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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle's speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle's speed changes, uses the preceding vehicle's upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time. [ABSTRACT FROM AUTHOR]

The current energy transition is characterized by a high level of consumer and prosumer involvement. Energy communities (ECs) are instruments that fit into this trend, as they organize the collective and mainly citizen-driven exchange of clean energy. Most stakeholder engagement research for ECs focuses on one aspect such as awareness raising or deployment acceptance. Hitherto, no specific research has been conducted on a participatory approach that can be applied throughout all the phases of an EC setup and for different purposes. In our study, we determine how the Multi-Actor Multi-Criteria analysis (MAMCA) methodology can be used as an engagement tool for this purpose. By linking potential engagement goals and their connected tools and techniques to the corresponding MAMCA step, we have created a multi-layered practical framework that can be used by all types of EC initiators for stakeholder engagement throughout the setup of a new EC. As this practical tool stimulates solutions that cater better to stakeholder needs, it can contribute to smoother deployment and an associated increase in ECs in the general system. A theoretical evaluation and a performed case study demonstrate the utility of the methodology that is developed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth comparison of the benefits and limitations of using a low-voltage DC (LVDC) microgrid versus an AC microgrid with regard to the integration of low-carbon technologies. To this end, a novel approach for charging electric vehicles (EVs) on low-voltage distribution networks by utilizing an LVDC backbone is discussed. The global aim of the conducted study is to investigate the overall energy losses as well as voltage stability problems on DC and AC microgrids. Both architectures are assessed and compared to each other by performing a power flow analysis. Along this line, an actual low-voltage distribution network with various penetration levels of EVs, combined with photovoltaic (PV) systems and battery energy storage systems is considered. Obtained results indicate significant power quality improvements in voltage imbalances and conversion losses thanks to the proposed backbone. Moreover, the study concludes with a discussion of the impact level of EVs and PVs penetration degrees on energy efficiency, besides charging power levels' impact on local self-consumption reduction of the studied system. The outcomes of the study can provide extensive insights for hybrid microgrid and EV charging infrastructure designers in a holistic manner in all aspects. [ABSTRACT FROM AUTHOR]

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle's energy consumption is notably reduced. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells' internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively. [ABSTRACT FROM AUTHOR]

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 e33.167 to e61.436 over an average of nine years for the Flanders region (Belgium). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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-meansquare 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. [ABSTRACT FROM AUTHOR]

This paper presents an integrated model for energy consumption and range estimation, capable of energy-efficient routing. This integrated model predicts the energy consumption on all road segments in the road network and applies shortest path algorithms to calculate energy-efficient routes. A temperature-dependent model of the battery internal resistance based on real-world data translates the energy consumption prediction into driving range. The graph representation of the road network is transformed from a node-based graph to an edge-based graph to allow cost allocation of one edge based on the characteristics of the preceding edge. The integrated model is used to perform an analysis of energy-efficient routes for a real-life scenario. The analysis showed that the energy-optimal route is different from the time- and distance-optimal route with energy-efficiency gains up to 37% for the chosen scenario. The energy-efficient route tends to lean toward a distance-optimal route in the case of low auxiliary consumption and to lean toward a time-optimal route in the case of high auxiliary consumption. The energy-efficient route generation is tested in real life for one case of the chosen scenario. The measured results showed a good match with the predicted values for the energy consumption with a 9% mean relative error and preserved the ranking of all routes in terms of the travel distance, travel time, and energy consumption. The proposed integrated model is a functional model for energy consumption in real-life that differentiates many energy consumption influencing factors and produces energy-efficient routes with good accuracy. [ABSTRACT FROM AUTHOR]