Search

In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s. [ABSTRACT FROM AUTHOR]

Fast charging is an essential stakeholder concern for achieving a deeper penetration of Electric Vehicles (EVs), as optimizing the charging times of conventional vehicles is as yet a bottleneck to be solved. An important drawback of EV's fast charging lies in the degradation suffered by the Li-ion Batteries (LIBs) at high charging currents. A deep understanding of the how these fast-charging activities affect the LIBs' degradation is necessary in order to design appropriate fast charging stations and EV powertrains for different scenarios and contexts. In this regard, the present paper analyzes the effect of fast charging on Libs' degradation under operation profiles from real driving cycles. Specifically, Battery Electric Buses (BEBs) driving profiles from three demos in European Cities (Gothenburg, Osnabrück and Barcelona) have been used in this analysis. In order to deduce the best practices for the design of the charging stations, different sizes for the chargers have been simulated, focusing on the analysis of the LIB degradation under each situation. Besides, for the design of the EV powertrain, different LIB sizes and LIB chemistries (Lithium Nickel Manganese Cobalt-NMC, Lithium Iron Phosphate-LFP, and Lithium Titanate Oxide-LTO) have also been proposed and compared in terms of LIB degradation. The results demonstrated that LTO batteries exhibited the lowest degradation, with capacity fade values under 1.5%/year in the nominal scenario (nominal charger and LIB sizes). As long as a full charging is ensured, reducing the fast charger size has been found to be a cost-effective measure, as the LTO degradation can be reduced at least to 1.21%/year. In addition, increasing the battery (BT) size has also been found to be a cost-effective approach for LTO batteries. In this case, it was found that for a 66% increase in capacity, the degradation can be reduced at least to 0.74%/year (more than 50% reduction). The obtained conclusions are seen as useful for the design of charging stations and EV's BT systems that undergo fast charging. [ABSTRACT FROM AUTHOR]

Solar-powered air conditioners offer a high potential for energy-efficient cooling with a high economic feasibility. They can significantly reduce the energy consumption in the building sector, which is essential to meet the greater ambition of reducing greenhouse gas emissions by 80% in the EU by 2050. This paper presents a computational model development capable of simulating the behaviour of a photovoltaic-assisted heat pump in different locations and working conditions. In addition, this model has been used to optimise a solar on-grid air conditioning system. The generated model has been validated with experimental data obtained in a real facility for a whole summer of operation (more than 100 tested days) in a Mediterranean climate (Alicante, Spain). According to the simulation results, the average Energy Efficiency Ratio (EER) of the system is 16.0, 10.7 and 7.8 in Barcelona, Madrid and Seville, respectively. The optimisation analysis has proven that the severity of the climatic region increases the costs as well as the optimum PV power to drive the AC unit. The obtained values for the the PV power and the annualised cost are 400 W and 506.2 € for Barcelona, 900 W and 536.7 € for Madrid, and 1300 W and 564.7 € for Seville. The annualised cost and the CO 2 emission levels are higher for the conventional system (no PV panels) than for the solar on-grid system, regardless of the installed PV power. This difference can be up to 66.64 € (10.55%) and 112.94 kg CO 2 (64.83%) per summer season in the case of Seville. [ABSTRACT FROM AUTHOR]

The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations. [ABSTRACT FROM AUTHOR]