Integrating Electric Vehicles in Microgrids: Overview on Hosting Capacity and New Controls

Lamedica, R.; Geri, A.; Gatta, Fabio Massimo; Sangiovanni, S.; Maccioni, M.; Ruvio, Alessandro

doi:10.1109/tia.2019.2933800

Cited by 54 publications

(19 citation statements)

References 41 publications

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“…When connected to the main grid, it is necessary to check the load imbalance, harmonic disturbances, and the potential of possible renewable energy sources for the decision of hosting capacity. Lamedica et al [9] overviews the methods regarding hosting capacity estimation and EV charging control schemes. Existing control schemes usually take as input status of charge (SoC) level, desired end time, charging cost, network congestion, local voltage, and distributed generation, be it local or global coordination.…”

Section: Research Methods 21 Background and Related Workmentioning

confidence: 99%

“…The unavoidable intermittency of renewable energy should be overcome so as to enhance the energyindependency of a target microgrid [8]. It is clear that the massive penetration of EVs can significantly reduce the creation of greenhouse gas, as the transport is the most responsible source of CO2 emissions [9]. Hence, microgrids are required to try to accommodate as many EVs as possible, making their charging more convenient and energy-efficient.…”

Section: Introductionmentioning

confidence: 99%

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Renewable energy allocation based on maximum flow modelling within a microgrid

Lee

Park

2022

IJECE

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This paper designs an energy allocation scheme based on maximum flow modeling for a microgrid containing renewable energy generators and consumer facilities. Basically, the flow graph consists of a set of nodes representing consumers or generators as well as a set of weighted links representing the amount of energy generation, consumer-side demand, and transmission cable capacity. The main idea lies in that a special node is added to account for the interaction with the main grid and that two-pass allocation is executed. In the first pass, the maximum flow solver decides the amount of the insufficiency and thus how much to purchase from the main grid. The second pass runs the flow solver again to fill the energy lack and calculates the surplus of renewable energy generation. The performance measurement result obtained from a prototype implementation shows that the generated energy is stably distributed over multiple consumers until the energy generation reaches the maximum link capacity.

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Section: Research Methods 21 Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Renewable energy allocation based on maximum flow modelling within a microgrid

Lee

Park

2022

IJECE

View full text Add to dashboard Cite

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“…2) BACKWARD SWEEP CALCULATION Starting from the terminal bus, update the current using Kirchhoff's current law to calculate the branch current (12).…”

Section: ) Nodal Current Calculationmentioning

confidence: 99%

Evaluation of Electric Vehicles Hosting Capacity Based on Interval Undervoltage Probability in a Distribution Network

Kim

Moon

2021

IEEE Access

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The rapidly growing market for electric vehicles (EVs) and chargers has a considerable influence on the operation of the distribution network. Accurate evaluation of the number of EVs penetrating the distribution network, that is, the hosting capacity (HC) of EVs, is beneficial to distribution network planning and efficient operation. Further, EVs have uncertainties in terms of arrival time, departure time, battery state of charge (SOC), and choice of charging station, which collectively affect the system difficult, but important, step of modeling EV uncertainties on system performance. This paper presents a methodology to model how many EVs can penetrate a low voltage distribution network, especially microgrid. First, EV load modeling incorporating uncertainties is performed by applying interval and affine arithmetic. Second, to analyze the effects of voltage, the same arithmetic methods are extended to perform power flow calculation. Thirdly, interval undervoltage probability (IUP) is introduced to evaluate the HC of the EVs. Subsequently, a voltage violation index (VVI) is introduced, and an EV HC evaluation method is proposed, assisting in the flexible operation of the network based on VVI. The utility of the proposed method is demonstrated by modeling the network of Seoul National University in South Korea, which is subsequently compared with the conventional approach of determining EV HC. The model incorporated Seoul National University's practical cable data, load data, and vehicle access data, along with the number of EVs penetrating the target network.INDEX TERMS Affine arithmetic, distribution network, electric vehicle, electric vehicle hosting capacity, interval arithmetic, uncertainties, under voltage probability.

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“…Transportation sector is the largest source of carbon emissions globally with the share of over a quarter of the total greenhouse gas production in the world. International efforts such as European commission's plans for making the continent climate‐neutral by 2050 necessitates replacing the internal combustion cars with EVs in the coming years 1 . Therefore, introducing large number of EVs to the grid requires taking proper actions regarding implementation of optimal power dispatch both from the utility and customers ends as uncontrolled EV charging could lead to instability of the grid 2 .…”

Section: Introductionmentioning

confidence: 99%