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Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic

Langenmayr, Uwe; Wang, Weimin; Jochem, Patrick

Abstract (englisch):
Increasing use of renewable energy leads to change in load flows from predictable generation and inelastic demand to more volatile and price-elastic patterns, especially on the distribution level. New applications such as electric vehicles further increase the demand of electricity. Therefore, a reliable, local control of load flexibilities is a key competence of future system operators. This paper presents a \textit{central planner -- decentral operator} approach to schedule local electricity flows. The central planner conducts a two-stage optimization to derive the demand limit and a corresponding battery schedule, while the decentral operator simply applies the battery schedule and heuristically reacts to unforeseen deviations between the forecasted and actual loads and power generation. Privacy concerns of the decentral planner are avoided as no private information is shared with the central planner. A relaxation factor and a reserve capacity for the battery are derived from a Monte Carlo simulation to consider the underlying uncertainties of load, photovoltaic generation, and electric vehicle charging. Our results show that the load of the decentral operator can be limited reliably for six days of the considered week and a maximum reduction of 2.6 kW (52\%) of peakload has been accomplished. ... mehr

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Postprint §
DOI: 10.5445/IR/1000125324
Frei zugänglich ab 17.10.2021
DOI: 10.1016/j.apenergy.2020.115972
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2020
Sprache Englisch
Identifikator ISSN: 0306-2619
KITopen-ID: 1000125324
Erschienen in Applied energy
Band 280
Seiten Art.-Nr.: 115972
Vorab online veröffentlicht am 16.10.2020
Schlagwörter PV-battery systems; Peak shaving; Uncertainty; Monte Carlo simulation; Electric vehicle; Optimization
Nachgewiesen in Scopus
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