equivalent emission and thermal and visual comfort are taken into account as the investigated criteria, and they are all evaluated in a simulation environment. The results show that for the modular residential buildings chosen as the case study and located in Tehran, Iran, having windows on the north and east façades is the best scheme. This alternative, which belongs to the B type, has about 40% and 37% lower heating and cooling energy demands than the C type’s foremost alternative. It is also able to provide about 10% better CO2 equivalent emission and 28% higher thermal comfort.

An increasing number of data sources and models to handle them call for transparency and openness in assessing their goodness and practical use for people [...]

Building performance from an energy and an environmental point of view is fundamental due to the large amount of GHG emissions related to the building sector [...]

This paper analyzes the intelligent use of time-varying electrical load via developing efficient energy utilization patterns using demand-side management (DSM) strategies. This approach helps distribution utilities decrease maximum demand and electrical energy billing costs. A case study of DSM implementation of electric energy utility for an educational building Alagappa Chettiar Government College of Engineering and Technology (ACGCET) campus was simulated. The new optimum energy load model was established for peak and off-peak periods from the system’s existing load profile using peak clipping and load shifting DSM techniques. The result reflects a significant reduction in maximum demand from 189 kW to 170 kW and a reduction in annual electricity billing cost from $11,340 to $10,200 (approximately 10%) in the upgraded system. This work highlights the importance of time of day (TOD) tariff structure consumers that aid reduction in their distribution system’s maximum demand and demand charges.

Owing to the current challenges in energy and environmental crises, improving buildings, as one of the biggest concerns and contributors to these issues, is increasingly receiving attention from the world. Due to a variety of choices and situations for improving buildings, it is important to review the building performance optimization studies to find the proper solution. In this paper, these studies are reviewed by analyzing all the different key parameters involved in the optimization process, including the considered decision variables, objective functions, constraints, and case studies, along with the software programs and optimization algorithms employed. As the core literature, 44 investigations recently published are considered and compared. The current investigation provides sufficient information for all the experts in the building sector, such as architects and mechanical engineers. It is noticed that EnergyPlus and MATLAB have been employed more than other software for building simulation and optimization, respectively. In addition, among the nine different aspects that have been optimized in the literature, energy consumption, thermal comfort, and economic benefits are the first, second, and third most optimized, having shares of 38.6%, 22.7%, and 17%, respectively.

In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m2/yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m2/yr in January to one and nearly zero kWh/m2/yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.

This pioneering Special Issue aims at providing the state-of-the-art on open energy data analytics; its availability in the different contexts, i.e., country peculiarities; and at different scales, i.e., building, district, and regional for data-aware planning and policy-making. Ten high-quality papers were published after a demanding peer review process and are commented on in this Editorial.

High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way.

EU targets for sustainable development call for strong changes in the current energy systems as well as committed protection of environmental resources. This target conflicts if a policy is not going to promote the compatible solutions to both the issues. This is the case of the additional renewable energy sources to be exploited for increasing the share in the electricity mix and in the gross final energy consumption. Solar energy is, currently, the cheapest solution in Southern European Countries, like Italy. In this paper, thanks to the availability of three open databases provided by National Institutions, the authors compared the historic trends and policy scenarios for soil consumption, electricity consumption, and renewable electricity production to check correlations. The provincial scale was chosen as resolution of the analysis. The deviations from the policy scenarios was then addressed to identify the demand for policy recommendations and pathways to promote in order to achieve the target for renewable electricity share as well as the reduction in soli consumption trend in 2030. The role of renewables integrated in the existing contexts, such as building integrated photovoltaics, is considered a key driver for solving this issue.

Electrification of the built environment is foreseen as a main driver for energy transition for more effective, electric renewable capacity firming. Direct and on-time use of electricity is the best way to integrate them, but the current energy demand of residential building stock is often mainly fuel-based. Switching from fuel to electric-driven heating systems could play a key role. Yet, it implies modifications in the building stock due to the change in the temperature of the supplied heat by new heat pumps compared to existing boilers and in power demand to the electricity meter. Conventional energy retrofitting scenarios are usually evaluated in terms of cost-effective energy saving, while the effects on the electrification and flexibility are neglected. In this paper, the improvement of the building envelope and the installations of electric-driven space heating and domestic hot water production systems is analyzed for 419 dwellings. The dwellings database was built by means of a survey among the students attending the Faculty of Architecture at Sapienza University of Rome. A set of key performance indicators were selected for energy and environmental performance. The changes in the energy flexibility led to the viable participation of all the dwellings to a demand response programme.

In the European Union (EU), where architectural heritage is significant, enhancing the energy performance of historical buildings is of great interest. Constraints such as the lack of space, especially within the historical centers and architectural peculiarities, make the application of technologies for renewable energy production and storage a challenging issue. This study presents a prototype system consisting of using the renewable energy from a photovoltaic (PV) array to compress air for a later expansion to produce electricity when needed. The PV-integrated small-scale compressed air energy storage system is designed to address the architectural constraints. It is located in the unoccupied basement of the building. An energy analysis was carried out for assessing the performance of the proposed system. The novelty of this study is to introduce experimental data of a CAES (compressed air energy storage) prototype that is suitable for dwelling applications as well as integration accounting for architectural constraints. The simulation, which was carried out for an average summer day, shows that the compression phase absorbs 32% of the PV energy excess in a vessel of 1.7 m3, and the expansion phase covers 21.9% of the dwelling energy demand. The electrical efficiency of a daily cycle is equal to 11.6%. If air is compressed at 225 bar instead of 30 bar, 96.0% of PV energy excess is stored in a volume of 0.25 m3, with a production of 1.273 kWh, which is 26.0% of the demand.

Stationary and dynamic heat and mass transfer analyses of building components are an essential part of energy efficient design of new and retrofitted buildings. Generally, a single constant thermal conductivity value is assumed for each material layer in construction components. However, the variability of thermal conductivity may depend on many factors; temperature and moisture content are among the most relevant ones. A linear temperature dependence of thermal conductivity has been found experimentally for materials made of inorganic fibers such as rockwool or fiberglass, showing lower thermal conductivities at lower temperatures. On the contrary, a nonlinear temperature dependence has been found for foamed insulation materials like polyisocyanurate, with a significant deviation from linear behavior. For this reason, thermal conductivity assumptions used in thermal calculations of construction components and in whole-building performance simulations have to be critically questioned. This study aims to evaluate how temperature affects thermal conductivity of materials in building components such as exterior walls and flat roofs in different climate conditions. Therefore, experimental conductivities measured for four common insulation materials have been used as a basis to simulate the behavior of typical construction components in three different Italian climate conditions, corresponding to the cities of Turin, Rome, and Palermo.

Power Grids face significant variability in their operation, especially where there are high proportions of non-programmable renewable energy sources constituting the electricity mix. An accurate and up-to-date knowledge of operational data is essential to guaranteeing the optimal management of the network, and this aspect will be even more crucial for the full deployment of Smart Grids. This work presents a data analysis of the electricity production at the country level, by considering some performance indicators based on primary energy consumption, the share of renewable energy sources, and CO2 emissions. The results show a significant variability of the indicators, highlighting the need of an accurate knowledge of operational parameters as a support for future Smart Grid management algorithms based on multi-objective optimization of power generation. The renewable share of electricity production has a positive impact, both on the primary energy factor and on the CO2 emission factor. However, a strong increase of the renewable share requires that the supply/demand mismatching issues be dealt with through appropriate measures.

Heating and cooling in buildings is a central aspect for adopting energy efficiency measures and implementing local policies for energy planning. The knowledge of features and performance of those existing systems is fundamental to conceiving realistic energy savings strategies. Thanks to Information and Communication Technologies (ICT) development and energy regulations’ progress, the amount of data able to be collected and processed allows detailed analyses on entire regions or even countries. However, big data need to be handled through proper analyses, to identify and highlight the main trends by selecting the most significant information. To do so, careful attention must be paid to data collection and preprocessing, for ensuring the coherence of the associated analyses and the accuracy of results and discussion. This work presents an insightful analysis on building heating systems of the most populated Italian region—Lombardy. From a dataset of almost 2.9 million of heating systems, selected reference values are presented, aiming at describing the features of current heating systems in households, offices and public buildings. Several aspects are considered, including the type of heating systems, their thermal power, fuels, age, nominal and measured efficiency. The results of this work can be a support for local energy planners and policy makers, and for a more accurate simulation of existing energy systems in buildings.

Urban waste management is one of the most challenging issues in energy planning of medium and large cities. In addition to the traditional landfill method, many studies are investigating energy harvesting from waste, not as a panacea but as a foreseeable solution. Thermo-chemical conversion to biogas, or even bio-methane under certain conditions, could be an option to address this challenge. This study focuses on municipal solid waste conversion to biogas as a local energy supply for the cities. Three urban models and their subdivision into urban areas were identified along with a typical Organic Fraction of Municipal Solid Waste (OFMSW) matrix for each urban area. Then, an energy analysis was carried out to provide an optimization map for an informed choice by urban policy-makers and stakeholders. The results highlighted how the urban context and its use could affect the opportunity to produce energy from waste or to convert it in fuel. So, in this case, sustainability means waste turning from a problem to a renewable resource.

Among the several typologies of storage technologies, mainly on different physical principles (mechanical, electrical and chemical), hydrogen produced by power to gas (P2G) from renewable energy sources complies with chemical storage principle and is based on the conversion of electrical energy into chemical energy by means of the electrolysis of water which does not produce any toxic or climate-relevant emission. This paper aims to pinpoint the potential uses of renewable hydrogen storage systems in Europe, analysing current and potential locations, regulatory framework, governments’ outlooks, economic issues, and available renewable energy amounts. The expert opinion survey, already used in many research articles on different topics including energy, has been selected as an effective method to produce realistic results. The obtained results highlight strategies and actions to optimize the storage of hydrogen produced by renewables to face varying electricity demand and generation-driven fluctuations reducing the negative effects of the increasing share of renewables in the energy mix of European Countries.

Energy enables the functioning of modern society. However, humanity’s reliance on fossil fuels since the industrial revolution has contributed to many societal problems including climate change, environmental degradation and pollution, and the transition to a renewable and carbon-free energy system is one of the grand challenges for the 21st century. The aim of this editorial is to outline the importance of a fast and transparent sharing of energy research and discuss key themes of the Energy Gateway of F1000Research.