Optimal sizing and placement of Electrical Vehicle charging stations to serve Battery Electric Trucks

For Norway to reach the emission limits in the Paris Agreement, a substantial amount of CO2 must be reduced. Road traffic alone accounts for a high percentage of the total emissions during 2021. This thesis will focus on electrifying the transport sector and analyzing charging infrastructure for heavy-duty electric vehicles. New charging infrastructure for heavy-duty Electric Vehicles (EVs) provides issues regarding profitability due to the currently low adaption rates. However, heavy-duty EVs use the same charging sockets as EVs. As a result, EVs may finance the charging infrastructure needed to increase the adaption of heavy-duty EVs. Projections from Norwegian grid operators suggest that the total electricity surplus is diminishing during the next years and will be negative by 2027. This highlights the importance of modeling the power system in combination with finding optimal locations for charging stations. This study uses prescriptive analytics to suggest optimal locations for charging infrastructure to maximize returned profits to motivate station builders to implement more charging stations. A soft-linking will be done with PyPSA-eur to model the power system, where the new infrastructure is added as an additional load. Analyzing the results, it is possible to see that charging infrastructure has the potential to become profitable as the adaption rate for heavy-duty EVs rise. The collaboration between the models offers an open-source tool for scholars, researchers, and planners to study how new charging infrastructure affects key components in the Norwegian power system and could be useful in modeling state-of-the-art technologies

Validation of optimal electric vehicle charging station allotment on IEEE 15-bus system

Introduction. The diminishing conventional energy resources and their adverse environmental impacts compelled the researchers and industries to move towards the nonconventional energy resources. Consequently, a drastic paradigm shift is observed in the power and transportation sectors from the traditional fossil fuel based to the renewable energy-based technologies. Considering the proliferation of electric vehicles, the energy companies have been working continuously to extend electric vehicle charging facilities. Problem. Down the line, the inclusion of electric vehicle charging stations to the electric grid upsurges the complication as charging demands are random in nature all over the grid, and in turn, an unplanned electric vehicle charging station installation may cause for the system profile degradation. Purpose. To mitigate the problem, optimum allocation of the charging stations in existing power distribution system in a strategic manner is a matter of pronounced importance in maintaining the system stability and power quality. In this paper, optimum allocation of electric vehicle charging stations in IEEE 15-bus system is studied in order to minimize the highest over and under voltage deviations. Methodology. Primarily, voltage stability analysis is carried out for identification of the suitable system nodes for the integration. Voltage sensitivity indices of all the system nodes are calculated by introducing an incremental change in reactive power injection and noting down the corresponding change in node voltage for all nodes. Henceforth, dynamic load-flow analysis is performed using a fast and efficient power flow analysis technique while using particle swarm optimization method in finding the optimal locations. Results. The results obtained by the application of the mentioned techniques on IEEE 15-bus system not only give the optimum feasible locations of the electric vehicle charging stations, but also provide the maximum number of such charging stations of stipulated sizes which can be incorporated while maintaining the voltage profile. Originality. The originality of the proposed work is the development of the objective function; voltage stability analysis; power flow analysis and optimization algorithms. Practical value. The proposed work demonstrates the detailed procedure of optimum electric vehicle charging station allotment. The experimental results can be used for the subsequent execution in real field.Вступ. Зменшення традиційних енергетичних ресурсів та їх несприятливий вплив на навколишнє середовище змусили дослідників і галузі промисловості перейти до нетрадиційних енергетичних ресурсів. Отже, в енергетичному та транспортному секторах спостерігається кардинальна зміна парадигми від традиційного викопного палива до технологій, що базуються на відновлюваних джерелах енергії. Беручи до уваги розповсюдження електромобілів, енергетичні компанії постійно працюють над розширенням потужностей для зарядки електромобілів. Проблема. Включення зарядних станцій для електромобілів до електричної мережі викликає ускладнення, оскільки вимоги до зарядки мають випадковий характер по всій електромережі, і, в свою чергу, незапланована установка зарядної станції для електромобілів може призвести до погіршення профілю системи. Мета. Щоб полегшити проблему, оптимальне розміщення зарядних станцій в існуючій системі розподілу електроенергії стратегічним чином є питанням надзвичайно важливого значення для підтримки стабільності системи та якості електроенергії. У цій роботі вивчається оптимальне розміщення зарядних станцій для електричних транспортних засобів в 15-шинній системі IEEE з метою мінімізації найвищих відхилень напруги вгору та донизу. Методологія. В першу чергу, проводиться аналіз стабільності напруги для ідентифікації відповідних вузлів системи для інтеграції. Показники чутливості до напруги всіх вузлів системи обчислюються шляхом введення поступової зміни подачі реактивної потужності та відмітки відповідної зміни вузлової напруги для всіх вузлів. Надалі динамічний аналіз потоку навантаження виконується за допомогою швидкого та ефективного методу аналізу потоку потужності, використовуючи метод оптимізації рою частинок для пошуку оптимальних місць розташування. Результати. Результати, отримані при застосуванні зазначених методів на 15-шинній системі IEEE, не тільки дають оптимально можливе розташування зарядних станцій електромобілів, але також забезпечують максимальну кількість таких зарядних станцій встановлених розмірів, які можна включити, зберігаючи профіль напруги. Оригінальність. Оригінальність запропонованої роботи полягає у розвитку цільової функції; у аналізі стабільності напруги; у алгоритмах аналізу та оптимізації потоку потужності. Практичне значення. Запропонована робота демонструє детальну процедуру оптимального розподілу станцій зарядки електромобілів. Результати експериментів можуть бути використані для подальшої реалізації в реальних умовах

Driver-aware charging infrastructure design

Public charging infrastructure plays a crucial role in the context of electrifying the private mobility sector in particular for urban regions. Against this background, we develop a new mathematical model for the optimal placement of public charging stations for electric vehicles in cities. While existing approaches strongly aggregate traffic information or are only applicable to small instances, we formulate the problem as a specific combinatorial optimization problem that incorporates individual demand and temporal interactions of drivers, exact positioning of charging stations, as well as various charging speeds, and realistic charging curves. We show that the problem can be naturally cast as an integer program that, together with different reformulation techniques, can be efficiently solved for large instances. More specifically, we show that our approach can compute optimal placements of charging stations for instances based on traffic data for cities with up to $600\,000$ inhabitants and future electrification rates of up to $15\%$

Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology

Stochastic Programming Models For Electric Vehicles’ Operation: Network Design And Routing Strategies

Logistic and transportation (L&T) activities become a significant contributor to social and economic advances throughout the modern world Road L&T activities are responsible for a large percentage of CO2 emissions, with more than 24% of the total emission, which mostly caused by fossil fuel vehicles. Researchers, governments, and automotive companies put extensive effort to incorporate new solutions and innovations into the L&T system. As a result, Electric Vehicles (EVs) are introduced and universally accepted as one of the solutions to environmental issues. Subsequently, L&T companies are encouraged to adopt fleets of EVs. Integrating the EVs into the logistic and transportation systems introduces new challenges from strategic, planning, and operational perspectives. At the strategical level, one of the main challenges to be addressed to expand the EV charging infrastructures is the location of charging stations. Due to the longer charging time in EVs compared to the conventional vehicles, the parking locations can be considered as the candidate locations for installing charging stations. Another essential factor that should be considered in designing the Electric Vehicle Charging Station (EVCS) network is the size or capacity of charging stations. EV drivers\u27 arrival times in a community vary depending on various factors such as the purpose of the trip, time of the day, and day of the week. So, the capacity of stations and the number of chargers significantly affect the accessibility and utilization of charging stations. Also, the EVCSs can be equipped by distinct types of chargers, which are different in terms of installation cost, charging time, and charging price. City planners and EVCS owners can make low-risk and high-utilization investment decisions by considering EV users charging pattern and their willingness to pay for different charger types. At the operational level, managing a fleet of electric vehicles can offer several incentives to the L&T companies. EVs can be equipped with autonomous driving technologies to facilitate online decision making, on-board computation, and connectivity. Energy-efficient routing decisions for a fleet of autonomous electric vehicles (AEV) can significantly improve the asset utilization and vehicles’ battery life. However, employing AEVs also comes with new challenges. Two of the main operational challenges for AEVs in transport applications is their limited range and the availability of charging stations. Effective routing strategies for an AEV fleet require solving the vehicle routing problem (VRP) while considering additional constraints related to the limited range and number of charging stations. In this dissertation, we develop models and algorithms to address the challenges in integrating the EVs into the logistic and transportation systems

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Optimal location of electric vehicle charging station and its impact on distribution network: A review

At present, the limited existence of fossil fuels and the environmental issues over greenhouse gas emissions have been directly affected to the transition from conventional vehicles to electric vehicles (EVs). In fact, the electrification of transportation system and the growing demand of EVs have prompted recent researchers to investigate the optimal location of electric vehicle charging stations (EVCSs). However, there are numerous challenges would face when implementing EVs at large scale. For instance, underdeveloped EVCSs infrastructure, optimal EVCS locations, and charge scheduling in EVCSs. In addition, the most fundamental EV questions, such as EV cost and range, could be partly answered only by a well-developed EVCS infrastructure. According to the literature, the researchers have been followed different types of approaches, objective functions, constraints for problem formulation. Moreover, according to the approaches, objective functions, constraints, EV load modeling, uncertainty, vehicle to grid strategy, integration of distributed generation, charging types, optimization techniques, and sensitivity analysis are reviewed for the recent research articles. Furthermore, optimization techniques for optimal solution are also reviewed in this article. In addition, the EV load impact on the distribution network, environmental impacts and economic impact are discussed