Applied Energy最新文献_第7页

Assessing climate change-induced variability in generation potential and droughts of renewable energy Systems in India 评估印度可再生能源系统发电潜力和干旱的气候变化引起的变异性

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127173

Aravinda De Chinnu Arul Babu , Venkatasailanathan Ramadesigan , Rangan Banerjee , Anoop Singh , Manajit Sengupta

Solar photovoltaic (PV) and wind energy systems are crucial for decarbonizing the electricity sector and achieving climate goals. However, these systems are weather-dependent, and ignoring the potential changes in their generation levels due to climate change could compromise achieving climate targets and meeting future electricity demand. This study evaluates the impact of climate change on the generation potential of wind and solar PV systems in India for three future periods, 2030 (2021–2040), 2050 (2041–2060), and 2070 (2061–2080) compared to the baseline year 2000 (1991–2010), under three emission scenarios: SSP245, SSP370, and SSP585. Solar PV generation levels consistently decline (up to 10 %) across all regions and scenarios. Wind energy shows more pronounced variability (−20 % to 30 %). The South and Southeastern regions of India show improvements in wind potential across all scenarios and time periods. This study also investigated the projected changes in the generation droughts of both energy systems. For solar PV, drought days increase across most regions (exceeding 500 days under SSP370 across the 20-year period). In contrast, wind energy sees a reduction in drought days, especially in parts of South and Southeast India (declines exceeding 50 days across different scenarios). For both energy systems, the patterns of generation drought and generation potential are similar, and indicate that Western and Northern India may be less favorable for the future expansion of solar PV and wind energy, respectively. These results highlight the need to account for the potential impacts in future capacity planning.

太阳能光伏（PV）和风能系统对于电力行业脱碳和实现气候目标至关重要。然而，这些系统是依赖天气的，忽视由于气候变化而导致的发电水平的潜在变化可能会影响实现气候目标和满足未来的电力需求。本研究评估了气候变化对印度未来三个时期(2030年（2021-2040年）、2050年（2041-2060年）和2070年（2061-2080年）与基准年2000年（1991-2010年）相比，在三种排放情景下（SSP245、SSP370和SSP585）风能和太阳能光伏系统发电潜力的影响。在所有地区和情况下，太阳能光伏发电水平持续下降（高达10%）。风能表现出更明显的可变性（- 20%至30%）。印度南部和东南部地区在所有情景和时间段内都显示出风能潜力的改善。本研究还调查了两种能源系统发电干旱的预测变化。对于太阳能光伏，大部分地区的干旱日数增加（在SSP370下，20年间干旱日数超过500天）。相比之下，风能的干旱天数减少，特别是在印度南部和东南部的部分地区（在不同的情况下减少超过50天）。对于这两种能源系统，干旱发电模式和潜在发电模式是相似的，这表明印度西部和北部可能分别不太有利于太阳能光伏和风能的未来扩展。这些结果强调了在未来容量规划中考虑潜在影响的必要性。

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引用次数: 0

Deep reinforcement learning for energy-efficient thermal management in 2U air-cooled server systems 2U风冷服务器系统节能热管理的深度强化学习

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127168

Pratheek Suresh, Kai-Wei Chang, Kim Boon Lua, Chi-Chuan Wang

Reinforcement learning offers a promising path for reducing the energy footprint of server cooling systems. This study develops a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework for the thermal management of a 2U air-cooled server. By assigning an independent agent to each fan and employing a centralized critic, the framework learns cooperative control strategies that eliminate redundant cooling. The agents’ learning is guided by a novel physics-informed reward function that divides the server’s thermal headroom into distinct operational zones, adding penalties to mitigate fan vibrations while dynamically balancing energy efficiency and thermal safety. To validate generalization, the MADDPG algorithm is trained in a simulation environment and subsequently deployed on experimental mock-up servers. A total of five configurations and power maps are used for validation. Each fan agent relies solely on local temperatures of its state space, while the centralized critic receives the global state of the server during training to penalize redundant cooling actions. The MADDPG controller reduced fan energy consumption by an average of 31.4 % compared to a conventional fan-table controller, while maintaining all component temperatures below their critical thresholds. The results also revealed that performance is highly dependent on server layout, with energy savings ranging from 43.8 % in centrally-located CPU configurations to 20.5 % when CPUs are at the chassis extremes, highlighting the importance of hardware-aware control policies.

强化学习为减少服务器冷却系统的能源足迹提供了一条有前途的途径。本研究开发了一个多代理深度确定性策略梯度（madpg）框架，用于2U风冷服务器的热管理。通过为每个风扇分配一个独立的代理并采用一个集中的评论家，该框架学习了消除冗余冷却的合作控制策略。代理的学习由一种新颖的物理信息奖励功能指导，该功能将服务器的热净空划分为不同的操作区域，增加惩罚以减轻风扇振动，同时动态平衡能源效率和热安全。为了验证泛化，在模拟环境中训练madpg算法，随后将其部署在实验模型服务器上。总共使用了五种配置和功率图进行验证。每个风扇代理仅依赖于其状态空间的局部温度，而集中式批评器在训练期间接收服务器的全局状态，以惩罚冗余的冷却动作。与传统的风扇表控制器相比，madpg控制器将风扇能耗平均降低了31.4%，同时将所有组件的温度保持在临界阈值以下。结果还显示，性能高度依赖于服务器布局，节能范围从中央CPU配置的43.8%到机箱极端CPU配置的20.5%，突出了硬件感知控制策略的重要性。

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引用次数: 0

Band degeneracy and convergence in high performing thermoelectric materials 高性能热电材料的能带简并与收敛

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127182

Dezhuang Ji , Natnael F. Haile , Xuan Li , Baosong Li , Moh’d Rezeq , Wesley Cantwell , Lianxi Zheng

Thermoelectric technology is a promising clean energy harvesting approach that enables solid-state collection of waste heat. However, its energy conversion efficiency remains low compared with conventional heat engines. Improving thermoelectric conversion efficiency is challenging because the governing parameters are strongly interdependent. Commonly adopted strategies, including reducing lattice thermal conductivity and optimizing carrier density, are mainly optimization approaches that cannot address the fundamental question of which materials possess intrinsically high thermoelectric performance. Microscopic analysis and transport studies have revealed that band degeneracy is a critical parameter determining the intrinsic merit of thermoelectric materials, a conclusion supported by numerous experimental reports. In this review, we analyze the band degeneracy characteristics of state-of-the-art high-performance thermoelectric materials, classified according to their crystal structures, as crystal symmetry is closely related to band degeneracy. We also discuss the widely explored band convergence strategies in engineering materials’ degeneracy. By highlighting the important role of band degeneracy in thermoelectric materials, this review aims to aid the discovery of new material candidates and the design of effective strategies to enhance thermoelectric performance.

热电技术是一种很有前途的清洁能源收集方法，可以实现固态收集废热。然而，与传统热机相比，其能量转换效率仍然较低。提高热电转换效率是具有挑战性的，因为控制参数是相互依赖的。通常采用的策略，包括降低晶格热导率和优化载流子密度，主要是优化方法，无法解决哪些材料具有高热电性能的根本问题。微观分析和输运研究表明，能带简并是决定热电材料内在性能的关键参数，这一结论得到了大量实验报告的支持。在这篇综述中，我们分析了最新的高性能热电材料的能带简并特性，根据它们的晶体结构进行分类，因为晶体对称性与能带简并密切相关。我们还讨论了在工程材料简并中被广泛探索的带收敛策略。通过强调带简并在热电材料中的重要作用，本文旨在帮助发现新的候选材料和设计有效的策略来提高热电性能。

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引用次数: 0

Buildings-to-grid with generalized energy storage: A multi-agent decomposed deep reinforcement learning approach for delayed rewards 具有广义能量存储的建筑物到网格：延迟奖励的多智能体分解深度强化学习方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127181

Jiahui Jin , Guoqiang Sun , Sheng Chen , Yaping Li , Hong Zhu , Wenbo Mao , Wenlu Ji

The growing penetration of distributed renewable energy and flexible building loads is intensifying the bidirectional building-to-grid (BtG) coupling. However, the inherent heterogeneity between electrochemical batteries and comfort-coupled thermal storage complicates coordinated control. To bridge this gap, the present study proposes a generalized energy storage system (GESS) that represents both devices with a common state of charge and generalized charge/discharge power. An adaptive self-loss term captures both battery self-discharge and temperature-dependent passive heat exchange. The model maps the generalized power of thermal energy storage to equivalent electrical power, while accounting for the thermal inertia of internal spaces and heating, ventilation, and air-conditioning systems. To address delayed rewards in the GESS, a multi-agent decomposed deep reinforcement learning approach is developed. The control problem is formulated as a sequential partially observable Markov decision process with a dual-critic architecture that redistributes immediate rewards to construct delayed rewards. Decentralized actors are optimized using a clipped surrogate objective with combined advantage estimates and control variate stabilization. Numerical experiments on the test system demonstrate that the proposed method enhances building profitability and reduces grid operating costs.

分布式可再生能源的日益普及和柔性建筑负荷的不断增加，加剧了建筑与电网的双向耦合。然而，电化学电池和舒适耦合蓄热之间固有的非均质性使协调控制变得复杂。为了弥补这一差距，本研究提出了一种通用储能系统（GESS），该系统代表具有共同充电状态和通用充放电功率的设备。自适应自损耗项捕获电池自放电和温度依赖的被动热交换。该模型将热能储存的广义功率映射为等效电力，同时考虑到内部空间和供暖、通风和空调系统的热惯性。为了解决GESS中的延迟奖励问题，提出了一种多智能体分解深度强化学习方法。控制问题被表述为一个序列部分可观察的马尔可夫决策过程，该决策过程具有双批评结构，将即时奖励重新分配到构造延迟奖励。分散的行为体使用一个具有综合优势估计和控制变量稳定化的截断代理目标进行优化。在测试系统上进行的数值实验表明，该方法提高了建筑盈利能力，降低了电网运行成本。

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引用次数: 0

Optimization scheduling model incorporating multivariate trapezoidal fuzzy parameters under wind power fluctuation patterns classification 风电波动模式分类下多元梯形模糊参数优化调度模型

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-05 DOI: 10.1016/j.apenergy.2025.127200

Yibo Wang , Qingqing Gao , Bowen Wang , Zhenyu Zhao , Chuang Liu , Junxiong Ge

With the rapid growth of grid-integrated renewable energy capacity, accurately characterizing its uncertainties has become essential for power system scheduling decision-making. To address this issue, based on the multivariate trapezoidal fuzzy parameters for reshaping the uncertainty of renewable energy, this paper proposes a low-carbon economic optimization scheduling method that considers fuzzy chance constraints. First, the distribution characteristics of forecast errors under different wind power fluctuation patterns are analyzed. Based on conventional single trapezoidal fuzzy parameters, a multivariate trapezoidal fuzzy parameter selection model is proposed according to the classification of wind power fluctuation patterns. Secondly, based on the analysis of green certificate-carbon joint trading mechanism, the optimization scheduling model is constructed with the system's total operating cost as the objective, considering multivariate trapezoidal fuzzy parameters under fuzzy chance constraints. Finally, through case studies, compared to traditional models, the scheduling scheme under the proposed model reduces the total system cost by 19.63 %, carbon emissions by 19.56 %, and the renewable energy curtailment rate increases by 3.62 %. The results demonstrate that the proposed model lowers the risk aversion level, while showing significant potential in improving both system economy and low-carbon performance.

随着可再生能源并网容量的快速增长，准确表征其不确定性已成为电力系统调度决策的关键。针对这一问题，本文基于多元梯形模糊参数重塑可再生能源的不确定性，提出了一种考虑模糊机会约束的低碳经济优化调度方法。首先，分析了不同风电功率波动模式下预测误差的分布特征。在传统的单梯形模糊参数的基础上，根据风电波动模式的分类，提出了一种多元梯形模糊参数选择模型。其次，在分析绿色证书-碳联合交易机制的基础上，考虑模糊机会约束下的多变量梯形模糊参数，以系统总运行成本为目标，构建了优化调度模型；最后，通过实例分析，与传统模型相比，所提模型下的调度方案使系统总成本降低19.63%，碳排放降低19.56%，可再生能源弃风率提高3.62%。结果表明，该模型降低了风险规避水平，同时在提高系统经济性和低碳绩效方面具有显著的潜力。

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引用次数: 0

Spatiotemporal sparse autoregressive distributed lag model with extended Regressors for regional wind power forecasting 具有扩展回归量的时空稀疏自回归分布滞后模型用于区域风电预测

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-04 DOI: 10.1016/j.apenergy.2025.127205

Ming Pei , Ruqing Gong , Lin Ye , Lei Chen , Yihui Sun , Yong Tang

Large-scale wind power forecasting is critical for the secure and economic dispatch of power systems, and the spatiotemporal correlation is suggested to improve forecasting accuracy. In this paper, a novel L₁-regularized and Correlation-Constrained Autoregressive Distributed Lag Model with Extended Regressors (LRCC-ARDLX) is proposed for sparse regional wind power prediction, which sufficiently considers spatiotemporal correlations of regional wind farms and effectively coordinates the heterogeneities in different farms. This method first designs a spatiotemporal correlation quantification model, including dynamic time warping, spatially representative wind farms, and their derived information screening, to provide a unified framework for the spatial interdependence of regional wind farms evolving. An autoregressive distributed lag (ARDL) model applicable to multiple wind farm inputs is developed based on this. By introducing spatial-temporally correlated multi-cluster prior information, an extended regressor is proposed to enhance the representation capability, and a two-stage spatial-temporal sparsity criterion is proposed to narrow the optimization scope, thus achieving rapid prediction of large-scale regional wind power while ensuring accuracy. Experiments involving 80 operating wind farms from 4 regions distributed over a wide spatial extent demonstrate the generalization and interpretation of LRCC-ARDLX compared to other commonly considered benchmarks.

大规模风电预测对电力系统的安全经济调度至关重要，提出了利用时空相关性提高预测精度的方法。本文提出了一种基于扩展回归量的l1正则化关联约束自回归分布滞后模型（LRCC-ARDLX），用于稀疏区域风电预测，充分考虑了区域风电场的时空相关性，有效地协调了不同风电场的异质性。该方法首先设计了包括动态时间规整、空间代表性风电场及其衍生信息筛选在内的时空相关量化模型，为区域风电场演化的空间相互依存关系提供了统一的框架。在此基础上建立了适用于多个风电场输入的自回归分布滞后（ARDL）模型。通过引入时空相关的多聚类先验信息，提出了扩展回归量来增强表征能力，提出了两阶段时空稀疏度准则来缩小优化范围，从而在保证精度的前提下实现了大范围区域风电的快速预测。涉及分布在广阔空间范围内的4个地区的80个运行风电场的实验表明，与其他通常考虑的基准相比，LRCC-ARDLX的泛化和解释效果更好。

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引用次数: 0

Subtransmission grid control via online feedback optimization 通过在线反馈优化控制子输电网

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-04 DOI: 10.1016/j.apenergy.2025.127142

Lukas Ortmann , Jean Maeght , Patrick Panciatici , Florian Dörfler , Saverio Bolognani

The increasing electric power consumption and the shift toward renewable energy resources demand for new ways to operate transmission and subtransmission grids. Online Feedback Optimization (OFO) is a feedback real-time control method that can be employed to enable optimal operation of these grids. Such controllers can maximize grid efficiency (e.g., minimizing curtailment) while satisfying grid constraints like voltage and current limits. The OFO control method is tailored and extended to handle discrete inputs and it is explained how to design an OFO controller for the subtransmission grid. A novel benchmark is presented and published that corresponds to the real French subtransmission grid on which the proposed controller is analyzed in terms of robustness against model mismatch, constraint satisfaction, and tracking performance. It is shown that OFO controllers can help utilize the grid to its full extent, virtually reinforce it, and operate it optimally and in real-time by using the flexibility offered by renewable generators connected to distribution grids.

不断增加的电力消耗和向可再生能源的转变需要新的方式来运行输电和亚输电电网。在线反馈优化（OFO）是一种实时反馈控制方法，可用于实现电网的优化运行。这种控制器可以最大限度地提高电网效率（例如，最小化弃电），同时满足电压和电流限制等电网约束。对OFO控制方法进行了调整和扩展，以处理离散输入，并说明了如何设计用于子输电网的OFO控制器。提出并发表了一个新的基准，该基准对应于实际的法国子输电网，在该基准上分析了所提出的控制器对模型失配的鲁棒性、约束满足性和跟踪性能。研究表明，OFO控制器可以利用可再生能源发电机连接到配电网所提供的灵活性，帮助充分利用电网，实际上加强电网，并优化实时运行电网。

引用次数: 0

Improving energy resilience in cellular base stations and critical infrastructures: A comprehensive review from multidimensional aspects 提高蜂窝基站和关键基础设施的能量弹性：从多维方面进行综合评述

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-04 DOI: 10.1016/j.apenergy.2025.127152

R. Bin Mofidul, M.J. Hossain, A. Zamee, M.M. Alam

The energy demands of cellular base stations have escalated significantly across successive network generations, with 5G and 6G deployments imposing substantially higher power requirements. In addition, the increasing frequency of extreme climate events has exposed the vulnerability of cellular base stations and their adjacent critical infrastructures, leading to cascading disruptions in communication and essential services. A critical analysis of previous surveys identifies the absence of an integrated framework that concurrently addresses service continuity, energy coordination, and adaptive risk mitigation. To address this gap, this review introduces multidimensional emergency resilience improvement techniques that adopt a unified, layered approach to achieve optimal resilience across electrical and communication systems. This approach enables resilient system design by hardening cellular base stations for robust communication services, providing guidance on energy regulation for hybrid and distributed power coordination, and implementing uncertainty-handling schemes for predictive and adaptive system control. This article comprehensively analyzes each dimension, identifies existing research gaps, and proposes an integrated energy-routing and control structure that ensures uninterrupted operation of cellular base stations and critical infrastructures, even during grid blackouts or natural disasters.

蜂窝基站的能源需求在连续几代网络中显著升级，5G和6G部署带来了更高的电力需求。此外，极端气候事件日益频繁，暴露了蜂窝基站及其邻近关键基础设施的脆弱性，导致通信和基本服务的级联中断。对以往调查的批判性分析表明，缺乏一个同时解决服务连续性、能源协调和适应性风险缓解问题的综合框架。为了解决这一差距，本综述介绍了多维应急恢复能力改进技术，该技术采用统一的分层方法来实现电气和通信系统的最佳恢复能力。该方法通过强化蜂窝基站以实现稳健的通信服务，为混合和分布式电力协调提供能量调节指导，并为预测和自适应系统控制实施不确定性处理方案，从而实现弹性系统设计。本文全面分析了每个维度，确定了现有的研究差距，并提出了一种集成的能量路由和控制结构，以确保蜂窝基站和关键基础设施的不间断运行，即使在电网停电或自然灾害期间。

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引用次数: 0

Adaptive energy scheduling strategy for port logistics systems: A dual-consolidation continual reinforcement learning approach 港口物流系统的自适应能源调度策略：双巩固持续强化学习方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-04 DOI: 10.1016/j.apenergy.2025.127169

Shiyu Wang , Yujing Zheng , Jing Chen , Zhaoxiang Li , Yuxiong Ji , Yuchuan Du

The utilization of clean energy and microgrids offers significant opportunities for the green transition of port logistics systems. However, the dual uncertainties in energy generation and consumption pose challenges for typical rule- or model-based energy scheduling methods. This study investigates the dynamic interactions among clean energy generation, energy storage, the main grid, and energy consumption within a port microgrid, and proposes a dual-consolidation continual reinforcement learning (DC-CRL) framework for adaptive energy scheduling in real time. The framework incorporates elastic weight consolidation to protect core parameters of the value function and policy distillation to regularize policy outputs, mitigating catastrophic forgetting and enhancing long-term adaptability. Meanwhile, a rolling reward window is introduced to improve decision quality by encouraging globally optimal behavior, and a dynamic action space is employed to ensure physical feasibility, allowing the agent to adjust its actions in real time according to the state of the storage device. To verify the performance of the proposed approach, we construct key baselines, including a rule-based allocation strategy and a system-optimal strategy with perfect foresight. Experimental results based on Shanghai Yangshan Port demonstrate that DC-CRL achieves a 5.90 % improvement in economic and environmental performance and a 15.0 % enhancement in convergence speed compared with the baseline methods. The case study further provides wind–solar configuration recommendations to support intelligent microgrid control and green port development.

清洁能源和微电网的利用为港口物流系统的绿色转型提供了重要机会。然而，能源生产和消费的双重不确定性对典型的基于规则或模型的能源调度方法提出了挑战。本文研究了港口微电网中清洁能源发电、储能、主电网和能耗之间的动态相互作用，提出了一种双巩固持续强化学习（DC-CRL）的自适应实时能源调度框架。该框架采用弹性权值巩固来保护价值函数的核心参数，采用政策蒸馏来规范政策输出，减轻灾难性遗忘，增强长期适应性。同时，引入滚动奖励窗口，通过鼓励全局最优行为来提高决策质量；采用动态动作空间来保证物理可行性，使智能体能够根据存储设备的状态实时调整动作。为了验证该方法的性能，我们构建了关键基线，包括基于规则的分配策略和具有完美预见的系统最优策略。基于上海洋山港的实验结果表明，与基线方法相比，DC-CRL方法的经济和环境性能提高了5.90%，收敛速度提高了15.0%。案例研究进一步提供了风能-太阳能配置建议，以支持智能微电网控制和绿色港口发展。

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引用次数: 0

Multi-task learning for solving OPF in an evolving environment 在不断变化的环境中求解OPF的多任务学习

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-04 DOI: 10.1016/j.apenergy.2025.127174

Yixiong Jia , Yi Wang , Yao Zhou

Optimal Power Flow (OPF) aims to minimize operating cost subject to AC network constraints, but its nonconvex, nonlinear nature makes the problem NP-hard, which in practice motivates solving simplified models or using conventional nonlinear solvers; both approaches typically fall short of real-time, high-quality AC-feasible solutions. By leveraging universal approximation ability with fast inference, modern machine-learning methods have become a promising direction for OPF surrogates. Despite the promising results achieved by existing methods, they typically focus on static scenarios without dataset distribution shifts. In contrast, practical power systems operate in an evolving environment where scenarios can change dynamically (e.g., generator outages), causing existing methods to fail to provide accurate OPF solutions. By proposing a multi-task learning framework, we aim to expand the application region of data-driven OPF methods. Specifically, to address the weak correlation across different scenarios, we model multiple scenarios simultaneously through shared network parameters in the proposed framework, with a hypernetwork facilitating knowledge transfer. Meanwhile, to address dataset imbalance under dynamic scenarios, we introduce an error-focused up-sampling method that resamples data exhibiting large deviations from the pre-trained model’s predictions. Furthermore, to balance the training process, we adopt an adaptive-weight algorithm that assigns trainable weights to each sample, updated alongside the hypernetwork weights in a dual ascent manner. Simulation results based on the 14-bus system and 118-bus system show that the proposed framework can provide optimality and feasibility-enhanced OPF solutions in near real time compared to existing methods with or without a post-processing step.

最优潮流（OPF）的目标是在交流网络约束下最小化运行成本，但其非凸、非线性特性使得问题np困难，这在实践中促使人们求解简化模型或使用传统的非线性求解器；这两种方法通常都无法提供实时、高质量的交流可行解决方案。利用快速推理的通用逼近能力，现代机器学习方法已成为OPF代理的一个有前途的方向。尽管现有方法取得了令人鼓舞的结果，但它们通常侧重于没有数据集分布变化的静态场景。相比之下，实际的电力系统运行在一个不断变化的环境中，场景可以动态变化（例如，发电机停机），导致现有方法无法提供准确的OPF解决方案。我们提出了一个多任务学习框架，旨在扩大数据驱动的OPF方法的应用范围。具体而言，为了解决不同场景之间的弱相关性，我们在提出的框架中通过共享网络参数同时对多个场景进行建模，并使用超网络促进知识转移。同时，为了解决动态场景下的数据不平衡问题，我们引入了一种以误差为中心的上采样方法，对与预训练模型预测偏差较大的数据进行重采样。此外，为了平衡训练过程，我们采用了一种自适应权重算法，该算法为每个样本分配可训练的权重，并以双上升方式与超网络权重一起更新。基于14总线系统和118总线系统的仿真结果表明，与没有后处理步骤的现有方法相比，所提出的框架可以提供近实时的最优性和可行性增强的OPF解决方案。

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引用次数: 0