Sustainable Energy Grids & Networks最新文献_第6页

Analysis on improvement of photovoltaic hosting capacity through the flexible connection policy 柔性接入政策对光伏装机容量的提升分析

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.segan.2025.102071

Jae Hyeon Shin , Jin Hyeok Kim , Seung Wan Kim , Dam Kim

The rapid integration of renewable energy sources, including photovoltaics (PV), presents operational challenges for distribution networks, such as reverse power flow, voltage fluctuations, and network congestion. In industrial parks, growing demand for on-site and shared renewables has spurred interest in deploying microgrids, where the concentration of variable generation creates hosting capacity constraints at feeder and substation. Conventional firm connection policies impose strict capacity limits based on worst-case scenarios, delaying interconnection and underutilization of the grid. To address these limitations, this study introduces a time-series bi-level optimization framework for evaluating flexible connection policies that allow controlled PV curtailment. A linearized power flow-based hosting capacity optimization model is developed and applied to evaluate maximum hosting capacity and optimize the siting of PV systems under firm and flexible connection cases. A case study on an IEEE 40-bus networked microgrid system demonstrates that allowing modest annual PV curtailment (1–11 %) can significantly enhance the hosting capacity of the network—up to 45 % greater than that achieved under firm connection approaches—while maintaining or even increasing the total annual renewable generation. Furthermore, an economic analysis reveals that although curtailment may slightly reduce developer profitability, significant savings from deferred grid upgrades provide substantial benefits to both microgrid and distribution system operators. Therefore, we establish a cost-effective pathway for large-scale renewable energy integration by proposing practical incentive mechanisms, such as net present value and benefit-cost ratio-based compensation. These findings emphasize the importance of strategically flexible connection policies in enabling efficient, economical, and high-capacity renewable energy integration into future power grids.

包括光伏（PV）在内的可再生能源的快速整合给配电网带来了运营挑战，如反向潮流、电压波动和网络拥塞。在工业园区，对现场可再生能源和共享可再生能源的需求不断增长，激发了人们对部署微电网的兴趣，在微电网中，可变发电的集中造成了支线和变电站的托管容量限制。传统的企业接入政策根据最坏的情况施加了严格的容量限制，延迟了电网的互联和未充分利用。为了解决这些限制，本研究引入了一个时间序列双级优化框架，用于评估允许可控光伏弃风的灵活连接策略。建立了基于线性潮流的托管容量优化模型，并将其应用于光伏系统在刚性和柔性连接情况下的最大托管容量评估和系统选址优化。对IEEE 40总线网络微电网系统的案例研究表明，允许适度的年度光伏削减（1 - 11% %）可以显着提高网络的承载能力-比固定连接方法实现的能力高出45% % -同时保持甚至增加年度可再生能源发电总量。此外，一项经济分析显示，尽管弃风可能会略微降低开发商的盈利能力，但推迟电网升级带来的大量节省为微电网和配电系统运营商提供了实质性的好处。因此，我们通过提出切实可行的激励机制，如净现值和基于收益成本比率的补偿，为大规模可再生能源整合建立了一条具有成本效益的途径。这些发现强调了战略上灵活的连接政策在实现高效、经济和高容量可再生能源整合到未来电网中的重要性。

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引用次数: 0

Raw measurement supervised learning transformer for anomaly detection of power system digital twin updates 用于电力系统数字孪生更新异常检测的原始测量监督学习变压器

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-11-28 DOI: 10.1016/j.segan.2025.102069

Zhiwei Shen, Felipe Arraño-Vargas, Georgios Konstantinou

Continuous updates are essential to ensure that a digital twin (DT) remains an accurate representation of its physical counterpart. The performance of DT applications heavily relies on how accurately the DT reflects its physical counterpart. DT updates, however, can be compromised by anomalous PT data stemming from physical twin (PT) measurements, communication malfunctions, and/or external attacks. Detecting such anomalies in PT data is crucial to ensuring the accuracy and reliability of DT, thereby generating only valid outcomes for associated applications. This paper proposes a detection method to identify anomalous PT data before its integration into the DT. The proposed raw measurement supervised learning Transformer (RM-SL-TF) facilitates a straightforward identification of PT data using raw measurements, eliminating the dependency on data preprocessing. The feasibility and effectiveness of the RM-SL-TF are demonstrated by using a power system digital twin (PSDT) that requires frequent updates. The resulting detection accuracy of anomalous PT data is comparable to, or even surpasses, that of other artificial intelligence (AI) algorithms that rely on input feature normalisation. By directly analysing raw measurements without normalising input features, the proposed approach is simpler, more flexible, and expandable, making it suitable for establishing and advancing the development and implementation of DTs for power systems and other industries.

持续更新对于确保数字孪生（DT）保持其物理对应物的准确表示至关重要。DT应用程序的性能在很大程度上依赖于DT如何准确地反映其物理对应物。然而，DT更新可能会受到来自物理孪生（PT）测量、通信故障和/或外部攻击的异常PT数据的破坏。检测PT数据中的此类异常对于确保DT的准确性和可靠性至关重要，从而为相关应用生成有效的结果。本文提出了一种在PT数据融入DT之前识别异常PT数据的检测方法。提出的原始测量监督学习转换器（RM-SL-TF）便于使用原始测量直接识别PT数据，消除了对数据预处理的依赖。通过使用需要频繁更新的电力系统数字孪生体（PSDT），验证了RM-SL-TF的可行性和有效性。由此产生的异常PT数据的检测精度与依赖于输入特征归一化的其他人工智能（AI）算法相当，甚至超过。通过直接分析原始测量而不规范化输入特征，所提出的方法更简单，更灵活，可扩展，使其适用于建立和推进电力系统和其他行业的dt的开发和实施。

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引用次数: 0

Pricing mechanism of localized distributed trading for household PV storage systems considering multi-agent interests 考虑多主体利益的户用光伏储能系统局部分布式交易定价机制

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-11-04 DOI: 10.1016/j.segan.2025.102035

Weijun Wang, Xinyu Wang, Haifeng Wang

The localized distributed trading model can effectively enhance power trading efficiency, reduce transaction costs, alleviate the operational pressure on public power grids, and facilitate the local consumption and rapid development of distributed energy. However, the core aspects of this model—such as the electricity pricing mechanism and transaction settlement—remain insufficiently defined, and the existing pricing strategies exhibit certain irrationalities. To address these issues, this study proposes a localized distributed trading for residential photovoltaic (PV)–storage systems that accounts for the interests of multiple stakeholders, coupled with a pricing mechanism incorporating demand-side response (DSR). An optimization model for the pricing mechanism is established with the dual objectives of maximizing the annual net profit of residential PV–storage systems and achieving the highest PV utilization rate. The study introduces an optimal period-partitioning method based on moving boundary techniques to segment PV generation levels into discrete time intervals, and applies a fuzzy Newton–Raphson algorithm combined with PSO to solve the model. This approach yields both the load distribution under DSR and the optimal trading price for the localized distributed trading model. Simulation results demonstrate that the proposed method increases the local PV consumption rate from 41.93 % to 78.49 %, boosts the revenue of residential PV–storage systems by 104.59 %, and reduces the overall electricity cost for residents by 29.66 %. These findings highlight the potential of the proposed model to promote the advancement of localized distributed trading and to contribute positively to China’s energy transition and green, low-carbon development.

本土化的分布式交易模式可以有效提高电力交易效率，降低交易成本，缓解公共电网的运行压力，有利于分布式能源就地消纳和快速发展。然而，该模型的核心部分，如电价机制和交易结算，仍然不够明确，现有的定价策略表现出一定的不合理性。为了解决这些问题，本研究提出了住宅光伏（PV）存储系统的本地化分布式交易，该交易考虑了多个利益相关者的利益，并结合了包含需求侧响应（DSR）的定价机制。以住宅光伏-储能系统年净利润最大化和光伏利用率最高为双重目标，建立定价机制优化模型。提出了一种基于移动边界技术的最优周期划分方法，将光伏发电水平划分为离散时间区间，并将模糊牛顿-拉斐尔算法与粒子群算法相结合对模型进行求解。该方法得到了DSR下的负荷分布和局部分布式交易模型的最优交易价格。仿真结果表明，该方法将当地光伏利用率从41.93 %提高到78.49 %，使居民光伏储能系统收益提高104.59 %，使居民总体电费成本降低29.66 %。这些发现凸显了所提出的模型在促进本地化分布式交易的推进以及为中国能源转型和绿色低碳发展做出积极贡献方面的潜力。

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引用次数: 0

An effective integrated optimal day-ahead and real-time power scheduling approach for hydrogen-based microgrid 基于氢基微电网的有效集成最优日前与实时电力调度方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-31 DOI: 10.1016/j.segan.2025.102039

Pasquale Vizza, Stanislav Fedorov, Anna Pinnarelli, Vittorio Bilotta, Maria Elena Bruni

The increasing penetration of renewable energy sources in power systems poses significant challenges for maintaining grid reliability, mainly due to the variability and uncertainty of solar and demand profiles. Microgrids, equipped with diverse storage technologies, have emerged as a promising solution to address these issues.This paper proposes an integrated day-ahead and real-time power scheduling approach for grid-connected microgrids equipped with both conventional and hydrogen-based ESSs. While existing strategies often address day-ahead and real-time scheduling separately or rely on a single storage technology, this work introduces a unified framework that exploits the complementary characteristics of batteries and hydrogen systems. The proposed approach is based on a novel two-stage stochastic optimization model, embedded within a hierarchical optimization framework to address these two intertwined problems efficiently. For the day-ahead scheduling, a two-stage stochastic programming energy management model is solved to optimize the microgrid schedule based on forecasted load demand and PV production profiles. Building upon the day-ahead schedule, another optimization model is solved, which addresses real-time power imbalances caused by deviations in actual PV production and load demand power profiles with respect to the forecasted ones, with the aim of minimizing operational disruptions. Simulation results demonstrate the validity of the proposed approach, achieving both cost reductions and minimal power imbalances. By dynamically adjusting energy flows and using both conventional batteries and hydrogen systems, the proposed approach ensures improved reliability, reduced operational costs, and enhanced integration of RES in microgrids. These findings highlight the potential of the proposed hierarchical framework to support the large-scale deployment of RES while ensuring resilient and cost-effective microgrid operations.

可再生能源在电力系统中的日益普及对维持电网可靠性提出了重大挑战，这主要是由于太阳能和需求概况的可变性和不确定性。配备了多种存储技术的微电网已经成为解决这些问题的一个有希望的解决方案。本文提出了一种集成了传统和氢基ess的并网微电网日前实时电力调度方法。虽然现有的策略通常分别解决日前和实时调度问题，或者依赖于单一的存储技术，但这项工作引入了一个统一的框架，利用了电池和氢系统的互补特性。该方法基于一种新的两阶段随机优化模型，嵌入到一个分层优化框架中，以有效地解决这两个相互交织的问题。针对日前调度问题，建立了基于负荷需求预测和光伏发电动态的两阶段随机规划能量管理模型，对微网调度进行了优化。在日前计划的基础上，求解了另一个优化模型，该模型解决了由于实际光伏生产和负载需求功率曲线相对于预测的偏差而导致的实时功率不平衡，目的是最大限度地减少运行中断。仿真结果证明了该方法的有效性，既降低了成本，又使功率不平衡最小化。通过动态调整能量流并同时使用传统电池和氢系统，该方法可确保提高可靠性，降低运营成本，并增强微电网中可再生能源的集成。这些发现强调了拟议的分层框架在支持可再生能源大规模部署的同时，确保弹性和成本效益高的微电网运行的潜力。

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引用次数: 0

A game theory-based edge device for renewable energy communities optimal management 基于博弈论的可再生能源社区优化管理边缘装置

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-31 DOI: 10.1016/j.segan.2025.102034

Giuseppe Sciumè, Francesco Montana, Eleonora Riva Sanseverino, Gaetano Zizzo

In most running implementations, Renewable Energy Communities aggregate consumers who share the use of renewable energy with the goal of reducing environmental impact. Each member, connected to the power grid via a single Point of Delivery, should optimize their consumption in order to achieve the community’s goal. However a comprehensive knowledge of the production capacities and consumption profiles of all members is required. To address this challenge, this paper proposes a distributed load scheduling method based on Game-Theory that achieves an optimal balance between individual and collective goals while preserving privacy and enabling energy services provision. The method distributes the computational workload among all users, making it feasible to implement on low-cost hardware devices. In addition, the method allows users to choose their own preferences regarding overall community goals. The proposed approach was evaluated in two case studies, showing that in a few iterations of the game, users reach an optimal equilibrium that not only maximizes individual profits but also satisfies community goals, without the need to share sensitive data.

在大多数正在运行的实现中，可再生能源社区将共享可再生能源使用的消费者聚集在一起，以减少对环境的影响。每个成员，通过一个单一的交付点连接到电网，应该优化他们的消费，以实现社区的目标。但是，需要全面了解所有成员的生产能力和消费概况。为了解决这一挑战，本文提出了一种基于博弈论的分布式负载调度方法，该方法在保护隐私和实现能源服务提供的同时，实现了个人和集体目标之间的最佳平衡。该方法将计算工作量分配给所有用户，使其能够在低成本硬件设备上实现。此外，该方法允许用户根据整体社区目标选择自己的偏好。在两个案例研究中对所提出的方法进行了评估，结果表明，在游戏的几次迭代中，用户达到了最优平衡，不仅使个人利润最大化，而且满足了社区目标，而不需要共享敏感数据。

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引用次数: 0

Impact of communication protocols on cost-optimization using home energy management systems 通信协议对家庭能源管理系统成本优化的影响

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-30 DOI: 10.1016/j.segan.2025.102038

Fabian Haslbeck , Nico Fuchs , Dirk Müller

Renewable electricity generation is volatile and requires flexibility in energy conversion and storage. In the residential sector, this flexibility can be incentivized by time-varying electricity prices. Recent literature has already investigated how a home energy management system (HEMS) can adapt residential electricity consumption to time-varying prices by controlling heat pumps or electric vehicles. Although the communication protocols of these devices can constrain their flexibility, none of the research has considered these influences yet. Thus, the novelty of the current study lies in quantifying the impact of real-world communication protocols on the performance of a cost-optimizing HEMS. For this, a HEMS system with a heat pump, electric vehicle, photovoltaic (PV) plant, and battery energy storage system is modeled. The HEMS uses a two-layer architecture to separate the communication protocol and the control strategy. The device communication layer abstracts the communication to the devices. The control layer uses a model predictive controller to minimize the total electricity costs of the system. To compare different communication protocols, a HEMS is simulated over one year using various consumption price offsets, system configurations, and packet loss probabilities. Results show that heat pumps can achieve the highest savings via Modbus direct load control with up to 6.3 % of the baseline costs. For electric vehicles, ISO 15118–20 shows the highest savings with up to 38.8 %, including battery degradation. However, the savings decrease as the consumption price offset increases.

可再生能源发电不稳定，在能源转换和储存方面需要灵活性。在住宅领域，这种灵活性可以通过时变电价来激励。最近的文献已经研究了家庭能源管理系统（HEMS）如何通过控制热泵或电动汽车来适应住宅用电量随时间变化的价格。虽然这些设备的通信协议限制了它们的灵活性，但目前还没有研究考虑到这些影响。因此，当前研究的新颖之处在于量化现实世界通信协议对成本优化HEMS性能的影响。为此，对一个包含热泵、电动汽车、光伏电站和电池储能系统的HEMS系统进行了建模。HEMS采用两层结构将通信协议和控制策略分离。设备通信层抽象了与设备之间的通信。控制层采用模型预测控制器使系统总电力成本最小。为了比较不同的通信协议，使用各种消耗价格补偿、系统配置和包丢失概率对HEMS进行了一年的模拟。结果表明，热泵可以通过Modbus直接负荷控制实现最高的节省，最多可节省基准成本的6.3%。对于电动汽车，ISO 15118-20显示最高的节省高达38.8%，包括电池退化。然而，储蓄随着消费价格抵消的增加而减少。

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引用次数: 0

Bounded rational bidding strategy of GenCo in electricity spot market based on prospect theory and distributional reinforcement learning 基于前景理论和分布式强化学习的电力现货市场有限理性竞价策略研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-28 DOI: 10.1016/j.segan.2025.102036

Qiyuan Liu , Donghan Feng , Yun Zhou , Yuanhao Feng , Quan Zhou

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this, we model GenCo decision-making with prospect theory (PT) and formulate a bilevel stochastic model for strategic bidding in the spot market. We further propose a distributional reinforcement learning (DistRL) framework to learn risk-aware bidding policies for bounded rational GenCo. The framework is validated on a 27-bus system from eastern China. Across 16 uncertainty scenarios with RE penetration ranging from 5 % to 80 %, our DistRL agent consistently achieves higher average returns and lower volatility than Deep Q-Network (DQN), Double DQN (DDQN), and prioritized experience replay DQN (PER-DQN) in every scenario. When integrating a gated recurrent unit (GRU) network, performance improves further, accompanied by a limited increase in training time. These results indicate that aligning DistRL with bounded rational preferences yields more robust bidding under market uncertainty.

随着可再生能源在电力系统中的日益普及，电力现货市场的不确定性越来越大，这给发电公司制定有效的竞价策略带来了重大挑战。现有研究大多假设发电公司是完全理性的决策者，忽略了不确定市场环境下非理性竞价行为的影响。为了解决这一问题，我们利用前景理论（PT）对发电公司决策进行建模，并建立了现货市场战略竞价的双层随机模型。我们进一步提出了一个分布式强化学习（DistRL）框架来学习有界理性发电公司的风险感知投标策略。该框架在中国东部的27辆公交系统上得到了验证。在16个不确定性场景中，RE渗透率从5%到80%不等，我们的DistRL代理在每个场景中都比Deep Q-Network （DQN）、Double DQN （DDQN）和优先体验重播DQN （PER-DQN）始终实现更高的平均回报和更低的波动性。当集成门控循环单元（GRU）网络时，性能进一步提高，伴随着有限的训练时间增加。这些结果表明，在市场不确定性下，将DistRL与有限理性偏好相结合可以产生更强的稳健竞价。

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引用次数: 0

A python toolbox for flexibility aggregation and disaggregation: PyFlexAD 一个用于灵活性聚合和分解的python工具箱：PyFlexAD

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102033

Emrah Öztürk , Kevin Kaspar , Timm Faulwasser , Karl Worthmann , Peter Kepplinger , Klaus Rheinberger

The increasing penetration of volatile renewables and growing electricity demand pose several challenges for power systems. Simultaneously, flexible devices – so called distributed energy resources (DER) – are becoming more widespread, making them attractive for providing ancillary services. The flexibility of a single device can be represented by a set of reference power profiles, and the flexibility of multiple devices by the summation of individual flexibility sets. However, set addition, also known as the Minkowski sum, is usually computationally intractable. This has led to the development of various approximation methods in the literature. The current study improves upon our previously published vertex-based inner approximation, by extending it to more general storage devices and hierarchical aggregation settings. We validate the efficacy and accuracy of the proposed method through case studies using real data and provide the source code of the algorithm as a Python package that enables the (dis-)aggregation of various flexible devices in real-world scenarios.

不稳定的可再生能源的不断渗透和不断增长的电力需求给电力系统带来了一些挑战。同时，灵活的设备——即所谓的分布式能源（DER）——正变得越来越普遍，这使得它们在提供辅助服务方面具有吸引力。单个设备的灵活性可以用一组参考功率曲线来表示，而多个设备的灵活性可以用单个灵活性集的总和来表示。然而，集合加法，也被称为闵可夫斯基和，通常是难以计算的。这导致了文献中各种近似方法的发展。当前的研究改进了我们之前发表的基于顶点的内部近似，将其扩展到更通用的存储设备和分层聚合设置。我们通过使用真实数据的案例研究验证了所提出方法的有效性和准确性，并提供了算法的源代码作为Python包，该包可以在真实场景中实现各种灵活设备的（非）聚合。

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引用次数: 0

Delay-structured noise robust dynamic mode decomposition for power system modal estimation with faulty PMU data 基于延迟结构噪声鲁棒动态模态分解的PMU故障模态估计

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102029

Rajarshi Roychowdhury , Xuan Wu , Mahesh S. Illindala

Power System Wide-area monitoring systems (WAMS) rely on high-resolution phasor measurement unit (PMU) data to enable real-time situational awareness and oscillation mode analyses. However, the integrity and availability of PMU data are increasingly threatened by communication failures, sensor malfunctions, and sophisticated adversarial attacks, leading to missing entries and corrupted measurements that can undermine conventional modal estimation techniques. This paper presents a robust algorithm for electromechanical mode identification that leverages advanced low-rank and delay-structured recovery methods to accurately extract system modes even when PMU datasets are severely degraded by both random missing data and adversarial corruption. The proposed approach is systematically evaluated under extreme scenarios with up to 70 % missing data and 25 % bad data, far exceeding the stress conditions considered in most prior studies. The proposed method was benchmarked against the three most widely used techniques in power system analysis: Prony, Matrix Pencil (MP), and Eigensystem Realization Algorithm (ERA). Results on real-world WAMS datasets demonstrate that the proposed method substantially outperforms these established algorithms in the presence of adversarial data, ensuring reliable mode estimation.

电力系统广域监测系统（WAMS）依靠高分辨率相量测量单元（PMU）数据来实现实时态势感知和振荡模式分析。然而，PMU数据的完整性和可用性日益受到通信故障、传感器故障和复杂的对抗性攻击的威胁，导致丢失条目和损坏的测量，从而破坏传统的模态估计技术。本文提出了一种鲁棒的机电模式识别算法，该算法利用先进的低秩和延迟结构恢复方法，即使PMU数据集因随机丢失数据和对抗性损坏而严重退化，也能准确提取系统模式。所提出的方法在极端情况下进行了系统评估，其中多达70%的数据缺失和25%的数据不良，远远超过了大多数先前研究中考虑的应力条件。提出的方法与电力系统分析中最常用的三种技术：proony、矩阵铅笔（MP）和特征系统实现算法（ERA）进行了基准测试。在实际WAMS数据集上的结果表明，在存在对抗性数据的情况下，所提出的方法实质上优于这些已建立的算法，确保了可靠的模式估计。

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引用次数: 0

Research on the multi-scenario potential analysis of long-duration energy storage in off-grid microgrids 离网微电网长时程储能多场景潜力分析研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.segan.2025.102030

Jialin Li , Shuxia Yang , Yu Hu , Xufeng Zhang , Min Yu , Mengyu Wang

Long-duration energy storage (LDES) plays a crucial role in ensuring the stability of high-penetration renewable energy systems. However, its application in off-grid microgrids has not been comprehensively examined, particularly in multi-scenario analyses. This study develops a linear programming model for various scenarios to investigate the application prospects of LDES in off-grid microgrids powered solely by photovoltaic (PV), wind turbine (WT), or a hybrid of both. The results show that in all scenarios, microgrid systems using LDES achieve lower costs than those with short-duration energy storage (SDES), demonstrating its advantages. Among different configurations, the photovoltaic-wind-hydrogen (PV-WT-HYD) system has the lowest cost, reducing expenses by 46.61 % compared to the most expensive lithium-ion storage. In terms of resource attributes, wind power better leverages the economic advantages of LDES. Specifically, during the transition from SDES to LDES, WT-LDES microgrids achieve cost reductions 46.94 % faster than PV-LDES microgrids. Furthermore, from a policy perspective, the northern China, southeast coastal region, and central China offer favorable conditions for developing PV-WT-HYD off-grid microgrids due to abundant wind and photovoltaic resources, lower hydrogen production costs, and suitable hydrogen storage conditions. Thus, targeted policies could be introduced to facilitate the adoption and development of LDES.

长时间储能（LDES）对于保证高渗透可再生能源系统的稳定性起着至关重要的作用。然而，其在离网微电网中的应用尚未得到全面研究，特别是在多场景分析中。本研究针对不同场景建立了线性规划模型，以探讨LDES在仅由光伏（PV）、风力涡轮机（WT）或两者混合供电的离网微电网中的应用前景。结果表明，在所有情况下，使用LDES的微电网系统比使用短时间储能（SDES）的微电网系统实现更低的成本，显示了其优势。在不同的配置中，光伏-风-氢（PV-WT-HYD）系统的成本最低，与最昂贵的锂离子储能相比，成本降低了46.61 %。在资源属性上，风电更好地发挥了LDES的经济优势。具体来说，在从SDES到LDES的过渡过程中，WT-LDES微电网的成本降低速度比PV-LDES微电网快46.94 %。此外，从政策角度看，中国北方、东南沿海和中部地区风能和光伏资源丰富，制氢成本较低，储氢条件适宜，为发展PV-WT-HYD离网微电网提供了有利条件。因此，可以采取有针对性的政策来促进采用和发展低成本经济系统。

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引用次数: 0