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Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid

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Veröffentlicht in: Energy reports 6(2020), 2 vom: Feb., Seite 680-685
Personen und Körperschaften: Kiptoo, Mark Kipngetich (VerfasserIn), Adewuyi, Oludamilare Bode (VerfasserIn), Elsayed, Mohammed Elsayed Lotfy (VerfasserIn), Ibrahimi, Abdul Matin (VerfasserIn), Senjyu, Tomonobu (VerfasserIn)
Titel: Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid/ Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Mohammed Elsayed Lotfy, Abdul Matin Ibrahimi, Tomonobu Senjyu
Format: E-Book-Kapitel
Sprache: Englisch
veröffentlicht:
2020
Gesamtaufnahme: : Energy reports, 6(2020), 2 vom: Feb., Seite 680-685
, volume:6
Schlagwörter:
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Quelle: Verbunddaten SWB
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Zusammenfassung: Optimal sizing with energy management strategy as a transition pathway towards a sustainable 100% renewable energy-based microgrid is investigated in this paper. Due to the challenges of intermittency of renewable energy, microgrid operations are complicated. Hence, in order to overcome some of the challenges facing microgrid planning and operations, optimal capacity sizing incorporated with energy management strategy considering time-ahead generation prediction is proposed. The system model consists of wind turbine (WT), solar photovoltaic (PV) and battery energy storage system (BESS). The generation forecasting output is used to reschedule the flexible demand resources (FDR) to reduce the mismatch between power demand and supply, and optimal sizing of components is performed jointly to determine the optimal capacity values of the PV, WT, and BESS for minimal investment costs. The optimization results for the scenarios with and without load shifting effects of FDRs are determined and analyzed for the case study. From the results obtained, the application of demand scheduling program using the generation forecasting outputs resulted in a cost-saving of 12.41%. The forecasting model is implemented using a random forest algorithm on python platform and the mixed-integer linear program on MATLAB® environment is used to model and solve the capacity sizing problem.
ISSN: 2352-4847
DOI: 10.1016/j.egyr.2019.11.137
Zugang: Open Access