Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits

Performance of gradient-based optimizer on charging station placement problem

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer

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

A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations

We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs.Ministerio de Ciencia e Innovación TIN2017-89842PMinisterio de Ciencia e Innovación PID2019-110455GB-I0

Electric vehicles charging infrastructure demand and deployment : challenges and solutions

Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Design and development of adaptive EV charging management for urban traffic environments

Due to the world’s shortage of fossil fuels, increasing energy demand, oil prices, environmental concerns such as climate change and air pollution, seeking for alternative energy has emerged as a critical study area. Transportation systems is one of the main contributors to air pollution and consumers of energy. Electric Vehicles (EVs) is considered as a highly desirable solution for a new sustainable transportation for many powerful advantages, such as energy efficient, environmentally friendly and may benefit from increased renewable energy technologies in the future. Despite all the acknowledged advantages and recent developments in terms of reducing the environmental impact, noise reduction and energy efficiency, the electric mobility market is still below the expectations. Among the most challenges that limit the market penetration of EVs as well as achieving a sustainable mobility system are the efficient distribution of adequate Charging Stations (CSs) and also determining the best CSs for EVs in metropolitan environments. This thesis is concerned in determining the optimal placement of EVCSs and the efficient assignment of EVs to CSs. To accomplish this, we thoroughly examine the interactions between EVs, CSs, and Electrical Grids (EGs). First, a novel energy efficient scheme to find the optimal placement of EVCSs are presented, based on minimizing the energy consumption of EVs to reach CSs. We then propose a comprehensive approach to find the optimal assignment of EVs to CSs based on optimization of EV users’ QoE. Finally, we proposed a reinforcement learning-based assignment scheme for EVs to CSs in urban areas, aiming at minimizing the total cost of charging EVs and reduce the overload on EGs. By comparing the obtained results of the proposed approaches with different scenarios and algorithms, it was concluded that the presented approaches in this thesis are effective in solving the problems of EVCS placement and EVs assignment

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC