Sustainable Energy Grids & Networks最新文献

Optimal reactive current compensation for smart grids using linear programming: A novel algorithm with theoretical and real-world data validation 基于线性规划的智能电网最优无功电流补偿：一种具有理论和实际数据验证的新算法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-11 DOI: 10.1016/j.segan.2025.102081

Francisco G. Montoya , Jorge Ventura , Xabier Prado , Jorge Mira

This paper presents an innovative optimization approach for reactive current compensation in modern distribution networks, based on a novel algorithmic solution using linear programming techniques. The proposed method determines optimal shunt compensator parameters by effectively linearizing nonlinear systems in high-harmonic environments without requiring negative reactive elements. Unlike traditional methods, this approach ensures reliable compensator values across diverse operational scenarios, making it particularly valuable for smart grid applications where power quality and energy efficiency are crucial. The theoretical framework is validated through comprehensive mathematical analysis and simulations, complemented by a real-world case study using data from an actual installation. Results demonstrate the method’s effectiveness in handling non-sinusoidal conditions through both theoretical cases and actual power system measurements. Furthermore, a parametric analysis of the real-world data reveals a key practical insight: a reduced-order compensator, targeting only the most dominant harmonics, can achieve nearly all of the source current reduction provided by a full compensator, thus offering an optimal trade-off between cost and performance. This research contributes to power systems theory by providing a computationally efficient and flexible approach for power quality enhancement in modern distribution systems.

本文提出了一种新颖的基于线性规划算法的现代配电网无功电流补偿优化方法。该方法在不需要负无功元件的情况下，通过对高谐波环境下的非线性系统进行有效线性化来确定最优并联补偿器参数。与传统方法不同，该方法可确保在各种操作场景中可靠的补偿器值，这对于电能质量和能源效率至关重要的智能电网应用特别有价值。理论框架通过全面的数学分析和模拟得到验证，并辅以使用实际安装数据的实际案例研究。理论算例和实际电力系统测量结果均表明了该方法在处理非正弦工况时的有效性。此外，对真实世界数据的参数分析揭示了一个关键的实用见解：仅针对最主要谐波的降阶补偿器可以实现全补偿器提供的几乎所有源电流减小，从而在成本和性能之间提供最佳权衡。该研究为现代配电系统的电能质量提高提供了一种计算效率高且灵活的方法，为电力系统理论的发展做出了贡献。

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引用次数: 0

A novel time-varying control method of renewable energy sources for smart grid efficiency enhancement 一种提高智能电网效率的可再生能源时变控制方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-11 DOI: 10.1016/j.segan.2025.102102

Xiaotong Ji , Dan Liu , Chang Ye , Ji Han , Bokai Zhou , Jiaming Guo , Bocheng Long , Yuqi Ao , Liangli Xiong

The rapid integration of renewable energy sources (RESs), such as photovoltaic (PV) and wind power generation (WPG), poses significant challenges to smart grids. Traditional control methods based on static or piecewise-linearized models are insufficiently adaptive to nonlinear and time-varying system behavior. This paper proposes a novel time-varying control strategy to enhance RES efficiency and coordination in smart grids. First, a control model is formulated considering both operational costs and system losses. To address system nonlinearities, a real-time sensitivity-based linearization scheme is developed to dynamically update the optimization model parameters as operating conditions evolve. Then, the optimality conditions of the time-varying optimization problem are derived, and a distributed control algorithm based on graph theory and finite-time convergence theory is proposed. The convergence of the algorithm is rigorously established through theoretical analysis. Finally, case studies are conducted on the IEEE 33-bus system and a real-world grid. The results demonstrate that the proposed method maintains generation–load deviation below 0.15 %, reduces operation cost and power loss by up to 8.5 % and 10.2 % compared with consensus, deep reinforcement learning (DRL), and droop control, and achieves RES consumption rates exceeding 85 % for WPG and 70 % for PV across representative scenarios.

光伏（PV）和风力发电（WPG）等可再生能源（RESs）的快速整合对智能电网提出了重大挑战。传统的基于静态或分段线性化模型的控制方法对非线性时变系统行为的适应性不足。本文提出了一种新的时变控制策略，以提高智能电网的可再生能源效率和协调性。首先，建立了考虑运行成本和系统损失的控制模型。为了解决系统的非线性问题，提出了一种基于实时灵敏度的线性化方案，根据工况变化动态更新优化模型参数。然后，推导了时变优化问题的最优性条件，提出了一种基于图论和有限时间收敛理论的分布式控制算法。通过理论分析，严格证明了算法的收敛性。最后，对IEEE 33总线系统和实际网格进行了案例研究。结果表明，与共识、深度强化学习（DRL）和下垂控制相比，该方法将发电负荷偏差保持在0.15 %以下，将运行成本和功率损耗分别降低8.5 %和10.2 %，并在代表性场景中实现了WPG超过85 %和PV超过70 %的RES消耗率。

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引用次数: 0

Integration of thermal energy harvesting in smart energy systems 智能能源系统中热能收集的集成

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-11 DOI: 10.1016/j.segan.2025.102098

Amir Karimdoost Yasuri

The rising global demand for energy efficiency and the urgency of climate change mitigation have intensified interest in waste-heat utilization. Thermal Energy Harvesting (TEH) offers a scalable pathway to recover otherwise lost thermal energy and integrate it into Smart Energy Systems (SES). In this study, a unified analytical framework is developed that combines quantitative modeling, literature-derived performance data, and predictive optimization to evaluate TEH performance across industrial, residential, and transportation sectors. Results show that thermoelectric generators achieve efficiencies of 5–8 % under moderate gradients, while organic Rankine cycles reach up to 20 % at higher temperatures. Integrating TEH within SES can enhance overall energy utilization by 10–15 % and reduce CO₂ emissions by approximately 9 %. The analysis identifies that system-level integration—linking material properties, thermodynamic design, and control intelligence—is more decisive for practical performance than isolated device improvements. The paper concludes by outlining research and policy priorities to advance hybridized, intelligent TEH solutions for sustainable and resilient energy infrastructures.

全球对能源效率的需求日益增加，以及缓解气候变化的紧迫性，加强了人们对废热利用的兴趣。热能收集（TEH）提供了一种可扩展的途径来回收原本损失的热能，并将其集成到智能能源系统（SES）中。在本研究中，开发了一个统一的分析框架，将定量建模、文献导出的性能数据和预测优化相结合，以评估工业、住宅和交通部门的TEH绩效。结果表明，热电发电机在中等梯度下的效率为5-8 %，而有机朗肯循环在较高温度下的效率可达20 %。在SES中整合TEH可以提高总体能源利用率10 - 15% %，减少二氧化碳排放量约9% %。分析表明，系统级集成——连接材料特性、热力学设计和控制智能——比孤立的设备改进对实际性能更具决定性。论文最后概述了研究和政策重点，以推进可持续和弹性能源基础设施的混合智能TEH解决方案。

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引用次数: 0

Coordinated scheduling mechanism of electric vehicle V2G and DR in integrated energy systems via deep reinforcement learning 基于深度强化学习的综合能源系统中电动汽车V2G和DR协调调度机制

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-10 DOI: 10.1016/j.segan.2025.102086

Chao He , Junwen Peng , Wenhui Jiang , Jiacheng Wang , Sirui Zhang , Yi Zhang , Hong Na

With the large-scale integration of electric vehicles (EVs) and the growing penetration of renewable energy, integrated energy systems (IES) are facing increased complexity in coordinated scheduling. This complexity arises from multi-source heterogeneity, heightened operational uncertainty, and the challenge of coordinating demand-side responses. To address these issues, we propose a coordinated optimization framework that integrates vehicle-to-grid (V2G) technology, demand response (DR) mechanisms, and carbon trading incentives. The framework facilitates dynamic coordination of flexible resources, such as EV charging/discharging, energy storage, grid electricity procurement, and heat pump loads. This improves operational flexibility, economic efficiency, and carbon reduction potential. To solve the multi-objective, non-convex optimization problem, we introduce a Deep Q-Network (DQN) algorithm from deep reinforcement learning. By utilizing policy learning, the algorithm dynamically optimizes operational decisions across various energy units, enabling adaptive scheduling in response to real-time system changes. Simulation results show that the proposed framework outperforms traditional rule-based and static strategies in terms of load regulation, carbon emission control, and operational cost. These findings highlight the broad applicability and scalability of the integrated scheduling mechanism with reinforcement learning for low-carbon dispatch in IES.

随着电动汽车的大规模并网和可再生能源的日益普及，综合能源系统协调调度的复杂性日益增加。这种复杂性来自于多来源的异质性、操作的不确定性以及协调需求侧响应的挑战。为了解决这些问题，我们提出了一个整合车辆到电网（V2G）技术、需求响应（DR）机制和碳交易激励机制的协调优化框架。该框架有利于电动汽车充放电、储能、电网购电和热泵负荷等灵活资源的动态协调。这提高了操作灵活性、经济效率和碳减排潜力。为了解决多目标非凸优化问题，我们引入了深度强化学习中的深度Q-Network （DQN）算法。通过利用策略学习，该算法动态优化各种能源单元的运营决策，实现对实时系统变化的自适应调度。仿真结果表明，该框架在负荷调节、碳排放控制和运行成本方面优于传统的基于规则和静态策略。这些发现突出了强化学习集成调度机制在IES低碳调度中的广泛适用性和可扩展性。

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引用次数: 0

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations 基于多阶段联合学习的微时刻推荐隐私保护能量优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-09 DOI: 10.1016/j.segan.2025.102100

Md Mosarrof Hossen , Aya Nabil Sayed , Faycal Bensaali , Armstrong Nhlabatsi , Muhammad E.H. Chowdhury

Human behavior significantly impacts domestic energy consumption, making it essential to monitor and improve these consumption patterns. Traditional methods often rely on centralized servers to gather and analyze consumption data, which can lead to significant privacy risks as personalized information becomes accessible online. To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. Leveraging FL enables efficient model training across diverse end-user applications while preserving data privacy. The proposed framework employs a two-stage FL training methodology, utilizing the DRED and QUD datasets, and achieves substantial performance improvements. A comparative evaluation of three FL algorithms (FedAvg, FedProx, Mime-lite) identifies the most suitable aggregation strategy. The model achieves robust performance, with approximately 98 % accuracy and F1-score in the second training stage. These findings demonstrate the effectiveness of FL in enabling personalized, privacy-preserving energy recommendations. The novelty of this work lies in combining micro-moment prediction with a multi-stage FL architecture tailored for smart home energy optimization. This study highlights the potential of FL to enhance energy efficiency and sustainability while safeguarding user privacy, paving the way for future research in energy optimization and sustainable living.

人类行为对国内能源消费有重大影响，因此必须监测和改善这些消费模式。传统的方法通常依赖于集中式服务器来收集和分析消费数据，这可能会导致重大的隐私风险，因为个性化信息可以在网上访问。为了应对这一挑战，本研究旨在通过提出一种创新的两阶段联邦学习（FL）框架来优化家庭能源消耗，同时保护数据隐私，该框架可提供基于实时微时刻的建议。利用FL可以在保护数据隐私的同时，跨不同的最终用户应用程序进行有效的模型训练。提出的框架采用两阶段FL训练方法，利用DRED和QUD数据集，并实现了实质性的性能改进。通过对三种FL算法（fedag, FedProx, Mime-lite）的比较评估，确定了最合适的聚合策略。该模型达到了鲁棒性，在第二阶段的训练中准确率约为98%，得分为f1。这些发现证明了FL在实现个性化、保护隐私的能源建议方面的有效性。这项工作的新颖之处在于将微矩预测与为智能家居能源优化量身定制的多级FL架构相结合。这项研究强调了FL在保护用户隐私的同时提高能源效率和可持续性的潜力，为未来能源优化和可持续生活的研究铺平了道路。

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引用次数: 0

Stackelberg game between charging stations and distribution networks with regional load forecasting and intelligent charging strategies 基于区域负荷预测和智能充电策略的充电站与配电网Stackelberg博弈

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-09 DOI: 10.1016/j.segan.2025.102093

Xiaocheng Wang , ZeLong Li , Qiaoni Han , Pengjiao Sun

In recent years, due to improper management of the relationship between charging stations (CSs) and distribution networks (DNs) in many areas, the fluctuation of power grid load has increased, which has affected the overall economic benefits of the power system. After analyzing the clear hierarchical relationship between CSs and DNs and their inherent rationality and selfishness, Stackelberg game is adopted. In this game, the DN tries to minimize its operating costs, while the goal of the CS is to maximize its profits. On the other hand, since it is difficult for DN to be aware of the load of each region in real time, this paper introduces regional load forecasting to help DN make more reasonable electricity pricing and power distribution plans. Moreover, due to the disorder and uncertainty of electric vehicle (EV) charging, the CS needs to control the charging behaviors of EVs, that is, the intelligent charging strategy is introduced to optimize the charging process, so as to ensure the load of the CS and improve its income. Finally, in order to solve the formulated Stackelberg game, the backward induction method is used to determine the optimal electricity purchase quantity of CSs and the optimal electricity price of DN through iteration. The simulation results show that the proposed method reduces the operating cost of DN by 20 % and increases the profit of CS by 18 %, and has significant advantages compared with other methods.

近年来，由于许多地区对充电站与配电网的关系管理不当，导致电网负荷波动增大，影响了电力系统的整体经济效益。在分析了CSs和dn之间清晰的层次关系以及它们内在的合理性和自私自利之后，采用Stackelberg博弈。在这个博弈中，DN的目标是最小化其运营成本，而CS的目标是最大化其利润。另一方面，由于DN难以实时了解各区域的负荷情况，本文引入区域负荷预测，帮助DN制定更合理的电价和配电方案。此外，由于电动汽车充电的无序性和不确定性，CS需要对电动汽车的充电行为进行控制，即引入智能充电策略对充电过程进行优化，从而保证CS的负载，提高CS的收益。最后，为了求解公式化的Stackelberg博弈，采用逆向归纳法，通过迭代确定CSs的最优购电量和DN的最优电价。仿真结果表明，该方法可使DN的运行成本降低20%，使CS的利润提高18%，与其他方法相比具有显著的优势。

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引用次数: 0

Voltage sensitivity-guided aggregation for virtual power plants via a model-data integration framework 基于模型-数据集成框架的虚拟电厂电压灵敏度引导聚合

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-09 DOI: 10.1016/j.segan.2025.102097

Xu Zhang , Wei Feng , Yanhui Zhang , Xuemei Dai

The interaction between virtual power plants (VPP) and distribution system operators is constrained by privacy preservation and voltage security requirements. Conventional dynamic operating envelopes (DOE) can safeguard privacy and voltage security, but they fail to guide VPP aggregation toward proactively mitigating voltage violations in distribution grids. This paper proposes a voltage sensitivity-guided aggregation driven by a model-data integration framework to address this limitation. The framework integrates a voltage-sensitivity affine model with data-driven uncertainty characterization, enabling aggregation with voltage regulation effects. Specifically, a voltage sensitivity affine model is established at the point of common coupling, where the stochastic factors of distributed energy resources are characterized using Gaussian mixture models combined with error propagation theory. The affine model is subsequently reformulated as a chance-constrained programming model, thus achieving the aggregation for VPP to ensure privacy preservation and voltage regulation. Case studies on the IEEE 33-bus distribution test system demonstrate that the proposed framework reduces aggregation costs and significantly enhances voltage regulation compared with conventional DOE-based aggregation approaches.

虚拟电厂（VPP）与配电系统运营商之间的交互受到隐私保护和电压安全要求的限制。传统的动态运行包络（DOE）可以保护隐私和电压安全，但它们无法引导VPP聚合主动减轻配电网中的电压违规。本文提出了一种由模型-数据集成框架驱动的电压灵敏度导向聚合来解决这一限制。该框架将电压敏感仿射模型与数据驱动的不确定性特征集成在一起，使聚合具有电压调节效果。具体而言，在共耦合点建立电压敏感仿射模型，利用高斯混合模型结合误差传播理论对分布式能源的随机因素进行表征。然后将仿射模型重新表述为机会约束规划模型，从而实现VPP的聚合，以确保隐私保护和电压调节。对IEEE 33总线配电测试系统的实例研究表明，与传统的基于doe的聚合方法相比，该框架降低了聚合成本，并显著提高了电压调节能力。

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引用次数: 0

Leveraging smart prosumers for grid resilience under high-impact low-probability events: A privacy-preserving optimization framework 利用智能产消者在高影响低概率事件下的电网弹性：一个隐私保护优化框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-09 DOI: 10.1016/j.segan.2025.102095

Zohreh Salmani Khankahdani , Mohammad Sadegh Ghazizadeh , Vahid Vahidinasab

Smart prosumers, equipped with generation, storage, and advanced communication infrastructure, have significant potential to provide grid services. However, effectively harnessing this potential in decentralized environments requires novel optimization frameworks that coordinate system operators with prosumers while preserving data privacy. To address this challenge, a two-layer hierarchical optimization structure is proposed to maximize grid service provision by smart prosumers under high-impact low-probability (HILP) events with minimal information exchange. In the first layer, smart prosumers, including Internet data centers and battery swapping stations, optimize and announce their available flexible capacities during emergencies. In the second layer, the distribution system operator (DSO) integrates these capacities into emergency operation planning, complemented by the dynamic routing of battery logistic trucks and the execution of distribution feeder reconfiguration (DFR) to restore power to customers in fault-affected areas. Implementation on the IEEE 69-bus distribution network demonstrates that the proposed hierarchical framework reduces load shedding by 44.82 % and emergency operation costs by 28.2 % while maintaining agent data confidentiality. These results are derived under deterministic conditions, assuming reliable communication, full prosumer participation, and accessible logistics. While uncertainties such as communication delays, partial participation, or disrupted transportation are not yet modeled, the framework provides a computationally efficient basis for decentralized resilience enhancement.

配备了发电、存储和先进通信基础设施的智能产消者具有提供电网服务的巨大潜力。然而，在分散的环境中有效利用这种潜力需要新的优化框架，以协调系统操作员与产消者，同时保护数据隐私。为了解决这一挑战，提出了一种两层分层优化结构，以最大限度地提高智能产消者在高影响低概率（HILP）事件下的电网服务提供，并减少信息交换。在第一层，智能产消者，包括互联网数据中心和电池交换站，在紧急情况下优化并公布其可用的灵活容量。在第二层，配电系统运营商（DSO）将这些能力整合到应急运营计划中，并辅以电池物流卡车的动态路由和配电馈线重新配置（DFR）的执行，以恢复故障影响区域客户的电力。在IEEE 69总线配电网上的实现表明，在保持代理数据保密性的同时，所提出的分层框架减少了44.82% %的减载和28.2% %的应急运行成本。这些结果是在确定性条件下得出的，假设可靠的通信，充分的产消参与，以及可访问的物流。虽然通信延迟、部分参与或运输中断等不确定性尚未建模，但该框架为分散的弹性增强提供了计算效率基础。

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引用次数: 0

Short-term optimal scheduling of wind-solar-hydro-storage systems under extreme heat scenarios with uncertainty consideration 考虑不确定性的极端高温条件下的风能-太阳能-蓄能系统短期优化调度

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-08 DOI: 10.1016/j.segan.2025.102096

Mingyue Zhang , Yang Han , Te Zhou , Yongchao Sun , Huaiyu Zhang , Congling Wang , Fan Yang

Extreme heat events threaten power system reliability by reducing hydropower output and intensifying load peaks. This study proposes a short-term scheduling framework for wind-solar-hydro-storage systems under such conditions. A hybrid forecasting model integrating bidirectional temporal convolutional networks (BiTCN), bidirectional long short-term memory (BiLSTM) with attention mechanism, and quantile regression forest (QRF) is developed to jointly predict wind speed, solar irradiance, and power load, thereby providing probabilistic scenarios. Based on these forecasts, a two-timescale scheduling framework is established, where the day-ahead stage employs an ε-constraint multi-objective programming approach to balance hydropower regulation, renewable energy absorption, and output smoothness, while the intraday stage adopts a rolling chance-constrained model updated every 15 min. To enhance climate adaptability, two adaptive modules are incorporated: an ε-bound feedback mechanism based on plan deviations and a thermal correction model utilizing the human comfort index to adjust temperature-sensitive outputs. A case study conducted on the Xiluodu Hydropower Station in Sichuan Province, China, under the extreme heat conditions of summer 2022 validates the effectiveness of the proposed framework. Tested on the highly fluctuating wind-speed dataset, the proposed BiTCN-BiLSTM-AM model achieves an R² of 0.930, representing improvements of 0.032 and 0.039 over the TCN-LSTM-AM and Transformer models, respectively. In terms of dispatch performance, compared with no-storage and static-dispatch strategies, renewable utilization increases from 92.023 % and 93.692–100 %, with total generation gains of 102.489 MW and 117.101 MW. These results demonstrate that the proposed approach enables robust, adaptive, and climate-resilient scheduling for clean-energy-dominated power grids.

极端高温事件通过降低发电量、加剧负荷峰值等方式威胁着电力系统的可靠性。本研究提出了在此条件下的风能-太阳能-水力蓄能系统的短期调度框架。建立了双向时间卷积网络（BiTCN）、具有注意机制的双向长短期记忆（BiLSTM）和分位数回归森林（QRF）相结合的混合预测模型，联合预测风速、太阳辐照度和电力负荷，从而提供概率情景。在此基础上，建立了双时间尺度调度框架，其中日前阶段采用ε约束多目标规划方法平衡水电调节、可再生能源吸收和输出平滑性，日内阶段采用滚动机会约束模型，每15 min更新一次。为了提高气候适应能力，系统采用了两个自适应模块：基于平面偏差的ε界反馈机制和利用人体舒适度调节温度敏感输出的热校正模型。以2022年夏季极端高温条件下的中国四川省溪洛渡水电站为例，验证了该框架的有效性。在高波动风速数据集上进行测试，所提出的BiTCN-BiLSTM-AM模型的R2为0.930，比TCN-LSTM-AM和Transformer模型分别提高0.032和0.039。在调度性能方面，与无存储和静态调度策略相比，可再生能源利用率分别提高了92.023 %和93.692-100 %，发电总增量分别为102.489 MW和117.101 MW。这些结果表明，所提出的方法能够实现以清洁能源为主的电网的鲁棒性、适应性和气候适应性调度。

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引用次数: 0

A hybrid model for efficient reliability assessment of power systems 电力系统高效可靠性评估的混合模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-08 DOI: 10.1016/j.segan.2025.102091

Adil Waheed, Jueyou Li

The reliability assessment of power systems ensures uninterrupted service and system stability. This paper proposes a hybrid approach consisting of Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks to predict key reliability indices, such as Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Loss of Load Frequency (LOLF). The proposed approach eliminates the need to solve multiple Optimal Power Flow (OPF) problems for each system state, thereby reducing computational time and complexity. In the training phase, the model learns from historical data and a limited set of pre-calculated OPF results. This process enables the model to capture the complex relationships between system states, load curtailment, and reliability indices. Once the training phase is complete, the model directly predicts reliability indices without the need to repeatedly solve OPF for every system state. Comparative analysis demonstrates that the proposed method achieves a high level of accuracy while significantly outperforming conventional techniques, such as Monte Carlo Simulation (MCS). The proposed model is also applied to well-known power systems, including the IEEE Reliability Test Systems (IEEE RTS, IEEE RTS-96) and the Saskatchewan Power Corporation (SPC) system in Canada. The results show that the MLP-LSTM model performs better and can solve OPF-based reliability assessments. Furthermore, the model reduces dependence on OPF and provides faster and more reliable analysis in real-time. This improvement facilitates better decision-making in power system planning and operations.

电力系统的可靠性评估保证了电力系统的不间断运行和稳定运行。本文提出了一种由多层感知器（MLP）和长短期记忆（LSTM）网络组成的混合方法来预测关键的可靠性指标，如负荷损失概率（LOLP）、预期未提供能量（EENS）和负荷损失频率（LOLF）。该方法消除了对每个系统状态求解多个最优潮流（OPF）问题的需要，从而减少了计算时间和复杂度。在训练阶段，模型从历史数据和一组有限的预先计算的OPF结果中学习。该过程使模型能够捕获系统状态、负荷削减和可靠性指标之间的复杂关系。一旦训练阶段完成，该模型就可以直接预测可靠性指标，而无需对系统的每个状态重复求解OPF。对比分析表明，该方法在显著优于蒙特卡罗模拟（MCS）等传统技术的同时，实现了较高的精度。该模型还应用于知名电力系统，包括IEEE可靠性测试系统（IEEE RTS, IEEE RTS-96）和加拿大萨斯喀彻温省电力公司（SPC）系统。结果表明，MLP-LSTM模型性能较好，能够解决基于opf的可靠性评估问题。此外，该模型减少了对OPF的依赖，提供了更快、更可靠的实时分析。这种改进有助于在电力系统规划和运行中更好地决策。

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引用次数: 0