English: Power system optimization is becoming a key enabler of secure, economical, and low-carbon operation in future power systems with high renewable penetration, widespread power-electronic interfacing, and rapidly growing flexibility resources such as energy storage, demand response, and electrified transport. At the same time, increasing nonlinearity, uncertainty, and operational diversity pose major challenges to power system planning, real-time operation, and market coordination. This track aims to publish methodological advances and practical studies on power system optimization for future low-carbon grids. We welcome submissions on AI-enabled power flow optimization, learning-accelerated optimal power flow, decomposition methods, scalable optimization algorithms, and structure-preserving modeling approaches that improve computational efficiency, feasibility, robustness, and interpretability. Papers addressing complex operating conditions are also encouraged, including unbalanced optimal power flow, robust optimization, stochastic optimization, distributionally robust optimization, hybrid optimization-control strategies, and AI-based tools for monitoring, diagnosis, and mitigation. Contributions on high-fidelity simulation and digital-twin-supported optimization are particularly welcome. This track also encourages studies on energy storage optimization and control across multiple services, including energy arbitrage, reserves, frequency regulation, congestion management, and renewable energy integration. Papers on electricity market participation, such as bidding strategies, settlement mechanisms, risk management, price forecasting, and price anomaly analysis, are also within scope. By linking advanced optimization, control, AI-based methods, and market mechanisms with practical power engineering needs, this special issue aims to provide actionable insights for resilient, secure, and economically efficient low-carbon power systems. Comprehensive review papers that synthesize recent progress in optimization methods, AI-based solvers, storage operation, and market applications are also encouraged.

中文: 在高比例新能源并网、大规模电力电子设备接入，以及储能、需求侧响应和电动交通等灵活性资源快速增长的背景下，电力系统优化已成为保障未来低碳电网安全、经济运行的关键驱动力。与此同时，日益增强的非线性、不确定性和运行多样性也给电力系统规划、实时运行和电力市场协调带来了重大挑战。本分论坛旨在汇集用于未来低碳电网的电力系统优化方法学前沿进展与工程应用研究。欢迎关于AI赋能的潮流优化、机器学习加速的最佳潮流（OPF）、分解算法、可扩展优化算法以及保持结构特征的建模方法等领域的稿件，以提高计算效率、可行性、稳健性和可解释性。同时，鼓励投稿关注复杂运行工况的研究，包括三相不对称最佳潮流、鲁棒优化、随机优化、分布鲁棒优化、优化-控制混合策略，以及基于人工智能的监测、诊断与抑制工具。此外，特别欢迎高保真仿真和数字孪生支撑的优化研究。本论坛还鼓励针对储能在能量套利、备用、调频、阻塞管理和新能源消纳等多场景下的优化与控制研究。涉及电力市场参与（如报价策略、结算机制、风险管理、价格预测及电价异常分析）的文章也在征稿范围内。本分论坛致力于将先进的优化、控制、人工智能方法及市场机制与实际电力工程需求紧密结合，为构建具韧性、安全且经济高效的低碳电力系统提供切实可行的解决方案。同时也欢迎对优化方法、AI求解器、储能运行和市场应用最新进展进行系统性总结的综述性论文。