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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]

Electric Vehicles (EVs) have seen significant growth in sales recently and it is not clear how power systems will support the charging of a great number of vehicles. This paper proposes a methodology which allows the aggregated EV charging demand to be determined. The methodology applied to obtain the model is based on an agent-based approach to calculate the EV charging demand in a certain area. This model simulates each EV driver to consider its EV model characteristics, mobility needs, and charging processes required to reach its destination. This methodology also permits to consider social and economic variables. Furthermore, the model is stochastic, in order to consider the random pattern of some variables. The model is applied to Barcelona's (Spain) mobility pattern and uses the 37-node IEEE test feeder adapted to common distribution grid characteristics from Barcelona. The corresponding grid impact is analyzed in terms of voltage drop and four charging strategies are compared. The case study indicates that the variability in scenarios without control is relevant, but not in scenarios with control. Moreover, the voltages do not reach the minimum voltage allowed, but the MV/LV substations could exceed their capacities. Finally, it is determined that all EVs can charge during the valley without any negative effect on the distribution grid. In conclusion, it is determined that the methodology presented allows the EV charging demand to be calculated, considering different variables, to obtain better accuracy in the results. [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]

Ground thermal energy is a sustainable source that can substantially reduce our dependency on conventional fuels for heating and cooling of buildings. To exploit this source, foundation sub-structures with embedded heat exchanger pipes are employed. Diaphragm wall heat exchangers are one such form of ground heat exchangers, where part of the wall is exposed to the basement area of the building on one side, while the other side and the further depth of the wall face the surrounding ground. To assess the thermal performance of diaphragm wall heat exchangers, a model that takes the wall geometry and boundary conditions at the pipe, basement, and ground surfaces into account is required. This paper describes the development of such a model using a weighting factor approach, known as Dynamic Thermal Networks (DTN), that allows representation of the three-dimensional geometry, required boundary conditions, and heterogeneous material properties. The model is validated using data from an extended series of thermal response test measurements at two full-scale diaphragm wall heat exchanger installations in Barcelona, Spain. Validation studies are presented in terms of comparisons between the predicted and measured fluid temperatures and heat transfer rates. The model was found to predict the dynamics of thermal response over a range of operating conditions with good accuracy and using very modest computational resources. [ABSTRACT FROM AUTHOR]