Track Ⅱ

Intelligent Energy Agents for New-Type Power Systems: Wind and Solar Power Forecasting, Electricity Price Modeling, and Energy Storage Optimization

面向新型电力系统的智慧能源智能体：风光功率预测、电价建模与储能优化

• Photovoltaic power forecasting (光伏功率预测)
  
• Wind power forecasting (风电功率预测)
  
• Electricity price forecasting (电价预测)
  
• Energy storage optimization (储能系统优化)
• Intelligent agent (智能体)
• Large language models (大语言模型)
  
• Weather foundation models (气象大模型)
  
• Time series forecasting (时序预测)
  
• Source-grid-load-storage coordination (源网荷储协同)
  

• Ultra-short-term, short-term, and medium/long-term forecasting of wind and solar power (风电、光伏功率超短期/短期/中长期预测 )
  
• Electricity price forecasting and trading decision-making in power markets (电力市场电价预测与交易决策)
  
• Modeling, dispatch, and coordinated optimization of energy storage systems (储能系统建模、调度与协同优化 )
  
• Renewable energy forecasting based on weather foundation models and multi-source data fusion (气象大模型与多源数据融合的新能源预测)
  
• Probabilistic forecasting and uncertainty quantification (概率预测与不确定性量化 )
• Multi-task learning, transfer learning, and automated machine learning for energy forecasting (多任务学习、迁移学习与自动机器学习在能源预测中的应用)
  
• Energy dispatch and control based on reinforcement learning and operations research (基于强化学习与运筹优化的能源调度与控制)
  
• LLM-agent-driven perception, forecasting, and decision-making for energy systems (大语言模型智能体驱动的能源系统感知、预测与决策)
  
• Integrated source-grid-load-storage operation and optimization (源网荷储一体化协同运行与优化)
  
• Virtual power plants, demand response, and distributed energy resource aggregation (虚拟电厂、需求响应与分布式能源聚合)

• Against the backdrop of the "dual carbon" goals and the construction of new-type power systems, the large-scale integration of wind and solar power couples output volatility with electricity price uncertainty, severely threatening grid security and renewable energy accommodation. This session targets researchers from universities and research institutes working on power/price forecasting, energy storage optimization, and energy agents, as well as engineers from grid, generation, and storage companies. By exchanging the latest advances along the integrated "forecasting–decision–optimization" pipeline, it aims to foster deployable solutions for renewable dispatch and storage coordination and to promote cross-disciplinary collaboration between artificial intelligence and power systems.
  

• 在"双碳"目标与新型电力系统建设背景下，风电、光伏大规模接入使出力的随机波动与电价不确定性相互耦合，严重威胁电网安全运行与新能源消纳。本单元面向从事功率/电价预测、储能优化与能源智能体研究的高校与科研院所人员，以及电网、发电与储能企业的工程师，围绕"预测—决策—优化"一体化链条交流最新成果，推动形成可落地的新能源调度与储能协同方案，促进人工智能与电力系统的交叉合作。