Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation

Ramadhani, Umar Hanif; Fachrizal, Reza; Shepero, Mahmoud; Munkhammar, Joakim; Widén, Joakim

doi:10.1016/j.scs.2021.103043

Cited by 49 publications

(15 citation statements)

References 40 publications

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“…The majority of the studies discussed above used deterministic approaches to characterise the impact of EVs on the power grid. The impact of EVs on the LV grid has been examined with the deterministic approach, and a limited number of studies have used a probabilistic approach for EV impact assessment on the grid [14][15][16][17].…”

Section: Related Studiesmentioning

confidence: 99%

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Impact of electric vehicles on low‐voltage residential distribution networks: A probabilistic analysis

Hungbo,

Gu,

Meegahapola

et al. 2023

IET Smart Grid

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The past two decades have seen a rapid increase in electric vehicles (EVs) for several reasons, such as policy directives to reduce carbon emissions in the transport sector and technology advancements in the EV industry. However, this has increased the load demand on the power grid, especially in the low‐voltage (LV) network, as most EVs are charged at EV owner premises. This paper investigates the impact of EVs on the LV residential distribution network using a probabilistic modelling framework. Probability distribution functions for EV charging power are derived using the United Kingdom (UK) EV dataset. The study has investigated multiple EV penetration levels, different probability distribution functions for EV charging representation, vehicle‐to‐grid (V2G), solar photovoltaic (PV) generation, and the volt‐var capability of the solar‐PV inverter. The results have shown that as EV penetration increases in the distribution network, there is a significant increase in transformer loading and a decrease in the steady‐state voltage levels. V2G has positively impacted the distribution network. A case study carried out on a real LV feeder with solar‐PV generation has shown how PV generation and volt‐var functionality of the PV inverter help reduce the impact of EV charging and V2G.

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Section: Related Studiesmentioning

confidence: 99%

“…A probabilistic load-flow technique was used in Ref. [14] to determine the impact of smart charging in an unbalanced LV distribution network. This study has demonstrated the benefits of smart charging and has also shown that concentrated allocation could adversely affect the LV network.…”

Section: Related Studiesmentioning

confidence: 99%

Impact of electric vehicles on low‐voltage residential distribution networks: A probabilistic analysis

Hungbo,

Gu,

Meegahapola

et al. 2023

IET Smart Grid

View full text Add to dashboard Cite

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“…The integration of EV charging with RES considering a variegated population of EV model and the capability of bidirectional power flow among vehicles has not been investigated in this reference. • In (Fachrizal and Munkhammar, 2020;Ramadhani et al, 2021), the authors investigate the probabilistic impact of EV SC, including the temporal and spatial variability of EV charging demand, household load, and PV system generation. The conclusions show that SC results in improved distribution system performance.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

et al. 2021

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The growing penetration of distributed renewable energy sources (RES) together with the increasing number of new electric vehicle (EV) model registrations is playing a significant role in zero-carbon energy communities’ development. However, the ever-larger share of intermittent renewable power plants, combined with the high and uncontrolled aggregate EV charging demand, requires an evolution toward new planning and management paradigms of energy districts. Thus, in this context, this paper proposes novel smart charging (SC) techniques that aim to integrate as much as possible RES generation and EV charging demand at the local level, synergically acting on power flows and avoiding detrimental effects on the electrical power system. To make this possible, a centralized charging management system (CMS) capable of individually modulating each charging power of plugged EVs is presented in this paper. The CMS aims to maximize the charging self-consumption from local RES, flattening the peak power required to the external grid. Moreover, the CMS guarantees an overall good state of charge (SOC) at departure time for all the vehicles without requiring additional energy from the grid even under low RES power availability conditions. Two methods that differ as a function of the EV power flow direction are proposed. The first SC only involves unidirectional power flow, while the second one also considers bidirectional power flow among vehicles, operating in vehicle-to-vehicle (V2V) mode. Finally, simulations, which are presented considering an actual case study, validate the SC effects on a reference scenario consisting of an industrial area having a photovoltaic (PV) plant, non-modulable electrical loads, and EV charging stations (CS). Results are collected and performance improvements by operating the different SC methods are compared and described in detail in this paper.

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“…The identified problems were mainly due to excess electricity in local balancing areas [2]. The excess of electric energy mainly causes a local voltage increase in the network above the normative values U n ± 10% [5,6]. The voltage increase is caused by the positive voltage drop on the impedance of the distribution network caused by the current flow from RES [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The excess of electric energy mainly causes a local voltage increase in the network above the normative values U n ± 10% [5,6]. The voltage increase is caused by the positive voltage drop on the impedance of the distribution network caused by the current flow from RES [5][6][7]. The problem of increased voltage in the low voltage network can be solved by rebuilding the network using larger diameters of cables with lower impedance (lower voltage drops, but at the cost of large financial outlays) [3].…”

Section: Introductionmentioning

confidence: 99%

Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid

2021

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Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.

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Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation

Cited by 49 publications

References 40 publications

Impact of electric vehicles on low‐voltage residential distribution networks: A probabilistic analysis

Impact of electric vehicles on low‐voltage residential distribution networks: A probabilistic analysis

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid

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