Research Direction I: Intelligent Analysis, Decision-Making, and Control for Next-Generation Power Systems

(1) Aggregation of Emerging Flexible Resources and Coordinated Control of Virtual Power Plants

This direction focuses on emerging flexible resources such as industrial loads, air-conditioning clusters, electric vehicles, and energy storage systems. The research investigates capability modeling, response characteristic identification, bi-level game–based coordination, and multi-agent collaborative mechanisms to support efficient VPP participation in frequency regulation, peak shaving, ancillary services, and renewable energy accommodation.

(2) Optimal Scheduling and Multi-Agent Trading Strategies under Electricity Market Mechanisms

Targeting spot markets, ancillary service markets, and load aggregator mechanisms, this direction develops market clearing models, price forecasting methods, game-theoretic optimization, and reinforcement-learning–based bidding strategies. The goal is to enable optimal trading decisions and multi-scenario market simulation for VPPs, generators, and demand-side participants.

(3) Intelligent Data Analytics and Forecasting for Power Systems

This direction applies deep learning, graph machine learning, and safe reinforcement learning to forecast key indicators including load, electricity prices, and power quality, as well as detect anomalies. It forms an intelligent analytical engine that supports system operations, planning, and demand response.

(4) Large Language Models (LLMs) and Knowledge-Enhanced Intelligent Decision-Making for Power Systems

Focusing on grid regulations, electrical equipment, market rules, and maintenance knowledge, this direction builds domain-specific LLMs and RAG-based systems for the power sector. Research topics include intelligent dispatch assistants, regulatory Q&A, market strategy generation, and equipment maintenance knowledge retrieval, enabling more intelligent, transparent, and explainable decision-making in power system operations.

Research Direction II: Intelligent Coordination and Autonomous Control of Unmanned Systems Swarms

(1) Task Allocation and Cooperative Planning for Large-Scale Unmanned Swarms

This direction investigates distributed task allocation, auction-based mechanisms, multi-task cooperative planning, and spatiotemporal scheduling for heterogeneous swarm systems. The aim is to ensure efficient execution and real-time coordination in large-scale multi-objective missions.

(2) Resilient Coordination and System Robustness in Open Unmanned Swarms

Addressing practical issues such as dynamic agent joining/exiting, communication interruptions, faulty nodes, and potential adversarial information, this direction develops resilient consensus, robust cooperation, and anti-interference coordination frameworks for open multi-agent systems. The focus is on ensuring stability and reliability in complex, constrained, or adversarial environments.

(3) Intelligent Game-Theoretic Decision-Making for Multi-Agent Unmanned Systems

Based on game theory, multi-agent decision-making, and strategy optimization, this direction studies autonomous strategy formation in cooperative, competitive, and mixed game environments. It aims to build game-theoretic decision models applicable to encirclement, surveillance, resource contention, and coordinated missions, enabling adaptive, interpretable, and optimal decision-making in complex tasks.

Research Direction III: Fundamental Theory of Distributed Networked Systems

(1) Coordination, Distributed Optimization, and Game-Theoretic Control in Open and Resilient Multi-Agent Systems

This direction investigates resilient coordination and distributed optimization/game-theoretic control in multi-agent systems subject to dynamic joining/exiting of agents, failures, adversarial information, and incomplete communication. It develops unified models for open MAS, analyzes controllability, reachability, stability, and optimality under dynamic topologies, and establishes algorithms capable of achieving consensus, distributed optimality, or Nash equilibrium under disturbances. The research integrates resilient consensus, f-local filtering, distributed gradient optimization, and learning-based game dynamics to support robust coordination for large-scale unmanned swarms, multi-area power system collaboration, and resource scheduling.

(2) Online Feedback Optimization and Real-Time Optimal Regulation in Dynamic Environments

This direction focuses on achieving continuous “online optimality” in systems operating under dynamic conditions, model uncertainties, and real-time disturbances. It develops online gradient control methods, closed-loop optimal regulation mechanisms, and steady-state optimal tracking frameworks. By integrating data-driven real-time strategies, disturbance rejection, and optimization dynamics, the research establishes online feedback optimization structures with provable convergence, stability, and robustness, applicable to real-time power system regulation, online task execution in unmanned systems, and dynamic resource allocation.