The large Electric Vehicle (EV) fleet penetrations can provoke several grid impact issues if no EV smart-charging is implemented. However, many EV smart-charging works assume an accurate prediction of input data, such as the EV driving patterns, which are highly uncertain. This paper addresses the impact and potential management of several uncertainties related to EV smart charging, such as photovoltaic (PV) generation, load demand, arrival state-of-charge (SOC), requested energy, and arrival and departure time of the EVs. The application of different levels of uncertainty budgets is proposed to account for the gradual impact of every uncertainty on smart charging performance. Moreover, potential uncertainty management is investigated with the use of robust optimization (RO) in predictive receding-horizon EV smart charging under the worst-case uncertainty level, and the ''price of robustness"is calculated. The results show that the EV driving uncertainties are more hazardous for the provided charging energy. In contrast, PV generation and load demand uncertainties have a significant impact mostly on the charging cost. Moreover, the price of robustness is very low for EV charging under every uncertainty case.

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This thesis aims to construct a systematic framework for integrating Electric Vehicles (EVs) into Low Voltage (LV) distribution grids. The ultimate goal is to develop a multi-functional, flexible and reliable smart charging (SC) algorithm enabling EV mass deployment in LV distribution grids. The framework for achieving the main research objective is segmented into several key parts:

- Conducting a thorough study on the EV mass deployment in distribution grids through grid load flow analysis.
- Performing a comparative investigation of representative heuristic EV charging tactics to establish a foundation for a smart charging algorithm.
- Developing a Power Transfer Distribution Factors (PTDF) based grid congestion prevention mechanism from the Distribution System Operator (DSO) perspective in anticipation of widespread EV connections.
- Designing, refining and validating a flexible, efficient and reliable hierarchical mixed integer programming (MIP) EV smart charging algorithm.
a. The developed algorithm is equipped with a passive stochasticity processing function and considers practical constraints in protocols such as IEC/ISO 15118 and IEC 61851-1. It is verified and assessed in a Power Hardware-In-the-Loop (PHIL) testbed.
b. Based on the experimental results, the algorithm's effectiveness is further enhanced in: charging current command levelling for a steadier charging process, upgrading grid balancing services, and acquiring a higher level of proximity to optimality. The stochasticity managing function is also upgraded for ad hoc admittance of (future) erratic charging events and self-correction of charging parameters.
c. The advanced EV smart charging algorithm is then assessed by comparing with uncontrolled and one heuristic charging method presented in part 2 above.@en

The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This article formulates a hierarchical mixed integer programming EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols, such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a power hardware-in-the-loop testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinized.

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Power control of flexible loads will play a significant role in energy transition. This work has developed a mixed-integer linear power control (MILP) model that manages electric vehicle (EV) chargers, heat pumps (HPs), and PV rooftops. The power control was tested with and without vehicle-to-grid (V2G) capabilities in different grid types, namely residential, commercial, and mixed grids. Moreover, the effect of different seasons and charger efficiencies was investigated. It was shown that grid characteristics such as EV parking times and building occupations can affect significantly the power control, e.g. the amount of imported and V2G power. Moreover, while V2G power is rarely used due to current V2G round-trip efficiency, future efficiency improvement can lead to a significant increase in V2G use. Finally, the seasonal effect had also a significant impact with Summer being characterized by higher exported and lower imported energy due to the high and prolonged PV power availability.

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Low Carbon Technologies (LCTs), such as Photovoltaics (PVs), Electric Vehicles (EVs), and Heat Pumps (HPs), are expected to cause a huge electric load in future distribution grids. This paper investigates the grid impact in terms of over-loading and nodal voltage deviations in different distribution grids due to increasing LCT penetrations. The major objectives are the identification of the most severe LCT, grid impact issue, seasonal effect, and vulnerable distributional area, considering the physical models of the LCTs. It is concluded that Winter is the most hazardous for the future grid impact, characterized by nearly 3 times higher over-loading and 2.5 times higher voltage deviations during high HP penetrations, while suburban areas are the most vulnerable. Moreover, while HPs seem to have, in general, a greater impact compared to EVs, EVs cause more prolonged violations. While this work follows a bottom-up approach, using detailed physical models, aggregated national data has also been acquired, which is often used by top-down approaches. Different grid impact issues have been compared for the two approaches in terms of magnitude and duration. While bottom-up approaches generate more pessimistic results regarding the magnitude of the violations, results about the duration of the violations can be contradictory.

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Smart-Charging of Electric Vehicles (EVs) is able to provide frequency regulation capacity services to the System Operator (SO) upon an automation generation control (AGC) signal. While the amount of available regulation capacity is of-fered in the Day-Ahead Market (DAM), there is high uncertainty on the actual amount of reserves that will be called in the Real-Time Market (RTM). This work focuses on aiding EV smart-charging to offer a consistent and reasonable amount of regulation capacity, taking into account the impact of potential future instantaneous called regulation reserves while also maintaining simplicity. The work also analyzes the results of different charger types with different characteristics and shows that they play an important role on the regulation provision. Finally, it has been shown that even though the regulation income is inevitably reduced (up to 66%), the Energy Management System (EMS) can still successfully charge the EV s and simultaneously provide regulation reserves with remuneration.@en

In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.

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This paper benchmarks the performance of three practical electric vehicle (EV) charging scheduling methods relative to uncontrolled charging (UNC) in low-voltage (LV) distribution grids. The charging methods compared are the voltage droop method (VDM), price-signal-based method (PSM) and average rate method (ARM). Trade-offs associated with the grid performance, charging demand fulfilment and economic benefits are explored for three different grid types and four increasing levels of EV penetration for summer and winter. This study was carried out using grid simulations of six existing Dutch distribution grids, and the EV charging demand was generated based on 1.5 M EV charging sessions; therefore, the findings of this research are relevant for actual case studies. The results suggest that the PSM can be a preferred strategy for achieving a charging cost reduction of 6–11% when the grid performance is not a bottleneck for the given EV penetration. However, it can lead to an increased peak loading of the grid under certain operational conditions, resulting in a charging energy deficiency ratio of 4–8%. The VDM should be preferred if user information on the parking time and energy demand is not consistently available, and if the mitigation of grid congestion is critical. However, both unfinished charging events and charging costs increase with the VDM. The ARM provides the best balance in the trade-offs associated with the mitigation of grid congestion and price reduction, as well as charging completion. This research provides a perception of how to select the most appropriate practical charging strategy based on the given system requirements. The outcome of this study can also serve as a benchmark for advanced smart charging algorithm evaluation in the future. @en

A simple and low-cost Power Hardware-in-the-Loop (PHIL) demonstrator is developed for the purpose of studying the impact of Electric Vehicle (EV) charging on low voltage distribution grids. An energy saving power circulating method with potential bi-directional function is proposed in this study as well. The distribution grid under test runs on a Digital Real Time Simulator (DRTS), and a controlled 3-phase voltage at one of the nodes is formed using a power amplifier. The practical setup consists of Electric Vehicle Supply Equipment (EVSE) and a system which emulates the charging behaviour of an EV, referred to as an EV emulator. These are integrated using a 15 kW back to back ac-dc converter based power router. Structure, performance and limitations of the test-bed components, communication protocols and signal processing are discussed.@en

Mass deployment of Electric Vehicles (EVs) can improve the loading characteristics of low voltage distribution grids with high Photovoltaic (PV) penetration. This impact is investigated in the paper from two point of views, namely, the EV charger type and the EV penetration level. Based on the measured usage data for home, public and semi-public EV chargers, it is highlighted that the ratio of the number of these charger types can influence the grid level impact of PV penetration. Using Monte-Carlo method with aggregated power balance model, it is suggested that the increase in percentage of public and semi-public chargers relative to home chargers can improve self-consumption of PV energy in the grid, thereby reducing the power mismatch due to excess local generation. A PowerFactory based simulation with real measurement based data on real German distribution grids reveals that the grids have no risk of congestion at all with 80% EV penetration, allowing for a possibility even higher EV penetration in the future. Furthermore, with the considered uncontrolled EV charging, it is observed that the grids experience reverse power flows due to excess PV generation. This excess PV energy reduces by about 5% with high EV penetration, indicating a future potential for targeted smart charging application for improving these benchmarked results.@en

This study aims to quantity the impact of uncontrolled charging of Electric Vehicles (EVs) on the low voltage distribution networks with increasing EV penetration levels. For this objective, key indicators are developed to show the magnitude, scale and duration of the impact on the distribution network. The disseminated results are based on the case study with actual data from the existing distribution networks. The findings of this paper can serve as a benchmark for determining the potential of smart EV charging algorithms and/or the extent of necessary infrastructural reinforcement that the grid operators must incorporate.@en

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead-acid (VRLA) battery and the LiFePO₄ (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge (SOC) and open circuit voltage (V_OC) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

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