Vehicle Charging Station Research Articles

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Achieving green mobility: Multi-objective optimization for sustainable electric vehicle charging

This study optimizes and evaluates a Photovoltaic-Wind-Battery/Electric Vehicle Charging Station (PVWB/EVCS) system using four Multi-Objective Optimization (MOO) techniques: MOPSO, NSGAII, NSGAIII, and MOEA/D. The main goals are to minimize the Total Net Present Cost (TNPC) and Loss of Power Supply Probability (LPSP) of the system, which are crucial for sustainable electric vehicle charging. The study analyzes the economic, operational, and sustainability aspects of the optimized system and compares it with HOMER software. The results show that NSGA-II is the best MOO technique for this problem, as it has the best performance and robustness. The Discounted Cash Flow analysis confirms the economic feasibility and sustainability of the optimized system over its lifetime. The technical analysis demonstrates the system's ability to use renewable energy from solar and wind sources, along with efficient energy storage and distribution. The study also conducts a sensitivity analysis to investigate the effects of changes in load, irradiance, wind speed, and component costs on the system performance. The findings reveal the system's resilience and adaptability under different scenarios, thus enhancing its suitability for renewable energy generation and electric vehicle charging. The study showed that the optimized PVWB/EVCS system is a promising solution for reducing reliance on non-renewable sources and promoting a more eco-friendly sustainable future.

Reliability Improvement of a Hybrid Electric Vehicle Integrated Distribution System

The recent trend in hybrid electric vehicles (HEV) has increased the need for vehicle charging stations (VCS) in the distribution system. In this condition, the additional load in the system leads to an increase in power loss, reduction in voltage and reliability of the system. The drawbacks of introducing this additional load can be rectified by integrating distributed generation (DG) into the distribution system. In this paper, the ideal location for placing DG is identified through the voltage stability index. The power loss minimization objective function is formulated with all the required constraints to estimate the size of DG required for the distribution system. Moreover, loss of load probability is used as a reliability assessment technique, through which the system reliability is analyzed after assessing the impact of integrating VCS and DG. Simulations are carried out to compare the following cases: a system without VCS and DG, a system that has only VCS and a system that has both VCS and DG. The IEEE 12-bus and 33-bus test systems are considered. In the 12-bus system with both VCS and DG, the power loss is reduced by 56% when compared with the system with only VCS, while the net reliability is also improved. The reliability of the system is evaluated for a 24 h load variation. The proposed work provides an efficient tool to improve the reliability of the system with support from DG.

Renewable energy curtailment practices in Jordan and proposed solutions

• Jordan is a pioneer in renewable energy, However, 17% of Jordan's wind energy capacity is curtailed. • Energy Curtailment means despite the ability to generate energy, the government stop generation. • In the case of energy curtailment, the government has to pay for this energy. • Curtailment energy could be used in charging electric cars. Curtailed energy could be used to improve techno-economic aspects and minimize emissions. Jordan is considered a leading country in renewable energy in the Middle East, particularly in wind energy. Due to the low energy demand during peak power generation, 17% of overall wind energy capacity is curtailed in Jordan. In this study, several energy storage systems are discussed to better usage of the curtailed energy which will eventually decrease the energy cost and reduce the carbon emissions. The Li-ion battery was used as a case study to store the curtailed energy produced from wind turbines in Jordan, where its capacity was designed to handle the curtailment for a typical day. The stored energy is then used to charge a significant number of electric vehicles, representing 11.8% of Jordan's total number of electric vehicles. An economic study, showed that the battery energy storage station is economically feasible solution. This would generate profit instead of loss and it will secure work opportunities if vehicle-charging stations were offered as a public-private partnership (PPP) project.

Enhancing system reliability by optimally integrating PHEV charging station and renewable distributed generators: A Bi-Level programming approach

A hybrid bi-level programming approach is presented in this paper to enhance the system reliability by optimally integrating the Vehicle Charging Stations (VCS) of Plug-in Hybrid Electric Vehicle (PHEV) and the Renewable Distributed Generation (RDG) simultaneously. A wide spectrum of requirement exists among the customers to have a continuity of supply in the presence of the fluctuating nature of RDS. Thus, a non-linear objective function is formulated to minimize the Energy Not Supplied (ENS) to the customers based on the various contingency analysis. Two notable contributions distinguish this work with existing endeavours. Firstly, Simultaneous selection of optimal place for both RDG and charging station are recognized. Succeeded by the consideration of simultaneous integration of VCS and RDG, a Hybrid Nelder-Mead Cuckoo Search (HNM-CS) algorithm based method is put into operation to minimize the ENS, which seamlessly diminishes the power loss and enhances the voltage magnitude of the system. The distribution systems of standard IEEE 33-bus and real time TamilNadu (TN) 84 bus are considered with different RDGs such as photovoltaic and fuel cell systems. Further, the operational cost of PHEVs scheduling in the VCS is analysed for a 24-h scenario. From the results obtained, the proposed method provides maximum advantage to the vehicle holder by placing and utilizing more RDGs and meanwhile it satisfies their preferences also.

Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems

With the increase in the number of electric vehicles (EV) on the street in the last years, the drivers of EVs are suffering from the problem of guiding themselves toward the nearest charging stations for recharging their batteries or finding the shortest routes toward their destinations. Although, the electric vehicle planning problem (EPP) is designed to achieve several transactions such as battery energy restrictions and the challenge of finding the nearest charging stations to the position of the electric vehicle. In this work, a new distributed system for electric vehicle routing is based on a novel driving strategy using a distributed Ant system algorithm (AS). The distributed architecture minimizes the total travelling path for the EV to attain the destination by proposing a set of the nearest charging stations that can be visited for recharging during his travels. Simulation result proved that our prototype is able to prepare optimal solutions within a reasonable time and forwarding EVs toward the nearest charging stations during their trips.

Portfolio Optimization of Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Stations with Sustainability Perspective Based on Cumulative Prospect Theory and MOPSO

Recently, an increasing number of photovoltaic/battery energy storage/electric vehicle charging stations (PBES) have been established in many cities around the world. This paper proposes a PBES portfolio optimization model with a sustainability perspective. First, various decision-making criteria are identified from perspectives of economy, society, and environment. Secondly, the performance of alternatives with respect to each criterion is evaluated in the form of trapezoidal intuitionistic fuzzy numbers (TrIFN). Thirdly, the alternatives are ranked based on cumulative prospect theory. Then, a multi-objective optimization model is built and solved by multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal PBES portfolio. Finally, a case in South China is studied and a scenario analysis is conducted to verify the effectiveness of the proposed model.

Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm

In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.

A Robust and Adaptive Detection Scheme for Interharmonics in Active Distribution Network

In the active distribution networks (ADN), the interharmonic distortion level is aggravated due to the growing penetration of distributed generations and wide application of power-electronic loads. The interharmonics, even at a low amplitude level, can cause power-quality problems, deteriorate the measurement accuracies of the advanced metering infrastructures, and increase the possibilities of power system oscillations. In this paper, an interharmonic detection scheme is proposed to effectively extract the interharmonic components of the power system even with the extremely noisy signals, and then to estimate their frequencies and magnitudes. In the scheme, a totally data-dependent threshold is proposed to detect interharmonics adaptively. This adaptive threshold is determined iteratively based on the statistical character of the background noise bins. And then the time-varying frequencies and magnitudes of the detected interharmonics are estimated using the time-domain method. Case studies show that the adaptive threshold is robust under the conditions with high noisy signal, and the proposed scheme can estimate precisely the frequencies and magnitudes of the interharmonics detected for both the steady-state and dynamic interharmonic conditions. Furthermore, the proposed scheme is also verified using the field 10-kV current signal of one fuel company and 0.4-kV voltage signal of one electric vehicle's charging station.

Analyzing the diffusion of eco-friendly vans for urban freight distribution

PurposeThe purpose of this paper is to investigate the diffusion dynamics of electric and hybrid commercial vans and its enabling factors in the city logistics (CL) contexts. The case of parcel delivery in Torino, Italy, is considered. Attention is paid to the influence on the choice of low impact vehicles of not only public strategies but also operational aspects characterizing urban freight distribution systems.Design/methodology/approachA System Dynamics model based on the Bass diffusion theory computes the number of adopters of low-emission vehicles together with the quantity of vans required and the associated economic savings. The model includes variables about freight demand, delivery frequency, van carrying capacity, routes, stops, distances traveled, and vehicle charging stations. A sensitivity analysis has been completed to identify the main diffusion levers. The focus is on advertising and other drivers, such as public contributions, taxes traditional polluting vehicles are subjected to, as well as on routing optimization strategies.FindingsAdvertising programs, green image, and word-of-mouth drive market saturation, although in a long time period. In fact, low-impact vehicles do not offer any economic advantage over traditional ones requiring higher investment and operating costs. Public incentives to purchase both green vehicles and charging stations, together with carbon taxes and a congestion charge affecting polluting vehicles, are able to shorten the adoption time. In particular, public intervention reveals to be effective only when it unfolds through a number of measures that both facilitate the use of environmentally friendly vehicles and discourage the adoption of traditional commercial vans. Route optimization also hastens the complete market saturation.Research limitations/implicationsThis work fosters research about the mutual relationships between the diffusion of low-emission commercial vehicles and the operational and contextual CL factors. It provides a structured approach for investigating the feasibility of innovative good vehicles that might be part of assessments of CL measures and requirements. Finally, the model supports studies about the cooperation among stakeholders to identify effective commercial vehicle fleets.Practical implicationsThis study fosters collaboration among CL players by providing a roadmap to identify the key factors for the diffusion of environmentally friendly freight vehicles. It also enables freight carriers to assess the operational and economic feasibility of adopting low-impact vehicles. Finally, it might assist public authorities in capturing the effects of new urban transportation policies prior to their implementation.Originality/valueMost of the current CL literature defines policies and analyzes their effects. Also, there are several contributions on the diffusion of low emission cars. The present study is one of the first works on the diffusion of low-impact commercial vehicles in urban areas by considering the associated key operational factors. A further value is that the proposed model combines operational variables with economic and environmental issues.

A New Model for Optimal Deployment of Electric Vehicle Charging and Battery Swapping Stations

It is recognized widely that the electric vehicle is of vast potential as an environment-friendly traffic tool. And the construction of the charging and battery swap stations is of critical importance for popularizing it. However, the deployment of the charging station is far from mature even though the construction is being undertaken in some cities. In order to reduce the cost and land use, many researchers and practitioners advocate an oil-electricity union, i.e., we can build the charging and battery swap stations at the existing gas stations. This paper proposes a new location model based on the set cover model taking the existing traditional gas station network as the candidate sites to determine the distribution of the charging and battery swap stations. A numerical experiment is presented to illustrate and verify the proposed model. Our model can help governments to promote the development of the electric vehicle.

Analysis and Research on Fire Risk of Electric Vehicle Charging and Swapping Station

According to the characteristics of cable and decoration materials, the typical fire scenes were built using the method of FDS large eddy simulation (LES)). The smoke movement, temperature change, visibility and CO distribution of different fire scenes were studied. The fire spreading and risk when the fire occurs in electric vehicle charging and swapping station (EVCSS) were analyzed, which provides an important technological support for effectively preventing fire and guiding evacuation in EVCSS.

The Sensing Technologies in the Electric Vehicle Charging and Exchanging Station

The Electric Vehicle Charging and Exchanging (EVCE) station is the important infrastructure for the Electric Vehicle (EV) industry. Meanwhile, the sensing technologies can effectively obtain the key parameters from kinds of smart devices, which make it very suitable for intelligent management in EVCE station system. In this paper, according to the environment and supporting technologies for EVCE station, we introduce the sensing technologies in detail, including technologies about EV battery sensing, EV sensing, charging/exchanging station sensing and charging pile sensing, etc. Practice shows that the sensing technology can effectively improve EVCE station intelligence.