The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Access to reliable, economical, and sustainable energy is a critical challenge in remote communities where infrastructure constraints and unreliability of renewable energy sources (RESs) complicate the possibility of having a stable supply. This study is motivated by the urgent need for intelligent, adaptive energy management systems that can ensure the reliability of the supply while maximizing the use of RESs. To meet this need, an adaptive and scalable multi-agent system (MAS) framework for hybrid energy systems can be employed. The system includes electric vehicle batteries (EVBs), hydrogen energy storage systems (HESSs), and battery energy storage systems (BESSs) and wind turbines (WTs) and PV. A hybrid backup architecture for energy supply continuity in low availability of RESs, in addition to vehicle-to-grid (V2G) functionality enabling EVBs to support grid stability. The MAS is evaluated under four scenarios: PV–WTs–BESSs, PV–WTs–BESSs–EVBs, PV–WTs–BESSs–HESSs, and PV–WTs–BESSs–EVBs–HESSs. Scenario 4 attains the lowest operating cost of $10,688.06, a reduction of 0.91% from scenario 1, in a 25 kW peak load microgrid. The artificial gorilla troops optimizer optimizes the real-time energy dispatch by learning to adjust to changing system conditions. Simulation results confirm that the proposed MAS improves cost-effectiveness, energy stability, and sustainability in constrained settings.

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms.

In this article, a nature inspired optimization approach is suggested, which is named Harris Hawks Approach (HHA). The prime inspiration of HHA is the mutual attitude and chasing way of Harris’ hawks in kind named surprise swoop. In this brilliant drawing, diverse hawks swoop victim cooperatively from distinct ways in an endeavor to startle it. Harris hawks can detect a sort of chasing paradigms rely on the spirited nature of scenarios and escaping styles of the victim. HHA is developed for handling the Economic Dispatch (ED) process. Since the ED is a non-linear, complex and obliged optimization process, its prime target is to lower the net generation cost while achieving the inequality and equality restrictions of the studied system. To prove the capabilities of the developed HHA, several test systems including 6, 13, 40 and 10 units have been implemented and tested for solving various ED problems. The simulation results clarify the potency of the HHA compared with other approaches illustrated recently in literature.