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Alternative fuels and energy vectors are becoming increasingly important in terms of technical, geopolitical, economic, and environmental aspects. In particular, gaseous fuels and vectors, such as fossil or synthetic natural gas (NG) blended with hydrogen, commonly help provide optimal strategies to reduce global and toxic emissions of internal combustion engines, owing to their adaptability, anti-knock capacity, lower toxicity of pollutants, reduced CO2 emissions, and costeffectiveness. However, diesel engines still represent the reference category among internal combustion engines in terms of maximum thermodynamic efficiency. The possibility offered by dual-fuel (DF) systems to combine the efficiency and performance of diesel engines with the environmental advantages of gaseous fuels has been the subject of extensive investigations. However, the simple replacement of diesel fuel with gaseous fuel does not allow for optimising the engine performance, owing to the high percentage of unburned gaseous fuel, which compromises the potential reduction of CO2; therefore, more complex combustion strategies should be realised. In this study, with the aim of improving the DF combustion process, an experimental investigation was performed to analyse low-temperature combustion (LTC), using NG and two enriched hydrogen-compressed NG blends as primary fuels. The LTC mode was activated by means of a very early advanced pilot injection and carried out in two close steps. The double pilot injection was used to control the energy release rate in the first combustion stage, thereby minimizing the increase of the rate of pressure and allowing the extension of the operation range under LTC. The experimental activity was also focused on analysing the particle emissions, as it is well known that these emissions, together with those of nitrogen oxide, constitute the main pollutants resulting from diesel fuel combustion. The results demonstrated the potential to reduce the unburned fuel, NOx, and particle emissions simultaneously, while maintaining equivalent CO2 emissions to a diesel-only engine. Both the timing and pressure of the pilot injection proved to be critical parameters for optimising the emissions and performance

Artículos en revistas A major aim of Capacity Remuneration Mechanisms (CRMs) is to lead the power system expansion towards the level of security of supply that the regulator considers adequate. When introducing a capacity mechanism, therefore, regulators must ensure that the resulting mix will actually provide the firmness pursued, in such a way that both the generation and the demand resources awarded with the capacity remuneration actually perform as expected when the system needs them. In order to achieve this goal, some experts stressed the importance of including performance incentives in the CRM design. However, first capacity mechanisms (implemented mainly in the American continent) did not pay enough attention to this aspect. Two decades of operation have evidenced the need for performance incentives and these instruments are, at this writing, at the centre of the regulatory discussion. On the basis of a model analysis, this article demonstrates how the introduction of properly designed explicit penalty schemes for under-delivery can positively impact the CRM outcomes, providing resources with effective incentives to maximise their reliability, discriminating against non-firm generation units, and therefore increasing the effectiveness of the mechanism in achieving its objectives. info:eu-repo/semantics/publishedVersion

The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.

Artículos en revistas As outlined in the Paris Agreement on climate change, efforts to mitigate and adapt to climate change will require new modes of development of the energy sector including the transformation and expansion of power systems to low-carbon and more resilient designs. However, there is a need for more systematic tools to support decision-making processes in the context of climate change impacts and adaptation strategies for the energy and power sectors. For instance, quantitative approaches should be developed and implemented for the assessment of the impacts and hedging strategies associated with the uncertainties inherent to energy and power planning problems. This study addresses the development and implementation of an integrated model-based system analysis, which uses general circulation models, global sensitivity analysis, and stochastic optimization techniques, for the optimal design and planning of the Colombian power system in view of submitted climate pledges and climate change adaptation. It was found that during the 2015 to 2029 time frame, climate change will likely reduce the capacity factor of hydropower generation by 5.5-17.1%. Additionally, it was established that the independent effects of three key uncertain parameters, i.e., capacity factor of hydropower generation, gas prices, and emission reduction target, account for ~96% of the variance in the total cost for the required expansion and operation of the power system. Furthermore, when uncertainty is taken into account, the optimal expansion strategy consists of rescheduling of investments in hydropower plants and investing more in carbon management technologies and renewable power plants to compensate for the uncertainty in hydropower generation, climate policy, and gas prices. info:eu-repo/semantics/publishedVersion

Vehicle emission models are relevant for evaluating the performance of vehicle technologies and help in the definition of environmental policies. This paper presents an improved emissions modelling approach (named VSP+M) by combining the vehicle specific power (VSP) with load-regime engine maps for each VSP mode. The new modelling enabled to link tailpipe emissions to vehicle and engine operating conditions, obtained from real driving emission (RDE) tests and on-board diagnosis (OBD) data. The parameters for the sizing of engine maps were optimised by means of Pareto frontiers to solve the trade-off between the minimisation of RMSE and emission factor errors in urban sections and total RDE trips. The CO2 emission factors errors were reduced up to 63% and 45% for urban and RDE sections, respectively. The NOx emission factor errors were reduced up to 15%, maintaining the same RMSE levels. Optimal engine maps sizing was found for every tested vehicle and for each engine type to be applied in other vehicles. This study demonstrates the potential to address declinations of the conventional VSP model based on engine operation or proxies to those variables by using the proposed approach. info:eu-repo/semantics/publishedVersion

Fleets of commercial vehicles for delivery services in urban areas constitute road transportation means which are required to run relatively short distances and to respect anti-pollution laws commonly imposed by many municipalities. For this kind of commercial applications, high efficiency and eco-friendly electric propulsion systems offer an interesting alternative to thermal engines. This paper is focused on the analysis of such solution, by presenting experimental results obtained with a ZEBRA battery based propulsion system, designed to power a specific urban unit within the category of electric commercial vehicles. A novel contribution is added to the relevant literature concerning battery based electric powertrains for road vehicles. The main novelty consists in a wide range of experimental results and performance analysis carried out with reference to the real behavior of both the whole propulsion system and each main component, when powering the commercial vehicle, on the urban part of the NEDC (New European Driving Cycle) standard driving cycle, at different slopes. The experimental results, expressed through electrical and mechanical parameters, are initially evaluated by means of a quasi-static numerical model of the electric powertrain and then experimentally verified with the support of a 1:1 scale laboratory dynamic test bench. The procedure followed and presented in this paper definitely demonstrates the good design and performance, obtained for the evaluated propulsion system, in satisfying the real energy and power requirements, specific of an urban use for delivery commercial vehicles, in terms of daily autonomy and slopes. The collections of experimental results, analyzed in the paper, represent in addition a useful set of data for simulation in order to build, verify and improve models in their outputs.

This paper is aimed to experimentally analyse the effectiveness of a hybrid storage system, when powering a commercial vehicle for urban use. The hybrid energy storage system is composed by two ZEBRA batteries, combined with an electric double layer capacitor (EDLC) module. The integration of those storage systems is obtained by means of a bidirectional DC/DC converter, which balances the electric power fluxes between batteries and super-capacitors, depending on the driving operative conditions. Modeling and simulations are preliminarily conducted with reference to the specific case study of an electric version of the Renault Master, supplied by the above described hybrid storage system. That theoretical activity allows the optimization of rule based energy management strategies for the hybrid energy storage system, in terms of the effectiveness in reducing the negative effects of high charging/discharging currents on battery durability. Then, the experimentation of the real power train, connected to the mentioned hybrid storage system, is carried out through a 1:1 laboratory test bench, able to perform the analysed energy management strategies on standard driving cycles, representative of the urban mission of the commercial vehicle under study. The obtained experimental results, expressed through electrical and mechanical parameters in a wide range of road operative conditions, show that the super-capacitors can improve the expected battery lifespan, with values of maximum effectiveness up to 52%, for driving patterns without negative road slopes. The procedure followed and presented in this paper definitely demonstrates the good performance of the evaluated hybrid storage system, controlled by the DC/DC power converter, to reduce the negative consequences of the power peaks associated with the urban use of commercial vehicles.

This paper is aimed to analyze design criteria, setting up, control strategies and experimental tests related to a power configuration of DC micro-grid for fast charging of full electric and plug in hybrid vehicles. The proposed DC fast charging architecture is derived by an analysis comparing the main characteristics of well known architectures, mainly based on AC and DC bus, taking also into account the integration of renewable energy sources (RESs) with stationary energy storage systems and fleets of road electric/hybrid vehicles. On the base of the proposed architecture a laboratory prototype of charging station has been realized by means of a 20. kW AC/DC bidirectional grid tie converter interconnected with two different power DC/DC converters of similar rated power. In this micro-grid architecture the AC/DC converter realizes a conversion stage at 790. V DC, whereas other two converters allow either the electric vehicle battery packs to be charged or an energy storage buffer to save electric energy and support the main grid during the fast charging operations. The laboratory tests described in this paper are mainly devoted to characterize the laboratory demonstrator, in different operative conditions, such as vehicle-to-grid (V2G), charging/discharging operations of different types of storage systems and fast charging operations of road electric vehicles. Then the study of the proposed power conversion architecture is focused on the evaluation of charging/discharging power, efficiency, energy flux management and its impact on the main grid. In addition proper control strategies are evaluated and implemented, allowing the proposed architecture to follow the required operations. The obtained experimental results demonstrate real advantages in terms of charging times and power requirements from the main grid, when adopting DC buffer architecture for fast charging operations. Finally, these results support the identification of a knowledge base, useful to evaluate energy management and control strategies to be adopted for DC charging stations and each one of their power converters in a smart grid scenario with distributed generation systems.