Optimization of smart grid communication systems

Abstract

This PhD research focuses on optimizing smart grid communication systems through the application of metaheuristic optimization algorithms, specifically Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), as well as advanced communication technologies such as Multiple-Input Multiple-Output (MIMO) and LoRa. The study aims to improve the reliability, efficiency, and security of real-time data exchange in critical smart grid components, including smart metering, home control, and Wide-Area Monitoring Systems (WAMS). In the first part, PSO and ACO are employed to optimize the placement of Data Aggregation Points (DAPs) in networks of 150 Z-wave smart meters deployed across various smart cities, with results showing that PSO provides faster execution, lower latency, and better cost-efficiency compared to ACO, especially in less complex networks. The second part introduces MIMO communication to improve data transmission accuracy and speed within WAMS, demonstrating performance gains in latency, data completeness, and correctness when compared with traditional systems. In the final phase, LoRa technology is utilized to support long-range, low-data-volume transmission for a proposed Wide-Area Network State Monitoring System. Using the IEEE 14-bus system with Phasor Measurement Units (PMUs), the study compares Single-Input Single-Output (SISO) and MIMO configurations under varying Signal-to-Noise Ratios (SNRs), revealing that MIMO significantly reduces the Bit Error Rate (BER) and that higher reporting rates further enhance data accuracy. Overall, the findings demonstrate the effectiveness of optimization and advanced communication techniques in building a more resilient, cost-effective, and high-performance smart grid communication infrastructure