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The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

This paper aims at evaluating the best allocation of potential biomethane generation for the decarbonization of the transport system, presenting a case study in Italy. The country has some peculiar features, such as several operating biogas plants, additional potential feedstock for biogas/biomethane generation, a well-developed natural gas network and established relevant natural gas uses in different final sectors, including transport. Based on current estimates for sustainable biomethane potential by 2030, ranging from 2.3 to 7.6 billion cubic meters depending on the scenario, the analysis compares technologies for the generation, distribution and final use of biomethane. The results of the analysis confirm the potential interesting contribution of biomethane in decarbonizing the Italian transport system: a billion cubic meters of biomethane can lead to 2.33–4.37 Mt CO 2 e savings, depending on the feedstock mix and the application. On a national basis, annual climate emission savings in 2030 range from 10.0 to 26.7 Mt CO 2 e , depending on the scenario. Additional 3.1–8.1 Mt CO 2 of emissions can be avoided if the CO 2 captured during the biomethane upgrading can be stored or reused. The proposed methodology could be used to extend the analysis to other countries, and to the European context. • We analyze the climate emissions of biomethane for different supply chains. • We evaluate the effects of feedstock, distribution strategy and electricity mix. • The results confirm the important role of biomethane for transport decarbonization. • We discuss the best allocation in transport segments based on emissions savings. • We compare alternative biomethane scenarios for Italy in 2030. [ABSTRACT FROM AUTHOR]

So far, energy planning methods faced several challenges in achieving a proper assessment due to the extreme variability of input conditions. To considerably decrease the computational efforts in achieving solutions, most of the analyses reported in the scientific literature assume constant costs and demand over the entire planning horizon in a typical year. However, results might not be perfectly aligned with real ones because of the incapability of some models to address several aspects like energy demand/production variability, energy and technology costs, efficiency degradation, and use of different energy carriers. This paper proposes a novel methodology that optimises both the long-term planning and short-term scheduling decisions in the management of a multi-energy carries community by means of a Mixed Integer Linear Programming model that also considers the modular design of technologies (e.g., technological devices selection from a discrete set of variants). Such a model has been applied to the case study of a University campus in Italy, whose historical demand data were used for its energy planning with a time horizon of 30 years. Three scenarios have been analysed: (i) the Business As Usual, (ii) the Sector-coupling scenario, and, finally, (iii) the Hydrogen deployment one. The results are obtained under different energy scenarios, showing the effectiveness of the methodology in dealing with multi-investment stages at different planning levels in a reasonable computational time. In particular, they showed that deploying more sustainable technologies would increase the cost of the electricity (between 43%–89%), while reducing other energy carriers' cost (about 60%) and lowering all energy carriers' carbon footprint (between 50%–80%). From a long-term perspective, (i) the use of sector-coupling technologies is beneficial from both economic and environmental points of view, and ii) dynamic variations of some parameters can strongly affect the deployment of high-cost technologies to be installed beyond 2030. • A novel multi-investment stage approach for multi-energy-carriers community is proposed. • Methodology validated on different energy-policy-driven scenarios with up to five energy-carries. • Manufacturer's datasheet-based size enables realistic modular design. • Automatic energy systems expansion aligned with fluctuating parameters. • Electricity cost increment (40%–80%) for the economic and environmental benefit of the other carriers (50%–90%). [ABSTRACT FROM AUTHOR]

[Display omitted] • Simulations provide reliable hydrogen production data for estimation of key indicators. • Hydrogen produced by electrolysis has larger EROEI than natural gas-based routes. • Green hydrogen appears the best solution in terms of LCOH for small-scale local production. • Green hydrogen performs better in terms of GWP, while other impacts should be improved. • TCO of hydrogen-fuelled vehicles is larger than diesel ones mainly due to purchase cost. Hydrogen is a versatile energy carrier and storage medium that may be employed in a variety of applications. According to the industrial processes used for its production, hydrogen may be labelled using different colours: (i) grey hydrogen, produced from natural gas using steam methane reforming (SMR), (ii) blue hydrogen, like the grey one, but with carbon capture and storage (CCS), (iii) green hydrogen, produced by water electrolysis using electricity from renewable sources only, (iv) "grid" hydrogen, produced by electrolysis using grid electricity. In this study, process simulation is used to solve material and energy balances, as well as to estimate capital and maintenance costs for each technology investigated. Then, process simulation outcomes are used to estimate three key performance indicators focusing on sustainability issues: the Energy Return on Energy Invested (EROEI), the Levelized Cost of Hydrogen (LCOH) and the Life Cycle Assessment (LCA). With reference to the case study of the Trieste port in Italy, the potential of synthesizing and utilizing hydrogen to fuel transportation activities within a port is examined. Based on the daily hydrogen consumption in fuel cells installed on locomotors and trucks, the design of the different processes considered is carried out, as well as their comparison in terms of EROEI, LCOH, and LCA. Furthermore, LCA and Total Cost of Ownership (TCO) evaluations for various hydrogen-fueled vehicles within the port are presented and compared to diesel-fueled ones to determine the impact of fuel-cell vehicles during operations. Results show that EROEI of hydrogen produced by electrolysis is larger than that produced by SMR with or without CCS. The LCOH for grey hydrogen is of the same order of magnitude of that of green or grid ones. The hydrogen compression step to 300 bar impacts on both energetic and economic performances. LCA indicates that the Global Warming Potential (GWP) of green hydrogen is at least half with respect to blue hydrogen, however other impact categories are less favourable. On the other hand, the TCO of hydrogen-fueled vehicles is higher than that of diesel-fueled ones, mainly because of the higher purchase costs. It is concluded that the methodology proposed in this paper, based on the evaluation of indicators at the design stage, is suitable for comparing hydrogen production processes. In addition, it is a powerful tool for policy decision-makers in defining the strategies for the development of hydrogen-based transport systems in port operations. [ABSTRACT FROM AUTHOR]

• Optimal design of multi-vector energy hubs in energy communities. • MILP model using a multi-objective function. • Application to a real case represented by a university campus. One of the main ways to reach the important targets of the European Commission in the energy field set by 2030 is to implement sustainable energy systems to cover electrical and thermal energy demands, as well as transportation needs, in urban areas. In the aforesaid scenario, multi-vector energy hubs consist of installing small and medium size power plants, mainly fed by renewable sources, and charging infrastructures for electric vehicles in sustainable urban districts. The optimal design of such systems can be faced from different points of view and using different methods, one of the most adopted being the development of mathematical programming algorithms typically aimed to minimize global costs and emissions. The contribution of this paper is to propose an innovative methodology to optimally design a multi-vector energy hub able to supply electricity and thermal energy, for space heating and cooling, to a set of buildings within a sustainable district acting as a local energy community. The developed mixed integer linear programming model is characterized by very detailed models of the technologies and by a multi-objective function which is proposed to evaluate the impact of the optimal solution on global costs and emissions. The proposed mathematical model is very comprehensive as it involves a wide range of energy technologies with detailed modelling by considering the impact of external ambient conditions and energy vector temperature values on the plant conversion efficiency, thus showing high replicability potential in real contexts. In the case study, the model is applied to a university campus in the North of Italy. [ABSTRACT FROM AUTHOR]

Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innovative solutions to the prototypal and experimental level. We take inspiration from the technology adopted in Italy, where the national regulatory authority actively supported the definition of a solution agreed upon by all the involved stakeholders. In this context, smart meters are enabled to convey data to low voltage end users through a power line communication channel (CHAIN 2) in near real-time. The aim of this paper is twofold. On the one hand, it describes the proof of concept that the channel underwent and its subsequent validation (with performances nearing 99% success rate). On the other hand, it defines a classification framework (I2MA) for post-metering value-added services, in order to categorize each use case based on both level of service and expected benefits, and understand its maturity level. As an example, we apply the methodology to the 16 use cases defined in Italy. The lessons learned from the regulatory, technological, and functional approach of the Italian experience bring us to the provision of recommendations for researchers and industry experts. In particular, we argue that a well-functioning post-metering value-added services' market can flourish when: (i) distribution system operators certify the measurements coming from smart meters; (ii) national regulatory authorities support the technological innovation needed for setting up this market; and (iii) service providers create customer-oriented solutions based on smart meters' data. • Smart meters' data can open a market of post-metering value-added services. • Access to electricity exchange data must be granted through a dedicated channel. • CHAIN 2 is a reliable and effective PLC-based communication channel. • Post-metering use cases can be classified based on level of service and benefits. • The Italian post-metering market structure can be regarded as a successful example. [ABSTRACT FROM AUTHOR]

• Weather files defined with different methodology and dataset are compared. • Projections of climate changes are considered in the construction of weather files. • The case study is a residential building in a cooling dominated climate. • The composition of weather files greatly influences the energy performance prevision. • Traditional efficiency measures are not resilient for the expected climate changes. The methodology, the site and the dataset as well as the emissions scenario considered in the weather file definition influence the numerical evaluation of efficiency measures resilience. With a complete statistical and critical approach, the paper analyzes the importance of these aspects by means of a residential case study simulated in Benevento, a city of south Italy. Using data monitored from 2015 to 2020, a current weather file is built with different methodologies. The comparison indicates that there is not repeatability of the year chosen as a reference for the various months and thus the resolution of the building energy balance could bring different results. Some future climate projections are also generated on medium (2050 s) and long (2080 s) term considering different emission scenarios. With long term projection, the heating degree days are reduced also of − 21% meanwhile the cooling degree days are more than double compared with the current condition. This suggests a remarked transition towards a dominant cooling climate for Benevento. Moreover, when the climate change is considered, the insulation intervention and the installation of double glazed low emissive window is not resilient because the heating energy need decreases also of −56%, but the cooling energy need increases of + 62% (2080 s). If the efficiency measures include also the cool roof and the external shadings, the cooling demand could be reduced until –33% in some scenarios (e.g. RCP 4.5-50th percentile) and increased (+31%) in some others (e.g. 2080 s). [ABSTRACT FROM AUTHOR]