This paper proposes a novel high voltage gain structure of DC-DC converter with soft-switching ability for photovoltaic (PV) applications. A small size coupled inductor with one magnet core is utilized to improve the voltage conversion ratio in the proposed converter. The converter has one active MOSFET with low conducting resistance ( $R_{DS}-_{ON}$ ), which in turn reduces the conduction losses and complexity of the control section. Due to the low input current ripple, the lifetime of the input PV panel is increased, and the maximum power point (MPP) of the PV panel can be easily tracked. The MOSFET’s zero-voltage and zero-current switching and diodes are the other countenance of the proposed converter, which improve its efficiency. Additionally, an improved Perturb and Observe MPP tracking (IP&O MPPT) algorithm is introduced to boost the extracted power of the input PV sources. To validate the performance of this converter, the operation modes principle, steady-state and efficiency survey, and comparison results with other same family converters are carried out. Finally, an experiential prototype is built with 20 V input, 200 V output, power rate of 200 W, and 50 kHz operating frequency to validate the mathematical analysis and effectiveness of the proposed structure. The efficiency of the proposed converter was estimated by over 95% at various power levels.

Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids’ components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

This work reports on H₂ fuel generation from sewage water using Cu/CuO nanoporous (NP) electrodes. This is a novel concept for converting contaminated water into H₂ fuel. The preparation of Cu/CuO NP was achieved using a simple thermal combustion process of Cu metallic foil at 550 °C for 1 h. The Cu/CuO surface consists of island-like structures, with an inter-distance of 100 nm. Each island has a highly porous surface with a pore diameter of about 250 nm. X-ray diffraction (XRD) confirmed the formation of monoclinic Cu/CuO NP material with a crystallite size of 89 nm. The prepared Cu/CuO photoelectrode was applied for H₂ generation from sewage water achieving an incident to photon conversion efficiency (IPCE) of 14.6%. Further, the effects of light intensity and wavelength on the photoelectrode performance were assessed. The current density (J_ph) value increased from 2.17 to 4.7 mA·cm⁻² upon raising the light power density from 50 to 100 mW·cm⁻². Moreover, the enthalpy (ΔH*) and entropy (ΔS*) values of Cu/CuO electrode were determined as 9.519 KJ mol⁻¹ and 180.4 JK⁻¹·mol⁻¹, respectively. The results obtained in the present study are very promising for solving the problem of energy in far regions by converting sewage water to H₂ fuel.