🤖 AI Summary
Addressing the challenges of global optimization and lack of modeling generality in smart grids, this paper proposes a context-free complex-system modeling paradigm. It pioneers the cross-scale integration of game theory with classical optimization techniques—including linear and dynamic programming—to establish a multi-level collaborative optimization model spanning generation, transmission, and consumption. The approach simultaneously captures structural complexity and preserves objective invariance, thereby enhancing computational scalability and policy adaptability without compromising robustness. Through simulation-driven validation, the model demonstrates efficient, high-fidelity optimization performance across diverse grid scales and topologies. This work provides a scalable theoretical framework and simulation platform enabling low-cost, high-accuracy deployment of optimization strategies in real-world smart grids.
📝 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.