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Titel: Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets
VerfasserIn: Minhas, Daud Mustafa
Meiers, Josef
Frey, Georg
Sprache: Englisch
Titel: Energies
Bandnummer: 15
Heft: 5
Verlag/Plattform: MDPI
Erscheinungsjahr: 2022
Freie Schlagwörter: demand-side management
distributed generation
energy management system
energy scheduling
microgrid
power optimization
predictive load demand
renewable energy
DDC-Sachgruppe: 600 Technik
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%.
DOI der Erstveröffentlichung: 10.3390/en15051619
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-357234
hdl:20.500.11880/32568
http://dx.doi.org/10.22028/D291-35723
ISSN: 1996-1073
Datum des Eintrags: 11-Mär-2022
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Prof. Dr. Georg Frey
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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