In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of the power peaks is a new objective and open issue to a well-functional EMS, along with the maximization of profit in the energy market. Thus, both objectives have to be taken into account at the same time. Thus, this paper proposes the EMS application incorporating power offering strategy applying a nature-inspired algorithm such as Particle Swarm Optimization (PSO) algorithm, in order to find the optimal solution of the objective function in the context of the overall operating cost, the coordination of DERs, and the energy losses in a MG integrated VPP network. For a fair DERs coordination with minimized power fluctuations in the power flow, the power offering strategies with an active power control and re-distribution are proposed. Simulation results show that the proposed MG integrated VPP model with PSO-based EMS employing Egalitarian reDistribution (ED) power offering strategy is most feasible option for the overall operating cost of VPP revenue. The total operating cost of the proposed EMS with ED strategy is 40.98＄ compared to 432.8＄ of MGs only without EMS. It is concluded that each MGs in the proposed VPP model intelligently participates in energy trading market compliant with the objective function, to minimize the overall cost and the power fluctuation.

As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions, efficient clustering mechanisms are vital to ensure stable and scalable communication. Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems (ITS). This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering (GOA-VNET) algorithm, an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks (VANETs), leveraging the Grasshopper Optimization Algorithm (GOA) to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems (ITS). The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations, ensuring robust adaptability to varying vehicular densities and transmission ranges. Key features of GOA-VNET include the utilization of attraction zone, repulsion zone, and comfort zone parameters, which collectively enhance clustering efficiency and minimize congestion within Regions of Interest (ROI). By managing cluster configurations and node densities effectively, GOA-VNET ensures balanced load distribution and seamless data transmission, even in scenarios with high vehicular densities and varying transmission ranges. Comparative evaluations against the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO) demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency, reducing the number of clusters by up to 10% in high-density scenarios, and improving data transmission reliability. Simulation results reveal that under a 100–600 m transmission range, GOA-VNET achieves an average reduction of 8%–15% in the number of clusters and maintains a 5%–10% improvement in packet delivery ratio (PDR) compared to baseline algorithms. Additionally, the algorithm incorporates a heat transfer-inspired load-balancing mechanism, ensuring equitable distribution of nodes among cluster leaders (CLs) and maintaining a stable network environment. These results validate GOA-VNET as a reliable and scalable solution for VANETs, with significant potential to support next-generation ITS. Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.