ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work. In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach

Generación y almacenamiento distribuidos de energía eléctrica como herramientas de flexibilidad para el sistema energético de la provincia de Mendoza: análisis basado en el modelo LEAP

En este trabajo, se simula mediante del modelo LEAP (Long-range Energy Alternatives Planning), a nivel regional y a largo plazo, el rol del almacenamiento de electricidad como herramienta de flexibilidad tendiente a mejorar la paulatina integración de fuentes renovables de energía variables al sistema eléctrico de la provincia de Mendoza; entre otros, consideramos los sistemas de generación centralizados y distribuidos con tecnología fotovoltaica, teniendo en cuenta la variabilidad temporal de la fuente. Se incluye el almacenamiento de energía diario vinculado a los vehículos eléctricos (con baterías de ion-litio) en su concepción de inyección a la red (V2G). En los resultados alcanzados se observa que, con las tasas de incorporación, tanto de vehículos eléctricos, como de instalaciones fotovoltaicas distribuidas, no se aprecian variaciones considerables por su inserción al sistema eléctrico. Por el contrario, se mejoran los indicadores medioambientales. Estos hallazgos sugieren la necesidad de abordar un enfoque de optimización.In this paper, we simulate, through the LEAP model (Long-range Energy Alternatives Planning), the role of energy storage at a regional and long-term horizon, as a flexibility tool tending to improve gradual integration of variable renewable energy sources into the electric system of Mendoza province; among others, we study centralized and distributed photovoltaic power systems, considering temporal variability of the source. Daily energy storage related to electric vehicles (by means of lithium-ion batteries) is represented by vehicle to grid (V2G) technology. The results show that, inclusion rates of both, electric vehicles and distributed photovoltaic power systems, do not present significant variations because of their share in the electrical system. However, environmental effects are improved. These findings suggest a further optimization approach.Asociación Argentina de Energías Renovables y Medio Ambiente (ASADES

Heuristic Storage System Sizing for Optimal Operation of Electric Vehicles Powered by Photovoltaic Charging Station

This paper discusses the utilisation of PV systems for electric vehicles charging for transportation requirements of smart cities. The gap between PV power output and vehicles charging demand is highly variable. Therefore, there is a need for additional support from a public distribution grid or a storage device in order to handle the residual power. Long term measurement data retrieved from a charging station for 15 vehicles equipped with a PV system were used in the research. Low and high irradiation seasons influenced the PV output. The charging demand of electric vehicles varied over the course of a year and was correlated to weather conditions. Therefore, the sizing and performance of a supportive storage device should be evaluated in a statistical manner using long period observations

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Distribution Transformer Stress in Smart Grid with Coordinated Charging of Plug-In Electric Vehicles

Coordinated charging of Plug-In Electric Vehicles (PEVs) in residential distribution systems is a new concept currently being explored in the wake of smart grids. Utilities are exploring these options as there are concerns about potential stresses and network congestions that may occur with random and uncoordinated multiple domestic PEV charging activities. Such operations may lead to degraded power quality, poor voltage profiles, overloads in transformer and cables, increased power losses and overall a reduction in the reliability and economy of smart grids. Future smart grids communication network will play an important role in PEV operation because the battery chargers can be remotely coordinated by the utility and harnessed for storing surplus grid energy and reused to support the grid during peak times. Based on a recently proposed PEV charging algorithm, this paper focuses on the impact of coordinated charging on distribution transformer loading and performance. Simulation results are presented to explore the ability of the PEV coordination algorithm in reducing the stress on distribution transformers at different PEV penetration levels. The performance of various distribution transformers within the simulated smart grid is examined for a modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs