International Journal of Electrical Power & Energy Systems最新文献_第7页

Innovative optimization of 765 kV transmission network upgrades: Enhancing dynamic stability and efficiency in power systems 765kv输电网升级创新优化：提高电力系统动态稳定性和效率

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111512

Mahdi Arabsadegh , Mostafa Rajabi Mashhadi

This research presents an advanced Reinforcement Learning-Graph Neural Network (RL-GNN) architecture for optimizing 765 kV transmission network upgrades. The architecture is specifically designed to address dynamic stability and efficiency challenges associated with high levels of renewable energy integration. The proposed methodology advances traditional optimization techniques through three key innovations: a dynamic reward mechanism that autonomously manages the tradeoff between stability and efficiency in real time; hierarchical graph processing methods that enable scalable implementation in large-scale systems; and adaptive control features that respond to fluctuations in renewable generation. Extensive evaluations using IEEE standard test systems and large-scale synthetic networks demonstrate significant advancements in critical metrics such as transmission efficiency, dynamic stability, and operational reliability. The architecture ensures strong compliance with N-1 security standards through topology-aware action masking and reduces computational complexity via optimized hierarchical processing. Validation against industry-standard tools and real-world grid data confirms the architecture’s effectiveness under practical operating conditions. Compared to conventional approaches, our paradigm offers superior performance in scenarios with high renewable penetration, providing grid operators with a robust and cost-effective decision-support tool for modernization and renewable integration. The architecture’s decisions are demonstrated to be interpretable and consistent with established power systems principles, while maintaining robust performance under unseen contingency scenarios. The capabilities highlight the potential for practical deployment of this architecture in contemporary power systems facing transitional challenges. These findings underscore the architecture’s potential for practical application in modern power systems experiencing transitional challenges.

本文提出了一种用于优化765kv输电网升级的高级强化学习图神经网络（RL-GNN）架构。该建筑是专门为解决与高水平可再生能源整合相关的动态稳定性和效率挑战而设计的。提出的方法通过三个关键创新来推进传统的优化技术：动态奖励机制，实时自主管理稳定性和效率之间的权衡；在大规模系统中实现可扩展的分层图处理方法；以及适应可再生能源发电波动的自适应控制功能。使用IEEE标准测试系统和大规模合成网络的广泛评估表明，在传输效率、动态稳定性和运行可靠性等关键指标上取得了重大进展。该体系结构通过拓扑感知的动作掩蔽确保了对N-1安全标准的强遵从性，并通过优化的分层处理降低了计算复杂度。针对行业标准工具和实际网格数据的验证证实了该体系结构在实际操作条件下的有效性。与传统方法相比，我们的范例在可再生能源渗透率高的情况下具有卓越的性能，为电网运营商提供了一个强大且具有成本效益的现代化和可再生能源整合决策支持工具。该体系结构的决策被证明是可解释的，并且与已建立的电力系统原则一致，同时在未知的突发情况下保持稳健的性能。这些能力突出了该架构在面临过渡挑战的当代电力系统中实际部署的潜力。这些发现强调了该架构在经历过渡挑战的现代电力系统中的实际应用潜力。

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引用次数: 0

An interval-based two-stage stochastic programming model for the unit commitment problem under net demand uncertainty 基于区间的两阶段随机规划模型求解净需求不确定性下的机组承诺问题

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111515

Hojin Jung , Jongheon Lee , Kyungsik Lee

The unit commitment problem aims to find a cost-minimizing on/off status and dispatch level for each generator while satisfying the electricity demand and operational requirements. To deal with net demand uncertainty modeled by a given probability distribution, scenario-based stochastic programming models where expected costs are minimized over sampled demand scenarios have been widely studied. However, they often suffer from excessive computational burden as the number of scenarios increases. To address this challenge, we propose an interval-based two-stage stochastic unit commitment model. In the model, the dispatch range for each generator is carefully determined along with its on/off status in each period, allowing dispatch decisions to be made independently across periods. To approximate the expected second-stage cost, we employ a discrete bounding approximation that obviates the need for scenario generation and provides controllable upper and lower bounds. Furthermore, we derive a compact Benders reformulation to reduce the size of the proposed model. Computational experiments on IEEE 118-bus systems show that the proposed model can substantially decrease computation time and achieve lower real-time operating costs compared to the conventional scenario-based two-stage stochastic unit commitment model, while maintaining a small approximation gap.

机组承诺问题的目标是在满足电力需求和运行要求的情况下，为每台发电机组找到成本最小的开/关状态和调度水平。为了处理由给定概率分布建模的净需求不确定性，基于场景的随机规划模型得到了广泛的研究，该模型在抽样需求情景中期望成本最小化。然而，随着场景数量的增加，它们往往会遭受过多的计算负担。为了解决这一挑战，我们提出了一个基于区间的两阶段随机单元承诺模型。在该模型中，每台发电机的调度范围及其在每个周期内的开/关状态都被仔细确定，从而允许跨周期独立做出调度决策。为了近似预期的第二阶段成本，我们采用了一个离散边界近似，避免了场景生成的需要，并提供了可控的上限和下限。此外，我们推导出一个紧凑的弯管机重新配方，以减少所提出的模型的尺寸。在IEEE 118总线系统上进行的计算实验表明，与传统的基于场景的两阶段随机单元承诺模型相比，该模型可以大幅减少计算时间，实现更低的实时运行成本，同时保持较小的近似间隙。

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引用次数: 0

RL-driven problem decomposition for computationally efficient AC optimal power flow 计算高效交流最优潮流的rl驱动问题分解

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111556

Ye-Eun Jang , Jaepil Ban , Young-Jin Kim , Chen Chen

Solving the AC optimal power flow (AC OPF) problem poses significant challenges in power system operations because of its inherent nonlinearity and complexity. This paper introduces a novel strategy to solve the AC OPF problem by utilizing a new problem decomposition framework combined with reinforcement learning (RL)-based cutting planes. The problem is decomposed into two sub-problems, DC OPF and AC power flow (AC PF) calculation sub-problems. To yield the AC-feasible solution, linear inequality constraints (i.e., cuts) are obtained by an RL agent and added into the DC OPF sub-problem. Then, the AC PF calculation is performed using the solution to the DC OPF sub-problem (i.e., power generation profiles) and voltage magnitude reference values, which is the output of the RL agent. Additionally, the action selection method is employed for the RL agent’s training efficiency. Case studies under various simulation scenarios are conducted to show the effectiveness of the proposed strategy compared to the conventional strategies. The simulation results indicate that the proposed strategy significantly enhances computational efficiency and solution feasibility compared to the conventional methods.

交流最优潮流（AC OPF）问题由于其固有的非线性和复杂性，在电力系统运行中具有重要的挑战性。本文采用一种新的问题分解框架，结合基于强化学习（RL）的切割平面，提出了一种解决AC OPF问题的新策略。将该问题分解为两个子问题：直流OPF和交流功率流计算子问题。为了得到交流可行解，通过RL代理获得线性不等式约束（即切割），并将其加入到DC OPF子问题中。然后，使用直流OPF子问题（即发电剖面）的解和电压幅值参考值进行交流PF计算，这是RL代理的输出。此外，为了提高RL agent的训练效率，还采用了动作选择方法。在不同的仿真场景下进行了案例研究，以表明所提出的策略与传统策略相比的有效性。仿真结果表明，与传统方法相比，该策略显著提高了计算效率和求解的可行性。

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引用次数: 0

Multi-power-level collaborative optimization considering high-entropy energy predictive supply in intelligent connected transportation system 考虑高熵能量预测供给的智能互联交通系统多功率协同优化

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111530

Junjie Hu , Chengming Xu , Jun Wu , Xuetao Liu , Xingyi Zhu

To maximize the efficient use of distributed energy, this paper proposes a multi-power-level collaborative scheduling model for Intelligent Connected Transportation System (ICTS), integrating high-entropy energy output forecasting. First, the interdependency between high-entropy energy and traffic flow is examined, and a dynamic spatial–temporal transformer model is applied to predict high-entropy energy output. Second, a hierarchical scheduling model for ICTS is developed based on the power-load characteristics of ICTS. The high-power-level scheduling layer employs a two-stage adaptive robust optimization (ARO) model to optimize the output of wind turbines (WT) and photovoltaic (PV) systems. Meanwhile, the low-power-level scheduling layer utilizes chance-constrained goal programming (CCGP) to address the uncertainty of high-entropy energy, based on predicted error distribution. The multi-power-level system is interconnected through energy routers to ensure operational stability. Finally, case study results demonstrate that the proposed strategy enhances the robustness and economic performance of ICTS, providing a reference for collaborative optimization in ICTS under uncertain environments.

为了最大限度地提高分布式能源的利用效率，提出了一种集成高熵能量输出预测的智能互联交通系统（ICTS）多功率级协同调度模型。首先，研究了高熵能与交通流的相互依存关系，并应用动态时空变压器模型对高熵能输出进行了预测。其次，基于ict的电力负荷特性，建立了ict的分层调度模型。大功率调度层采用两阶段自适应鲁棒优化（ARO）模型对风力发电机组和光伏发电系统的输出进行优化。同时，低功耗调度层利用基于预测误差分布的机会约束目标规划（CCGP）来解决高熵能量的不确定性。多功率级系统通过能量路由器互联，保证系统运行的稳定性。最后，实例研究结果表明，该策略增强了ict系统的鲁棒性和经济性，为不确定环境下的ict协同优化提供了参考。

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引用次数: 0

Stability of the Theta method for systems with multiple time-delayed variables 多时滞变量系统的Theta方法的稳定性

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2026.111560

Andreas Bouterakos, Georgios Tzounas

The paper focuses on the numerical stability and accuracy of implicit time-domain integration (TDI) methods when applied for the solution of a power system model impacted by time delays. Such a model is generally formulated as a set of delay differential algebraic equations (DDAEs) in non index-1 Hessenberg form. In particular, the paper shows that numerically stable ordinary differential equation (ODE) methods, such as the trapezoidal and the Theta method, can become numerically unstable when applied to a power system that includes a significant number of delayed variables. Analysis reveals how numerical stability is primarily governed by the number of delayed variables and the strength of their Jacobian coefficients, whereas the influence of the delay magnitudes themselves is found to be comparatively minor. Numerical stability is examined through a scalar test delay differential equation, as well as through a matrix pencil approach that accounts for the DDAEs of any given dynamic power system model. Simulation results are presented in a case study based on the IEEE 39-bus system, as well as the real-world scale model of the All-Island Irish Transmission System (AIITS).

研究了隐式时域积分法在求解受时滞影响的电力系统模型时的数值稳定性和精度。这种模型一般被表述为一组非指标-1 Hessenberg形式的延迟微分代数方程（DDAEs）。特别是，本文表明，数值稳定的常微分方程（ODE）方法，如梯形法和Theta法，在应用于包含大量延迟变量的电力系统时可能变得数值不稳定。分析揭示了数值稳定性主要由延迟变量的数量及其雅可比系数的强度决定，而发现延迟大小本身的影响相对较小。数值稳定性是通过一个标量测试延迟微分方程，以及通过矩阵铅笔的方法，说明任何给定的动态电力系统模型的DDAEs检查。本文给出了基于IEEE 39总线系统的仿真结果，以及全岛爱尔兰输电系统（AIITS）的实际比例模型。

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引用次数: 0

A new decentralized control structure for generation scheduling in a multi-energy virtual power plant considering its interactions with an electric vehicle aggregator and a demand response provider 考虑与电动汽车聚合器和需求响应提供者交互的多能量虚拟电厂发电调度分散控制结构

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111539

Zahra Sadat Mirjamali Khozaghi , Abbas Seifi , Gevork B. Gharehpetian

This paper presents a new hierarchical structure for decentralized decision-making in a Multi-Energy Virtual Power Plant (MEVPP). The proposed framework identifies three main stakeholders: the MEVPP authority, an Electric Vehicles Aggregator (EVA), and a Demand Response Provider (DRP), to clarify their roles in MEVPP generation scheduling. A bi-level optimization model is developed to illustrate the hierarchical relationships among these agents. The MEVPP authority, acting as the leader, manages daily energy generation scheduling for distributed energy resources and sets energy prices. The EVA and DRP agents serve as followers, aiming to optimize their operations based on energy prices and collaborate within the context of home energy capacity management. The EVA determines the optimal charging and discharging schedules for electric vehicles and assigns them to different charging stations via the Vehicle-to-Home (V2H) mechanism. The DRP makes decisions on the consumers’ participation rate in the demand response program and adjusts household energy consumption accordingly. A new Internal System Marginal Pricing (ISMP) scheme is used in the pricing strategy set by the MEVPP authority for energy transactions with the two consuming agents. An iterative algorithm is designed to solve the bi-level model with interconnected followers to find a Stackelberg-Nash equilibrium. To compare the centralized and decentralized control structures, we implement six structural scenarios and analyze their results. The proposed bi-level model addresses existing challenges related to consumer decision-making authority and information privacy. Additionally, it reduces the consumers’ costs up to 30 % and lowers peak energy demand up to 9 %, compared to single-level centralized models.

提出了一种新的多能源虚拟电厂（MEVPP）分散决策层次结构。拟议的框架确定了三个主要利益相关者：MEVPP当局，电动汽车聚合商（EVA）和需求响应提供商（DRP），以明确他们在MEVPP发电调度中的角色。建立了一个双层优化模型来说明这些智能体之间的层次关系。MEVPP作为领导者，管理分布式能源的日常发电计划和设定能源价格。EVA和DRP代理作为追随者，旨在根据能源价格优化其运营，并在家庭能源容量管理的背景下进行协作。EVA确定电动汽车的最佳充放电时间表，并通过车辆到家庭（V2H）机制将其分配到不同的充电站。DRP决定消费者对需求响应计划的参与率，并相应地调整家庭能源消耗。在MEVPP机构与两个消费主体的能源交易定价策略中，采用了一种新的系统内部边际定价（ISMP）方案。设计了一种迭代算法来求解具有相互关联追随者的双层模型，以求得Stackelberg-Nash均衡。为了比较集中式和分散式控制结构，我们实现了六种结构场景并分析了它们的结果。提出的双层模型解决了与消费者决策权和信息隐私相关的现有挑战。此外，与单级集中式模型相比，它可将消费者的成本降低30%，峰值能源需求降低9%。

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引用次数: 0

Unifying temporal continuity and structural decomposition for stealthy FDIA detection in smart grids via implicit neural representations 基于隐式神经表征的智能电网FDIA隐身检测的时间连续性和结构分解

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111507

Siliang Zhao , Wuman Luo , Qin Shu , Fangwei Xu

False data injection attacks (FDIAs) pose significant threats to power system security by stealthily manipulating measurements to bypass traditional residual-based detectors. While recent time-series detection methods improve robustness by modeling temporal continuity, they often rely on labeled attack data or static architectures with limited adaptability. This paper proposes a fully unsupervised FDIA detection framework based on implicit neural representations (INRs), which model multivariate power system measurements as continuous functions of time. A transformer-based hypernetwork dynamically generates INR parameters, enabling the model to adapt to varying operating conditions without retraining. To unify temporal continuity and structural decomposition, the proposed framework couples continuous-time INR modeling with a hierarchical trend-seasonal-residual representation and a measurement-type-aware residual structure. Anomalies are identified via a weighted reconstruction error using type-normalized anomaly scores. Extensive experiments on IEEE 14-bus and 118-bus test systems demonstrate that the proposed method achieves near-perfect detection accuracy against both optimization-based and controllable FDIAs, significantly outperforming existing supervised, semi-supervised, and unsupervised baselines. Moreover, the INR model exhibits strong sensitivity to subtle, temporally-localized perturbations and remains robust under different attack amplitudes. To the best of our knowledge, this is the first FDIA detection framework leveraging INR-based continuous-time modeling with transformer-driven parameterization, offering a scalable, interpretable, and topology-agnostic solution for securing modern power grids.

虚假数据注入攻击（FDIAs）通过秘密操纵测量值来绕过传统的基于残差的检测器，对电力系统的安全构成重大威胁。虽然最近的时间序列检测方法通过建模时间连续性来提高鲁棒性，但它们通常依赖于标记的攻击数据或适应性有限的静态架构。本文提出了一种基于隐式神经表示（INRs）的全无监督FDIA检测框架，该框架将电力系统的多变量测量数据建模为时间的连续函数。基于变压器的超网络动态生成INR参数，使模型无需再训练即可适应不同的操作条件。为了统一时间连续性和结构分解，该框架结合了具有分层趋势-季节-残差表示和测量类型感知残差结构的连续时间INR建模。通过使用类型归一化异常分数的加权重建误差来识别异常。在IEEE 14总线和118总线测试系统上的大量实验表明，所提出的方法在基于优化和可控的ffdi下都达到了近乎完美的检测精度，显著优于现有的监督基线、半监督基线和无监督基线。此外，INR模型对细微的、时间局部的扰动表现出很强的敏感性，并且在不同的攻击幅度下保持鲁棒性。据我们所知，这是第一个利用基于inr的连续时间建模和变压器驱动参数化的FDIA检测框架，为保护现代电网提供了可扩展、可解释和拓扑不可知的解决方案。

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引用次数: 0

Enhancing global search of honey badger algorithm for High-Accuracy Lithium-Ion battery modeling and SOC estimation 基于蜜獾算法的高精度锂离子电池建模和SOC估计的增强全局搜索

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2026.111566

Wei-Lun Yu , En-Jui Liu , Jen-Yuan Chang

Accurate modeling and reliable SOC estimation are central to battery management system design. Lithium-ion batteries show nonlinear behavior and create a multimodal search landscape that makes complex optimization challenging. The honey badger algorithm (HBA) converges rapidly in complex optimization problems but exhibits a strong exploitation bias that limits global exploration. To improve solution stability and search robustness, the modified honey badger algorithm (MHBA) with Brownian-motion is proposed to enhance global search capability. MHBA is first tested on 23 standard optimization functions to assess robustness and is then applied to lithium-ion battery parameter identification, where performance is compared with four advanced algorithms in terms of best fitness, mean fitness, standard deviation and search behavior. Subsequently, the MHBA-optimized parameters are incorporated into extended Kalman filter to examine the impact of modeling accuracy on SOC estimation under three temperature conditions (0 °C, 25 °C and 50 °C), across different discharge rates and initial SOC conditions. The results show that the RMSE values obtained using MHBA and HBA are 1.512 mV and 1.553 mV, respectively. In SOC estimation validation, the MHBA-based parameters improve estimation accuracy by up to 53.564 %. These results indicate that MHBA not only effectively enhances search capability but also provides a reliable foundation for the development of advanced battery management system.

准确的建模和可靠的SOC估计是电池管理系统设计的核心。锂离子电池表现出非线性行为，造成了多模态搜索环境，使复杂的优化变得具有挑战性。蜂蜜獾算法（HBA）在复杂的优化问题中收敛迅速，但表现出强烈的开发偏见，限制了全局探索。为了提高解的稳定性和搜索的鲁棒性，提出了基于布朗运动的改进蜜獾算法（MHBA），增强了全局搜索能力。MHBA首先在23个标准优化函数上进行了测试，以评估鲁棒性，然后将其应用于锂离子电池参数识别，并在最佳适应度、平均适应度、标准差和搜索行为方面与四种高级算法进行了比较。随后，将mhba优化后的参数纳入扩展卡尔曼滤波器，在不同的放电速率和初始荷电状态下，在三种温度条件下（0°C、25°C和50°C），研究建模精度对荷电状态估计的影响。结果表明，使用MHBA和HBA获得的RMSE值分别为1.512 mV和1.553 mV。在SOC估计验证中，基于mhba的参数将估计精度提高了53.564%。这些结果表明，MHBA不仅有效地增强了搜索能力，而且为开发先进的电池管理系统提供了可靠的基础。

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引用次数: 0

Decentralized peer-to-peer transactive energy management and game-theoretic market operation of an integrated electrical-thermal system in networked multi-carrier microgrids under uncertainty 不确定条件下网络化多载波微电网一体化电热系统的分散点对点交互能量管理与博弈论市场运行

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111546

Hamidreza Zareshahi, Shahram Jadid

This study presents a novel decentralized energy management framework for Networked Multi-Carrier Microgrids (NMCMG), addressing critical gaps in privacy-preserving peer-to-peer (P2P) energy trading for multiple energy carriers, including electricity and thermal energy. Unlike conventional centralized approaches, which require extensive data sharing and compromise privacy, the proposed model leverages a two-stage optimization strategy combining the Alternating Direction Method of Multipliers (ADMM) and game theory to ensure secure, scalable, and economically fair energy exchanges among microgrids. The framework focuses on addressing limitations of prior NMCMG energy management systems, particularly in decentralized P2P markets, which often lack consideration for privacy, multiple uncertainties, and fair profit-sharing mechanisms. The first stage employs ADMM to optimize microgrid energy management while preserving operational privacy, eliminating the need for centralized data aggregation and respecting microgrid autonomy. The second stage integrates bargaining game theory to establish dynamic pricing mechanisms in the P2P market, ensuring fair profit distribution based on each microgrid’s contribution, incentivizing active engagement in energy trading. Additionally, robust optimization is applied to handle uncertainties in renewable generation and demand fluctuations, avoiding the computational complexity of probabilistic methods and enhancing real-time applicability. The proposed model not only reduces operational costs but also improves grid stability, reduces carbon emissions, minimizes load shedding, and enhances customer satisfaction. Practical results demonstrate a significant reduction in the total daily cost of microgrids, achieving approximately 13% savings through implementing the proposed innovations. Furthermore, the model exhibits robust stability, maintaining correct energy management even under critical conditions such as microgrid outages. By integrating advanced energy storage and cooperative game theory, this research provides a practical, scalable solution for real-world NMCMG deployment, bridging the gap between theoretical research and industrial implementation.

本研究提出了一种用于网络多载波微电网（NMCMG）的新型分散能源管理框架，解决了多种能源载体（包括电力和热能）在保护隐私的点对点（P2P）能源交易中的关键空白。与传统的集中式方法不同，该模型需要广泛的数据共享和损害隐私，该模型利用两阶段优化策略，结合交替方向乘数法（ADMM）和博弈论，以确保微电网之间安全、可扩展和经济公平的能源交换。该框架的重点是解决以前的NMCMG能源管理系统的局限性，特别是在分散的P2P市场中，这些市场往往缺乏对隐私、多重不确定性和公平利润分享机制的考虑。第一阶段采用ADMM优化微电网能源管理，同时保护运营隐私，消除对集中数据聚合的需求，并尊重微电网的自主权。第二阶段整合议价博弈理论，建立P2P市场的动态定价机制，确保基于每个微电网贡献的公平利润分配，激励积极参与能源交易。此外，采用鲁棒优化方法处理可再生能源发电中的不确定性和需求波动，避免了概率方法的计算复杂度，增强了实时性。所提出的模型不仅降低了运营成本，还提高了电网稳定性，减少了碳排放，最大限度地减少了负荷下降，提高了客户满意度。实际结果表明，通过实施拟议的创新，微电网的每日总成本显著降低，节省了约13%。此外，该模型表现出强大的稳定性，即使在微电网中断等关键条件下也能保持正确的能源管理。通过整合先进的储能和合作博弈论，本研究为现实世界的NMCMG部署提供了一个实用的、可扩展的解决方案，弥合了理论研究与工业实施之间的差距。

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引用次数: 0

Investigating flexibility contracts in electricity markets considering single-price and 15-minute imbalance settlement 考虑单一价格和15分钟不平衡结算的电力市场弹性合约研究

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-01-01 DOI: 10.1016/j.ijepes.2025.111541

Milad Mousavi , Jin Zhong , Sarah Rönnberg

Flexibility Contracts (FlexCons), in the context of power and energy systems, aim to address challenges arising from uncertainties in electricity consumption and variable renewable generation using a bilateral short-term instrument. Through such contracts, electricity retailers (RETs) and variable renewable energy producers (VREPs) agree to provide power flexibility to one another to reduce the impact of imbalances on their respective decision-making processes. In this paper, two four-stage decision-making problems are developed for RETs and VREPs to analyze their participation in FlexCons alongside the day-ahead market (DAM), intraday market (IDM), and balancing market (BLM). The proposed models incorporate uncertainties in market prices, electricity consumption, and renewable generation through scenario sets and a stochastic decision-making approach. Additionally, the framework includes single-price and 15-minute imbalance settlement, as well as location-specific considerations within the system. Subsequently, the outcomes of these decision-making problems are integrated into the market-clearing processes of DAM, IDM, and BLM to assess the positive and negative impacts of such bilateral transactions. A two-bus illustrative example and the IEEE 24-bus RTS system are used for simulations. The results indicate higher profit for FlexCons’ participants by 1.6%, lower flexibility prices in the BLM by 6.8%, and an overall reduction in system costs by 4% when FlexCons are used.

弹性合同（FlexCons），在电力和能源系统的背景下，旨在通过双边短期工具解决电力消耗的不确定性和可变的可再生能源发电所带来的挑战。通过此类合同，电力零售商（ret）和可变可再生能源生产商（vrep）同意为彼此提供电力灵活性，以减少不平衡对各自决策过程的影响。在本文中，为RETs和vrep开发了两个四阶段决策问题，以分析他们与日前市场（DAM），日内市场（IDM）和平衡市场（BLM）一起参与FlexCons。所提出的模型通过情景集和随机决策方法纳入了市场价格、电力消耗和可再生能源发电的不确定性。此外，该框架还包括单一价格和15分钟不平衡结算，以及系统内的特定位置考虑。随后，这些决策问题的结果被整合到DAM、IDM和BLM的市场清算过程中，以评估此类双边交易的积极和消极影响。采用双总线示例和IEEE 24总线RTS系统进行仿真。结果表明，FlexCons参与者的利润提高了1.6%，BLM中的灵活性价格降低了6.8%，系统成本总体降低了4%。

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