Predictive Control of PV/Battery System under Load and Environmental Uncertainty

Salem Batiyah, Roshan Sharma, Sherif Abdelwahed, Waleed Alhosaini and Obaid Aldosari
Additional contact information
Salem Batiyah: Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Industrial, Almadina 46452, Saudi Arabia
Roshan Sharma: Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Sherif Abdelwahed: Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA
Waleed Alhosaini: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Obaid Aldosari: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, Najd 11991, Saudi Arabia

Energies, 2022, vol. 15, issue 11, 1-16

Abstract: The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.

Keywords: power management; model predictive control; dc microgrid; photovoltaic; battery energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/11/4100/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/11/4100/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:11:p:4100-:d:830538

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().