Suitable law-based location selection of high-power electric vehicles charging stations on the TEN-T core network for sustainability: a case of Poland

Electric vehicles (EV) have become widespread especially in recent years. However, the infrastructure required for charging these vehicles is inadequate and the necessary investment strategies need to be determined for the effective establishment of this infrastructure. Electric vehicle charging stations are a fundamental component of sustainable transportation systems. However, in parallel with the increasing EV demand, determining appropriate investment strategies to support the installation of these stations is a critical need. This study aims to reveal optimal investment strategies for the efficient establishment of electric vehicle charging stations. This study aims to identify the optimal investment strategies for the effective establishment of EV charging stations. Hence, it addresses the growing demand for sustainable and environmentally friendly transportation solutions. With the increasing adoption of electric vehicles, the development of an efficient EV charging infrastructure becomes critical. Thus, it is intended to determine the most suitable strategies for building efficient charging station infrastructures. The motivation for this research arises from the necessity to strengthen the existing charging infrastructure in response to the rising number of electric vehicles and the need for sustainable transport solutions. Electric vehicle charging stations are vital components of sustainable mobility, making it essential to evaluate the investment strategies that will enable their successful establishment and operation. To achieve the study’s goal, a detailed analysis has been conducted using the decision-making trial and evaluation laboratory (DEMATEL) technique. The findings from this analysis emphasize that technological improvement is the most crucial factor in enhancing the performance of EV charging infrastructure projects. Technological advancements such as faster charging technologies, greater energy efficiency, and better user interfaces are paramount to ensuring the success of these projects. In addition to technological improvements, financial performance and legal effectiveness also play significant roles in the efficient establishment of EV charging stations. These factors directly impact the feasibility and long-term sustainability of the projects. On the other hand, customer expectations, although important, are found to have the least weight in this regard. In conclusion, this study underscores the importance of focusing on technological advancements, alongside financial and legal factors, to effectively drive the establishment of these stations.

A decision-centric approach for techno-economic optimization and environmental assessment of standalone and grid-integrated renewable-powered electric vehicle charging stations under multiple planning horizons

Planning of fast charging infrastructure for electric vehicles in a distribution system and prediction of dynamic price

Due to rapid development of the economy, resource scarcity and environmental contamination are becoming increasingly significant. Electric vehicles have become a main direction for positive development. Charging stations are an energy supplement infrastructure for electric vehicles. How to provide reasonable electric vehicle charging station locations and capacity plans for different regions is the theme of this article. Based on the idea of gradual cover and the location of the charging stations, we propose a distance satisfaction function to improve the model. As the supporting infrastructure for electric vehicles, the location of electric vehicle charging stations has highly important significance for the promotion of electric vehicles. To solve the problem of location selection of an electric vehicle charging station and fully cover the demand point, we used the set coverage model and distance satisfaction function to propose a set coverage model based on distance satisfaction.

The forecasts of the Ministry of Energy regarding the development of the charging infrastructure for electric vehicles in Poland envisage that, over the next years, ca. 6,000 new normal power charging points (under 22 kW) and ca. 400 high power charging points (over 22 kW) will be built. There are also other very optimistic projections indicating a sharp increase in the number of electric vehicle charging points in Poland. This paper presents the characteristics of electric vehicle charging points and stations, websites allowing for electric vehicle charging stations to be searched for in Poland, as well as the current situation and problems related to the charging stations for electric vehicles in Poland.

To improve the electric vehicle charging infrastructure and accelerate the development of electric vehicles, the optimization of electric vehicle charging stations’ location and size are studied in this paper. The annual comprehensive cost of society, including user cost and charging station cost, is regarded as the objective function. Weight coefficients are added to the function for increasing the proportion of user cost. An optimized mathematical model for electric vehicle charging stations based on users’ benefit is established. The improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is adopted to solve this mathematical mode, and result which contain the optimal location and size of electric vehicle charging stations is obtained. Finally, an actual area is taken as the case study to optimize the location and size of electric vehicle charging stations by solving the mathematical model proposed in this paper with the improved QPSO algorithm. Rationality and validity of the model are well improved by the scientific reasonable result.

The rapid expansion of electric vehicle (EV) charging infrastructure has introduced significant vulnerabilities to cyber‐physical threats, raising concerns about the resilience of both charging and smart power grid systems. This paper presents an innovative investigation into the resilience of EV charging infrastructure using a real‐time co‐simulation testbed, integrating both power network models and communication protocols such as IEC 61850. The study addresses gaps in existing research by implementing a realistic smart grid environment that incorporates EVs, charging stations and communication networks to simulate cyber‐physical interactions. Key cyber‐attacks, such as remote charging station status and configuration manipulations and their impact on it, are analysed in real‐time simulations. Results show that even a relatively small attack utilising an IEEE 9‐bus system with two EV charging stations can severely disrupt grid stability. The paper also explores various attacks targeting EV infrastructure, including charging stations, communication protocols, and management systems. The combined effects of cyber‐attacks on power consumption and current variation highlight the critical importance of ensuring that charging infrastructure can adapt to sudden changes in demand while maintaining operational integrity.

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors such as driver behavior, location of charging stations, electricity pricing etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. Several studies have modelled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This thesis aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using the NetLogo software tool is employed in this thesis to closely mimic the human aggregate behaviour and its influence on the load demand due to charging of EVs. EV charging stations where the EV charging takes place will play an important role in the energy management of smart cities. Private and commercial EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. Hence, after the successful modelling of EV charging behavior using agent based approaches, a hybrid optimization algorithm for energy storage management is proposed as an application. This algorithm shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of PV integrated charging stations with on-site ESS. The algorithm comprises three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data and optimization for minimizing the operating cost of an EV charging station integrated with PV and ESS. An extensive simulation study is carried out with private and commercial EV charging load model obtained from the agent based modeling approach, in the context of Singapore, to check the effectiveness of this algorithm. Furthermore, a detailed analysis of the subsidy and incentive to be given by the government agencies for a higher penetration of PV systems is also presented. This work would aid in planning of adoption of PV integrated EV charging stations with on-site ESS which would be expected to take place of traditional gas stations in future.

Chapter 13 - Sizing and feasibility analysis of a self-governing hybrid electric vehicle charging system

Review on techno-socio-economic studies of electric vehicles in electrical energy systems

Electric vehicle (EV) charging infrastructure must be effective and favorable to the environment as a result of the transition towards sustainable transportation. This paper examines the concept of a sustainable recharge infrastructure that utilizes solar and wind energy. With the growing prevalence of hybrid and electric vehicles, the need for dependable and quick-charging solutions has become essential. Traditional charging stations powered by the utility have a limited capacity and can burden the existing electrical infrastructure. This study proposes a sustainable approach that incorporates solar and wind energy to power EV charging stations in order to resolve these issues. Reduced reliance on fossil fuels can be achieved through the use of renewable energy sources, resulting in lower greenhouse gas emissions and a more sustainable transportation ecosystem. Additionally, the paper discusses the potential benefits, challenges, and considerations associated with the implementation of such a sustainable charging infrastructure. The findings demonstrate the importance of integrating renewable energy into EV charging systems, paving the way for a cleaner and sustainable transportation future. Keywords: electric vehicle charging infrastructure, sustainable transportation, renewable energy, solar energy, wind energy, hybrid vehicles, electric vehicles, fast-charging solutions

To reduce the negative effects caused by electric vehicles (EVs), this paper proposes a coordinated charging strategy for batteries in EV charging station. According to the charging characteristics of EV batteries, the strategy is established to minimize the sum of squares of the substation, which could consider the number of battery chargers, EVs' needs for batteries, power limit of EV charging and switching station and substation, node voltage and so on. By combining the method of power flow linearization and genetic algorithm, the optimal charging starting time of each battery can be obtained quickly, which could reduce the sum of squares and P-V difference of power grid. The correctness and efficiency of the proposed strategy are validated by a EV charging and switching station as a case.

Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real?world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, ie, data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (eg, hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, eg, when another charging station or a place of interest is created. On the basis of such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

The rising demand for electric vehicles (EVs), motivated by their environmental benefits, is generating an increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. Such investment decisions, which include EV charging station locations and capacities, and the timing of such investments require robust estimates of future travel demand and EV battery range constraints. This paper develops and implements a framework to establish an optimal schedule and locations for new charging stations and decommissioning gasoline refueling stations over a long-term planning horizon, considering the uncertainty in future travel demand forecasts and the driving range heterogeneity of EVs. A robust mathematical model is proposed to solve the problem by minimizing not only the worst-case total system travel cost but also the total penalty for unused capacities of charging stations. This study uses an adaptation of the cutting-plane method to solve the proposed model. Based on two key decision criteria (travelers’ cost and charging supply sufficiency), the results indicate that the robust scheme outperforms its deterministic counterpart.