Smart management of PEV charging enhanced by PEV load forecasting

Xydas, Erotokritos, Marmaras, Charalampos, Cipcigan, Liana Mirela ORCID: https://orcid.org/0000-0002-5015-3334 and Jenkins, Nicholas ORCID: https://orcid.org/0000-0003-3082-6260 2015. Smart management of PEV charging enhanced by PEV load forecasting. Rajakaruna, Sumedha, Shahnia, Farhad and Ghosh, Arindam, eds. Plug In Electric Vehicles in Smart Grids, Power Systems, Springer, pp. 139-168. (10.1007/978-981-287-317-0_5)

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Abstract

According to the U.K. Department for Transport, the 97 % of transport energy consumption comes from the usage of oil. Therefore, a fuel diversification is needed to improve the energy security, and plug-in electric vehicles (PEVs) seem promising in giving an alternative solution. However, PEV owners need electric power from the grid in order to recharge the batteries of their vehicles. PEV charging load is a new type of demand, influenced by additional factors such as travel and driving patterns. Average travel distance within a day, the connection and disconnection time and the PEV’s power consumption will directly affect the daily load curve. This chapter proposes a decentralized control algorithm to manage the PEV charging requests. The aim of the control algorithm is to achieve a valley-filling effect on the demand curve, avoiding a potential increase in the peak demand. The proposed model includes an algorithm for PEV short term load forecasting. This forecast contributes to the effectiveness of the control model. Through different case studies, the performance of the proposed model is evaluated and the value of the PEV load forecasting as part of the PEV load management process is illustrated.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher:	Springer
ISBN:	9789812873163
ISSN:	16121287
Last Modified:	27 Oct 2022 10:12
URI:	https://orca.cardiff.ac.uk/id/eprint/69281

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