A modern DC microgrid often comprises renewable energy sources (RESs) such as Photovoltaic (PV) generation units, battery energy storage systems (BESSs), and local load, and it is also connected to the utility grid through a point of common coupling (PCC). While most existing approaches have to rely on communication links to achieve desirable control performance, this paper proposes a novel control strategy without resorting to the communication links. This is achieved by assigning BESSs as master units and regulating the DC bus voltage with a novel state-of-charge (SoC)-based droop control, where the BESSs coordinate the slave units (e.g. RES, utility grid) with the aid of the DC bus signaling (DBS) technique to avoid overcharging and over-discharging of these BESSs. In the proposed droop control, the reference voltage for these BESSs is designed for coordinated operation between BESSs and utility grid, it is maintained constant in normal SoC range, which can reduce DC voltage variation. Droop coefficients designed for SoC balance of BESS are dynamically adjusted based on their own SoC values. Furthermore, the preset maximum deviation between the reference voltage and DC bus voltage ensures reliable coordinated operation. Real-time hardware-in-loop (HIL) experiments considering three different scenarios are conducted to validate the effectiveness of the propose method.

An error criterion is essential in the process of parameter extraction of photovoltaic (PV) modules by fitting I–V curves, which exerts a huge influence on the accuracy of the extracted parameters. This paper proposes a new integrated current–voltage error criterion, named EC-I&V(x), which takes into account the intrinsic I–V properties of the PV module. The deviation in both current and voltage is considered by combining the mean squared error of the current and voltage in different data regions. Four optimization methods are used to validate the proposed error criterion, including guaranteed convergence particle swarm optimization, differential evolution, shuffled complex evolution, and an artificial bee colony algorithm. Different methods with the proposed error criterion are applied to synthetic I–V curves with variable error levels and measured I–V data under different operating conditions. Comparing with the traditional current based error criterion, more accurate results are obtained by using the proposed EC-I&V(x) at different error levels for different optimization methods. The proposed EC-I&V(x) not only improves the accuracy of each extracted parameter but also improves the accuracy of the estimated I–V property near maximum power points.

The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is essential for solar power forecasting and ensuring grid stability. The traditional method based on the single-diode model is inconvenient and complex because the current-voltage equation is implicit. In this paper, a novel method combining an artificial neural network (ANN) with an explicit analytical model (EAM) is proposed for predicting the I-V characteristics of PV modules under different operating conditions. The EAM makes it efficient to obtain the I-V curves from the estimated model parameters due to its simplicity and explicit expression. The ANN based on the EAM is composed of a three-layer feedforward neural network, in which the inputs are solar irradiation and module temperature and the outputs are the four parameters in EAM. Once the ANN is built and trained by using the measured I-V curves, the shape parameters and I-V curve are predicted by only reading solar irradiation and temperature without solving any nonlinear implicit equations. The accuracy and capability of the proposed method are verified by the experimental data for different types of PV modules. Moreover, the dependence of shape parameters in the EAM on solar irradiation and temperature is investigated first.

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