Impact of electric vehicle charging – An agent‐based approach

Abstract

Several European countries have launched programs to increase the market penetration of battery electric vehicles (BEV). Similarly, politicians in Switzerland have targeted a 15% BEV share of new car registrations by 2022. As each electric car increases the power demand, new challenges are posed to the operation of existing distribution grid infrastructure. Here, a new bottom-up physical approach is presented that couples agent-based traffic simulations through an unsteady vehicle power consumption model with distribution grid power flow simulations. The impacts on hourly powerline loads from charging a car fleet with an 8.5% BEV share are quantified in the real distribution grid for the canton of Zurich. The grid is composed of 12,000 buses and 9,800 powerlines, providing power to 398,000 individual customers. Results indicate that the risk of overloaded powerlines is highest in low-level distribution grids. In our most critical future scenario, with simultaneous 8.5% BEV charging at 8 pm with 11 kW, peak line loads reach up to 132% of rated capacity. Hence, in a potential energy transition towards a decarbonised future, each individual distribution grid could face critical loads at specific temporal and spatial bottlenecks. Thus, grids should be assessed individually to limit uncertainties and risks of critical power system situations.

Charging infrastructure facilitate a large-scale Introduction of electric vehicle in urban areas using hybrid technique: A RBFNN-SPOA approach

“…Also, it considers three types of agents, like electric vehicle drivers, non-electric vehicle car owners, and CPO are all in the CS environment. The interaction among these agents is evaluated by three processes, such as charging process [44][45][46] that means the EV drivers communicate with presented CS as well as other EV drivers, procedure of buying a vehicle for non-electric vehicle car owners, installment process of new CS through the CPO. At first, charging behaviour of agent is modelled by RBFNN based on the large dataset on actual charging patterns.…”

Preventive maintenance decision model of electric vehicle charging pile based on mutation operator and life cycle optimization

“…Relevant supporting charging facilities are also becoming more and more important. It is necessary to build convenient charging infrastructure to improve the user charging experience 1,2 . As the infrastructure for national development, the safety, reliability, and compatibility of electric vehicle charging facilities have been highly valued by the regulatory authorities, automobile enterprises, transportation departments, and other relevant units.…”

“…It is necessary to build convenient charging infrastructure to improve the user charging experience. 1,2 As the infrastructure for national development, the safety, reliability, and compatibility of electric vehicle charging facilities have been highly valued by the regulatory authorities, automobile enterprises, transportation departments, and other relevant units. At present, the planning, construction and operation, and maintenance of electric vehicle charging facilities still face many problems, for example, the operation and maintenance level of charging stations is relatively backward, the state assessment technology of charging piles is lagging behind, the failure rate of charging piles is high, there are many unqualified items in routine inspection, and some charging piles have safety hazards, and so on.…”

Research on distribution network operation status evaluation technology based on new sensing measurement method

“…However, once the assessment accuracy decreases, the network staff may not be able to adjust the operating state of the distribution network, resulting in the existence of distribution network faults. Therefore, it is imperative to scientifically and reasonably evaluate the operation status of the distribution network by using accurate measurement data [3,4] .…”