Search

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]

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]

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 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]

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]

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]