Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

Photovoltaic (PV) technology has been gaining an increasing amount of attention as a renewable energy source. Irradiation and temperature are the two main factors which impact on PV system performance. When partial shading from the surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Moreover, shading affects the pattern of the power–voltage (P–V) characteristic curve to contain more than one power peak, causing difficulties when developing maximum power point tracking. Consequently, shading leads to a hotspot in which spreading the hotspot widely on the PV panel’s surface increases the heat and causes damage to the panel. Since it is not possible to access the circuit inside the PV cells, indirect measurement and fault detection methods are needed to perform them. This paper proposes the global maximum power point tracking method, including the shading detection and tracking algorithm, using the trend of slopes from each section of the curve. The effectiveness was confirmed from the dynamic short-term testing and real weather data. The hotspot-detecting algorithm is also proposed from the analysis of different PV arrays’ configuration, which is approved by the simulation’s result. Each algorithm is presented using the full mathematical equations and flowcharts. Results from the simulation show the accurate tracking result along with the fast-tracking response. The simulation also confirms the success of the proposed hotspot-detection algorithm, confirmed by the graphical and numerical results.

The dependence of distributed generations (DGs) on climate conditions and fluctuating load demands are the challenges for the implementation of battery energy storage systems (BESSs) in islanded microgrids. BESS participation in system frequency regulation becomes one of the solutions to those challenges. Frequency regulation by BESS can be realized by applying the load-frequency control (LFC) in BESS. However, this participation clearly poses problems for the battery state of charge (SOC), as the battery is often overcharged or undercharged. In this paper, a control that maintains SOC at a certain level is introduced. This control strategy focuses on the battery operation function, which is determined from five control scenarios. All scenarios are achieved by applying the droop reference shifting method in the LFC to allow battery operation change. Control verification is carried out on an islanded microgrid system that experiences load demand changes and photovoltaic (PV) output power changes. The test results show that the SOC is maintained at 45–75% by applying load and PV power variations. The results correspond to the predetermined criteria control.

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO₂ concentrations. Our models accurately estimated temperature, humidity, and CO₂ concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors.

The emergence of Distributed Generation (DG) in the electric system has brought about the appearance of the islanding phenomenon. In AC networks, there are a lot of Islanding Detection Methods (IDMs) have been studied. However, not too much IDMs in DC networks have been published because of the absence of frequency and reactive power. The hybrid IDM based on injected perturbation signal and rate of change of power output is proposed. This IDM can detect islanding condition not only in the worst case (the power of load and PV are equal) but also in another case (the power of load is greater than the power of PV). It can be applicable to both single and multi-PV operation scenarios. Besides, the effectiveness of the proposed method is verified by simulation in Matlab/Simulink.

This paper focuses on autonomous multi-islanded entities and the seamless reconnection to the main grid as the self-healing ability of the future power system. The minimization of power quality issues (mainly that of voltage, frequency, and harmonics) in such entities based on controllers, with or without intercommunication, is also an important part of this paper. The future power system, with the significant penetration of distributed generations (DGs), can rapidly respond to any problem occurring within it by separating into autonomous islanded entities to prevent the disconnection of DGs. As a result, high-quality and continuous power is supplied to consumers. Finally, future research that is necessary for the realization of the future power system is discussed.