Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

Abstract

To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%.

A critical review on contemporary power electronics interface topologies to vehicle‐to‐grid technology: Prospects, challenges, and directions

“…Technically speaking, the V2G concept treats EVs as storage devices by allowing electricity to flow in both directions as per a predefined timetable to carry out supportive works and provide ancillary services to the power grid [10,11]. In essence, V2G signifies that personal EVs have the potential to transform into mobile, self-controlled resources that can regulate power flow [12]. When owners require them, they function as vehicles, but when it is rush hour, they switch to energy sources and recharge in the evening or during off-peak times.…”

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

“…J 2 = P pv,loss 2 (19) During optimization, a schedule for the next day is generated using a perfect prediction of the consumption profile and PV production.…”

“…In some studies, only smart unidirectional charging was examined [13]; in others, the bidirectional charging of EVs [14][15][16][17][18][19]. Some focused on a building energy system ( [18]), while others examined the impact on local grids (commercial, industrial, and parking lots) [20,21].…”

“…Minhas et al [19] presented a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a model predictive intelligent home energy management system comprising EVs and PV. In their study, the authors found that electricity energy costs from the grid supply could be reduced by 13%.…”