Operation of Plug-In Electric Vehicles for Voltage Balancing in Unbalanced Microgrids

Carpinelli, G.; Mottola, Fabio; Proto, Daniela; Russo, Angela

doi:10.5772/intechopen.68894

Cited by 1 publication

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [ 21 ] concluded that V2G technology could enhance the coordination between the HMG sectors. The utilization of EV batteries to balance an unbalanced microgrid was introduced in [ 22 ]. Mortaz and Valenzuela [ 23 ] utilized Bender’s decomposition to calculate the operational costs to find the optimal size and number of the V2G parking lots via Nelder–Mead optimization algorithm with conclusions that V2G had enhanced the long-term electricity cost.…”

Section: Introductionmentioning

confidence: 99%

“…As discussed above, some existing approaches optimize economic operations by building exhausting stochastic models, e.g., [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Others take an easier way of considering entire EVs battery resources as passive loads, e.g., [ 27 , 28 , 29 , 30 , 31 , 32 ], burning up a valuable energy compensation resource in rush hours.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators

Abdel-Hakim

Abo‐Elyousr

2022

Sensors

View full text Add to dashboard Cite

In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system’s parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50–85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system’s operational data.

show abstract

Section: Introductionmentioning

confidence: 99%