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Technical Brief

Estimation-Based Maximum Power Point Tracking in a Self-Balancing Photovoltaic Battery Energy Storage System

[+] Author and Article Information
Partha P. Mishra

Department of Mechanical and Nuclear Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: partha.p.mishra@gmail.com

Michelle Denlinger

Department of Mechanical and Nuclear Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: mak5497@psu.edu

Hosam K. Fathy

Department of Mechanical and Nuclear Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: hkf2@engr.psu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received January 12, 2018; final manuscript received May 7, 2019; published online June 5, 2019. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 141(10), 104503 (Jun 05, 2019) (8 pages) Paper No: DS-18-1020; doi: 10.1115/1.4043756 History: Received January 12, 2018; Revised May 07, 2019

This paper examines the problem of controlling the exchange of current in photovoltaic-plus-storage systems to achieve photovoltaic (PV) maximum power point tracking (MPPT). This work is motivated by the need for MPPT algorithms that are less costly and complex to implement in PV farms with integrated battery energy storage. We study the online optimal control of a “hybrid” PV/lithium (Li)-ion battery integration topology that is self-balancing in nature. The self-balancing behavior ensures that the state of charge (SOC) across different cells balances to the same stable equilibrium value without needing any balancing power electronics, thereby significantly reducing the integration cost. The DC–DC converters in this hybrid system are controlled to achieve PV MPPT that maximizes energy generation and storage. However, sensing needs for traditional MPPT controllers can render the hybrid system unnecessarily complex and costly. We surmount this problem by: (i) developing a novel model-based PV power estimation algorithm that only requires voltage measurement, and (ii) using this algorithm together with extremum-seeking (ES) control to achieve closed-loop, estimation-based PV MPPT. Simulation case studies show that this estimation-based MPPT controller is able to harness more than 99% of the maximum available solar energy under different irradiation profiles.

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Figures

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Fig. 1

(a) Traditional energy storage integrated PV farm and (b) self-balancing PV/Li-ion battery integration topologies. Part (b), top shows a hybrid string consisting of a number of individual units connected in series and providing power to an external load or to the grid. Part (b), bottom shows a single hybrid unit that contains a PV array connected to a single Li-ion cell through the connecting device. (c) Equivalent electrical circuit models for (i) PV arrays, (ii) Li-ion cells, and (iii) connecting devices.

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Fig. 2

Schematic of an estimation-based MPPT controller. Measured battery voltage and knowledge of external load current allow the estimator to estimate the PV generated power, which is used by an ES-based MPPT controller to control the conversion ratio of the DC–DC converter in a hybrid unit.

Grahic Jump Location
Fig. 3

Behavior of a hybrid string subjected to a step change in irradiation. The controller is able to track the increase in MPP while the system reaches equilibrium at final irradiation.

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Fig. 4

Behavior of a hybrid string when a single string level voltage measurement is used for total PV power estimation and a common conversion ratio is calculated by the MPPT controller

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