In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Solar Photovoltaic (PV) systems are degraded in terms of efficiency by increment in their temperature. To keep away from efficiency degradation regarding the temperature increase, various thermal management techniques have been introduced to keep the temperature low. Besides improvement in electrical efficiency, the overall efficiency can be enhanced by using the extracted thermal energy from the cell. The extracted heat in these systems, known as PV/Thermal (PV/T), can be applied for some purposes including water or air heating. This article reviews the works on the PV/T systems exergy analysis and discusses their findings. Based on the findings of the reviewed works, different factors such as the system configuration, used components and elements, and working conditions affect the exergy efficiency of these systems. As an example, use of coolants with improved thermal features, i.e., nanofluids, can cause improvement in the exergy efficiency. In addition to the nanofluid, making use of the thermal energy storage unit can further enhance the exergy efficiency. Furthermore, it has been observed that the materials of nanostructures can be another element that influences the enhancement of exergy efficiency. Moreover, the usage of some components such as glazing can lead to avoidance of thermal energy loss that would be beneficial from an exergy point of view. Finally, according to the reviewed works and knowledge of the authors, some suggestions are represented for future works in this field.

Power generating entities’ connection to utility grids requires power converters to achieve high efficiency and low injected current harmonic distortion. The control of the power converter plays a crucial role in the grid-tied power converter’s performance. Various control techniques for grid-tied inverters ranging from classical to intelligent are introduced in several exist. Evaluating the current state and trend in grid-tied power inverters and related control methods, research shows that most works in this area focus on grid integration using the close-loop and other advanced control approaches. This is because these control methods are preferred since they provide adequate performance in case of uncertainties in the system. This investigation can aprove that PQ open-loop control technique can operate sufficiently and cost-effectively in grid-tied renewable and alternative power systems under normal operating conditions. Hence, this paper aims to assess the performance of a centralized single-stage grid-tied three-level diode clamped inverter connected to a PV-Fuel cell unit. An active and reactive power open-loop control scheme is employed to operate the inverter and achieves a current harmonic distortion below 5%. The system comprises a 150 kW/700 V PV, a 150 kW/1400 V fuel cell, a 265 kW multilevel inverter operating at a rated voltage of 415 V, and an LCL filter. Two operating scenarios are adopted to investigate the system’s responses further. In the first scenario, a local load of 509.2 kW is supplied from the PV-fuel cell inverter. The load also receives the grid’s power to meet the demand as the PV-fuel cell inverter provides only 265 kW. Whereas in the other scenario, the PV-fuel cell unit provides power to supply a local load while transporting the surplus to the grid. The results reveal the developed model’s good performance with a current harmonic distortion of 0.33%.

The simplicity and excellent dynamic performance of Direct Torque Control (DTC) make Induction Motor (IM) drives attractive for many applications that require precise torque control. The traditional version of DTC uses hysteresis controllers. Unfortunately, the nature of these controllers prevents the optimization of the inverter voltage vectors inside the flux hysteresis band. The inverter voltage vector optimization can produce fast torque response of the IM drive. This research proposes two torque optimization methods for IM systems utilizing DTC. Analysis and Matlab simulations for the proposed optimization methods prove that the torque and, consequently, the speed responses, are greatly improved. The performances of the drive system controlled by the proposed optimization methods and the traditional DTC are compared. Conversely, the effects of the parameters on the proposed optimization methods are introduced. The proposed methods greatly improve the torque and speed dynamic performances against the traditional DTC technique. However, one of the proposed optimization methods is more sensitive to IM parameter variations than the other.

The demand for electricity is rapidly growing and renewable energy sources such as solar, wind and tidal energy can compensate the demand to a substantial level. Among these, solar energy is abundant, scalable and is cheaper. The generated energy can be used in an efficient way if the DC output is directly supplied to the load instead of converting it to AC. Every electrical system is capable of operating in DC and, for example, energy efficient Light Emitting Diode (LED) lights have become popular as they provides more lumens with less power consumption and also can be directly operated from DC. LED lighting system in large commercial buildings has irradiance levels which vary sigificantly during operation. Extracting maximum power from the energy system and maintaining constant voltage output at different loads is another challenge. This paper proposes a solar Photo Voltaic (PV)-based energy system including Battery Energy Storage System (BESS) for supplying LED lamps to a commercial building through a modified high gain Luo converter. The Perturb and Observe control algorithm has been used for maximum power extraction from a PV cell whereas PI (Proportional Integral) controllers maintain constant output voltage from PV–BESS against different irradiance levels. To supply the desired voltages to the LED lighting system, a modified high gain Luo converter is designed. To make the output voltage constant at different load currents, PI and Sliding Mode Controllers (SMC) are designed with the help of the state-space average model. It is found that the sliding mode controller outperforms the PI controller in terms of behavior in the transient period and tracking capability. The system is simulated using MATLAB/Simulink^®. The Sliding Mode Controller has a 95% less transient period and is 75% faster in tracking capability when compared to other controllers. The system could be incorporated with the PV source to obtain green energy.