CORTEXA
← Browse
arxiveess.SY2026-07-16Cited by 17

Distributed Cooperative Control of BESSs in AC and DC Hybrid Microgrid and Its Energy Internet Paradigm

Yalin Zhang, Zhongxin Liu, Zengqiang Chen

An AC and DC hybrid microgrid, which inherits advantages of AC and DC microgrids and discards some disadvantages, is considered to be the most promising power network structure and gradually applied in the community. Usually, the AC subgrid and the DC subgrid are interconnected by Bidirectional Interlink Power Converters (BILPCs). Besides, in view of the different droop characteristics of AC subgrid and the DC subgrid, it is necessary to design a suitable distributed secondary controller for an AC and DC hybrid microgrid. Accordingly, this paper proposes a flexible and scalable distributed control framework for an AC and DC hybrid battery energy storage system (ADHB) with BILPCs in an Energy Internet (EI) paradigm. An ADHB governed by multi agent systems via a cloud server can reach the State-of-Charge balance, proportional power sharing, frequency and voltage restoration. The proposed control framework provides the group play-and-plug by adding or removing an inter-MASs interaction link. For a single BILPC in an ADHB, active/reactive power, frequency and voltage are adjusted by an AC BESS. For the parallel BILPCs in EI, a decentralized secondary control scheme is proposed. Communication delay issues and stability are analysed. Then, the relevant simulation results verify the correctness of the proposed scheme.

View free PDFSource page

Related papers

arxiveess.SY2026-07-24

Fast Frequency Services from HVDC-Connected Offshore Wind Power Plants: A Review in the European Context

Zhenghua Xu, George Alin Raducu, Behnam Nouri, Oscar Saborío-Romano, Nicolaos A. Cutululis

The rapid expansion of offshore wind energy is central to the European Union's climate-neutrality targets, with High Voltage Direct Current-connected offshore wind power plants (HVDC-OWPPs) becoming increasingly important for integrating gigawatt-scale renewable generation over l…

View free PDFSource page
arxivcs.ROeess.SY2026-07-24

Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics

Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros

Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nomin…

View free PDFSource page
arxivcs.NIcs.MAeess.SY2026-07-24

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

View free PDFSource page
arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

View free PDFSource page
arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

View free PDFSource page