Applied Energy最新文献_第4页

Nationwide insights on solar deployment trends and spatial inequalities revealed by vision transformer models 由视觉变压器模型揭示的太阳能部署趋势和空间不平等的全国见解

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127411

Rajanie Prabha , Zhecheng Wang , Chad Zanocco , June Flora , Ram Rajagopal

The transition to renewable energy is essential for achieving sustainable development goals and mitigating the impacts of climate change. Solar energy, in particular, has emerged as a viable and cost-effective option, yet its adoption exhibits significant disparities. While existing research has extensively explored equity issues in energy access, a critical gap remains in understanding the broader distributional disparities in residential rooftop solar adoption at local, state, and national scales. This study addresses this gap by quantifying spatial inequalities in rooftop solar adoption, leveraging a novel dataset created using vision transformers to identify rooftop-installed photovoltaic (PV) systems across the United States through 2022. By employing Lorenz curves, an approach traditionally used to analyze income inequality, the distribution of residential rooftop solar at multiple geographic and temporal scales is assessed to uncover areas with disproportionate adoption levels. This analysis identifies key factors that drive these inequalities, including economic conditions, policy frameworks, and demographic characteristics. The findings reveal pronounced inequalities in solar adoption within and between counties and states, despite a doubling of rooftop PV installations nationwide between 2017 and 2022 from 1.47 to 2.95 million systems. While policies and incentives have helped some disadvantaged communities overcome barriers related to lower household income, they have often fallen short of achieving broader spatial equality. The dataset, DeepSolar-3M, has been released as an open resource to support policymakers, researchers, energy analysts, and others in advancing data-driven energy solutions.

向可再生能源过渡对于实现可持续发展目标和减轻气候变化的影响至关重要。特别是太阳能，已成为一种可行和成本效益高的选择，但其采用情况却存在很大差异。虽然现有的研究已经广泛地探讨了能源获取中的公平问题，但在理解地方、州和国家范围内住宅屋顶太阳能采用的更广泛的分布差异方面，仍然存在一个关键的差距。本研究通过量化屋顶太阳能采用的空间不平等来解决这一差距，利用使用视觉变压器创建的新数据集来识别到2022年美国各地屋顶安装的光伏（PV）系统。通过采用洛伦兹曲线（一种传统上用于分析收入不平等的方法），评估了住宅屋顶太阳能在多个地理和时间尺度上的分布，以揭示采用水平不成比例的地区。该分析确定了导致这些不平等的关键因素，包括经济条件、政策框架和人口特征。调查结果显示，尽管在2017年至2022年期间，全国屋顶光伏安装量翻了一番，从147万套增加到295万套，但县和州内部和州之间的太阳能采用存在明显的不平等。虽然政策和激励措施帮助一些处境不利的社区克服了与家庭收入较低有关的障碍，但它们往往未能实现更广泛的空间平等。该数据集名为DeepSolar-3M，已作为开放资源发布，以支持政策制定者、研究人员、能源分析师和其他人推进数据驱动的能源解决方案。

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引用次数: 0

Decision-focused learning integrated with data-driven robust optimization for energy management 以决策为中心的学习与数据驱动的能源管理鲁棒优化相结合

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2025.127343

Guotao Wang , Zhenjia Lin , Xiaoqing Zhong , Haoran Ji , Peng Li , Yuntian Chen , Jinyue Yan

Addressing uncertainties in power systems is critical for enhancing renewable energy integration and ensuring overall system reliability. Existing research typically treats forecasting and optimization as separate processes, without effectively integrating the impact of predictive accuracy on the robustness and quality of downstream energy management decisions. To fill this gap, this study proposes a novel framework, Artificial Intelligence for Robust Optimization (AIROpti), which tightly integrates a forecasting model with a subsequent data-driven robust optimization model. Two cases are examined: a distributed energy system comprising twenty households and a market level energy system. Both analyses account for uncertainty in demand and electricity prices. Regarding predictive performance, the day ahead load forecasting model attains a symmetric mean absolute percentage error (SMAPE) of 17.99 % for the aggregated demand across twenty households and 3.82 % at the market level, demonstrating high accuracy. However, our experiments also show that improved forecast accuracy does not necessarily translate into more robust downstream energy management. AIROpti yields a 25.94 % to 44.34 % reduction in regret relative to high accuracy prediction models and a 3.99 % to 75.74 % reduction relative to traditional decision-focused learning baselines. Moreover, it enhances the robustness of operational strategies, thereby reducing worst case performance bounds by 13.77 % and 59.71 % across the two cases. These results highlight AIROpti's ability to produce forecasts that prioritize the robustness of energy management rather than merely achieving higher prediction accuracy.

解决电力系统中的不确定性问题对于提高可再生能源并网和确保整个系统的可靠性至关重要。现有的研究通常将预测和优化视为独立的过程，没有有效地整合预测准确性对下游能源管理决策的稳健性和质量的影响。为了填补这一空白，本研究提出了一个新的框架，即稳健优化人工智能（AIROpti），它将预测模型与随后的数据驱动稳健优化模型紧密集成在一起。本文研究了两个案例：一个由20户家庭组成的分布式能源系统和一个市场级能源系统。这两种分析都考虑到了需求和电价的不确定性。在预测性能方面，日前负荷预测模型对20个家庭的总需求达到17.99%的对称平均绝对百分比误差（SMAPE），在市场水平上达到3.82%，显示出较高的准确性。然而，我们的实验也表明，提高预测精度并不一定转化为更稳健的下游能源管理。与高精度预测模型相比，airpti的遗憾率降低了25.94%至44.34%，与传统的以决策为中心的学习基线相比，该模型的遗憾率降低了3.99%至75.74%。此外，它还增强了运营策略的鲁棒性，从而在两种情况下将最坏情况的性能界限分别降低了13.77%和59.71%。这些结果突出了airpti的预测能力，它优先考虑能源管理的稳健性，而不仅仅是实现更高的预测精度。

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引用次数: 0

Physics-informed neural network for dynamic energy flow calculation in integrated electricity and gas systems 集成电、气系统中动态能量流计算的物理信息神经网络

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127374

Shangyang He , Suhan Zhang , Yuanzheng Li , Wei Gu , Chi-yung Chung

The global decarbonization is driving integrated energy systems (IES) toward more efficient and low-carbon operations, with tighter coupling between natural gas systems (NGS) and electric power systems (EPS) to accommodate diverse renewable sources and energy carriers. The bidirectional energy flow problem in IES with a partial differential algebraic equations (PDAE) form remains a challenge for model-based solvers due to privacy concerns and computational complexity, as it may require full parameters and dozens of minutes or hours to obtain a feasible solution in large-scale systems. To address this obstacle, this study proposes a physics-informed neural operator for energy flow calculations in IES. A novel Differential-Algebraic Gas Flow Neural Operator (DAGFNO) is proposed to embed physical constraints of PDAE into the neural operator, which not only obtains accurate heterogeneous gas states but also provides a privacy-preserved interface for EPS analysis. Besides DAGFNO, we also developed a novel Masked Differential and Algebraic Coupling Constraint Loss function (MDACloss) to represent the degree of constraint violation and enable its parallel computing ability through the masking technique. By doing so, the MDACloss could guarantee the satisfaction of constraints in the energy flow calculation of IES obtained by the DAGFNO as much as possible. Case studies on two NGS and EPS coupled IESs reveal the effectiveness of the proposed method.

全球脱碳正在推动综合能源系统（IES）朝着更高效、更低碳的方向发展，天然气系统（NGS）和电力系统（EPS）之间的耦合更加紧密，以适应多样化的可再生能源和能源载体。由于隐私问题和计算复杂性，具有偏微分代数方程（PDAE）形式的IES中的双向能量流问题对于基于模型的求解者来说仍然是一个挑战，因为它可能需要完整的参数和数十分钟或数小时才能在大规模系统中获得可行的解。为了解决这一障碍，本研究提出了一种用于IES中能量流计算的物理信息神经算子。提出了一种新的微分代数气体流动神经算子（DAGFNO），该算子将PDAE的物理约束嵌入到神经算子中，不仅得到了准确的非均相气体状态，而且为EPS分析提供了一个隐私保护接口。除了DAGFNO之外，我们还开发了一种新的掩模微分代数耦合约束损失函数（MDACloss）来表示约束违反的程度，并通过掩模技术使其具有并行计算能力。这样，MDACloss可以尽可能地保证DAGFNO得到的IES能量流计算约束的满足。对两个NGS和EPS耦合的IESs进行了实例研究，结果表明了该方法的有效性。

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引用次数: 0

Load restoration strategy for post-disaster distribution networks considering cyber-physical-traffic coupling with multi-resource collaboration 考虑网络-物理-流量耦合和多资源协作的灾后配电网负荷恢复策略

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127373

Ying Wang, Chunming Liu, Yulong Zhao, Xinyu Li, Yuhan Wu

Extreme events can trigger coupled failures across distribution, information, and traffic networks, thereby compromising the safe operation of distribution networks. Mobile energy storage systems (MESS) provide spatiotemporal flexibility in energy supply and operate in a complementary manner to fixed power sources. Unmanned aerial vehicles (UAVs) offer mobile communication capabilities and facilitate rapid communication recovery. Additionally, fault repair can achieve stepwise load restoration through dynamic network reconfiguration. However, the integrated scheduling of these emergency resources across interdependent systems remains an urgent research challenge. Therefore, this paper proposes a post-disaster load restoration strategy for distribution networks, incorporating multi-resource collaborative scheduling under cyber-physical-traffic coupled failure scenarios. First, a cyber-physical-traffic coupled failure architecture is established. Second, the impact of traffic network failures on vehicle routing is considered, and Dijkstra's algorithm is employed to compute travel times. Simultaneously, to address the reduction in situational awareness and control caused by information network failures, a Location Set Covering Problem (LSCP) algorithm is adopted to optimize UAV site selection. A collaborative scheduling model is subsequently developed, integrating ESS, GAS, MESS, UAVs, and fault repair. The model aims to minimize both load shedding and the cost of emergency resource scheduling, using a multi-timeframe optimization approach to dynamically determine MESS deployment locations and fault repair sequences. Finally, simulations based on a modified IEEE-33-bus distribution system demonstrate that the proposed strategy can effectively reduce post-disaster load losses and shorten load restoration times.

极端事件可能引发配电网、信息网和通信网的耦合故障，从而危及配电网的安全运行。移动储能系统（MESS）提供了能源供应的时空灵活性，并以一种与固定电源互补的方式运行。无人驾驶飞行器（uav）提供移动通信能力并促进快速通信恢复。此外，故障修复可以通过动态网络重构实现负荷逐步恢复。然而，这些应急资源在相互依赖的系统之间的综合调度仍然是一个紧迫的研究挑战。为此，本文提出了一种在网络-物理-流量耦合故障场景下，结合多资源协同调度的配电网灾后负荷恢复策略。首先，建立了网络-物理-流量耦合故障体系结构。其次，考虑交通网络故障对车辆路径的影响，采用Dijkstra算法计算出行时间；同时，针对信息网络故障导致的态势感知和控制能力下降的问题，采用位置集覆盖问题（LSCP）算法对无人机的选址进行优化。随后建立了集成ESS、GAS、MESS、无人机和故障修复的协同调度模型。该模型采用多时间框架优化方法，动态确定MESS的部署位置和故障修复顺序，旨在最大限度地减少负载减少和应急资源调度成本。最后，基于改进的ieee -33总线配电系统的仿真表明，该策略可以有效地减少灾后负荷损失，缩短负荷恢复时间。

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引用次数: 0

Hybrid Reinforcement Learning for occupant-centric building control: A review and deployment framework for co-optimizing energy, comfort, and indoor air quality 以乘员为中心的建筑控制的混合强化学习：共同优化能源、舒适度和室内空气质量的审查和部署框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127392

Majid Mohsenpour, Yangang Xing

The operational phase of buildings represents a major share of global energy consumption, underscoring its importance in achieving sustainability goals. Reinforcement learning, with its ability to manage both continuous and discrete control tasks, shows strong performance for enhancing building systems' efficiency. Existing reviews on reinforcement learning applications in building energy systems primarily focus on heating, ventilation, and air conditioning systems and often overlook critical distinctions between system-centric and occupant-centric control strategies, as well as the role of data acquisition for training. To address these gaps, this study conducts systematic review of reinforcement learning and hybrid reinforcement learning approaches to answer the question of how reinforcement learning methods improve the performance of heating, ventilation, and air conditioning systems, lighting systems, and window systems in terms of energy efficiency, thermal comfort, and indoor air quality. This study summarizes the states, actions, rewards, and performance of reinforcement learning methods. Through a critical analysis of more than seventy papers, this review distinguishes between system-centric and occupant-centric control models in terms of publication trends, design frameworks, and simulation and co-simulation tools. This review also goes beyond the simulation stage and investigates reinforcement learning challenges and methods, training strategies, and data-collection techniques for real-world deployment. In addition, this study proposes a novel co-adaptive reinforcement learning framework for further research on real-world deployment, considering occupants as the core of the design stage. Finally, this study identifies and discusses ten future research directions, outlining current limitations and opportunities for advancing reinforcement learning in building system control.

建筑的运营阶段占全球能源消耗的主要份额，强调了其在实现可持续发展目标方面的重要性。强化学习具有管理连续和离散控制任务的能力，在提高建筑系统的效率方面表现出很强的性能。现有的关于建筑能源系统中强化学习应用的评论主要集中在供暖、通风和空调系统上，往往忽视了以系统为中心和以乘员为中心的控制策略之间的关键区别，以及数据采集在培训中的作用。为了解决这些差距，本研究对强化学习和混合强化学习方法进行了系统回顾，以回答强化学习方法如何在能效、热舒适和室内空气质量方面改善供暖、通风和空调系统、照明系统和窗户系统的性能。本研究总结了强化学习方法的状态、动作、奖励和性能。通过对70多篇论文的批判性分析，本综述在出版趋势、设计框架、仿真和联合仿真工具方面区分了以系统为中心和以乘员为中心的控制模型。这篇综述也超越了模拟阶段，研究了现实世界部署的强化学习挑战和方法、训练策略和数据收集技术。此外，本研究提出了一种新的自适应强化学习框架，用于进一步研究现实世界的部署，将居住者作为设计阶段的核心。最后，本研究确定并讨论了十个未来的研究方向，概述了目前在建筑系统控制中推进强化学习的局限性和机会。

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引用次数: 0

Enhancing power grid resilience through weather-aware security constraints: A deep reinforcement learning approach with hybrid CNN-GRU architecture 通过天气感知安全约束增强电网弹性：基于CNN-GRU混合架构的深度强化学习方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127363

Yingjun Wu , Junyu Feng , Xuejie Chen , Yujian Ye , Zhiwei Lin , Jiangfan Yuan , Xueyan He , Zhengxi Yin , Jiayan Lu

Extreme weather events increasingly challenge the operational resilience of distribution systems by introducing dynamic and uncertain security limits (SLs), alongside data sparsity. Traditional model-based approaches often rely on static assumptions and require complete system modeling, making them difficult to adapt to rapidly evolving weather-induced constraints. To address these limitations, this paper proposes a model-free resilience enhancement framework based on deep reinforcement learning (DRL), integrating real-time weather-aware SL identification and adaptive dispatch. First, an ensemble Bagging-XGBoost model is developed to classify weather severity levels and determine whether static or dynamic SLs should be applied, enabling scenario-adaptive SL switching. Second, a hybrid convolutional neural network–gated recurrent unit (CNN-GRU) model, enhanced by transfer learning, is designed to accurately estimate dynamic SLs under varying weather conditions. The CNN captures spatial meteorological patterns, while the GRU models temporal evolution; transfer learning improves generalization under limited training data. Third, the dispatch problem is formulated as a constrained Markov decision process (CMDP), and solved using a primal–dual deep deterministic policy gradient (PD-DDPG) algorithm that explicitly incorporates SL constraints into the policy learning process. An attention-based meteorological data reconstruction model is further integrated to enhance the quality of input data and training efficiency. Case studies on the improved IEEE-123 test feeder demonstrate that the proposed method reduces average load loss by 23.30 % and 12.10 % compared to CNN-only and GRU-only baselines, respectively. Moreover, it achieves an 88.77 % improvement in computational efficiency over conventional model-based resilience strategies, highlighting its robustness and applicability under limited data and high-impact weather conditions.

极端天气事件通过引入动态和不确定的安全限制（SLs）以及数据稀疏性，日益挑战配电系统的运行弹性。传统的基于模型的方法通常依赖于静态假设，并且需要完整的系统建模，这使得它们难以适应快速变化的天气引起的约束。为了解决这些限制，本文提出了一个基于深度强化学习（DRL）的无模型弹性增强框架，集成了实时天气感知SL识别和自适应调度。首先，开发了一个集成Bagging-XGBoost模型，用于对天气严重程度进行分类，并确定应该应用静态还是动态SL，从而实现场景自适应SL切换。其次，设计了一种混合卷积神经网络门控循环单元（CNN-GRU）模型，通过迁移学习进行增强，以准确估计不同天气条件下的动态SLs。CNN捕获空间气象模式，GRU模拟时间演变；迁移学习提高了有限训练数据下的泛化能力。第三，将调度问题表述为约束马尔可夫决策过程（CMDP），并使用原始对偶深度确定性策略梯度（PD-DDPG）算法求解，该算法将SL约束明确纳入策略学习过程。进一步集成了基于关注的气象数据重构模型，提高了输入数据的质量和训练效率。对改进后的IEEE-123测试馈线的案例研究表明，与cnn和gru基线相比，该方法分别降低了23.30%和12.10%的平均负载损耗。此外，与传统的基于模型的弹性策略相比，该方法的计算效率提高了88.77%，突出了其在有限数据和高影响天气条件下的鲁棒性和适用性。

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引用次数: 0

Heterogeneous attention-driven deep reinforcement learning for solving EVRPs with pickup-delivery and time windows 基于异构注意力驱动的深度强化学习求解带取货和时间窗的evrp

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127355

Qingshu Guan , Hui Cao , Tiansen Niu , Lixin Jia , Dapeng Yan , Badong Chen

Electric vehicle routing problems (EVRPs) have attracted growing interest in the pursuit of sustainable transportation, driven by the environmental benefits and energy efficiency of electric vehicles (EVs). Nevertheless, mainstream approaches predominantly focus on minimizing travel distance rather than propulsion energy and overlook key logistical constraints such as time windows and pickup-delivery demands, which are critical in modern express operations. To address these limitations, we investigate an energy-optimal EVRP with pickup-delivery and time windows (EVRP-PDTW) and develop a high-resolution energy consumption model that integrates time-dependent driving dynamics, detailed road information, and battery charging behavior. Building on this foundation, we cast the routing task as a Markov decision process and propose a Heterogeneous Attention-driven Deep Reinforcement Learning (HA-DRL) framework. The encoder leverages a heterogeneous attention mechanism to capture role-specific interactions among depots, customers, and charging stations, while the decoder incorporates a dynamic-aware context embedding to capture state transitions and temporal feasibility. We analyze how these design choices structure the decision space and align the learned policy with the underlying energy model, thereby explaining the observed energy savings. Experiments on synthetic and real-world datasets show that HA-DRL outperforms a suite of heuristic and DRL-based methods by a clear margin, reducing average energy consumption by 51.30 kWh (a 6.43% improvement in optimality gap) over the competitive NCS approach in large-scale scenarios involving 200 customers and 40 charging stations. These achievements underscore the promise of HA-DRL in advancing energy-aware routing solutions for real-world EV logistics systems.

在电动汽车的环境效益和能源效率的推动下，电动汽车路径问题（evrp）在追求可持续交通的过程中引起了越来越多的关注。然而，主流方法主要侧重于最小化运输距离，而不是推进能量，并忽略了关键的物流限制，如时间窗口和取件交付需求，这在现代快递业务中至关重要。为了解决这些限制，我们研究了一种具有取车交付和时间窗口的能量最优EVRP (EVRP- pdtw)，并开发了一个高分辨率的能量消耗模型，该模型集成了随时间变化的驾驶动态、详细的道路信息和电池充电行为。在此基础上，我们将路由任务转换为马尔可夫决策过程，并提出了一个异构注意力驱动的深度强化学习（HA-DRL）框架。编码器利用异构关注机制来捕获仓库、客户和充电站之间特定角色的交互，而解码器则结合动态感知上下文嵌入来捕获状态转换和时间可行性。我们分析了这些设计选择如何构建决策空间，并将学习到的策略与潜在的能源模型结合起来，从而解释了观察到的能源节约。在合成数据集和真实数据集上的实验表明，HA-DRL明显优于一套启发式和基于drl的方法，在涉及200个客户和40个充电站的大规模场景中，与竞争的NCS方法相比，HA-DRL平均能耗降低了51.30 kWh（最优性差距提高了6.43%）。这些成就凸显了HA-DRL在为现实世界的电动汽车物流系统推进能源感知路由解决方案方面的前景。

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引用次数: 0

Integrating green hydrogen production and electrical energy storage in energy communities under uncertainty 在不确定的能源社区中整合绿色制氢和电能储存

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127389

M. Ferrara , F. Mottola , D. Proto , A. Ricca , M. Valenti

This paper addresses the integration of green hydrogen production and electrical energy storage in renewable energy communities. An optimal approach is proposed for sizing an electrolyzer and a battery energy storage system within the community which includes photovoltaic generation and loads. The method tackles key planning challenges by incorporating uncertainty handled through decision theory techniques. Multiple scenarios are defined based on variations in photovoltaic generation, load demand, and electricity price profiles to capture a wide range of operating conditions. The proposed planning model includes scheduling strategies aimed at facilitating the integration of the distributed resources by coordinating their power flows, with the dual objectives of maximizing green hydrogen production and enhancing energy sharing within the community. The scheduling is solved through mixed-integer linear programming, whose combination with decision theory reduces computational effort by exhaustively considering a range of scenarios through their probabilities of occurrence and distinct characteristics. To evaluate the impact of the resource contributions under the community’s self-consumption incentive policy, the paper includes the formulation of shared energy models for three distinct system configurations, each adapted from a general framework to address the specific characteristics of the respective configurations. The results of numerical applications provide evidence of the effectiveness of the proposed procedure and present an analysis of economic viability. The analysis shows that, as the hydrogen selling price increases, the optimal planning procedure leads to increased hydrogen production, which in turn boosts the net economic benefit. The proposed approach provides a flexible decision–support tool for planners and policymakers, enabling tailored insights into optimal system design based on the specific objectives and available information.

本文讨论了可再生能源社区中绿色制氢和电能存储的整合。提出了一种优化的方法来确定电解槽和电池储能系统在包括光伏发电和负载的社区内的尺寸。该方法通过结合决策理论技术处理的不确定性来解决关键的规划挑战。根据光伏发电、负荷需求和电价概况的变化定义了多种方案，以捕获广泛的运行条件。所提出的规划模型包括调度策略，旨在通过协调其电力流来促进分布式资源的整合，同时实现最大化绿色氢气生产和增强社区内的能源共享的双重目标。采用混合整数线性规划方法求解调度问题，该方法与决策理论相结合，充分考虑了各种场景的发生概率和不同特征，减少了计算量。为了评估社区自我消费激励政策下资源贡献的影响，本文建立了三种不同系统配置的共享能源模型，每种模型都采用一般框架来解决各自配置的具体特征。数值应用的结果证明了所提出的程序的有效性，并对经济可行性进行了分析。分析表明，随着氢气销售价格的提高，最优规划程序导致氢气产量增加，从而提高净经济效益。所提出的方法为规划者和决策者提供了一个灵活的决策支持工具，使他们能够根据具体目标和现有信息量身定制最佳系统设计。

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引用次数: 0

A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability 太阳临近预报的深度学习综述：提高准确性、可靠性和可解释性

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-09 DOI: 10.1016/j.apenergy.2026.127378

Zhijin Wang , Senzhen Wu , Yaohui Huang , Ruyu Liu , Xiufeng Liu

Solar nowcasting (0–6 hour horizons) is critical for grid stability, ramp-rate control, and energy storage optimization as renewable penetration accelerates. This systematic review analyzes 120 peer-reviewed studies (2015–2024) selected via PRISMA protocol from 8245 initial records, evaluating CNN-based spatial feature extraction, RNN-based temporal modeling, and hybrid multi-modal fusion architectures alongside emerging paradigms including federated learning, diffusion models, physics-informed neural networks, and foundation models. Modern deep learning achieves 20–40% improvement over persistence baselines (skill scores 0.25–0.45), with physics-aware designs substantially reducing violations of radiative constraints. However, critical gaps persist: only 18% of studies release code publicly, preprocessing pipelines remain undocumented, probabilistic evaluation using proper scoring rules (CRPS, Brier score) with calibration diagnostics is inconsistently applied, and benchmark datasets concentrate in North America and Europe, limiting generalizability. We establish explicit connections between forecast skill and operational value (reserve costs, curtailment reduction, ramp compliance, battery cycling) and quantify deployment constraints (inference latency

<

60s, energy consumption 5–300W, edge versus cloud architectures). Key recommendations include mandatory release of preprocessing pipelines with datasets, standardized probabilistic evaluation protocols with condition-specific analyses, physics-informed architectures with radiative constraints, multi-objective optimization balancing accuracy against computational cost and carbon footprint, and federated learning for privacy-preserving collaboration. This review provides an evidence-based roadmap toward reproducible, physically consistent, and operationally valuable solar nowcasting essential for reliable renewable energy integration.

随着可再生能源渗透的加速，太阳能临近预报（0-6小时视界）对电网稳定性、斜坡率控制和储能优化至关重要。本系统综述分析了通过PRISMA协议从8245条初始记录中选择的120项同行评议研究（2015-2024），评估了基于cnn的空间特征提取、基于rnn的时间建模、混合多模态融合架构以及新兴范例，包括联邦学习、扩散模型、物理信息神经网络和基础模型。现代深度学习在持久性基线（技能得分0.25-0.45）的基础上实现了20-40%的改进，物理感知设计大大减少了对辐射约束的违反。然而，关键的差距仍然存在：只有18%的研究公开发布代码，预处理管道仍然没有记录，使用适当评分规则（CRPS， Brier评分）的概率评估与校准诊断的应用不一致，基准数据集集中在北美和欧洲，限制了普遍性。我们在预测技能和运营价值（储备成本、减少弃风、斜坡合规性、电池循环）之间建立了明确的联系，并量化了部署约束（推断延迟<；60s、能耗5-300W、边缘与云架构）。主要建议包括强制发布带有数据集的预处理管道，带有特定条件分析的标准化概率评估协议，带有辐射约束的物理信息架构，多目标优化平衡计算成本和碳足迹的准确性，以及用于隐私保护协作的联邦学习。这篇综述提供了一个基于证据的路线图，以实现可重复的、物理上一致的、操作上有价值的太阳能临近预报，这对可靠的可再生能源整合至关重要。

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引用次数: 0

A capacity renting framework for shared energy storage considering peer-to-peer energy trading among prosumers with privacy protection 基于隐私保护的消费者点对点能源交易的共享储能容量租用框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-09 DOI: 10.1016/j.apenergy.2026.127368

Yingcong Sun , Laijun Chen , Yue Chen , Mingrui Tang , Shengwei Mei

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.

共享储能系统（ESS）为解决可再生分布式发电机（dg）发电与产消者电力需求之间的时间不平衡提供了一种有希望的解决方案。然而，随着DG渗透率的上升，空间能源失衡变得越来越明显，需要在共享ESS框架内整合点对点（P2P）能源交易。在这种情况下出现了两个关键挑战：缺乏有效的机制，以及由于数据通信增加而导致隐私保护的更大困难。本研究提出一种考虑产消费者P2P能源交易的共享ESS容量租用框架。在提出的框架中，生产消费者可以参与P2P能源交易，并从共享ESS中租用容量。建立了一个广义纳什博弈模型来模拟交易过程和产消者之间的竞争互动，并证明了该博弈的变分均衡等价于一个二次规划问题的最优解。为了解决隐私保护问题，采用Paillier密码系统的乘法器交替方向法（ADMM）解决了这一问题。最后，通过数值仿真验证了P2P能源交易对共享ESS框架的影响，验证了所提隐私保护算法的有效性。

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引用次数: 0