Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid

摘要

In practical engineering, the communication networks of industrial systems are complex, and system models are generally unavailable. To overcome the requirement of mathematical models, several artificial-intelligence-based algorithms in multi-access edge computing are introduced for the performance optimization control of a benchmark microgrid in this paper. First, a neural-network-based identification scheme is proposed to combine with the online adaptive dynamic programming learning method, which avoids the requirement of system models. However, the identification errors are not taken into consideration. Next, to realize the model-free purpose without using the identification schemes, an online dual-network-based action-dependent heuristic dynamic programming method and a critic-only Q-learning approach are presented. Finally, the optimal control strategy is applied to a benchmark microgrid system to demonstrate the effectiveness of performance optimization. (C) 2020 Elsevier B.V. All rights reserved.