Special Session

Special Session I: Resilience enhancement strategy of power system against extreme events 电力系统应对极端事件的弹性增强策略

In the face of an increasingly unpredictable climate and the rising frequency of extreme weather events, ensuring the resilience of power systems has become a paramount concern for utilities, governments, and researchers. Power system resilience refers to the ability of the grid to withstand, adapt to, and rapidly recover from disruptive events, minimizing the impact on electricity supply and maintaining critical services. This special session covers different comprehensive resilience enhancement strategies to address the challenges posed by extreme events. 面对日益不可预测的气候和日益频繁的极端天气事件，确保电力系统的弹性已成为公用事业、政府和研究人员最关心的问题。电力系统弹性是指电网承受、适应和迅速从破坏性事件中恢复的能力，最大限度地减少对电力供应的影响，并维持关键服务。本次特别会议涵盖了应对极端事件挑战的不同综合增强韧性战略。

Related Topic征稿相关主题:
1) Fault modeling and fault propagation mechanism under extreme events极端事件下的故障建模与故障传播机制;
2) Resilience evaluation of new-type power system新型电力系统弹性评估;
3) Power system planning against extreme events针对极端事件的电力系统规划;
4) Power system operation during extreme events极端事件下电力系统运行;
5) Optimal restoration of power system after extreme events极端事件后电力系统的最优恢复;;
6) AI-based applications for improving power system resilience提高电力系统弹性的人工智能应用.

Special Session II: Electricity market with a high share of clean energy and energy storage 清洁能源和储能份额较高的电力市场

The electricity market can optimize the resource allocation of the power sector in different time scales. Different countries have developed their respective market structures and mechanisms. However, most electricity markets are designed for fossil-fuel-dominated power systems. With the increase of clean energy (e.g., hydropower, wind power, and solar power) and energy storage (e.g., pumped hydro and electrochemical), existing market designs might become incompetent to integrate renewables and guarantee power loads. Therefore, it is necessary to study the topic of the electricity market with a high share of clean energy and energy storage.电力市场可以在不同的时间尺度上优化电力部门的资源配置。各国发展了各自的市场结构和机制。然而，大多数电力市场都是为化石燃料主导的电力系统设计的。随着清洁能源(如水电、风电、太阳能)和储能(如抽水蓄能、电化学)的增加，现有的市场设计可能无法整合可再生能源并保证电力负荷。因此，有必要研究清洁能源和储能占比高的电力市场这一课题。

Related Topic相关主题:
• Long-term market for clean energy and energy storage清洁能源和储能的长期市场
• Day-ahead and real-time market with a high share of clean energy清洁能源高份额的日前实时市场
• Modeling of hydropower, wind, solar, or storage in the electricity market 电力市场中水电、风能、太阳能或储能的建模
• Ancillary service market design 辅助服务市场设计
• Pricing of clean energy and energy storage 清洁能源与储能定价
• Market design with a high share of clean energy 清洁能源高占有率的市场设计
• Coordination of different markets 不同市场的协调
• Electricity market simulation 电力市场模拟
• Market behaviors analysis 市场行为分析
• Efficient market cleaning technologies 高效的市场清洁技术

Special Session III: Control and Optimization of New Type Power Systems 新型电力系统的控制与优化 

In the context of rapid global energy transformation, this session focuses on the research and practical applications related to new type power systems. Here, in-depth discussions revolve around innovative control techniques that can adeptly handle the fluctuating power flows resulting from the extensive integration of renewable energy sources. Advanced control and optimization strategies are devised to guarantee the stability and reliability of new type power systems, regardless of the intermittent nature of renewables. This session, indeed, paves the way for the construction of a cleaner, more efficient, and reliable power ecosystem, fostering a vibrant environment for knowledge exchange and innovation generation. 在全球能源加速转型的大背景下，本次会议的主题是新型电力系统相关的研究与实际应用。会议围绕创新控制技术展开深入研讨，这些技术能够妥善应对因大规模接入可再生能源而产生的功率波动问题。尽管可再生能源发电存在间歇性不稳定问题，但依靠先进的控制和优化策略，能够全方位保障新型电力系统稳定、可靠地运行。本次会议不仅将会为构建更清洁、更高效、更可靠的电力生态系统奠定基础，同时也会为知识交流与创新营造活跃的环境。

Related Topic征稿相关主题:
• Variable Renewable Energy Sources Forecasting 可变可再生能源预测
• Economic Dispatch Optimization 经济调度优化
• Control Strategies for Different Energy Storage Technologies 不同储能技术的控制策略
• Control and Optimization of Integrated Energy System 综合能源系统的控制与优化
• Aggregation and Optimization of Virtual Power Plant 虚拟电厂的聚合与优化
• Energy Management of the Integrated Energy System and the Virtual Power Plant 综合能源系统和虚拟电厂的能源管理
• Collaborative Modeling and Optimization of Power Transmission and Carbon emissions 电力传输和碳排放的协同建模与优化

Special Session IV: Nexus Between Smart Energy and Intelligent Transportation Systems under Dual Carbon Target 双碳”目标下的智慧能源-智慧交通耦合

Moving towards a low-carbon future, the proliferation of electric/hydrogen vehicles and the wide deployment of energy refueling stations would greatly deepen the interdependence of energy and transportation systems, accelerating the formation of integrated energy-transportation system (IETS). The development in intelligent transportation system is creating many emerging concepts such as car sharing and autonomous driving. These factors would profoundly reshape the human-driving dominated IETS. Meanwhile, advances in power-to-hydrogen, PV-ESS integrated charging station, etc. have also enriched the connotation in energy sector. The above revolution calls for advanced modeling and simulation techniques, as well as opening up a new direction in the IETS research field. 在全球低碳愿景的驱动下，电动汽车/氢燃料汽车的快速渗透和能源补给站的广泛部署将大大加深能源系统与交通系统之间的相依性，推动集成能源-交通系统（IETS）的形成。与此同时，智慧交通系统的发展带来了许多新兴概念，如车辆共享和自动驾驶，这些因素将深刻重塑以人工驾驶为主导的IETS。此外，电转氢、光伏-储能一体化充电站等技术的发展也极大丰富了能源领域的内涵。上述变革亟需配套发展先进的建模和仿真技术，同时也为IETS领域开辟了新的研究方向。

Related Topic征稿相关主题:
-Modeling and simulation of IETS/ IETS的建模与仿真技术
-Low carbon operation & planning of IETS/ IETS的低碳运行与规划
-Mechanism design for stimulating flexibilities in IETS / 面向IETS灵活性挖掘的机制设计
-Synergy analysis between intelligent transportation system and smart energy system智慧交通系统与智慧能源系统协同分析
-Resilience enhancement and vulnerability analysis of IETS / IETS韧性增强与脆弱性分析
-Cyber-physical analysis of IETS / IETS信息-物理分析

Special Session V: AI-aided control for more electric transportation system 面向电气化交通系统的智能辅助控制

This special session explores the cutting-edge integration of artificial intelligence with control systems for advancing electrified transportation. As the world moves toward sustainable mobility, the complexity of managing electric transportation systems demands innovative solutions. This session brings together researchers, engineers, and practitioners to discuss how AI technologies can enhance the control, efficiency, and reliability of electric vehicles, charging infrastructure, and integrated transportation networks. 本专题旨在探讨人工智能与控制系统在推动电气化交通中的前沿融合。随着全球迈向可持续出行，管理电气化交通系统的复杂性需要创新解决方案。该专题汇聚了研究人员、工程师和实践者，共同探讨如何利用人工智能技术提升电动车辆、充电基础设施及综合交通网络的控制效率和可靠性。

Machine learning approaches for optimizing power management and distribution in electric vehicles, advanced predictive control strategies for battery management systems, AI-driven solutions for electric fleet operations, intelligent charging infrastructure coordination, and robust control methods for electric propulsion systems. The session welcomes contributions addressing both theoretical frameworks and practical implementations, with particular emphasis on real-world applications and challenges in transitioning to more electric transportation systems. Topics of interest include but are not limited to the following. 人工智能可全面赋能交通电气化，包括电动车辆电能管理与优化分配、先进的电池管理系统预测控制、面向电动车队运营的人工智能驱动解决方案、智能充电基础设施协调，以及电力推进系统的鲁棒控制。本专题欢迎涵盖理论框架与实际应用的研究成果，特别关注向更多交通电气化系统转型过程中面临的理论与实际挑战。感兴趣的主题包括但不限于以下内容：

Related Topic相关主题:
• Deep learning techniques for dynamic power allocation and energy management 深度学习技术在动态功率分配与能源管理中的应用
• Reinforcement learning applications in electric vehicle control systems 强化学习在电动车辆控制系统中的应用
• AI-enhanced fault detection and predictive maintenance 基于人工智能的故障检测与预测性维护
• Smart charging strategies and vehicle-to-grid integration 智能充电策略与车网协同集成
• Neural network-based control for electric propulsion systems 基于神经网络的电力推进系统控制
• Multi-agent systems for coordinating electric transportation networks 多智能体系统在电气化交通网络协调中的应用
• Hybrid AI approaches for improving system reliability and efficiency 混合人工智能方法用于提高系统可靠性与效率
• Real-time optimization of electric vehicle performance and range 电动车辆性能与续航的实时优化
• AI solutions for electric public transportation systems 面向电动公共交通系统的人工智能解决方案
• Intelligent control strategies for electric aerospace applications 电动航空应用的智能控制策略

Special Session VI: Planning and Operation of Low-Carbon Integrated Energy Systems with Uncertainties 考虑不确定性的低碳综合能源系统的规划与运行

This Special Session is concerned with the research on the key fundamental issues of low-carbon integrated energy systems (LCIESs) planning and operation under uncertainties, which consist of four parts: (1) Modeling of LCIESs, in inclusion of modeling the dynamic behaviors of various energy devices as well as their connections to form an IES model; (2) development of high-dimensional multi-objective stochastic optimization algorithms; (3) development of decision-making support for determination of the final optimal solution for the planning and operation of LCIESs under uncertainties, selected from the Pareto sets of the multi-objective optimization computation, and (4) mechanism design for distributed LCIES to participate in the electricity market under various interest entities. 本次专栏关注低碳综合能源系统（LCIESs）在不确定性条件下的规划与运行的关键基础问题，内容包括四个部分：1. 低碳综合能源系统的建模，包括对各种能源设备的动态行为建模以及它们之间的连接，形成综合能源系统（IES）模型； 2. 开发高维多目标随机优化算法，以应对系统中的不确定性； 3. 决策支持系统的开发，用于从多目标优化计算的帕累托解集中选择最终的最优解，以实现低碳综合能源系统的规划与运行； 4. 分布式低碳综合能源系统参与电力市场的机制设计，考虑不同利益主体的参与。
The referred methods could be applied to plan a power network based LCIESs and investigate the economy and reliability of LCIESs under uncertainties, which could be achieved using distributed CHP and CCHP, heat storages, cool storages and hydrogen storages, and investigate the smooth peaks and valleys of power generation and loads, respectively.

This Special Session aims to publish high-quality, original research papers in the overlapping fields of: 这些方法可应用于基于电力网络的低碳综合能源系统规划，并研究其在不确定性条件下的经济性和可靠性。具体而言，可以通过分布式热电联产（CHP）、冷热电联产（CCHP）、热储存、冷储存和氢储存等技术，分别研究发电和负荷的削峰填谷。本次专栏旨在发表高质量、原创性的研究论文，涵盖以下交叉领域的前沿内容：

Related Topic征稿相关主题:
• Advanced Simulation Models of LCIESs: Leveraging big data and machine learning to develop next-generation simulation models that accurately represent the dynamics of low-carbon technologies within integrated systems 低碳综合能源系统的先进仿真模型：利用大数据和机器学习技术开发下一代仿真模型，精准地模拟低碳技术在综合系统中的动态行为
• Uncertainty Modeling: Focusing on characterizing the uncertainties of massive new energy sources like solar and wind power, and their impact on the stability and reliability of LCIES不确定性建模：专注于对太阳能、风能等大规模新能源的不确定性特征进行刻画，并分析其对低碳综合能源系统稳定性与可靠性的影响
• Optimization Techniques for Energy Efficiency: Exploring cutting-edge optimization algorithms that aim to maximize energy efficiency and minimize carbon emissions 能源效率优化技术：探索前沿的优化算法，旨在最大化能源效率并最小化碳排放
• Data-Enhanced Decision-Making Support: Covers the incorporation of data analytics into decision support tools to facilitate informed decision-making under uncertainty数据驱动的决策支持：将数据分析融入决策支持工具，助力在不确定性条件下做出明智的决策
• Coupling of Heterogeneous Energy Sources: Analyzing and optimizing the synergistic operations of various energy types within an IES to enhance overall system performance and energy efficiency 异质能源耦合：分析并优化综合能源系统中不同类型能源之间的协同运行，以提升整体系统性能和能源效率
• Multi-Market Mechanism Design: Designing coupling mechanisms between different markets (e.g., electricity and carbon markets) to enhance the consumption rate of new energy sources and align with low-carbon goals. 多市场机制设计：设计电力市场与碳市场等不同市场之间的耦合机制，以提高新能源的消纳率，并与低碳目标相一致

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