IEEE Transactions on Power Systems最新文献

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Efficient High-Order Participation Factor Computation via Batch-Structured Tensor Contraction 基于批结构张量收缩的高效高阶参与因子计算

IF 7.2 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Power Systems

Pub Date : 2025-11-10 DOI: 10.1109/TPWRS.2025.3631327

Mahsa Sajjadi;Kaiyang Huang;Kai Sun

Participation factors (PFs) quantify the interaction between system modes and state variables, and they play a crucial role in various applications such as modal analysis, model reduction, and control design. With increasing system complexity, especially due to power electronic devices and renewable integration, the need for scalable and high-order nonlinear PF (NPF) computation has become more critical. This paper presents an efficient tensor-based method for calculating NPFs up to an arbitrary order. Traditional computation of PFs directly from normal form theory is computationally expensive—even for second-order PFs—and becomes infeasible for higher orders due to memory constraints. To address this, a tensor contraction–based approach is introduced that enables the calculation of high-order PFs using a batching strategy. The batch sizes are dynamically determined based on the available computational resources, allowing scalable and memory-efficient computation.

参与因子（PFs）量化了系统模式和状态变量之间的相互作用，它们在模态分析、模型简化和控制设计等各种应用中起着至关重要的作用。随着系统复杂性的增加，特别是电力电子器件和可再生集成，对可扩展和高阶非线性PF （NPF）计算的需求变得更加迫切。本文提出了一种基于张量的计算任意阶npf的有效方法。直接从范式理论计算PFs的传统方法在计算上是昂贵的，即使对于二阶PFs也是如此，并且由于内存限制，对于高阶PFs变得不可行。为了解决这个问题，引入了一种基于张量收缩的方法，该方法可以使用批处理策略计算高阶pf。批大小是根据可用的计算资源动态确定的，允许可扩展和内存高效的计算。

引用次数: 0

2025 Index IEEE Transactions on Power Systems 电力系统学报

IF 7.2 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Power Systems

Pub Date : 2025-11-10 DOI: 10.1109/TPWRS.2025.3628151

引用次数: 0

Quality-Diversity Learning Enabled Multi-Alternative Unit Commitment Optimization 质量-多样性学习实现多备选单元承诺优化

IF 7.2 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Power Systems

Pub Date : 2025-11-10 DOI: 10.1109/TPWRS.2025.3631269

Yixi Chen;Jizhong Zhu;Cong Zeng

This letter proposes a novel quality-diversity learning (QDL) method for multi-alternatives unit commitment (UC) optimization. Existing UC methods focus solely on finding a single global optimum, neglecting insights from alternative solutions with competitive performance. In contrast, QDL maintains a multi-cell agent archive populated with multiple high-performing UC policies, each sharing the same objective while evolving to explore distinct behavioral regions, enabling simultaneous optimization of solution quality and diversity. The resulting diverse solutions catering to various dispatch preferences not only enhance operational preparedness, but also allow rapid retrieval of alternatives if feasibility tests fail. Case studies on several standard test systems confirm the effectiveness of the method.

本文提出了一种新的多备选单元承诺（UC）优化的质量多样性学习（QDL）方法。现有的UC方法只关注于寻找单一的全局最优，而忽略了从具有竞争性能的替代解决方案中获得的见解。相比之下，QDL维护了一个多单元代理存档，其中填充了多个高性能UC策略，每个策略共享相同的目标，同时发展以探索不同的行为区域，从而同时优化解决方案质量和多样性。由此产生的满足各种调度偏好的各种解决方案不仅增强了作战准备，而且在可行性测试失败时还可以快速检索备选方案。几个标准测试系统的案例研究证实了该方法的有效性。

引用次数: 0

Online Spatiotemporal Ensemble Learning for Load Forecasting Against Anomalous Events 针对异常事件的在线时空集成学习负荷预测

IF 7.2 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Power Systems

Pub Date : 2025-11-10 DOI: 10.1109/TPWRS.2025.3631329

Yaqi Zeng;Pengfei Zhao;Di Cao;Zhe Chen;Weihao Hu

This letter proposes a novel online spatiotemporal ensemble learning framework that can rapidly adapt to load pattern changes caused by abnormal events. Unlike existing online learning approaches that focus solely on temporal dependencies, the proposed method also exploits spatial correlations across different regions to achieve fast convergence. An online complementary learning network that can instantly adapt to new patterns while recalling similar historical knowledge is first built as the basic forecast expert to extract spatial and temporal features. The two information streams are then combined using an online convex programming framework, which is further solved by exponentiated gradient descent and reinforcement learning methods. Experiments on real-world electricity load datasets from the COVID-19 period demonstrate the proposed method's effectiveness.

本文提出了一种新的在线时空集成学习框架，该框架可以快速适应异常事件引起的负载模式变化。与现有的仅关注时间依赖性的在线学习方法不同，该方法还利用了不同区域之间的空间相关性来实现快速收敛。首先建立一个在线互补学习网络，该网络可以在回顾相似的历史知识的同时立即适应新的模式，作为基本预测专家提取时空特征。然后使用在线凸规划框架将两个信息流组合起来，并通过指数梯度下降和强化学习方法进一步求解。在COVID-19期间的实际电力负荷数据集上进行的实验证明了该方法的有效性。

引用次数: 0

Distributed Continuous Time-Varying Optimization for Microgrids with Heterogeneous Renewable Energy Systems 异构可再生能源微电网的分布式连续时变优化