A Distributed AI Framework for Nano-Grid Power Management and Control

Abstract

Due to their minimal environmental impact, green energy sources like wind turbines and solar panels are increasingly utilized in power systems. However, the power they generate is highly variable, leading to unpredictable fluctuations in power supply. Additionally, advanced smart functions in consumer devices and their unpredictable usage patterns contribute to similar fluctuations in power consumption. These fluctuations present a significant challenge to the stability and quality of the power grid, creating a complex issue of power imbalance that becomes harder to manage. Innovative management and control approaches are necessary to address these challenges and thus support the shift to sustainable energy sources. Artificial intelligence (AI) techniques are increasingly proposed as promising solutions, albeit mostly implemented as isolated solutions within centralized power control systems. To effectively manage the complex and often large scale power systems, this paper advocates the use of a Distributed AI (DAI) framework as imperative in enhancing their agility and stability. An illustrative Nano-Grid example (including the potential use of battery sources in extreme scenarios) is adopted to demonstrate the framework's utility, and a number of power control strategies to safeguard the power system against the variability of both power generators and loads are theoretically formulated and then realized within the proposed framework. Linear Programming, Ant Colony Optimization, Genetic Algorithms, and Particle Swarm Optimization techniques are experimented with, and through simulations, the utility of the DAI framework is demonstrated. The findings underscore the effectiveness and potential benefits of the proposed framework in ensuring the safe and effective operation of power systems with the use of particle swarm optimization amid fluctuating energy scenarios with a small to large number of devices in the nano-grid.