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Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Journal 02 Nov 2021Publisher:MDPI AGJournal:Sensors, volume 21, page 7,295 (eissn: 1424-8220,

Authors: Maria Fotopoulou; Dimitrios Rakopoulos; Orestis Blanas;

doi: 10.3390/s21217295

pmid: 34770613

pmc: PMC8587728

Day Ahead Optimal Dispatch Schedule in a Smart Grid Containing Distributed Energy Resources and Electric Vehicles

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Abstract

This paper presents a day ahead optimal dispatch method for smart grids including two-axis tracking photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS) and electric vehicles (EV), which serve as additional storage systems in vehicle to grid (V2G) mode. The aim of the day ahead schedule is the minimization of fuel-based energy, imported from the main grid. The feasibility of the proposed method lies on the extensive communication network of the smart grids, including sensors and metering devices, that provide valuable information regarding the production of the distributed energy resources (DER), the energy consumption and the behavior of EV users. The day ahead optimal dispatch method is applied on a smart grid in order to showcase its effectiveness in terms of sustainability, full exploitation of DER production and ability of EVs to act as prosumers.

Related Organizations

Centre for Research and Technology Hellas
Greece

Keywords

energy management, Chemical technology, V2G, TP1-1185, Article, distributed energy resources, smart grid, day ahead optimization

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	26
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%