Combined solar charging stations and energy storage units allocation for electric vehicles by considering uncertainties

Abstract

Abstract-Plug-in electric vehicles (PEVs) are becoming a key feature of smart grids. PEVs will be embedded in the network as a mobile load-storage with probabilistic behavior. In order to manage PEVs as flexible loads, charging stations (CSs) have essential roles. In this paper, a new method for optimal sitting and sizing of solar CSs using energy storage (ES) options is presented. Also, behavior of PEVs in the presence of other loads, electricity price and solar power generation uncertainties are considered. The proposed optimization model maximizes the distribution company (DisCo) benefit by appropriate use of CSs, maximizes the benefit of CSs owners and minimizes the power loss, load demand and voltage sags during peak times considering different technical constraints. The optimization variables are the location and capacity of CSs (consists of solar units and energy storage systems). In this paper, charge-discharge actions of PEVs are regulated based on time-of-use demand response programs. In order to solve the optimization problem considering uncertainty of load growth, number of EVs, electricity price, initial state of charge in PEV batteries and solar power generation, genetic algorithm method using Monte-Carlo simulation is used. The simulation results show that the proposed method has several advantages for DisCo and owners of CSs.

Neural network based novel controller for hybrid energy storage system for electric vehicles

“…PI and ANN controller [21] This paper explored a optimization based new strategy for the benefit of distribution company name Discos with the aid of proper selection and use of charging station CSs that helps in reducing the losses, voltage dips and load demand during peak hours conditions. In problem formulation various parameters i.e.…”

System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

“…In addition to the above reviewed papers regarding the impact, cost, and profit of ECSs, papers investigated key technologies in various ECS systems, energy management sizing and control issues in practical applications are reviewed in the rest of this subsection. The capacity and configuration design of ECSs were explored in [92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111]. A Erlang-loss system and stochastic programming were used to model uncertainties including EV, PV-based power generation and electricity prices in [92], in which attribute sets and a clustering-based scenario generation method were used to generate the scenarios for the investigation.…”

“…In [99], the hybrid optimization of multiple energy renewables (HOMER) software was used to analyze CO 2 reduction and possible cost savings in an ECS design example in Bangladesh, in which PV and biogas were investigated. The Monte Carlo method and a genetic algorithm (GA) were used to find the best ECS designs in [100]. In [101], a second order conic programming problem was formulated and used to find a comprehensive optimization ECS model.…”