With the increasing penetration of distributed energy resources, electric vehicles, and prosumers, the demand for secure, scalable, and low-latency transaction platforms in smart grid–based transactive energy systems has grown substantially. Although blockchain technology offers a promising solution for decentralized energy trading, the choice of consensus mechanism critically determines system performance and practical feasibility. This study evaluates the relative performance of Clique, Istanbul Byzantine Fault Tolerance (IBFT), and proof of work (PoW) consensus algorithms to identify the most suitable approach for transactive energy applications in a smart grid environments. Using a five-node blockchain network, key performance indicators (KPIs)—namely, latency, throughput, and transaction failure rate—are quantified for essential market functions, including bidding, market clearing, payment settlement, and balance queries. The results demonstrate that permissioned consensus mechanisms significantly outperform PoW in term of responsiveness, scalability, and reliability. Among the evaluated approaches, IBFT exhibits the highest throughput and the lowest latency, making it the most suitable choice for real-time and near-real-time energy market operations. Clique delivers satisfactory performance in small-scale deployments but exhibits scalability limitations as transaction volumes increase. In contrast, PoW suffers from excessive latency and high failure rates, rendering it unsuitable for smart grid service operations. Overall, the findings indicate that permissioned blockchain platforms employing IBFT can effectively support efficient, secure, and scalable transactive energy markets for future smart grid infrastructures.

The rapid integration of distributed energy resources and power-electronic-interfaced loads requires sophisticated planning techniques for modern radial distribution systems (RDS). In this paper, we propose a comprehensive multi-objective optimization framework for the coordinated placement and size of electric vehicle charging stations (EVCS), distributed generations (DGs), and soft open points (SOPs) in RDS. The developed objective function concurrently minimizes loss, improves the voltage profile, enhances the power factor, reduces harmonic distortion, and maximizes the utilization of substation capacity, subject to operational and technical constraints. Teaching Learning Based Optimization (TLBO) and Harris Hawks Optimization (HHO) were used and compared to evaluate solution resilience and convergence efficiency. The outcomes showed that system coordination consistently improved network efficiency, voltage stability, feeder balance, and power quality. Coordinated multi-device integration delivered better technical performance and ensured steady operation within regulatory voltage and harmonic limits. The robustness and dependability of the suggested optimization framework were validated through statistical analysis, which showed that HHO outperformed TLBO in terms of convergence behavior and solution quality. In addition to improving technical performance, the suggested framework supports the development of sustainable power systems by encouraging the integration of renewable energy sources, grid modernization, and the adoption of electrified transportation. The study supports SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action). Overall, the suggested coordinated optimization approach offers a scalable and long-lasting solution for active distribution networks prepared for the future.