Smart Energy Grid Optimization for Climate Modeling Using Terahertz Communication and Quantum-Inspired Multi-objective Dragonfly Algorithm

Effective climate modelling and sustainable energy management depend on linking technology for smart energy grids with high-speed communication networks. The aim of this work is to provide a novel framework improving the gathering and processing of meteorological data in real-time by means of integration of smart energy grid optimization and communication at THz. THz communication allows for the transmission of large amounts of environmental data in a short time between central climate modelling platforms and distributed sensors due to its very high bandwidth and low-latency. The proposed study is the Quantum-Inspired Multi-objective Dragonfly Algorithm (QMDA) that integrates swarm intelligence search behaviour of the Dragonfly Algorithm with quantum-inspired operators that include superposition-based representation and probabilistic position updating to augment exploration potential, convergence rate, and flexibility in the complex optimization problems. This can assist us to effectively control this complex system. QMDA, which has been developed to handle several different objectives vying for attention, is intended to assist in controlling many competing objectives, such as minimizing energy loss, lowering latency, and maximizing data throughput, while maintaining efficient load balancing across the energy grid. In terms of convergence speed, solution variety, and adaptation to dynamic conditions, the simulation findings show that QMDA outperforms considerably traditional single-objectual methods and heuristic procedures. The suggested methodology ensures lowest energy waste and more rapid completion of climate simulation feedback loops by means of simultaneous optimization of energy distribution and THz communication channels. This method sets a basis for the future generation of sustainable infrastructure, characterized by the convergence of high-frequency transmission and sophisticated algorithms to enable precise climate forecasting. The proposed method achieves the energy loss by 13%, latency by 12 (ms), data throughput by 10 Gbps, load balancing index by 0.9%, convergence speed by 51%, diversity score by 0.8, adaptability by 0.77%, and absorption loss by 8 dB. The proposed methodology minimizes energy waste compared to the current techniques, with up to a 13% energy loss reduction and a decrease in latency, throughput, and load balancing, confirmed by the simulation results.