Sustainable Energy Grids & Networks最新文献

Coordinated curtailment of uncontrollable distributed energy resources in isolated power systems 孤立电力系统中不可控分布式能源的协同弃电

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-27 DOI: 10.1016/j.segan.2026.102124

Phivos Therapontos , Savvas Panagi , Charalambos A Charalambous , Petros Aristidou

The escalating integration of renewable energy sources (RES) into isolated, low-inertia power systems presents considerable challenges to maintaining frequency stability. To preserve operational security, system operators often impose stringent requirements that can necessitate RES curtailment, particularly during periods of low demand. While such measures predominantly affect large-scale distributed energy resources (DERs), prolonged curtailment scenarios may also compel output reductions from numerous small-scale, often uncontrollable, DERs (UDERs). Prevailing control strategies for UDERs typically rely on the deployment of dedicated control and communication hardware at each UDER site, incurring significant capital expenditure and implementation complexity. This paper introduces a novel methodology for the coordinated curtailment of UDERs, which circumvents the need for such supplementary equipment. The proposed approach utilizes the system frequency as an implicit communication conduit, leveraging the inherent active power-frequency (P-f) response capabilities of UDER inverters. A data-driven framework is employed to optimize a global active power-frequency reduction characteristic, tailored from historical operational data. This characteristic is subsequently implemented in a decentralized manner by individual UDERs, thereby effectively mitigating investment costs and cybersecurity vulnerabilities associated with conventional control architectures. The performance and efficacy of the proposed methodology are demonstrated through dynamic simulations on a model of the isolated, low-inertia power system of Cyprus.

将可再生能源（RES）逐步整合到孤立的低惯性电力系统中，对保持频率稳定性提出了相当大的挑战。为了保证运行安全，系统运营商通常会施加严格的要求，这可能导致RES的缩减，特别是在低需求时期。虽然这些措施主要影响大规模分布式能源（DERs），但长期弃风情景也可能迫使许多小规模、通常不可控的分布式能源（DERs）减少产量。UDER的主流控制策略通常依赖于在每个UDER站点部署专用控制和通信硬件，这导致了大量的资本支出和实施复杂性。本文介绍了一种新的方法来协调削减uder，从而避免了对这些补充设备的需要。该方法利用系统频率作为隐式通信通道，利用UDER逆变器固有的有源工频（P-f）响应能力。基于历史运行数据，采用数据驱动框架优化全局有源工频降低特性。该特性随后由各个uder以分散的方式实现，从而有效降低与传统控制架构相关的投资成本和网络安全漏洞。通过对塞浦路斯孤立的低惯性电力系统模型的动态仿真，证明了所提出方法的性能和有效性。

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引用次数: 0

The overvoltage-driven blackout of the Iberian Peninsula on 28th April 2025 2025年4月28日伊比利亚半岛的过电压驱动停电

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-27 DOI: 10.1016/j.segan.2026.102125

L. Rouco, F.M. Echavarren, E. Lobato

The Iberian Peninsula blackout on 28th April 2025 occurred due to cascading disconnection of renewable generation with power factor control, triggered by overvoltage generation protections. This paper describes a conceptual model to explain the primary phenomenon that occurred, which we have called an overvoltage-driven blackout. While the phenomenon of voltage collapse, or more precisely undervoltage collapse, is widely discussed in the scientific literature, the phenomenon of an overvoltage-driven blackout is new. An illustrative 3-bus small-scale power system is provided to better understand the evolution of bus voltages in an overvoltage-driven blackout, identifying the critical factors that can lead a power system to a blackout caused by overvoltage. The conceptual model is applied to the state of the Iberian Peninsula electricity system at 12:30 on 28th April 2025, preceding the blackout. The paper will show how, with the loss of renewable generation, the growth of bus voltages exhibits the same pattern as the one identified in the 3-bus small-scale system. A new safety metric (margin to overvoltage-driven blackout) is defined and computed. The paper will demonstrate how the system operated with an insufficient safety margin, leading to an overvoltage-driven blackout, due to a lack of sufficient synchronous reactive power absorption capacity in the central and southern parts of Spain, as well as the low-loaded transmission grid in those regions.

2025年4月28日，伊比利亚半岛发生大停电，原因是由过压发电保护触发的具有功率因数控制的可再生能源发电级联断开。本文描述了一个概念模型来解释发生的主要现象，我们称之为过电压驱动停电。虽然电压崩溃现象，或者更准确地说是欠压崩溃现象在科学文献中被广泛讨论，但过电压驱动的停电现象是新的。为了更好地理解在过电压驱动的停电中母线电压的演变，提供了一个说明性的3母线小型电力系统，确定了可能导致电力系统由过电压引起的停电的关键因素。该概念模型应用于2025年4月28日12:30的伊比利亚半岛电力系统状态，即大停电之前。本文将展示，随着可再生能源发电的损失，母线电压的增长如何呈现出与3母线小规模系统中确定的相同模式。定义并计算了一种新的安全度量（过电压驱动停电余量）。本文将展示由于西班牙中部和南部缺乏足够的同步无功吸收能力以及这些地区的低负荷输电网，该系统如何在安全裕度不足的情况下运行，从而导致过压驱动的停电。

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引用次数: 0

Planning hybrid renewable energy systems under uncertain grid interconnection conditions 不确定并网条件下的混合可再生能源系统规划

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-27 DOI: 10.1016/j.segan.2026.102131

Majd Olleik, Amir Boushahine, Kareem Abou Jalad

Achieving universal access to affordable and clean energy is a central goal of the United Nations Sustainable Development Agenda. However, in many developing and fragile contexts, large populations remain disconnected from the national grid or experience highly unreliable supply. Hybrid Renewable Energy Systems (HRES) offer a viable off-grid solution, but their long-term planning is complicated by uncertainty around future grid interconnection both in timing and technical or economic conditions. This paper proposes a decision analysis framework that incorporates grid interconnection uncertainty into off-grid HRES planning. The framework employs a receding horizon approach that combines deterministic and stochastic optimization models while integrating early asset retirement decisions and residual value assessments. A case study from Lebanon, a country with persistent electricity sector challenges, demonstrates the framework’s utility. Results highlight three key insights: (i) incremental planning enables better adaptation to evolving grid conditions, (ii) planning the HRES for the worst-case scenario of no grid availability and then adjusting once the grid is available is a valid heuristic, and (iii) policy interventions that improve market liquidity for used HRES assets can mitigate the risks associated with uncertain grid interconnection, enhancing the economic resilience of off-grid investments. These findings offer both methodological and policy contributions to energy planning in uncertain and underserved regions.

普及负担得起的清洁能源是联合国可持续发展议程的中心目标。然而，在许多发展中国家和脆弱的环境中，大量人口仍然与国家电网脱节，或者经历着极不可靠的供应。混合可再生能源系统（HRES）提供了一种可行的离网解决方案，但由于未来电网在时间和技术或经济条件上的不确定性，其长期规划变得复杂。提出了一种将并网不确定性纳入离网HRES规划的决策分析框架。该框架采用了一种将确定性和随机优化模型相结合的后退地平线方法，同时集成了早期资产退役决策和剩余价值评估。黎巴嫩是一个电力行业持续面临挑战的国家，其案例研究证明了该框架的实用性。结果突出了三个关键的见解：(i)增量规划能够更好地适应不断变化的电网条件；（ii）针对无电网可用的最坏情况规划HRES，然后在电网可用后进行调整是一种有效的启发式方法；（iii）提高已用HRES资产的市场流动性的政策干预可以减轻与不确定电网互联相关的风险，增强离网投资的经济弹性。这些发现为不确定和服务不足地区的能源规划提供了方法和政策贡献。

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引用次数: 0

A new convex Stackelberg game theory oriented optimization model for resilient day-ahead planning of distribution network by optimal distributed generation pricing and incentive-based demand response program 基于最优分布式发电定价和基于激励的需求响应方案的配电网弹性日前规划新凸Stackelberg博弈优化模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-26 DOI: 10.1016/j.segan.2026.102134

Saeed Behzadi , Mehdi Naserian

Following severe disasters in distribution systems, distribution network operators (DNOs) should employ various methods to minimize load-shedding. A highly effective strategy is offering incentive-oriented rewards to consumers to reduce load in critical conditions. In this paper, two case studies have been compared under low-probability and high-impact (LPHI) outage conditions to indicate the impact of incentive-based demand response program (IBDRP) on load restoration. In the best case, the offered optimal incentive reward price to the consumers is determined based on the optimal pricing of distributed generation (DG) in critical conditions. These proposed prices have been obtained by taking into account the optimal benefit view of both consumers and DNOs. To reach an optimal solution for day-ahead pricing in resilient distribution system planning according to this point of view, the Stackelberg game theory (SGT) is utilized. On the other side, accurate day-ahead network load forecasting is obtained by using machine learning and classical methods. In addition, all the formulations have been convexified and implemented in GAMS software and tested in the IEEE 33-bus system. Finally, the Pareto optimization scenarios have been considered, and the optimal solution is reached by the fuzzy satisfying method.

在配电网发生重大灾害后，配电网运营商应采取各种措施将负荷减少到最小。一个非常有效的策略是向消费者提供以激励为导向的奖励，以减少关键条件下的负荷。本文比较了低概率和高影响（LPHI）停电条件下的两个案例，以表明基于激励的需求响应计划（IBDRP）对负荷恢复的影响。在最优情况下，根据临界条件下分布式发电的最优价格确定向消费者提供的最优激励奖励价格。这些建议的价格是在考虑了消费者和dno的最佳利益观点后得出的。根据这一观点，利用Stackelberg博弈论（SGT）求解弹性配电系统规划中日前电价问题的最优解。另一方面，利用机器学习和经典方法，获得了准确的日前网络负荷预测。此外，所有的公式都在GAMS软件中进行了凸化和实现，并在IEEE 33总线系统中进行了测试。最后，考虑了Pareto优化方案，并采用模糊满足法得到了最优解。

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引用次数: 0

Reconstructing hourly power profiles from monthly billing data: A neural network framework with two-phase validation 从月度账单数据重构每小时电力概况：一种两阶段验证的神经网络框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-23 DOI: 10.1016/j.segan.2026.102122

Morteza Aghahadi , Alessandro Bosisio , Edoardo Dacco , Davide Falabretti , Andrea Ruffini , Alessandro Cirocco

Electrical grid planning requires accurate hourly power consumption profiles, yet utilities typically possess only monthly billing data. This study presents a neural network framework for reconstructing detailed hourly power profiles from aggregated monthly consumption features. Feature engineering transforms hourly consumption into 46 monthly aggregated features, including tariff-based totals and distribution ratios. Principal Component Analysis and K-means clustering identify 14 distinct user behavioral patterns. Three neural network architectures are systematically compared: Multi-Layer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit networks. The methodology employs temporally separated validation, using 2022 data for training and 2023 data for validation, thereby assessing robustness to inter-annual variations in weather, economic conditions, and consumer behavior. Among the evaluated models, the Gated Recurrent Unit achieved the best overall performance with an

R^{2}

of 0.87 and a 40% reduction in mean squared error compared to XGBoost. For peak load estimation, which is critical for grid capacity planning, the proposed approach achieves a peak error of 18.3% for high-consumption users. Clustering stability analysis and evaluation across extreme user segments (high-consumption, high-volatility, and low-consumption) further confirm the robustness of the proposed methodology.

电网规划需要精确的每小时电力消耗概况，但公用事业公司通常只拥有每月的账单数据。本研究提出了一个神经网络框架，用于从汇总的月度消费特征重建详细的每小时电力概况。特征工程将每小时的消费转换为46个月的聚合特征，包括基于资费的总量和分配比率。主成分分析和K-means聚类识别出14种不同的用户行为模式。系统地比较了三种神经网络结构：多层感知器、长短期记忆和门控循环单元网络。该方法采用时间分离验证，使用2022年数据进行训练，使用2023年数据进行验证，从而评估对天气、经济条件和消费者行为年际变化的稳健性。在所评估的模型中，门控循环单元获得了最佳的整体性能，R2为0.87，与XGBoost相比，均方误差降低了40%。对于电网容量规划至关重要的峰值负荷估计，该方法对高消费用户的峰值误差为18.3%。跨极端用户群体（高消费、高波动和低消费）的聚类稳定性分析和评估进一步证实了所提出方法的鲁棒性。

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引用次数: 0

A multi-objective approach for integrated energy hub with waste incineration plants integrating carbon capture technology, and electric vehicle parking lots under uncertainties 不确定条件下垃圾焚烧厂集成碳捕集技术和电动汽车停车场的综合能源枢纽多目标研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-23 DOI: 10.1016/j.segan.2026.102121

Phan Thanh Vinh , Truong Hoang Bao Huy , Pham Van Phu , Namchul Cho , Daehee Kim

The rapid growth of municipal solid waste (MSW) and the urgent need for carbon emissions reduction present major challenges in sustainable energy system design. Waste incineration plants (WIPs), a key component of waste-to-energy (WtE) systems, convert MSW into usable energy. However, they generate considerable carbon emissions during the combustion process. Addressing these issues, this paper explores the potential of WtE technologies coupled with carbon capture (CC) and electric vehicle infrastructure within an integrated energy hub (IEH). A multi-objective mixed-integer linear programming paradigm for an IEH model is proposed where WIP-CC collaboration, renewable energy sources, power conversion components, storage system and electric vehicle parking lots are fully utilized. The augmented

ε

-constraint approach is employed for effectively solving the multi-objective IEH problem. In the proposed model, three objective functions are investigated: operation cost, emission tax, and export index (EI). The proposed IEH achieves an operation cost of $13,370.25, emission tax of $128.89, and an EI of 0 in the deterministic case. When considering uncertainties, the hybrid stochastic-IGDT model is applied to choose an optimal plan based on the characteristics of the uncertain parameters. Both risk-averse and risk-seeking strategies are studied to evaluate the trade-offs between solution robustness and performance, enabling more informed decision-making under varying levels of uncertainty.

城市固体废物的快速增长和碳减排的迫切需要对可持续能源系统的设计提出了重大挑战。垃圾焚烧厂（wip）是垃圾发电（WtE）系统的关键组成部分，将城市生活垃圾转化为可用的能源。然而，它们在燃烧过程中会产生相当多的碳排放。为了解决这些问题，本文探讨了在综合能源枢纽（IEH）中，WtE技术与碳捕获（CC）和电动汽车基础设施相结合的潜力。提出了一种充分利用WIP-CC协作、可再生能源、电力转换组件、存储系统和电动汽车停车场的IEH模型多目标混合整数线性规划范式。采用增广ε约束方法有效地解决了多目标IEH问题。在该模型中，研究了三个目标函数：运营成本、排放税和出口指数。在确定性情况下，所提出的IEH的运营成本为13370.25美元，排放税为128.89美元，EI为0。在考虑不确定性时，采用混合随机- igdt模型，根据不确定参数的特点选择最优方案。研究了风险规避和风险寻求策略，以评估解决方案鲁棒性和性能之间的权衡，从而在不同程度的不确定性下实现更明智的决策。

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引用次数: 0

Optimal capacity planning for grid‐connected power‐to‐hydrogen integrated energy system considering dynamic hydrogen production efficiency 考虑动态制氢效率的并网电氢一体化能源系统最优容量规划

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-14 DOI: 10.1016/j.segan.2026.102120

Jizhe Dong , Chongshan Xu , Meng Zhu , Yanbin Zhang , Zehao Zhao , Ziyang Hao , Shunjie Han

With the rapid expansion of hydrogen energy applications, the demand for high-precision modeling of hydrogen production systems has become increasingly urgent. In capacity planning for integrated energy systems (IESs), neglecting dynamic hydrogen production efficiency (DHPE) leads to planning results that deviate from the actual performance and impair the resource allocation. This paper proposes a capacity planning model for power-to-hydrogen IES that accounts for DHPE by incorporating the nonlinear relationship between the input power of an electrolyzer and its production efficiency. Additionally, a solution method is presented to address the problems of the model being unsolvable and having slow solution speed. Case studies, based on real operational and publicly available data, demonstrate that the DHPE model generates more reasonable planning solutions than the static hydrogen production efficiency model, and the operational levelized cost of hydrogen is reduced by approximately 0.3 %–3.9 %, while the renewable energy self-consumption increases by approximately 2.5 %–6.5 %.

随着氢能应用的迅速扩大，对制氢系统高精度建模的需求日益迫切。在综合能源系统容量规划中，忽略动态制氢效率会导致规划结果偏离实际性能，影响资源配置。通过考虑电解槽输入功率与其生产效率之间的非线性关系，提出了考虑DHPE的电制氢系统容量规划模型。此外，针对模型不可解和求解速度慢的问题，提出了一种求解方法。基于实际运行和公开数据的案例研究表明，与静态制氢效率模型相比，DHPE模型产生了更合理的规划方案，氢气的运行平准化成本降低了约0.3 % -3.9 %，而可再生能源自用增加了约2.5 % -6.5 %。

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引用次数: 0

Dynamic security region modeling via conditional style transfer and causal representation learning 基于条件风格迁移和因果表征学习的动态安全区域建模

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-07 DOI: 10.1016/j.segan.2026.102119

Zixuan Zheng , Yuan Zeng , Junzhi Ren , Chao Qin , Changgang Li , Wei Xu , Weilun Ni

The increasing complexity of power system operations poses challenges for traditional dynamic security region (DSR) modeling, particularly in critical sample transferability, boundary accuracy, and interpretability. This paper proposes a unified framework integrating conditional style transfer learning (CSTL), deep modeling, and causal inference to enhance DSR characterization across varying scenarios. First, a perturbation-style transfer method using conditional diffusion effectively generalizes critical samples across operating scenarios. Second, a deep feedforward neural network (DFNN) is employed to model complex nonlinear DSR boundaries, aided by a stability margin index for quantitative state assessment. The model achieves high accuracy, low latency, and strong noise robustness, supporting online deployment. Finally, to improve the physical interpretability, we incorporate a causal representation learning mechanism combining inverse probability weighting, doubly robust estimation, conditional shapley values and counterfactual inference. This enables causal attribution of perturbations to boundary variations and supports counterfactual analysis. Validation on the IEEE 39-bus and IEEE 145-bus systems confirms the effectiveness and scalability of the proposed approach.

电力系统运行的复杂性日益增加，对传统的动态安全区域（DSR）建模提出了挑战，特别是在关键样本可转移性、边界精度和可解释性方面。本文提出了一个整合条件风格迁移学习（CSTL）、深度建模和因果推理的统一框架，以增强不同场景下的DSR表征。首先，使用条件扩散的微扰式传递方法有效地概括了不同操作场景下的关键样本。其次，采用深度前馈神经网络（DFNN）对复杂非线性DSR边界进行建模，并辅以稳定裕度指标进行状态定量评估；该模型精度高、时延低、噪声鲁棒性强，支持在线部署。最后，为了提高物理可解释性，我们引入了一种结合反概率加权、双鲁棒估计、条件shapley值和反事实推理的因果表示学习机制。这使得扰动的因果归因到边界变化，并支持反事实分析。在IEEE 39总线和IEEE 145总线系统上的验证证实了该方法的有效性和可扩展性。

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引用次数: 0

An energy management strategy for integrated energy system based on data-driven and game theory methods 基于数据驱动和博弈论方法的综合能源系统能源管理策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-02 DOI: 10.1016/j.segan.2025.102118

Xun Xu, Zhenguo Shao, Feixiong Chen, Guoyang Cheng

To address the challenge of imperfect market coordination in multi-energy-coupled integrated energy systems (IESs) under uncertainty, especially the unresolved conflicts among stakeholders and the insufficient protection of disadvantaged participants within current market frameworks, an energy management strategy based on data-driven and game theory methods is proposed. Firstly, to optimize the benefits for both individual and collective stakeholders, a tri-level multi-energy management model is developed using multi-game framework, providing a novel approach to capturing interactions among diverse entities. Secondly, to handle the uncertainty of renewable energy, a data-driven distributionally robust chance constraint (DRCC) method is introduced, which uniquely combines dynamic Bayesian network (DBN) with imprecise Dirichlet model (IDM) and applies it to mixed ambiguity set that integrates desirable properties of different ambiguity sets. Finally, fixed-point theory is used to establish the existence of game equilibrium, and a Gauss-Seidel algorithm with adaptive inertia weight, combined with the alternating direction method of multipliers, is proposed to solve the multi-game model while ensuring the privacy of all parties. Case studies demonstrate that the DBN-IDM reduces the conservatism of parameter selection for the DRCC, and the proposed energy management strategy and improved Gauss-Seidel algorithm enhance participant benefits and accelerate convergence.

针对不确定条件下多能耦合综合能源系统市场协调不完善的挑战，特别是当前市场框架下利益相关者之间的冲突未解决和弱势参与者保护不足的问题，提出了一种基于数据驱动和博弈论方法的能源管理策略。首先，为了优化个体利益相关者和集体利益相关者的利益，利用多博弈框架建立了一个三级多能量管理模型，提供了一种捕捉不同实体之间相互作用的新方法。其次，针对可再生能源的不确定性，提出了一种数据驱动的分布式鲁棒机会约束（DRCC）方法，该方法将动态贝叶斯网络（DBN）与不精确狄利克雷模型（IDM）独特地结合起来，并将其应用于综合了不同模糊集所需特性的混合模糊集。最后，利用不动点理论建立了博弈均衡的存在性，并提出了一种自适应惯性权重的Gauss-Seidel算法，结合乘数交替方向法，在保证各方隐私的前提下求解多博弈模型。实例研究表明，DBN-IDM降低了DRCC参数选择的保守性，提出的能量管理策略和改进的Gauss-Seidel算法提高了参与者的利益，加快了收敛速度。

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引用次数: 0

Exploring dimensional distinctions of residential heat load profiles using an unsupervised machine learning clustering framework 使用无监督机器学习聚类框架探索住宅热负荷分布的维度差异

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-01-02 DOI: 10.1016/j.segan.2025.102117

Vasilis Michalakopoulos , Elissaios Sarmas , Viktor Daropoulos , Giannis Kazdaridis , Stratos Keranidis , Vangelis Marinakis , Dimitris Askounis

Decarbonizing the heating sector is central to achieving the energy transition, as heating systems provide essential space heating and hot water in residential and industrial environments. A major challenge lies in effectively profiling large clusters of buildings to improve demand estimation and enable efficient Demand Response schemes. This paper addresses this challenge by introducing a novel unsupervised machine learning framework for clustering residential heating load profiles, focusing on natural gas space heating and hot water preparation boilers, while analyzing five different dimensions: boiler usage, heating demand, weather conditions, building characteristics, and user behavior. Three distance metrics, Euclidean Distance, Dynamic Time Warping, and Derivative Dynamic Time Warping, are applied and evaluated using established clustering indices. The proposed method is assessed considering 29 residential buildings in Greece equipped with smart heating controllers throughout a calendar heating season (i.e. 210 days). This study demonstrates that Dynamic Time Warping is demonstrably the most suitable metric. A subsequent correlation analysis of the clustering results from each dimension reveals strong, time-dependent relationships between boiler usage, heat demand and temperature, identifying them as the most important and correlated directions. These findings shed light on heating load behavior, establishing a solid foundation for developing more targeted and effective demand response programs.

供暖部门脱碳是实现能源转型的核心，因为供暖系统为住宅和工业环境提供必要的空间供暖和热水。一个主要的挑战在于有效地分析大型建筑群，以改善需求估计和实现有效的需求响应计划。本文通过引入一种新的无监督机器学习框架来解决这一挑战，该框架用于聚类住宅供暖负荷概况，重点关注天然气空间供暖和热水准备锅炉，同时分析五个不同的维度：锅炉使用、供暖需求、天气条件、建筑特征和用户行为。三种距离度量，欧几里得距离，动态时间翘曲，和导数动态时间翘曲，应用和评估使用已建立的聚类指标。在整个日历采暖季节（即210天）中，对希腊29座配备智能供暖控制器的住宅建筑进行了评估。研究表明，动态时间翘曲是最合适的度量。随后对每个维度的聚类结果进行相关性分析，揭示了锅炉使用、热需求和温度之间强烈的时间依赖关系，将它们确定为最重要和相关的方向。这些发现揭示了热负荷行为，为制定更有针对性和更有效的需求响应计划奠定了坚实的基础。

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引用次数: 0