An analysis of multi objective energy scheduling in PV-BESS system under prediction uncertainty

Unnikrishnan Raveendrannair, Monika Sandelic, Ariya Sangwongwanich, Tomislav Dragicevic, Ramon Costa Castello, Frede Blaabjerg

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Energy storage systems (ESS) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out,in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria.

Original languageEnglish
JournalIEEE Transactions on Energy Conversion
Number of pages10
ISSN0885-8969
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Artificial neural networks
  • battery management
  • Decision making
  • Degradation
  • Energy management
  • grid congestion degradation
  • Mathematical model
  • Model predictive control
  • Predictive models
  • PV system
  • State of charge

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