Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid

Shunlong Xiao, Mohammad B. Shadmand, Robert Balog

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper presents the operational control strategy of multi-string photovoltaic (PV) systems in a dc microgrid by using model predictive control (MPC). Due to the intermittent nature of solar energy, battery energy storage system with unify-controlled charger is integrated to the dc microgrid to compensate the fluctuating generated PV power and to regulate the dc-bus voltage. A two-string PV system is considered in this paper to evaluate the performance of the proposed controller. The proposed MPC for the dc microgrid consists: maximum power point tracking (MPPT) of PV arrays and control of a bidirectional dc-dc converter for charging/discharging the battery energy storage system. The controller tracks the maximum power point (MPP) of each PV string under dynamic weather condition and ensures to deliver the required power by the load using the battery system in case of insufficient power generated by the PV systems. The MPC of the battery system stabilizes the bus voltage by considering the state of charge (SoC) of battery and evaluating the direction of power flow from battery system to the dc bus and vice versa. By using the characteristics of MPC with ability to add design constraints, the proposed system ensures power distribution among PV arrays and evaluates the battery SoC for changing the mode of operation to achieve a reliable energy source for the local loads. The practical feasibility of the proposed controller is studied for several case studies and verified experimentally by implementing the controller using dSPACE 1007 platform.

Original languageEnglish
Title of host publication2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1284-1290
Number of pages7
ISBN (Electronic)9781509053667
DOIs
Publication statusPublished - 17 May 2017
Event32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017 - Tampa, United States
Duration: 26 Mar 201730 Mar 2017

Other

Other32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017
CountryUnited States
CityTampa
Period26/3/1730/3/17

Fingerprint

Model predictive control
Controllers
Energy storage
Electric potential
Solar energy

Keywords

  • DC Microgrid
  • Energy Storage System (ESS)
  • Model Predictive Control (MPC)
  • Renewable Energy Resource (RES)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Xiao, S., Shadmand, M. B., & Balog, R. (2017). Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid. In 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017 (pp. 1284-1290). [7930861] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APEC.2017.7930861

Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid. / Xiao, Shunlong; Shadmand, Mohammad B.; Balog, Robert.

2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1284-1290 7930861.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xiao, S, Shadmand, MB & Balog, R 2017, Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid. in 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017., 7930861, Institute of Electrical and Electronics Engineers Inc., pp. 1284-1290, 32nd Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2017, Tampa, United States, 26/3/17. https://doi.org/10.1109/APEC.2017.7930861
Xiao S, Shadmand MB, Balog R. Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid. In 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1284-1290. 7930861 https://doi.org/10.1109/APEC.2017.7930861
Xiao, Shunlong ; Shadmand, Mohammad B. ; Balog, Robert. / Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid. 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1284-1290
@inproceedings{1623984d6a674ee3975e47e5c3797198,
title = "Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid",
abstract = "This paper presents the operational control strategy of multi-string photovoltaic (PV) systems in a dc microgrid by using model predictive control (MPC). Due to the intermittent nature of solar energy, battery energy storage system with unify-controlled charger is integrated to the dc microgrid to compensate the fluctuating generated PV power and to regulate the dc-bus voltage. A two-string PV system is considered in this paper to evaluate the performance of the proposed controller. The proposed MPC for the dc microgrid consists: maximum power point tracking (MPPT) of PV arrays and control of a bidirectional dc-dc converter for charging/discharging the battery energy storage system. The controller tracks the maximum power point (MPP) of each PV string under dynamic weather condition and ensures to deliver the required power by the load using the battery system in case of insufficient power generated by the PV systems. The MPC of the battery system stabilizes the bus voltage by considering the state of charge (SoC) of battery and evaluating the direction of power flow from battery system to the dc bus and vice versa. By using the characteristics of MPC with ability to add design constraints, the proposed system ensures power distribution among PV arrays and evaluates the battery SoC for changing the mode of operation to achieve a reliable energy source for the local loads. The practical feasibility of the proposed controller is studied for several case studies and verified experimentally by implementing the controller using dSPACE 1007 platform.",
keywords = "DC Microgrid, Energy Storage System (ESS), Model Predictive Control (MPC), Renewable Energy Resource (RES)",
author = "Shunlong Xiao and Shadmand, {Mohammad B.} and Robert Balog",
year = "2017",
month = "5",
day = "17",
doi = "10.1109/APEC.2017.7930861",
language = "English",
pages = "1284--1290",
booktitle = "2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Model predictive control of multi-string PV systems with battery back-up in a community dc microgrid

AU - Xiao, Shunlong

AU - Shadmand, Mohammad B.

AU - Balog, Robert

PY - 2017/5/17

Y1 - 2017/5/17

N2 - This paper presents the operational control strategy of multi-string photovoltaic (PV) systems in a dc microgrid by using model predictive control (MPC). Due to the intermittent nature of solar energy, battery energy storage system with unify-controlled charger is integrated to the dc microgrid to compensate the fluctuating generated PV power and to regulate the dc-bus voltage. A two-string PV system is considered in this paper to evaluate the performance of the proposed controller. The proposed MPC for the dc microgrid consists: maximum power point tracking (MPPT) of PV arrays and control of a bidirectional dc-dc converter for charging/discharging the battery energy storage system. The controller tracks the maximum power point (MPP) of each PV string under dynamic weather condition and ensures to deliver the required power by the load using the battery system in case of insufficient power generated by the PV systems. The MPC of the battery system stabilizes the bus voltage by considering the state of charge (SoC) of battery and evaluating the direction of power flow from battery system to the dc bus and vice versa. By using the characteristics of MPC with ability to add design constraints, the proposed system ensures power distribution among PV arrays and evaluates the battery SoC for changing the mode of operation to achieve a reliable energy source for the local loads. The practical feasibility of the proposed controller is studied for several case studies and verified experimentally by implementing the controller using dSPACE 1007 platform.

AB - This paper presents the operational control strategy of multi-string photovoltaic (PV) systems in a dc microgrid by using model predictive control (MPC). Due to the intermittent nature of solar energy, battery energy storage system with unify-controlled charger is integrated to the dc microgrid to compensate the fluctuating generated PV power and to regulate the dc-bus voltage. A two-string PV system is considered in this paper to evaluate the performance of the proposed controller. The proposed MPC for the dc microgrid consists: maximum power point tracking (MPPT) of PV arrays and control of a bidirectional dc-dc converter for charging/discharging the battery energy storage system. The controller tracks the maximum power point (MPP) of each PV string under dynamic weather condition and ensures to deliver the required power by the load using the battery system in case of insufficient power generated by the PV systems. The MPC of the battery system stabilizes the bus voltage by considering the state of charge (SoC) of battery and evaluating the direction of power flow from battery system to the dc bus and vice versa. By using the characteristics of MPC with ability to add design constraints, the proposed system ensures power distribution among PV arrays and evaluates the battery SoC for changing the mode of operation to achieve a reliable energy source for the local loads. The practical feasibility of the proposed controller is studied for several case studies and verified experimentally by implementing the controller using dSPACE 1007 platform.

KW - DC Microgrid

KW - Energy Storage System (ESS)

KW - Model Predictive Control (MPC)

KW - Renewable Energy Resource (RES)

UR - http://www.scopus.com/inward/record.url?scp=85020054182&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020054182&partnerID=8YFLogxK

U2 - 10.1109/APEC.2017.7930861

DO - 10.1109/APEC.2017.7930861

M3 - Conference contribution

SP - 1284

EP - 1290

BT - 2017 IEEE Applied Power Electronics Conference and Exposition, APEC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -