Search Results (28)

Further advances in hardware and software features are needed to optimize battery and thermal management systems to allow for the execution of longer trips in electric vehicles. This paper assesses the economic and environmental impacts of the following features: eco-charging, eco-driving, smart fast charging, predictive thermal powertrain and cabin conditioning, and an advanced heat pump system. A Total Cost of Ownership (TCO) and externalities calculation is carried out on two passenger cars and one light commercial vehicle (LCV). The energy consumption data from the vehicles are based on experiments. The analysis shows more benefits for the LCV, while the smart fast-charging feature on the car shows a slight increase in TCO. However, negative results did not contribute significantly compared to the ability to install a smaller battery capacity for similar use. Full article

The current paper defines a framework for the introduction of automated frequency restoration reserve services, enabled by vehicle-to-grid technology, into the business model of an entity owning and operating a network of semi-public Electric Vehicle Supply Equipment. It assesses the profitability of this introduction by performing a case study based on the real-life electric vehicle charging data from the EVSE network located in a hospital parking lot. From the results of the study, it is clearly visible that the introduction of vehicle-to-grid-enabled automated frequency restoration reserve services has a significant positive incremental profitability; however, this is heavily dependent on the plug-in ratio of the charging network, determined by electric vehicle users’ behavior. Full article

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production. Full article

Even though eco-efficiency (EE) is already applied to various energy systems, so far, no study investigates in detail the hourly, marginal and seasonal impacts of a decentralized energy system. This study assesses the hourly EE of the Research Park Zellik (RPZ), located in the Brussels metropolitan area for 2022 composed of photovoltaic installations, wind turbines and batteries. A cradle-to-grave life cycle assessment (LCA) to identify the carbon footprint (CF) and a levelized cost of electricity (LCOE) calculation is conducted. An existing design optimization framework is applied to the RPZ. Consumption data are obtained from smart meters of five consumers at the RPZ on a one-hour time resolution for 2022 and upscaled based on the annual consumption of the RPZ. As the EE is presented as the sum of the CF and the LCOE, a lower EE corresponds to an economically and environmentally preferable energy system. In a comparative framework, the developed method is applied to two different case studies, namely, (i) to an energy system in Vega de Valcerce in Spain and (ii) to an energy system in Bèli Bartoka in Poland. The average EE of the RPZ energy system in 2022 is 0.15 per kWh, while the average EE of the Polish and Spanish energy systems are 1.48 and 0.36 per kWh, respectively. When analyzing four selected weeks, both the LCOE and CF of the RPZ energy system are driven by the consumption of the Belgian electricity grid mix. In contrast, due to the very low LCOE and CF of the renewable energy sources, in particular wind turbines, the RPZ energy system’s EE benefits and lies below the EE of the Belgium electricity grid mix. Full article

Increasing penetration of electric vehicles brings a set of challenges for the electricity system related to its energy, power and balance adequacy. Research related to this topic often requires estimates of charging demand in various forms to feed various models and simulations. This paper proposes a methodology to simulate charging demand for different driver types in a local energy system in the form of time series of charging sessions. The driver types are extracted from historical charging session data via data mining techniques and then characterized using a kernel density estimation process. The results show that the methodology is able to capture the stochastic nature of the drivers’ charging behavior in time, frequency and energy demand for different types of drivers, while respecting aggregated charging demand. This is essential when studying the energy balance of a local energy system and allows for calculating future demand scenarios by compiling driver population based on number of drivers per driver type. The methodology is then tested on a simulator to assess the benefits of smart charging. Full article

Power outages of the electricity grid threaten the proper operation of critical infrastructure such as hospitals. To cope with this problem, emergency diesel generators (DGs) are often used to guarantee continuous and resilient electricity supply, resulting in increased costs and greenhouse gas (GHG) emissions. Thus, this study aims to investigate the economic feasibility of both reducing and replacing emergency diesel generators with solar photovoltaic (PV) systems, battery energy storage systems (BESS) and demand-side management. A mixed-integer quadratically constrained program is used to find the optimal configuration in terms of capacities of new assets, as well as the optimal scheduling of both BESS and flexible loads, that minimises the levelised cost of energy (LCOE). The model is applied to an existing hospital and its surrounding community located in Gulu, Uganda. The results show that full replacement of the DGs will require an additional 500 kWp of PV and 1591 kWh of BESS. This new configuration will decrease LCOE by 26% compared to the actual situation, with a simple payback time of 6.2 years and a reduction of 74% in GHG emissions. Full article

The current paper defines a framework for the introduction of frequency containment reserve (FCR) services, enabled by vehicle-to-grid (V2G) technology, into the business model of an entity owning and operating electric vehicle (EV) charging infrastructure. Moreover, the defined framework can also be extrapolated, with minor adjustments, to the business models of different core participants of the EV charging business ecosystem. This study also investigates the financial factors impacted by this introduction, eventually evaluating its financial profitability under given assumptions and comparing it to the profitability of the traditional business model of an entity owning and operating a unidirectional EV charging infrastructure. The current research shows that offering additional V2G-enabled FCR services can be potentially more profitable than the existing unidirectional approach if the V2G technology reaches its maturity phase with mass market adoption and economies of scale. Full article

Electric vehicles (EV) are foreseen as one major technology toward decarbonizing the mobility sector. At the same time, Vehicle to Grid (V2G) technology opens a new market for EV owners. This article identifies the impacts of providing V2G services on the Total Cost of Ownership (TCO) of EVs. Thus, we studied EVs in private, semi-public and public charging cases, considering two different V2G revenue streams. The included V2G services were: (i) local load balancing to balance the peaks and valleys of the electricity demands of buildings and (ii) an imbalance service to enhance grid stability. In this paper, the impact of these two V2G services is quantified and considered in the TCO calculations. To the authors’ knowledge, no comparable study incorporating the same V2G services exists in the literature. The TCO is calculated with real-life data for four different EVs currently available in the market. As a result, the V2G TCO ranges from €33.167 to €61.436 over an average of nine years for the Flanders region (Belgium). Full article

The rapid growth of the electrical vehicle (EV) market over the last decade has rendered the existence and accuracy of the business models of the EV charging market a critical factor for a company’s success. To address this issue, this paper presents a quantification framework for the business models of the core participants of the EV charging market, defining the factors that directly influence their revenues and costs and providing two sets of earnings before interest and taxes (EBIT) formulas: explicit and implicit. The explicit formulas would be useful for business analytics of the current participants of the EV charging market, while the implicit could be applied by the new entrants, to make reliable predictions based on the benchmark data. These formulas include factors that have not been previously addressed in the literature such as different prices per type of charger, the annual consumed amount of energy per charger and their utilization rate among others. Finally, this research applies the defined framework on an EBIT scenario of an archetypical charge point operator, based on real-life data. Full article

The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG. Full article

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW. Full article

Today, there are many recent developments that focus on improving the electric vehicles and their components, particularly regarding advances in batteries, energy management systems, autonomous features and charging infrastructure. This plays an important role in developing next electric vehicle generations, and encourages more efficient and sustainable eco-system. This paper not only provides insights in the latest knowledge and developments of electric vehicles (EVs), but also the new promising and novel EV technologies based on scientific facts and figures—which could be from a technological point of view feasible by 2030. In this paper, potential design and modelling tools, such as digital twin with connected Internet-of-Things (IoT), are addressed. Furthermore, the potential technological challenges and research gaps in all EV aspects from hard-core battery material sciences, power electronics and powertrain engineering up to environmental assessments and market considerations are addressed. The paper is based on the knowledge of the 140+ FTE counting multidisciplinary research centre MOBI-VUB, that has a 40-year track record in the field of electric vehicles and e-mobility. Full article

Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced. Full article

The potential environmental impacts of producing and using future electric vehicles (EVs) are important given their expected role in mitigating global climate change and local air pollutants. Recently, studies have begun assessing the effect of potential future changes in EVs supply chains on overall environmental performance. This study contributes by integrating expected changes in future energy, iron, and steel production in the life cycle assessment (LCA) of EVs. In this light, the study examines the impacts of changes in these parameters on producing and charging future EVs. Future battery electric vehicles (BEV) could have a 36–53% lower global warming potential (GWP) compared to current BEV. The change in source of electricity generation accounts for 89% of GWP reductions over the BEV’s life cycle. Thus, it presents the highest GWP reduction potential of 35–48%. The use of hydrogen for direct reduction of iron in steelmaking (HDR-I) is expected to reduce vehicle production GWP by 17% compared to current technology. By accounting for 9% of the life cycle GWP reductions, HDR-I has the second-highest reduction potential (1.3–4.8%). The results also show that the potential for energy efficiency improvement measures for GWP reduction in vehicle and battery manufacture would be more beneficial when applied now than in the distant future (2050), when the CO₂ intensity of the EU electricity is expected to be lower. Interestingly, under the same conditions, the high share of renewable energy in vehicle supply chains contributed to a decrease in all air pollution-related impact categories, but an increase in toxicity-related categories, as well as land use and water consumption. Full article

The system under consideration in this paper consists of a photovoltaic (PV) array, described as having a 10 kWp capacity, battery storage, and connection to the grid via a university grid network. It is stated that the system meets a local load of 4–5 kVA. The system is in Ethiopia, and the authors give details of the location and solar resource to provide information to assess its performance. However, the performance assessment will be specific to the details of the installation and the operational rules, including the variable nature of the load profile, charging and discharging the battery storage, and importing from and exporting to the university grid. The nearby load is mostly supplied from PV and grid sources, and hence the battery installed is found to be idle, showing that the PV together with storage battery system was not utilized in an efficient and optimized way. This in turn resulted in inefficient utilization of sources, increased dependency of the load on the grid, and hence unnecessary operational expenses. Therefore, to alleviate these problems, this paper proposes a means for techno-economic optimization and performance analysis of an existing photovoltaic grid-connected system (PVGCS) by using collected data from a plant data logger for one year (2018) with a model-based Matlab/Simulink simulation and a hybrid optimization model for electric renewables (HOMER) software. According to the simulation result, the PVGCS with 5 kWp PV array optimized system was recommended, which provides a net present cost (NPC) of 5770 (€/kWh), and a cost of energy (COE) of 0.087 (€/kWh) compared to an existing 10 kWp PV system, which results in a NPC value of 6047 (€/kWh) and COE of 0.098 (€/kWh). Therefore, the resulting 5 kWp PV system connected with a storage battery was found to be more efficient and techno-economically viable as compared to the existing 10 kWp PVGCS plant. Full article