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A game-theoretic model for flexibility-constrained renewable energy communities in local energy trading with smart distribution networks 基于智能配电网的可再生能源社区柔性交易博弈模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-24 DOI: 10.1016/j.segan.2025.102105

Sahar Mobasheri , Masoud Rashidinejad , Amir Abdollahi , Mojgan MollahassaniPour , Sobhan Dorahaki

Renewable Energy Communities (RECs) play a critical role in advancing the energy transition towards a decentralized, distributed, and increasingly digitalized energy system. Through local energy trading within the distribution network, RECs have the potential to significantly enhance the flexibility of the energy system. This interaction, however, introduces complex challenges between REC operators and distribution network operators, necessitating robust analytical approaches. Leveraging Stackelberg game theory, this study models the hierarchical relationship between these entities, positioning the REC operator as the leader and the distribution network operator as the follower. To address the inherent uncertainties in renewable energy resources, Multi-Objective Information Gap Decision Theory (MO-IGDT) is employed, alongside flexibility constraints to ensure stability and efficiency in the system amidst fluctuations in REC output power. A bilevel optimization model, initially formulated as a mixed-integer linear program, is simplified into a single-level problem using Karush-Kuhn-Tucker (KKT) conditions. The findings underscore the benefits of integrating Community Energy Storage (CES) with renewable energy sources within an REC, demonstrating a 3.39 % increase in profits and a significant 51.23 % reduction in dependency on the upstream grid, highlighting the potential of RECs to enhance both economic and operational resilience in modern energy systems.

可再生能源社区（rec）在推动能源向分散、分布式和日益数字化的能源系统过渡方面发挥着关键作用。通过配电网内的本地能源交易，RECs有可能显著提高能源系统的灵活性。然而，这种相互作用在REC运营商和分销网络运营商之间引入了复杂的挑战，需要强大的分析方法。利用Stackelberg博弈论，本研究建立了这些实体之间的等级关系模型，将REC运营商定位为领导者，将配电网运营商定位为追随者。为了解决可再生能源资源固有的不确定性，采用多目标信息缺口决策理论（MO-IGDT），并结合柔性约束来保证系统在REC输出功率波动时的稳定性和效率。利用KKT条件将两层优化模型简化为单层优化问题，该优化模型最初是一个混合整数线性规划。研究结果强调了在REC内将社区能源存储（CES）与可再生能源相结合的好处，表明利润增加3.39% %，对上游电网的依赖显著减少51.23 %，突出了REC在提高现代能源系统的经济和运营弹性方面的潜力。

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引用次数: 0

Comparative analysis of machine learning methods for residential net load forecasting of solar-integrated households 机器学习方法在太阳能集成家庭净负荷预测中的比较分析

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-24 DOI: 10.1016/j.segan.2025.102106

Panagiotis Herodotou , Georgios Tziolis , George Makrides , George E. Georghiou

Accurate short-term net load forecasting (STNLF) of residential buildings with increased solar photovoltaic (PV) power penetration is critical for enabling reliable operation and enhancing grid stability. This paper presents a systematic comparative analysis of twelve deep learning and machine learning (ML) models for day-ahead net load forecasting, evaluated using data from a pilot study involving 68 households in Cyprus equipped with grid-connected PV systems. The proposed approach utilized historical, weather, and temporal features derived from the dataset. A rigorous evaluation procedure was followed, including cross-validation, recursive forecasting, and multiple error metrics. Results indicate that the random forest (RF) algorithm exhibited the best performance, with normalized root mean square error of 5.71 %, normalized relative to the range of observed net load values. RF achieved this due to its robustness in capturing non-linear interactions and its ability to handle mixed feature types. In contrast, the gated recurrent unit (GRU) network presented higher adaptability to sudden weather changes, attributed to its sequential learning structure and memory capabilities. The differences in model performance were verified with Diebold-Mariano test, indicating the superiority of recurrent and ensemble models over the simpler baselines. Feature importance analysis showed that lagged net load features were important in all models, but deep learning (DL) models better captured the impact of temporal and weather variables more effectively. The systematic approach for STNLF in PV-integrated residential buildings used in this study extends to the broader field of solar-integrated residential microgrids, promoting adaptable, interpretable models for effective energy management and renewable energy integration.

随着太阳能光伏发电（PV）的普及，住宅建筑的短期净负荷准确预测（STNLF）对于实现可靠运行和提高电网稳定性至关重要。本文对用于日前净负荷预测的12种深度学习和机器学习（ML）模型进行了系统的比较分析，并使用了一项涉及塞浦路斯68个配备并网光伏系统的家庭的试点研究数据进行了评估。该方法利用了数据集中的历史、天气和时间特征。遵循严格的评估程序，包括交叉验证、递归预测和多个误差度量。结果表明，随机森林（RF）算法表现出最好的性能，相对于观测到的净负荷值范围归一化的均方根误差为5.71 %。RF实现这一目标是由于其在捕获非线性相互作用方面的鲁棒性以及处理混合特征类型的能力。相比之下，门控循环单元（GRU）网络由于其顺序学习结构和记忆能力，对突发天气变化具有更高的适应性。通过Diebold-Mariano检验验证了模型性能的差异，表明循环模型和集合模型优于简单基线。特征重要性分析表明，滞后净负荷特征在所有模型中都很重要，但深度学习（DL）模型更有效地捕获了时间和天气变量的影响。本研究中使用的光伏集成住宅建筑STNLF系统方法扩展到太阳能集成住宅微电网的更广泛领域，促进了有效能源管理和可再生能源整合的适应性、可解释模型。

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引用次数: 0

A sequential conic programming algorithm for calculating voltage stability margins 计算电压稳定裕度的顺序二次规划算法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-23 DOI: 10.1016/j.segan.2025.102108

Long Fu , Gexiang Zhang , Jianping Dong , Gang Wang , Zhao Yang Dong , Yaran Li

With the increasing power demand and major blackout events, power systems are operating under more stressed conditions, approaching their stability limits. Voltage stability margin (VSM) characterizes a measure of distance to the power flow insolvability/infeasibility boundary that needs to be precisely calculated and effectively monitored, yet it can be challenging considering varying operational constraints and loading scenarios. Focusing on static power flow equations in this paper, a novel sequential conic programming (SCP) algorithm is proposed based on linear approximations of non-convex functions for an optimization-based VSM calculation. Compared with existing methods, the performance of proposed SCP is more robust against different operating scenarios where desired features of being initialization-free, exact, scalable, and applicable can be appropriately achieved. Multiple test cases validate the advantages and effectiveness of the proposed approach.

随着电力需求的增加和重大停电事件的发生，电力系统的运行压力越来越大，接近其稳定极限。电压稳定裕度（VSM）是一种距离潮流不可解/不可行的边界的度量，需要精确计算和有效监测，但考虑到不同的运行约束和负载情况，它可能具有挑战性。针对静态潮流方程，提出了一种基于非凸函数线性逼近的序贯二次规划（SCP）优化算法。与现有方法相比，所提出的SCP在不同操作场景下的性能更加健壮，可以适当地实现无初始化、精确、可扩展和适用的期望特性。多个测试用例验证了所提出方法的优点和有效性。

引用次数: 0

Development of grid-compliance metric for reliable integration of fast charging stations in power networks 电网快速充电站可靠集成的电网顺应性指标研究

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-20 DOI: 10.1016/j.segan.2025.102088

Salman Harasis , Irfan Khan , Ahmed Massoud

With the fast deployment of electric bus fleets in public transportation, efficient energy consumption and grid impact have become major concerns and challenges. Moreover, due to the accelerated need for fast charging, low-voltage distribution networks show insufficient hosting capacity for E-transportation systems, which encourages stations to connect to the medium voltage lines. In addition to the voltage level constraints, many factors associated with fast charging affect the grid interaction level and the maximum charging power that can be applied to charge the fleets. Although several studies have analyzed voltage deviations, harmonics, and renewable support individually, there remains a lack of a comprehensive grid-compliance evaluation methodology that can holistically quantify these impacts for large-scale charging stations. Therefore, this paper proposes a reliable grid interaction framework for E-bus fleets and develops a novel grid impact metric to ensure efficient charging power with minimal grid impact in a PV grid-connected system. The measures include voltage profile, charging power, and grid-injected harmonics. This work examines an optimal charging strategy to address fast charging challenges, featuring novel performance indices that quantify the grid impact and PV power generation. The proposed strategy is demonstrated by evaluating the charging station deployed at the IEEE 34-node network under different voltage levels. The proposed IGIM is demonstrated on the IEEE 34-node test feeder, where results show that MV connection significantly outperforms LV in terms of grid hosting capacity, which reduces voltage deviations by more than 50 %. Harmonic analysis reveals that constant-current mode charging up to 80 % SoC complies better with IEEE-519 limits than constant-voltage mode. In addition, PV-assisted charging increases self-consumption by up to 60 %.

随着电动公交车队在公共交通中的快速部署，高效的能源消耗和对电网的影响已经成为主要的问题和挑战。此外，由于对快速充电的需求加快，低压配电网络对电子交通系统的承载能力不足，这鼓励了车站连接到中压线路。除了电压水平的限制外，与快速充电相关的许多因素也会影响电网交互水平和可用于为车队充电的最大充电功率。尽管有几项研究分别分析了电压偏差、谐波和可再生能源支持，但仍然缺乏一种全面的电网合规性评估方法，可以全面量化这些对大型充电站的影响。因此，本文提出了一种可靠的电动公交车队电网交互框架，并开发了一种新的电网影响指标，以确保光伏并网系统中有效的充电功率和最小的电网影响。这些措施包括电压分布、充电功率和电网注入谐波。这项工作研究了一种解决快速充电挑战的最佳充电策略，具有量化电网影响和光伏发电的新型性能指标。通过评估不同电压水平下部署在IEEE 34节点网络中的充电站，验证了所提出的策略。提出的IGIM在IEEE 34节点测试馈线上进行了验证，结果表明，中压连接在电网承载能力方面明显优于低压连接，减少了50%以上的电压偏差 %。谐波分析表明，恒流模式充电高达80 % SoC比恒压模式更符合IEEE-519限制。此外，pv辅助充电增加了高达60% %的自我消耗。

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引用次数: 0

BESS and PV systems application for optimal microgrid operation with frequency security constraints BESS和PV系统在具有频率安全约束的微电网优化运行中的应用

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-19 DOI: 10.1016/j.segan.2025.102103

Mehrdad Bagheri-Sanjareh, Marjan Popov

Battery energy storage systems (BESSs) have been used in AC Microgrids (AMGs) for frequency control (FC) and energy management (EM). AMGs with low inertia might suffer large frequency deviations with high rates without the required reserve power for FC. This paper proposes a linear model for the optimal operation of grid-connected AMGs considering frequency security constraints. BESS and photovoltaic systems both participate in primary FC (PFC) and EM. PVSs can decrease their generation in power surplus conditions. They can release the energy of their DC-link capacitors in power shortage conditions. Through coordinated use of BESS and PVSs, the required BESS power for PFC decreases considerably, which allows the BESS to participate in EM more effectively and hence reduces the AMG operational cost. Frequency simulation studies show that PVSs can considerably assist BESS for PFC. Moreover, the optimization results show that without PVSs' support, load shedding is unavoidable which increases the AMG operation cost significantly. In this regard, deterministic and stochastic optimization show that PVSs' participation in PFC results in 24 % and 24.2 % reduction in the AMG operation cost compared to those when BESS is only used for PFC. Therefore, the PVSs' assist in PFC, even though short, has large impact on the optimal operation of the AMG.

电池储能系统（bess）已在交流微电网（amg）中用于频率控制（FC）和能量管理（EM）。低惯性的amg在没有FC所需的备用功率的情况下，可能会遭受高速率的大频率偏差。本文提出了考虑频率安全约束的并网AMGs优化运行的线性模型。BESS和光伏系统都参与初级FC （PFC）和EM。PFC可以在电力剩余条件下减少其发电量。它们可以在电力短缺的情况下释放直流链路电容器的能量。通过协调使用BESS和pss， PFC所需的BESS功率大大降低，这使得BESS能够更有效地参与EM，从而降低AMG的运行成本。频率仿真研究表明，PVSs对pfc的BESS有很大的辅助作用，优化结果表明，没有PVSs的支持，系统的减载不可避免，大大增加了AMG的运行成本。因此，确定性优化和随机优化表明，与BESS仅用于PFC相比，pfs参与PFC可使AMG运行成本降低24% %和24.2% %。因此，pfs参与PFC的时间虽短，但对AMG的优化运行影响较大。

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引用次数: 0

Enhancing grid resilience to extreme events: A synergistic framework integrating vegetation dynamics and microgrid capabilities 增强电网对极端事件的弹性：整合植被动态和微电网能力的协同框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-16 DOI: 10.1016/j.segan.2025.102094

Umar Salman , Zongjie Wang

Grid resilience against extreme weather events is critical for utilities and operators. Overhead distribution lines are particularly vulnerable due to secondary damage caused by falling trees or branches during such events. This paper proposes a vegetation dynamics integrated-resilience assessment framework incorporating microgrid capabilities to address these challenges. The methodology introduces a tree failure model that accounts for tree characteristics in assessing pole and line fragility. Grid resilience is evaluated under four extreme event scenarios, superstorms, hurricanes, earthquakes, and ice storms, considering both islanded and microgrid-operating conditions. Simulation case studies on an IEEE 69-node radial distribution system, performed using Monte Carlo simulations, have demonstrated the effectiveness of the vegetation dynamics integrated-resilience assessment framework in integrating vegetation dynamics for comprehensive vulnerability assessments of power systems. Across cases and events, distributed generation reduced EDNS by 60 %–100 % and LOLP by 60 %–95 %, with the largest gains in hurricane/earthquake conditions, underscoring the importance of DG siting relative to event centers and network bottlenecks. This approach provides practical insights for mitigating disruptions in power distribution systems caused by extreme events.

电网抵御极端天气事件的弹性对公用事业和运营商至关重要。在这种情况下，由于倒下的树木或树枝造成的二次损坏，架空配电线路特别脆弱。本文提出了一个结合微电网能力的植被动态综合恢复力评估框架来应对这些挑战。该方法引入了一个树木失效模型，该模型在评估杆和线的脆弱性时考虑了树木的特征。电网弹性评估在四种极端事件情景下，超级风暴、飓风、地震和冰暴，同时考虑孤岛和微电网的运行条件。通过对IEEE 69节点径向配电系统的蒙特卡罗模拟，验证了植被动态-恢复力综合评估框架在整合植被动态进行电力系统综合脆弱性评估方面的有效性。在案例和事件中，分布式发电将EDNS降低了60% - 100%，将LOLP降低了60% - 95%，在飓风/地震条件下收益最大，强调了DG选址相对于事件中心和网络瓶颈的重要性。这种方法为减轻极端事件造成的配电系统中断提供了实用的见解。

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引用次数: 0

A novel bilevel model for service restoration in distribution systems integrating technical constraints and the energy market environment 考虑技术约束和能源市场环境的配电系统服务恢复新二层模型

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-12-13 DOI: 10.1016/j.segan.2025.102092

Etiane O.P. Carvalho , Wandry R. Faria , Leonardo H. Macedo , Gregorio Muñoz-Delgado , Javier Contreras , Benvindo R. Pereira Junior , João Bosco A. London Junior

This paper introduces a bilevel programming model for service restoration in distribution systems, integrating private distributed generations (DGs) and market strategies. The upper-level problem minimizes costs associated with unsupplied loads and voltage regulator parameters, while the lower-level problem maximizes the profits of DG owners. By incorporating realistic market-based pricing to incentivize privately owned DGs during contingencies, the model addresses the gap in current literature, where DG ownership and production costs are often overlooked. Validation using a 53-node test system under multiple fault scenarios demonstrates the model’s effectiveness in achieving cost-efficient restoration and providing fair compensation to DG owners. This approach ultimately enhances the resilience and reliability of distribution systems.

本文提出了一种结合私有分布式代（dg）和市场策略的配电系统服务恢复双层规划模型。上层问题最小化与未供电负载和稳压器参数相关的成本，而下层问题最大化DG所有者的利润。通过结合现实的市场定价来激励突发事件中的私有DG，该模型解决了当前文献中的空白，即DG所有权和生产成本经常被忽视。在多个故障场景下使用53节点测试系统验证了该模型在实现成本效益恢复和为DG所有者提供公平补偿方面的有效性。这种方法最终提高了配电系统的弹性和可靠性。

引用次数: 0

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