It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical / mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning ( DSP ) . It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete / discarded products. This paper discusses the recent progress of disassembly sequencing planning including new theories and methods in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence ( AI ) methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies.

This paper proposes a secure management framework for optimal scheduling and operation of energy systems in smart cities, which will bring balance in modern power systems. The expanded infiltration of energy systems along with stochastic performance and high data transfer rate can bring new challenges that may severely affect precise energy management. This research work investigates the optimal and secure operation of energy systems in the presence of smart transportation systems in a correlated energy system. To achieve the maximum efficiency from the transportation system, the ideas of vehicle-to-subway (V2S) and vehicle-to-grid (V2G) are utilized to develop a bilateral power flow strategy (charging/discharging or idle mode) to provide the chief charging requirements of energy systems. In this regard, the proposed model suggests a novel stochastic architecture based on unscented transformation (UT) to handle the operational uncertainties of the transportation systems when considering the correlated power generation of wind turbines (WTs). Moreover, blockchain technology is deployed to guarantee the security of data transfer within the smart city. Hence, there is a secure collaboration within all sub-systems in the smart cities, which has triggered the integrated management in this structure. The simulation results are discussed on a typical smart city test system to show the quality of the proposed model.

Wind energy is predicted to account for a higher share of the world’s total power generation in the future. However, as wind power becomes more prevalent in the grid, it poses new challenges in terms of grid reliability and stability. This opens up new possibilities for the development of control methods capable of supporting the grid during voltage disruptions as well as enhancing power quality issues. This paper proposes a sliding mode control scheme for a direct-drive PMSG based wind energy conversion system. Nonlinear Sliding Mode Control (SMC) has the merit of robustness and good disturbance rejection capability, making it effective in responding to grid disturbances. The SMC chattering effect, on the other hand, increases the overall harmonic distortion injected into the grid. In this paper, the demerit of SMC has been minimized with the proper selection of SMC reaching law and the inclusion of an LCL filter and its dynamics in the design of the SMC control law. Moreover, MATLAB/Simulink simulation results have shown that the proposed control strategy has a better performance than the optimally tuned proportional-integral control during grid voltage disturbances.

Multi-terminal high voltage DC transmission currently represents a leading technology in long-distance power transmission systems. Among the main technical challenges facing such technology, DC fault isolation, permitting different grounding schemes, providing interoperability, and high DC voltage stepping between different HVDC networks, and allowing high-speed power reversal without power interruption especially when connecting the pre-existing voltage source converters (VSC) and line commutated converters (LCC)-based HVDC networks. This paper introduces a new modular multilevel converter (MMC) based front-to-front DC-DC converter to interconnect two different types (LCC/VSC) of HVDC networks. The proposed topology comprises a voltage source MMC (VS-MMC) and a current source MMC (CS-MMC), while both are coupled via an AC link including the isolating transformer. The proposed topology can successfully provide an uninterruptible bi-directional power flow, high DC voltage stepping with a DC fault blocking capability, and low number of semiconductors due to the usage of only half-bridge SMs. The system design is provided with a detailed mathematical analysis. Furthermore, two active power control methodologies are proposed and compared. The first control technique is simpler and entails lower passive elements, while the second technique ensures a zero reactive power over the full range of active power flow. Furthermore, Losses analysis and comparison are provided between the two proposed control techniques. Finally, Control-Hardware-in-the-Loop (CHiL) test validation is employed to confirm the validity of the proposed system under healthy as well as different fault scenarios.

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

Smart grids (SG) allow users to plan and control device usage patterns optimally, thereby minimizing power costs, peak-to-average ratios (PAR), and peak load demands. The present study develops a typical framework of a home energy management system (HEMS) for SG scenarios using newly limited and multi-limited planning approaches for domestic users. Time-of-use pricing (TOUP) is used to develop, handle, and manage the optimization problem properly. As a capable method for optimizing the proposed problem, this paper uses a robust meta-heuristic algorithm named wind-driven optimization algorithm (WDOA) and compares it to the other optimization algorithms in order to demonstrate its efficiency. In addition, it integrates a rooftop photovoltaic (PV) system with the system in order to show that all devices are cost-effective if managed properly. Eight diverse case studies are analyzed using a variety of time planning algorithms. The simulation results advocate for the quality and high performance of the proposed model by minimizing the total cost and managing energy consumption economically.