Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. Note to Practitioners —This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs.

Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources' ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

This paper aims at proposing an efficient and versatile application of Petri nets (PNs) either alone without global positioning system (GPS) as in (GPS-free) system or together with the navigation system (GPS-registered) to conveniently provide a proper energy management strategy for hybrid electric vehicles (HEVs) of high hybridization level and serial architecture. A comparison between the PN strategy and two fuzzy logic strategies is performed in terms of fuel consumption and convergence time. In this paper, short and long trip types of 30 km mainly urban and 240 km mostly highway are considered with an initial state of charge (SoC) of 50% and different daily driving cycles or various standard the New York City Cycle, the New European Driving Cycle, US06 driving cycles. Both kinds of battery management strategies, GPS-free and GPS registered, are demonstrated and compared through simulation studies using the MTCsim software. Dealing with both types of trips, the simulation results significantly illustrate the superiority of the novel GPS-registered methodology's efficiency toward improving the HEV's energy management and reducing its fuel consumption besides the relative economic feasibility and structural simplicity features. Over one week duration, the GPS allows reaching the desired final SoC with acceptable errors and reducing the fuel consumption for both daily short and weekend long trips. The originality of this paper is proposing a hybrid GPS/rule-based approach to reduce the fuel consumption during daily driving trips that present about half of the professional travels in 2008 according to the French Sustainable Development Division. This novel strategy is developed on the basis of the recorded GPS data from past trips and the batteries' final recharging capacities.

Photovoltaic panels play a pivotal role in the renewable energy sector, serving as a crucial component for generating environmentally friendly electricity from sunlight. However, a persistent challenge lies in the adverse effects of rising temperatures resulting from prolonged exposure to solar radiation. Consequently, this elevated temperature hinders the efficiency of photovoltaic panels and reduces power production, primarily due to changes in semiconductor properties within the solar cells. Given the depletion of limited fossil fuel resources and the urgent need to reduce carbon gas emissions, scientists and researchers are actively exploring innovative strategies to enhance photovoltaic panel efficiency through advanced cooling methods. This paper conducts a comprehensive review of various cooling technologies employed to enhance the performance of PV panels, encompassing water-based, air-based, and phase-change materials, alongside novel cooling approaches. This study collects and assesses data from recent studies on cooling the PV panel, considering both environmental and economic factors, illustrating the importance of cooling methods on photovoltaic panel efficiency. Among the investigated cooling methods, the thermoelectric cooling method emerges as a promising solution, demonstrating noteworthy improvements in energy efficiency and a positive environmental footprint while maintaining economic viability. As future work, studies should be made at the level of different periods of time throughout the years and for longer periods. This research contributes to the ongoing effort to identify effective cooling strategies, ultimately advancing electricity generation from photovoltaic panels and promoting the adoption of sustainable energy systems.

This paper puts forward the implementation of an intelligent type II fuzzy PID (T2-FPID) controller tweaked with a water cycle algorithm (WCA), subjected to an error multiplied with time area over integral (ITAE) objective index for regularizing the variations in frequency and interline power flow of an interconnected power system during load disturbances. The WCA-based T2-FPID is tested on a multi-area (MA) system comprising thermal-hydro-nuclear (THN) (MATHN) plants in each area. The dynamical behavior of the system is analyzed upon penetrating area 1 with a step load perturbation (SLP) of 10%. However, power system practicality constraints, such as generation rate constraints (GRCs) and time delays in communication (CTDs), are examined. Afterward, a territorial control scheme of a superconducting magnetic energy storage system (SMES) and a unified power flow controller (UPFC) is installed to further enhance the system performance. The dominancy of the presented WCA-tuned T2-FPID is revealed by testing it on a widely used dual-area hydro-thermal (DAHT) power system model named test system 1 in this paper. Analysis reveals the efficacy of the presented controller with other approaches reported in the recent literature. Finally, secondary and territorial regulation schemes are subjected to sensitivity analysis to deliberate the robustness.

Disputed supply chains, inappropriate weather and low investment, followed by the Russian invasion of Ukraine, has led to a phenomenal energy crisis, especially in the Horn of Africa. Accordingly, proposing eco-friendly and sustainable solutions to diversify the access of electricity in the Republic of Djibouti, which has no conventional energy resources and is completely energy-dependent on its neighboring countries, has become a must. Therefore, the implementation of sustainable renewable and energy storage systems is nationally prioritized. This paper deals, for the first time, with the exploitation of such an affordable and carbon-free resource to produce hydrogen from wind energy in the rural areas of Nagad and Bara Wein in Djibouti. The production of hydrogen and the relevant CO₂ emission reduction using different De Wind D6, Vestas and Nordex wind turbines are displayed while using Alkaline and Proton Exchange Membrane (PEM) electrolyzers. The Bara Wein and Nagad sites had a monthly wind speed above 7 m/s. From the results, the Nordex turbine accompanied with the alkaline electrolyzer provides the most affordable electricity production, approximately 0.0032 $/kWh for both sites; this cost is about one per hundred the actual imported hydroelectric energy price. Through the ecological analysis, the Nordex turbine is the most suitable wind turbine, with a CO₂ emission reduction of 363.58 tons for Bara Wein compared to 228.76 tons for Nagad. While integrating the initial cost of wind turbine implementation in the capital investment, the mass and the levelized cost of the produced green hydrogen are estimated as (29.68 tons and 11.48 $/kg) for Bara Wein with corresponding values of (18.68 tons and 18.25 $/kg) for Nagad.