A Context-Free Smart Grid Model Using Complex System Approach
Soufian Ben Amor, Alain Bui, Guillaume Guerard
TL;DR
The paper addresses the challenge of optimizing smart grids amid complexity and heterogeneity. It proposes a context-free complex-system model that decomposes the grid into three layers and employs distributed, knapsack-like optimization, auction-based energy booking, and multi-agent simulation to coordinate local decisions toward global objectives. Key contributions include the formal global objective, the three-layer grid architecture, an iterative resource-allocation framework, and DSM/utility-based strategies validated by initial simulations. The results suggest convergence toward supply-demand equilibrium with a scalable, adaptable framework, with future work aimed at economics, learning, and reduced computational demand.
Abstract
Energy and pollution are urging problems of the 21th century. By gradually changing the actual power grid system, smart grid may evolve into different systems by means of size, elements and strategies, but its fundamental requirements and objectives will not change such as optimizing production, transmission, and consumption. Studying the smart grid through modeling and simulation provides us with valuable results which cannot be obtained in real world due to time and cost related constraints. Moreover, due to the complexity of the smart grid, achieving global optimization is not an easy task. In this paper, we propose a complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels. Thanks to this combination, the optimization can be achieved with flexibility and scalability, while keeping its generality.