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Optimising green hydrogen injection into gas networks: Decarbonisation potential and influence on quality-of-service indexes 优化向天然气网络注入绿色氢气:脱碳潜力及对服务质量指数的影响
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-16 DOI: 10.1016/j.segan.2024.101543
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. The model is designed to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe index (WI) and higher heating value (HHV)) within admissible limits. This study also presents the maximum injection allowable of hydrogen correlated with the gas quality index variation. The model has been applied to a case study of a gas network with four distinct scenarios and implemented using Python. The findings of the case study quantify the maximum permitted volume of hydrogen in the network, the total savings in natural gas, and the reduction in carbon dioxide emissions. Lastly, a sensitivity analysis of injected hydrogen as a function of the Wobbe index (WI) and Higher Heating Value (HHV) limits relaxation.
本文介绍了一个数学模型,旨在优化天然气输配网络的运行,其中考虑了在多个节点注入氢气的问题。该模型旨在优化氢气注入量,使压力、天然气流量和天然气质量指标(沃伯指数(WI)和高热值(HHV))保持在允许范围内。本研究还提出了与气体质量指数变化相关的最大允许氢气注入量。该模型应用于一个天然气网络的案例研究,包含四种不同的情况,并使用 Python 实现。案例研究的结果量化了网络中允许的最大氢气量、节省的天然气总量以及减少的二氧化碳排放量。最后,对注入氢气作为沃伯指数(WI)和高热值(HHV)限制松弛函数的敏感性进行了分析。
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引用次数: 0
Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty 不确定性条件下不平衡配电网络中考虑能源枢纽的多能源载体优化运行
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101538
This article presents a two-stage stochastic programming model to address the dispatching scheduling problem in an energy hub, considering an unbalanced active low-voltage (LV) network. A three-phase version of the second-order cone relaxation of DistFlow AC optimal power flow (AC-OPF) is employed to incorporate unbalanced network constraints, while the objective minimizes the Local Energy Community (LEC) operational cost. The model results have been validated using OpenDSS, encompassing energy losses, voltage levels, and active/reactive power. Likewise, a comparative analysis between the three-phase model and the traditional single-phase model, using a modified version of the IEEE European LV Test Feeder as a case study, reveals interesting differences, such that the single-phase model underestimates voltage limits during photovoltaic (PV) system operation and overestimates energy purchased from the main grid, compared with the three-phase model. Similarly, the comparison results reveal that discrepancies between the single and three-phase models intensify as the power injected from PV systems rises. This notably impacts the total energy purchased from the grid, battery operation, and the satisfaction of thermal consumption through electricity. Finally, while the three-phase model offers valuable insights into security levels for voltage and grid energy purchase, its longer computational time makes it more suitable for strategic use rather than daily operational frameworks.
本文提出了一种两阶段随机编程模型,用于解决能源枢纽中的调度调度问题,该模型考虑了不平衡的有源低压(LV)网络。该模型采用了 DistFlow 交流最优功率流 (AC-OPF) 二阶圆锥松弛的三阶段版本,以纳入不平衡网络约束,同时目标最小化本地能源社区 (LEC) 运营成本。模型结果已通过 OpenDSS 验证,包括能量损失、电压水平和有功/无功功率。同样,以修改版的 IEEE 欧洲低压试验馈线为案例,对三相模型和传统单相模型进行了比较分析,发现了有趣的差异,例如,与三相模型相比,单相模型低估了光伏(PV)系统运行期间的电压限制,并高估了从主电网购买的能源。同样,比较结果表明,随着光伏系统注入功率的增加,单相和三相模型之间的差异也会加剧。这明显影响了从电网购买的总能量、电池运行以及通过电力满足热能消耗。最后,虽然三相模型为电压和电网能源购买的安全等级提供了有价值的见解,但其较长的计算时间使其更适用于战略用途,而非日常运行框架。
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引用次数: 0
Optimal design model for a public-private Renewable Energy Community in a small Italian municipality 意大利小城市公私可再生能源社区的优化设计模型
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101545
Energy communities (ECs) are currently seen as an important pathway to increase the participation of citizens in the energy transition. The present work proposes a mixed integer linear programming (MILP) optimization model that provides the optimal design of a renewable energy community (REC) in terms of best technologies and chosen members. Different objective functions are investigated so that the REC’s design can be studied from different perspectives. The first objective is related to the minimization of total annualized costs (TAC) while the second one regards the maximization of the shared energy. The model considers one year as time horizon with a timestep of one hour. A case study is defined by considering the municipality of Plodio, located in the northwest of Italy, as the host of a potential REC. A total of 11 possible users are introduced, including municipality and residential users. In cost-optimized scenarios, the REC design is characterized by fewer users but has the maximum installation of PV modules. However, most of the revenues are obtained due to the selling of electricity and not due to its sharing. When the shared energy is maximized, all the candidate members are chosen and technologies such as wind turbines and batteries are exploited to increase the number of periods characterized by the injection of electricity into the grid. It is also noted that higher electricity prices increase the profitability of the investment. Finally, it is shown that the inclusion of an industrial user positively influences energy-sharing indicators.
能源社区(EC)目前被视为提高公民参与能源转型的重要途径。本研究提出了一个混合整数线性规划(MILP)优化模型,从最佳技术和所选成员的角度对可再生能源社区(REC)进行优化设计。研究了不同的目标函数,以便从不同角度研究可再生能源社区的设计。第一个目标与总年化成本(TAC)最小化有关,第二个目标则与共享能源最大化有关。该模型的时间跨度为一年,时间步长为一小时。案例研究将位于意大利西北部的普洛迪奥市作为潜在 REC 的所在地。共引入了 11 个可能的用户,包括市政用户和居民用户。在成本优化方案中,REC 设计的特点是用户较少,但光伏组件安装量最大。然而,大部分收入是通过出售电力获得的,而不是通过分享电力获得的。当共享能源最大化时,选择所有候选成员,并利用风力涡轮机和电池等技术,增加向电网注入电力的时段。此外,我们还注意到,较高的电价会提高投资的盈利能力。最后,研究表明,工业用户的加入会对能源共享指标产生积极影响。
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引用次数: 0
Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements 结合电网时间序列测量中的异常点和变化点检测,获取更好的负荷估计值
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101540
In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.
在本文中,我们提出了自动过滤异常和开关事件的新方法,以改进电网系统中的负荷估算。通过利用无监督方法和有监督优化,我们的方法优先考虑了可解释性,同时确保了在未见数据上的稳健性和通用性。通过实验,二进制分割法检测变化点和统计过程控制法检测异常点的组合成为最有效的策略,特别是以新颖的顺序方式进行组合时。结果表明,如果不使用过滤功能,显然会浪费潜力。自动负载估计也相当准确,约 90% 的估计值误差在 10% 以内,在测试集中的 60 次测量中,只有一次在最小和最大负载估计中出现重大失误。我们的方法具有可解释性,因此特别适用于关键基础设施规划,从而加强决策过程。
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引用次数: 0
Distributed photovoltaic power forecasting based on personalized federated adversarial learning 基于个性化联合对抗学习的分布式光伏发电功率预测
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-14 DOI: 10.1016/j.segan.2024.101537
Existing distributed photovoltaic (PV) power forecasting methods fail to address the impact of sample scarcity and heterogeneity in PV power data. Moreover, training a single prediction model proves challenging to meet the personalized forecasting needs of different PV stations in distributed environments. This paper proposes a personalized federated generative adversarial network (PFedGAN)-based DPV power forecasting method. Leveraging the federated learning (FL) framework, it achieves collaborative training of prediction models among DPV stations while preserving data privacy. y introducing generative adversarial networks (GAN) and personalized strategy optimization into the FL training process, it alleviates data scarcity issues and reduces the impact of data heterogeneity. A TimesNet-DeepAR (TNE-DeepAR) power prediction model is designed, where the TimesNet module extracts correlations between PV power data from different time periods, and the DeepAR module facilitates PV power prediction, mitigating the effects of meteorological factors' multi-periodic variations on PV power. Experimental results show that the proposed hybrid prediction model reduces the average mean absolute percentage error (MAPE) by 30–40 % compared to single models. The proposed approach reduces the MAPE by 9.79 % compared to traditional methods and by 49.62 % for PV stations with scarce data.
现有的分布式光伏(PV)功率预测方法未能解决光伏功率数据中样本稀缺性和异质性的影响。此外,要满足分布式环境中不同光伏电站的个性化预测需求,训练单一预测模型具有挑战性。本文提出了一种基于联合生成对抗网络(PFedGAN)的个性化 DPV 功率预测方法。它利用联合学习(FL)框架,在保护数据隐私的同时,实现了 DPV 站之间预测模型的协同训练。在 FL 训练过程中引入生成对抗网络(GAN)和个性化策略优化,缓解了数据稀缺问题,降低了数据异质性的影响。设计了 TimesNet-DeepAR(TNE-DeepAR)功率预测模型,其中 TimesNet 模块提取了不同时间段光伏功率数据之间的相关性,DeepAR 模块促进了光伏功率预测,减轻了气象因素多周期变化对光伏功率的影响。实验结果表明,与单一模型相比,所提出的混合预测模型可将平均绝对百分比误差 (MAPE) 降低 30-40%。与传统方法相比,所提出的方法可将 MAPE 降低 9.79%,对于数据稀缺的光伏电站,可将 MAPE 降低 49.62%。
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引用次数: 0
Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity 包含实时动态托管能力影响的主动配电网运行可靠性和非确定弹性估计
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101541
Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.
人们日益认识到,主动配电网络对实现可持续发展目标至关重要。传统上,托管容量被认为是规划分布式能源资源整合的静态措施。这项工作引入了动态寄存容量的概念,即根据不稳定的现代电网条件,反复重新评估寄存容量。动态托管容量的引入有助于测试从最小到 100 % 的功率注入变化,维持电力系统的管理参数限制。这就需要对运行可靠性进行评估,并提高态势感知能力,以实现最佳的功率注入和平衡。为实现基于动态托管能力的运行可靠性分析,提出了基于概率分布函数的混合蒙特卡罗模拟。由于该方法可在各种不确定因素下对系统性能进行全面、准确的评估,因此太阳能光伏发电和负载调整的改善率达到 85-90%。该框架的验证包括预测随时间变化的运行可靠性指数,以及与时间无关的可靠性指数,即动态负载损失概率、动态负载损失预期、动态负载损失持续时间、动态负载损失频率、动态电网裕度和动态电网依赖性。这使得电网裕度的评估结果提高了 30%,促进了可靠的不确定性处理能力。此外,由于太阳能光伏分布式能源资源的不确定性,还提出了期望最大化算法来评估非确定弹性。非确定性弹性评估测试了 80% 的反弹率,显示了更好的适应性和鲁棒性。整个分析在 MATLAB 中进行,使用台风硬件在环实时平台进行了验证,并与现有文献进行了比较,以证明其有效性。
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引用次数: 0
A reinforcement learning-based energy management strategy for fuel cell electric vehicle considering coupled-energy sources degradations 基于强化学习的燃料电池电动汽车能源管理策略,考虑耦合能源退化问题
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101548
An effective energy management strategy (EMS) is crucial for fuel cell electric vehicles (FCEVs) to optimize fuel consumption and mitigate fuel cell (FC) aging by efficiently distributing power from multiple energy sources during vehicle operation. The Proton Exchange Membrane Fuel Cell (PEMFC) is a preferred main power source for fuel cell vehicles due to its high power density, near-zero emissions, and low corrosivity. However, it is expensive, and its lifespan is significantly affected by rapid power fluctuations. To address this issue, the proposed method of minimizing instantaneous cost (MIC) reduces the frequency of abrupt changes in the FC load. Additionally, by analyzing driving condition characteristics, the Ensemble Bagging Tree (EBT) facilitates real-time recognition (WCI) of composite conditions, thereby enhancing the EMS's adaptability to various operating conditions. This paper introduces an advanced EMS based on double-delay deep deterministic policy gradient (TD3) deep reinforcement learning, which considers energy degradation, economic efficiency, and driving conditions. Training results indicate that the TD3-based policy, when integrated with WCI and MIC, not only achieves a 32.6 % reduction in FC system degradation but also lowers overall operational costs and significantly accelerates algorithm convergence.
有效的能源管理策略(EMS)对燃料电池电动汽车(FCEV)至关重要,它可以在车辆运行期间有效分配来自多种能源的电力,从而优化燃料消耗量并缓解燃料电池(FC)老化。质子交换膜燃料电池(PEMFC)具有高功率密度、近零排放和低腐蚀性等优点,是燃料电池汽车首选的主要动力源。然而,它价格昂贵,而且其寿命会受到快速功率波动的严重影响。为解决这一问题,提出了最小化瞬时成本(MIC)的方法,以降低 FC 负载突然变化的频率。此外,通过分析驾驶条件特征,集合袋装树(EBT)可促进复合条件的实时识别(WCI),从而增强 EMS 对各种运行条件的适应性。本文介绍了一种基于双延迟深度确定性策略梯度(TD3)深度强化学习的先进 EMS,它考虑了能源退化、经济效率和驾驶条件。训练结果表明,将基于 TD3 的策略与 WCI 和 MIC 相结合,不仅能使 FC 系统退化率降低 32.6%,还能降低总体运营成本,并显著加快算法收敛速度。
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引用次数: 0
Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops 多加油站可持续城际旅行的电动汽车充电策略优化问题
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101546
Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.
由于电动汽车(EV)的续航里程有限和充电站稀缺,考虑长途旅行的电动汽车(EV)驾驶员仍然面临续航里程焦虑。因此,确保所选路线的可行性并确定最佳充电策略变得非常重要。作为为电动汽车驾驶员提供决策支持的一个重要方面,本研究针对电动汽车充电策略问题(EVCSP)提出了一种混合整数线性规划(MILP)方法,并结合了片断线性近似技术,以应对非线性充电时间带来的挑战。所提出的优化模型(即 CSPM)可确定在指定路线上为电动汽车充电的地点、时间和充电量,从而最大限度地减少旅行时间和成本。大规模测试问题的求解时间和图尔基耶案例研究揭示了 CSPM 的鲁棒性和可靠性。此外,案例研究还采用了两种多目标优化方法(加权求和法和词典法),并对结果进行了分析。结果表明,旅行成本对所选的收费策略更为敏感,在各种收费策略中的波动幅度为 46.09%,而旅行时间则更具弹性,最大波动幅度为 19.77%。与完全充电策略的对比分析表明,CSPM 可将旅行时间缩短 60.1%,将成本效率提高 105.72%。
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引用次数: 0
Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo 基于可靠性框图-序列蒙特卡罗的陆上风电场直流集电系统可靠性评估
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101549
The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.
全直流风力发电系统有效克服了传统交流风力发电系统的谐波谐振、无功功率传输等问题,具有广阔的发展前景。作为上述系统的关键组成部分,采集系统的可靠性对整个陆上风电场的安全稳定运行至关重要。本文首先研究了陆上风电场直流采集系统的关键设备和拓扑结构。其次,综合考虑风电场内部组件和外部环境,构建了基于气象因素的组件停运概率模型,为风电场直流采集系统的可靠性评估提供准确数据。利用可靠性框图详细分析了陆上风电场直流采集系统不同拓扑结构的内部逻辑连接。然后,提出了一种基于可靠性方框图--等式蒙特卡罗的陆上全直流风电场采集系统可靠性评估方法。最后,以一个 50 兆瓦的陆上风电场为样本,对比分析了不同收集系统拓扑结构的可靠性评估结果。结果表明,陆上风电场直流收集系统的可靠性具有显著优势。
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引用次数: 0
Optimization of offshore wind farm cluster transmission system topology based on Stackelberg game 基于 Stackelberg 博弈的海上风电场集群输电系统拓扑优化
IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101542
Offshore wind energy is pivotal in the global energy transition, with a global installed capacity reaching 64.3 GW by 2022 and an expected annual increase of 60.2 GW over the next decade. This study aims to optimize the topology of transmission systems (TS) for offshore wind farm (OWF) clusters using Stackelberg game theory. The OWF investor (OWFI) acts as the leader, optimizing investment returns while considering wake effects, and the offshore TS operator (OTSO) follows by adjusting transmission strategies to reduce costs. The analysis includes the wake effects within OWF clusters and their impact on power generation efficiency. Simulation results demonstrate that the proposed model can balance stakeholder interests and enhance the economic viability of OWF clusters, showing a potential increase in net present value (NPV) by up to 30 %. This study validates the practical application of the Stackelberg game model in optimizing OWF cluster TS topology, contributing to more efficient and cost-effective renewable energy integration.
海上风能在全球能源转型中举足轻重,到 2022 年,全球装机容量将达到 64.3 千兆瓦,预计未来十年每年将增加 60.2 千兆瓦。本研究旨在利用斯塔克伯格博弈论优化海上风电场(OWF)集群的输电系统(TS)拓扑结构。海上风电场投资者(OWFI)作为领导者,在考虑尾流效应的同时优化投资回报,而海上风电场运营商(OTSO)则通过调整输电策略来降低成本。分析包括 OWF 集群内的唤醒效应及其对发电效率的影响。模拟结果表明,所提出的模型可以平衡利益相关者的利益,提高 OWF 集群的经济可行性,净现值 (NPV) 有可能增加 30%。这项研究验证了斯塔克尔伯格博弈模型在优化 OWF 簇 TS 拓扑中的实际应用,有助于提高可再生能源整合的效率和成本效益。
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引用次数: 0
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