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This paper's research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car's charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization model for the integration of polygeneration systems into energy communities (ECs), by analyzing a case study. The concept of ECs is increasingly seen as beneficial for reducing global energy consumption and greenhouse gas emissions. Polygeneration systems have the potential to play a crucial role in this context, since they are known for producing multiple energy services from a single energy resource, besides the possibility of being fed also by renewable energy sources. However, optimizing the configuration and operation of these systems within ECs presents complex challenges due to the variety of technologies involved, their interactions, and the dynamic behavior of buildings. Therefore, the aim of this work is developing a mathematical model using a mixed integer linear programming (MILP) algorithm to optimally design and operate polygeneration systems integrated into ECs. The model is applied to a case study of an EC comprising nine buildings in a small city in the northeast of Italy. The work rests on the single- and multi-objective optimization of the polygeneration systems taking into account the sharing of electricity among the buildings (both self-produced and/or the purchased from the grid), as well as the sharing of heating and cooling between the buildings through a district heating and cooling network (DHCN). The main results from the EC case study show the possibility of reducing the total annual CO2 emissions by around 24.3% (about 1.72 kt CO2/year) while increasing the total annual costs by 1.9% (about 0.09 M€/year) or reducing the total annual costs by 31.9% (about 1.47 M€/year) while increasing the total annual CO2 emissions by 2.2% (about 0.16 kt CO2/year). The work developed within this research can be adapted to different case studies, such as in the residential–commercial buildings and industrial sectors. Therefore, the model resulting from this work constitutes an effective tool to optimally design and operate polygeneration systems integrated into ECs. [ABSTRACT FROM AUTHOR]

Efficient management of renewable energy resources is imperative for promoting environmental sustainability and optimizing the utilization of clean energy sources. This paper presents a pioneering European-scale study on energy management within renewable energy communities (RECs). With a primary focus on enhancing the social welfare of the community, we introduce a reinforcement learning (RL) controller designed to strategically manage Battery Energy Storage Systems (BESSs) and orchestrate energy flows. This research transcends geographical boundaries by conducting an extended analysis of various energy communities and diverse energy markets across Europe, encompassing different regions of Italy. Our methodology involves the implementation of an RL controller, leveraging optimal control theory for training and utilizing only real-time data available at the current time step during the test phase. Through simulations conducted in diverse contexts, we demonstrate the superior performance of our RL agent compared to a state-of-the-art rule-based controller. The agent exhibits remarkable adaptability to various scenarios, consistently surpassing existing rule-based controllers. Notably, we illustrate that our approach aligns with the intricate patterns observed in both Italian and European energy markets, achieving performance levels comparable to an optimal controller assuming perfect theoretical knowledge of future data. [ABSTRACT FROM AUTHOR]

The paper explores the potential of the main state economic incentive (Conto Termico) designed to promote the use of renewable energy sources for heating and cooling purposes in residential buildings, aiming to reduce primary air emissions from residential biomass combustion (RBC). For this purpose, an emission reduction scenario involving the technological transition of biomass residential heating appliances was implemented in the lowland small‐medium municipalities of the Lombardy region in Italy, where households are connected to the natural gas grid but RBC is often practiced for economic reasons, contributing to severe air pollution problems related especially to particulate matter (PM) emissions. This technological turnover scenario (i.e. all biomass appliances in the area replaced by state‐of‐the‐art pellet appliances) resulted in a significant reduction of particulate matter emissions, while having only a minimal impact on NOx emissions. The incentivized cost for technological turnover (€15/kgPM10 avoided) was in line with the social costs associated with health impacts related to PM10 in a rural setting such as the study area. Given the one‐off investment support provided by a recently adopted national decree (DM 15/09/2022), biomethane emerges as a promising renewable energy alternative to decrease PM and NOx emissions from RBC in Lombardy region, characterized by an extensive natural gas distribution network and potential waste feedstock for biomethane production. Despite the still elevated production costs, an analysis restricted to the incentivized segment reveals that supporting biomethane production through investment incentives is more cost‐effective (€8/kgPM10 avoided) than promoting technological turnover. [ABSTRACT FROM AUTHOR]

The development of new technological innovations for eco-friendly vehicles combined with the usage of renewable energy sources is essential for mitigating the environmental impact of freight transport. In this context, this paper investigates the opportunities for implementing the eHighway system, a novel recent technology designed to supply new hybrid trucks. This technology uses overhead catenary heavy-duty vehicles that are supplied with electric energy from overhead power lines through a pantograph that is positioned at the top of the truck. A novel bi-level multi-objective network electrification design (BM-NED) model is proposed to assess the environmental benefits and opportunities of adopting eHighways, considering the limited budgetary resources for road infrastructure electrification. Still, the implementation of eHighways requires collaboration between public and private stakeholder interests. The upper level considers multiple objectives aiming at minimizing the total travel cost, infrastructure, and environmental costs and maximizing the average traffic density of OC hybrid trucks on electrified arcs, whereas the lower level is the traffic assignment model. The Elitist multi-objective Genetic Algorithms are used as a solution approach for the multi-objective optimization and the Pareto front of the non-dominated solutions have been generated. Results of the model, tested on a part of a motorway network in the Veneto region in Italy, show that the implementation of the eHighway system can lead to an average emission reduction of about 66%, considering all Pareto-optimal solutions. Furthermore, a sensitivity analysis has been carried out by giving different weights to the objective functions that can be a basis for decision-makers regarding the adoption of this new technology. • The implementation of the eHighway system is investigated to evaluate its benefits. • A novel bi-level multi-objective network electrification design model is proposed. • The model is tested on a part of a motorway network in the Veneto region (Italy). • Sensitivity analysis evaluates different solutions for potential decision-makers. • The study gives insights for policy makers regarding eHighway adoption. [ABSTRACT FROM AUTHOR]

• Novel layout to be easily adopted by existing residential districts. • The novel layout can reduce the primary energy consumption of the residential district. • A primary energy saving index of 38 % and payback period of 2.2 years is achieved. • Power to heat strategy increases the district self-consumed electricity by 15%. • The power to heat strategy is able to further exploit the excess renewable electricity. This paper aims at reducing both electricity and space heating demands of residential districts. It specifically investigates the emerging paradigm, based on renewable energy communities, for existing residential districts. In particular, a novel renewable energy community layout is analyzed. This layout integrates a district heating network and the power to heat control strategy. The proposed system is designed to prevent any major refurbishment inside the apartments, using the existing high temperature space heating system. This arrangement is compared with more conventional renewable energy community layouts. Detailed dynamic models for both the residential district and the proposed layouts are developed using the TRNSYS software. Results show that renewable energy community is an extremely profitable paradigm for promoting the use of renewables at residential buildings scale. Moreover, the layout integrating energy community, district heating network, and Power-to-Heat strategy achieves the best results with a payback period of 2.2 years and a primary energy saving of 38 %. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals are significantly increasing investments in the production of energy from renewable sources (RESs). To this end, the supply of monetary incentives by public institutions has increased sharply. This flow of money inevitably attracts the attention of criminal organizations (henceforth COs) that use their power to increase the volumes of investments, while public authorities might react by deciding not to make investments in RESs in areas at risk of distorted use of incentives. In this context, the research question is as follows: does the presence of COs slow down or encourage investment in RESs? Until now, this topic has received little attention from researchers, at least in the European Union. In particular, the presence of COs is particularly pervasive in the economic system of Italy. Given the heterogeneity of this country, a spatial econometric approach was used, taking into account geographical dependency relationships and their impact on the relevant variables. The main result of the research shows a negative relationship between Italian areas with higher CO levels and RES investments. In other words, investments are discouraged in these regions. This situation is detrimental to the target regions in terms of sustainable development and increasing the gross national product (GNP). Furthermore, we found that micro-crime cannot in any way influence investments in RESs. [ABSTRACT FROM AUTHOR]

The transition towards energy efficiency measures and green energy sources is strongly fostered by the European Union. Italy is among the EU countries that have heavily invested in renewable energy sources, more than doubling their share in gross final energy consumption. In particular, biogas has a pivotal role in the generation of electricity and can also be upgraded into biomethane, with a higher and more stable energy content. In this study, the sustainability of the supply chain of electricity from biogas in Italy has been thoroughly analyzed in the broader framework of the ARCADIA (Life Cycle Approach in Public Procurement and Italian LCA Database for Resource Efficiency) project. The environmental assessment, carried out by means of Life Cycle Assessment (LCA), provides a two-fold perspective. Firstly, it allows us to identify the main hotspots of the investigated system, such as the cultivation of dedicated crops, and to provide useful insights for improving environmental performance. Furthermore, a focus on the modeling of the dataset related to the production of electricity from biogas within the Italian electricity mix represents a step ahead in the LCA research, filling the lack of site-specific databases for reliable LCA results. [ABSTRACT FROM AUTHOR]

The current study evaluates the effect of financial development (FD), economic growth (GDP), trade globalization (TGLO), renewable energy consumption (REC), and human capital (HC) on ecological footprint (ECF) in Italy using data between 1985 and 2018. We further assess the combined impact of trade globalisation and human capital on ecological footprint as well as the combined effect of trade globalisation and human capital on ecological footprint. In order to explore these associations, the present research utilizes autoregressive distributed lag (ARDL) to catch both long and short-run associations between ecological footprint and its drivers. Furthermore, we use dynamic OLS (DOLS) and fully modified OLS (FMOLS) long-run estimators as robustness checks. Lastly, causality at different frequencies is captured using the frequency domain causality test. The outcomes of the ARDL estimator disclose that (a) a surge in ecological footprint is attributed to an increase in economic growth; (b) a decrease in ecological footprint is attributed to an increase in renewable energy, trade globalisation, and human capital; (c) the combined impact of human capital and trade globalisation lessen ecological footprint, and (d) combined effect of trade globalisation and human capital mitigate ecological footprint in Italy. Furthermore, the outcomes of the causality test revealed that all the exogenous variables can significantly predict ecological footprint in the long run with the exemption of financial development. Based on the study's findings, an SDG-focused policy framework is advocated. [ABSTRACT FROM AUTHOR]

Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability. [ABSTRACT FROM AUTHOR]

This paper aims to broaden the concept of Net Zero Energy Building (NZEB), assessing its suitability to the emerging grid-based energy sharing models. The energy exchange between two buildings with shifted load schedules, namely, a new residential NZEB (prosumer) and an existing school building (consumer), is analysed to investigate energy sharing dynamics. The buildings are in Southern Italy, a region with high solar radiation. The model of the existing school building is calibrated on measured data and the study is performed by dynamic energy simulation. Three energy sharing scenarios based on the design of a photovoltaic system installed on the roof of the NZEB have been investigated, i.e., PV system peak power designed: to satisfy the NZEB target on annual basis; to satisfy the NZEB target on monthly basis; to fully exploit the available roof area of the NZEB. Considering annual and monthly net energy balances, oversizing the NZEB PV system leads to the compensation of a high share of school building non-renewable energy consumption. However, self-consumption analysis performed on hourly basis shows that increasing the PV system size negatively affects the self-consumption rate of the renewable energy community, and the presence of the school building only mitigates this effect. [ABSTRACT FROM AUTHOR]

The paper aims to analyze the evolution of oil prices and renewable energy production in Italy during the first wave of the COVID-19 pandemic crisis with daily data for the period January 2020-September 2020 through several time series techniques (long memory test and spectral causality analysis) and Wavelet Analysis tools. Italy has been the first country all over the world to be severely hit by the pandemic, reacting immediately with strong restrictive measures. The applied results show that oil prices and renewable energy sources were highly correlated during the pandemic shock. Moreover, causality tests reveal a unidirectional flow running from solar, hydro, and wind sources to oil prices, highlighting the relevance of the effect of the energy transition on the oil market. It is also imperative for a country that is a net energy resources importer to achieve a more sustainable way of production and accelerate the energy transition process, especially during phases of high fossil fuel prices. [ABSTRACT FROM AUTHOR]

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]

Wastewater treatment plants are highly energy-intensive systems. This research uses Life Cycle Assessment (LCA) to determine the impacts generated during the operation of a wastewater treatment plant. Three different scenarios are analyzed: a baseline scenario that considers a conventional activated sludge treatment technology exploiting data from an existing plant located in central Italy, a second scenario that involves the implementation of MBR technology applied to the baseline scenario, and finally a third scenario that consists of the addition of an anaerobic digester that allows energy recovery from biogas production, followed by a photovoltaic plant capable of supplying the plant energy demand. Global warming potential, eutrophication, and acidification are the environmental categories considered most relevant to emissions. The results showed that the effluent had the highest impact in terms of CO2 equivalent in all three situations due to the presence of N2O. Since emissions from biological processes, transportation, and wastewater are almost similar in all three scenarios, it is preferable to focus on the environmental impacts associated with energy consumption. The third scenario involves careful resource management and the use of treatment technologies that allow for a reduction in the use of nonrenewable energy sources in favor of renewable ones. [ABSTRACT FROM AUTHOR]

Renewable energy communities (RECs) have been gaining momentum around the world, as a way to promote sustainable development and combat climate change. These communities are typically composed of individuals, businesses, and organizations that come together to invest in and promote the use of renewable energy sources, such as solar, wind, and hydraulic power. This article focuses on the benefits that renewable energy communities bring to a territory through the diffusion of renewable energy systems, tackling different issues like local depopulation, increasing energy prices, and a lack of jobs, while reducing greenhouse gas emissions and improving air quality. The novelty of this article lies in the results from the first-of-its-kind national call within the Next Appennino program, part of the National Complementary plan, aimed at the formation and proliferation of RECs in the area severed by the 2009 and 2016 earthquakes in Italy, as a way to add social, environmental, and sustainable value to the reconstruction of this territory. The data refer to the municipality of Castelraimondo and Unione Montana dei Monti Azzurri (a consortium of mountain municipalities), both located in the Marche region. Analysis of the consumer and prosumer energy needs, as well as the quantification of the exploitable production from the new renewable generators installed, showed that a total of 6.134 GWh/year can be shared by the community. [ABSTRACT FROM AUTHOR]

The EU has planned the phase-out of new vehicles based on internal combustion engines in favor of high-efficiency battery electric vehicles (BEV) by 2035 (Fit for 55 package). However, many doubts remain about the effectiveness of this choice for each country of the Union in terms of CO2 emissions reduction, as each State is characterized by a different carbon intensity related to the production of electricity needed to manufacture and recharge vehicles. This study seeks to explore the Italian case. To this aim, carbon intensities related to electricity production were calculated considering both the Italian electricity mix production in 2022 and those envisaged in 2035, considering two energy scenarios based on different introductions of renewable energy sources (RES). Afterward, the values obtained were adopted for determining the CO2 emissions related to the whole production process of battery systems in Italy (emissions from mining and refining, scrap materials, and final assembly included) by comparing some of the most up-to-date Life-Cycle Assessment (LCA) analyses related to the manufacturing cycle of the batteries. Finally, the results were adopted to calculate the starting carbon debit for A, B, C, and M car segments for Mild Hybrid, Full Hybrid, and Full Electric powertrains. At the same time, statistical road fuel/electricity consumption data were collected and overall CO2 emissions were calculated for the same vehicles adopting a dynamic approach and plotted for a defined distance, so as to determine break-even points with respect to the cumulative (i.e., from battery and road) carbon emissions. The results showed that advantages related to electric vehicles are significant only if a low carbon intensity related to electricity production is reached by means of a very high introduction of RES, thus keeping the door open for innovative hybrid powertrain technologies, if fed with low carbon fuels. [ABSTRACT FROM AUTHOR]

The present communication is focused predominantly on important R&D solutions relevant to renewable energy technologies covering the following: (i) Innovative heat transfer fluid and thermal storage technology based on a molten salt mixture developed by ENEA for large-scale heat storage. The system uses a parabolic trough collector, compared with diathermic oil, which allows higher operating temperature, resulting in significant benefits to the plant's operation, safety and the environment. (ii) The world's first solar disk powered by air micro turbine developed by ENEA. (iii) An innovative steam-explosion prototype plant installed at ENEA for the pre-treatment of lignocellulosic biomass and the fractionation of bio components to generate ethanol from lignocellulosic material using hemicellulose and lignin. (iv) The production of hydrogen-enriched biogas using steam as the gasification agent, which helps in obtaining nearly nitrogen-free product gas and with a high calorific value of around 12 MJ/Nm3 dry gas and a high percentage of hydrogen (up to 55%) while using steam as the gasifying agent in the presence of a catalyst. (v) A rotary kiln plant, with the main purpose being to develop and optimize a thermo-chemical process to convert used rubber tyres so as to recover material and energy, as well as other solid products, with high value-added "Activated carbon" and synthesis gas. [ABSTRACT FROM AUTHOR]