Jichen Zhang

Jichen Zhang, Linwei Sang, Yinliang Xu et al. · tsinghua

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.

Junhui Rao, Yi Liu, Jichen Zhang et al.

This paper proposes a novel analytical framework, denoted the Multiport Analytical Pixel Electromagnetic Simulator (MAPES). MAPES enables efficient and accurate prediction of the electromagnetic (EM) performance of arbitrary pixel-based microwave (MW) and RFIC structures. Unlike the Internal Multiport Method (IMPM), which optimizes only connecting elements within a fixed, gap-separated pixel skeleton, MAPES operates directly on the all-pixel presence/absence formulation used in recent MW/RFIC design. This is enabled by diagonal virtual pixels, an occupancy-to-load mapping, and a multi-layer/via port-level formulation that have no counterpart in IMPM. By introducing virtual pixels and diagonal virtual pixels and inserting virtual ports at critical positions, MAPES captures all horizontal, vertical, and diagonal electromagnetic couplings within a single multiport impedance matrix. Only a small set of full-wave simulations (typically about 1% of the datasets required by AI-assisted EM emulators) is needed to construct this matrix. Subsequently, any arbitrary pixel configuration can be evaluated analytically using a closed-form multiport relation without additional full-wave calculations. The proposed approach eliminates data-driven overfitting and ensures accurate results across all design variations. Using MAPES, comprehensive examples for single- and double-layer PCBs and CMOS processes (180 nm and 65 nm) confirm that high prediction accuracy with 600-2000$\times$ speed improvement is achieved compared to CST simulations. Owing to its efficiency, scalability, and reliability, MAPES provides a practical and versatile tool for AI-assisted MW circuit and RFIC design across diverse fabrication technologies.