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State-of-the-art review of MPPT techniques for hybrid PV-TEG systems: Modeling, methodologies, and perspectives 混合PV-TEG系统MPPT技术的最新综述:建模、方法和观点
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.005
Bo Yang , Rui Xie , Jinhang Duan , Jingbo Wang

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation. Of the currently available technologies, solar energy has promising application prospects owing to its merits of being clean, safe, and sustainable. Solar energy is converted into electricity through photovoltaic (PV) cells; however, the overall conversion efficiency of PV modules is relatively low, and most of the captured solar energy is dissipated in the form of heat. This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells. To overcome the shortcomings, an efficient approach involves combining a PV cell with a thermoelectric generator (TEG) to form hybrid PV-TEG systems, which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs. Based on a thorough examination of the literature, this study comprehensively reviews 14 maximum power point tracking (MPPT) algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion, namely conventional, mathematics-based, metaheuristic, artificial intelligence, and other algorithms. This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

开发替代可再生能源技术对于缓解气候变化和促进能源转型至关重要。在目前可用的技术中,太阳能具有清洁、安全和可持续的优点,具有很好的应用前景。太阳能通过光伏电池转化为电能;然而,光伏组件的整体转换效率相对较低,并且捕获的大部分太阳能以热的形式耗散。这不仅降低了太阳能电池的发电效率,而且可能对光伏组件的电气参数和光伏电池的使用寿命产生负面影响。为了克服这些缺点,一种有效的方法包括将PV电池与热电发电机(TEG)结合以形成混合PV-TEG系统,其通过降低PV模块的操作温度同时提高PV系统的能量转换效率,并且通过利用从PV系统产生的废热经由TEG发电来增加功率输出。在全面查阅文献的基础上,本研究全面回顾了目前应用于混合PV-TEG系统的14种最大功率点跟踪(MPPT)算法,并将其分为五大类进行进一步讨论,即传统算法、基于数学的算法、元启发式算法、人工智能算法和其他算法。这篇综述旨在启发混合PV-TEG系统的MPPT算法的先进思想和研究。
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引用次数: 0
GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN 基于自关注机制的GIS局部放电数据增强方法
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.007
Qinglin Qian , Weihao Sun , Zhen Wang , Yongling Lu , Yujie Li , Xiuchen Jiang

The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids. This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information. Subsequently, the VAE structure was introduced into the traditional GAN, and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN. Then, the self-attention mechanism was integrated into the VAE-GAN, and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state. Finally, the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples. The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis. The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method. The structure similarity index measure (SSIM) index is increased by 4.5% and 21.7%, respectively, and the average accuracy of fault diagnosis is increased by 22.9%, 9%, 5.7%, and 6.5%, respectively. The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.

地理信息系统局部放电故障诊断的可靠性对电网的安全稳定运行至关重要。本研究提出了一种基于自注意机制的数据增强方法,以优化VAE-GAN方法,解决局部放电样本不足和不同缺陷之间分布不平衡的问题。首先,使用非二次采样轮廓变换(NSCT)算法对超高频和光学局部放电信号进行融合,获得信息更丰富的光电融合相位分辨局部放电(PRPD)光谱。随后,将VAE结构引入到传统的GAN中,并利用VAE优异的隐层特征提取能力来指导GAN的生成。然后,将自注意机制集成到VAE-GAN中,并使用Wasserstein距离和梯度惩罚机制来优化网络损失函数,并将样本集扩展到平衡状态。最后,使用KAZE和极坐标分布熵方法提取扩展样本。将集合的特征向量代入长短期记忆(LSTM)网络进行局部放电故障诊断。实验结果表明,该方法的样本生成质量和故障诊断结果明显优于传统的数据增强方法。结构相似性指数测度(SSIM)指数分别提高了4.5%和21.7%,故障诊断的平均准确率分别提高了22.9%、9%、5.7%和6.5%。本研究提出的数据增强方法可为GIS局部放电故障诊断提供参考。
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引用次数: 0
An adaptive control strategy for microgrid secondary frequency based on parameter identification 基于参数辨识的微电网二次频率自适应控制策略
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.006
Yong Shi , Yin Cheng , Bao Xie , Jianhui Su

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy

复杂的微电网结构和时变条件等因素导致微电网的机械建模出现问题,使基于模型的控制器优化变得困难。因此,本研究提出了一种基于参数识别的二次频率自适应控制策略,该策略使用在线参数识别方法实时识别微电网中的参数。识别出的参数然后在次级频率自适应控制器中使用,以优化实时控制器性能。该方法实现了微电网运行状态下控制器的自适应优化,并应用于参数未知的微电网,对控制器参数进行调整。最后,通过仿真实验验证了模型的准确性和自适应控制策略的调频效果
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引用次数: 0
Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression 基于改进k-means算法的电动汽车动态分组控制风力波动抑制
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.003
Yang Yu , Mai Liu , Dongyang Chen , Yuhang Huo , Wentao Lu

To address the significant lifecycle degradation and inadequate state of charge (SOC) balance of electric vehicles (EVs) when mitigating wind power fluctuations, a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm. First, a swing door trending (SDT) algorithm based on compression result feedback was designed to extract the feature data points of wind power. The gating coefficient of the SDT was adjusted based on the compression ratio and deviation, enabling the acquisition of grid-connected wind power signals through linear interpolation. Second, a novel algorithm called IDOA-KM is proposed, which utilizes the Improved Dingo Optimization Algorithm (IDOA) to optimize the clustering centers of the k-means algorithm, aiming to address its dependence and sensitivity on the initial centers. The EVs were categorized into priority charging, standby, and priority discharging groups using the IDOA-KM. Finally, an two-layer power distribution scheme for EVs was devised. The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals. The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles. The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals, smoothing wind power fluctuations, mitigating EV degradation, and enhancing the SOC balance.

为了解决电动汽车在缓解风电波动时生命周期显著退化和荷电状态(SOC)平衡不足的问题,提出了一种基于改进k-means算法的电动汽车动态分组控制策略。首先,设计了一种基于压缩结果反馈的摆动门趋势(SDT)算法来提取风电的特征数据点。SDT的选通系数根据压缩比和偏差进行调整,从而能够通过线性插值获取并网风电信号。其次,提出了一种新的算法IDOA-KM,该算法利用改进的Dingo优化算法(IDOA)来优化k-means算法的聚类中心,旨在解决其对初始中心的依赖性和敏感性。使用IDOA-KM将电动汽车分为优先充电组、备用组和优先放电组。最后,设计了一种电动汽车的双层配电方案。上层确定三个EV组的充电/放电顺序及其相应的功率信号。下层基于最大充电/放电功率或SOC均衡原理将功率信号分配给每个EV。仿真结果证明了所提出的控制策略在准确跟踪电网功率信号、平滑风电波动、缓解电动汽车退化和增强SOC平衡方面的有效性。
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引用次数: 0
Review of multi-objective optimization in long-term energy system models 长期能源系统模型中的多目标优化研究进展
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.010
Wenxin Chen , Hongtao Ren , Wenji Zhou

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues. The conventional optimization of long-term energy system models focuses on a single economic goal. However, the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives, such as environmental, social, and energy security. Therefore, a multi- objective optimization of long-term energy system models has been developed. Herein, studies pertaining to the multi- objective optimization of long-term energy system models are summarized; the optimization objectives of long-term energy system models are classified into economic, environmental, social, and energy security aspects; and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers. Finally, the key development direction of the multi-objective optimization of energy system models is discussed.

建模和优化长期能源系统可以为涉及公共利益问题的各种能源和环境政策提供解决方案。长期能源系统模型的传统优化侧重于单一的经济目标。然而,能源系统日益复杂的需求需要综合考虑多个维度的目标,如环境、社会和能源安全。因此,开发了一种长期能源系统模型的多目标优化方法。本文综述了长期能源系统模型的多目标优化研究;长期能源系统模型的优化目标分为经济、环境、社会和能源安全方面;并根据决策者的偏好表达式对多目标优化方法进行了分类和解释。最后,讨论了能源系统模型多目标优化的关键发展方向。
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引用次数: 0
Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network 基于改进CEEMDAN方法和生成对抗插值网络的风电数据缺失插值模型
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.001
Lingyun Zhao , Zhuoyu Wang , Tingxi Chen , Shuang Lv , Chuan Yuan , Xiaodong Shen , Youbo Liu

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g., grid frequency and voltage), which in turn affect the stable operation of a power system. However, owing to external factors (such as weather), there are often various anomalies in wind power data, such as missing numerical values and unreasonable data. This significantly affects the accuracy of wind power generation predictions and operational decisions. Therefore, developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry. In this study, the causes of abnormal data in wind power generation were first analyzed from a practical perspective. Second, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components. Finally, a complete wind power generation time series was reconstructed. Compared to traditional methods, the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations

风电输出的随机性和波动可能会导致重要参数(如电网频率和电压)的变化,进而影响电力系统的稳定运行。然而,由于外部因素(如天气),风电数据往往存在各种异常,如数值缺失和数据不合理。这严重影响了风力发电预测和运营决策的准确性。因此,开发和应用可靠的风电插值方法对促进风电行业的可持续发展具有重要意义。本研究首先从实际角度分析了风力发电数据异常的原因。其次,提出了一种改进的带自适应噪声的完全集成经验模式分解(ICEEMDAN)方法,该方法使用生成对抗性插值网络(GAIN)网络对风力发电进行预处理,并对缺失的风力发电子分量进行插值。最后,重构了一个完整的风力发电时间序列。与传统方法相比,所提出的ICEEMDAN-GAIN组合插值模型具有更高的插值精度,可以有效地减少风力发电序列波动带来的误差影响
{"title":"Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network","authors":"Lingyun Zhao ,&nbsp;Zhuoyu Wang ,&nbsp;Tingxi Chen ,&nbsp;Shuang Lv ,&nbsp;Chuan Yuan ,&nbsp;Xiaodong Shen ,&nbsp;Youbo Liu","doi":"10.1016/j.gloei.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.gloei.2023.10.001","url":null,"abstract":"<div><p>Randomness and fluctuations in wind power output may cause changes in important parameters (e.g., grid frequency and voltage), which in turn affect the stable operation of a power system. However, owing to external factors (such as weather), there are often various anomalies in wind power data, such as missing numerical values and unreasonable data. This significantly affects the accuracy of wind power generation predictions and operational decisions. Therefore, developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry. In this study, the causes of abnormal data in wind power generation were first analyzed from a practical perspective. Second, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components. Finally, a complete wind power generation time series was reconstructed. Compared to traditional methods, the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"6 5","pages":"Pages 517-529"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71766834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system 基于模糊控制和神经网络的永磁同步风力发电系统最大功率点跟踪转子转速控制器
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.004
Min Ding , Zili Tao , Bo Hu , Meng Ye , Yingxiong Ou , Ryuichi Yokoyama

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system, the maximum power point cannot be easily determined in a timely manner. This study proposes a maximum power reference signal search method based on fuzzy control, which is an improvement to the climbing search method. A neural network-based parameter regulator is proposed to address external wind speed fluctuations, where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions. Finally, the effectiveness of this method is verified via Simulink simulation

在永磁同步风力发电系统中,当风速发生显著变化时,无法及时确定最大功率点。本文提出了一种基于模糊控制的最大功率参考信号搜索方法,该方法是对爬升搜索方法的改进。提出了一种基于神经网络的参数调节器来解决外部风速波动问题,其中调整比例积分控制器的参数,以准确监测不同风速条件下的最大功率点。最后,通过Simulink仿真验证了该方法的有效性
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引用次数: 0
Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT 基于DBN-Elman结合改进PSO-HHT的风速预报模型
Q4 ENERGY & FUELS Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.002
Wei Liu , Feifei Xue , Yansong Gao , Wumaier Tuerxun , Jing Sun , Yi Hu , Hongliang Yuan

Random and fluctuating wind speeds make it difficult to stabilize the wind-power output, which complicates the execution of wind-farm control systems and increases the response frequency. In this study, a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network, the Elman neural network, and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm. The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks. Although the complexity of the model is high, the accuracy of wind-speed prediction and stability are also high. The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.

随机和波动的风速使风电输出难以稳定,这使风电场控制系统的执行复杂化并增加了响应频率。在本研究中,将深度置信网络、Elman神经网络和使用改进的粒子群优化算法修改的Hilbert-Huang变换相结合,开发了一种新的风电场短期风速预测模型。实验结果表明,所提出的深度神经网络的预测结果优于浅层神经网络。尽管该模型的复杂性很高,但风速预测的准确性和稳定性也很高。该模型有效地提高了风电场短期风速预测的准确性。
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引用次数: 0
A collaborative approach to integrated energy systems that consider direct trading of multiple energy derivatives 考虑多种能源衍生品直接交易的综合能源系统的协作方法
Q4 ENERGY & FUELS Pub Date : 2023-08-01 DOI: 10.1016/j.gloei.2023.08.004
Jianhui Wang , Guangqing Bao , Peizhi Wang , Shoudong Li

The cooperative model of a multi-subject Regional Integrated Energy System (RIES) is no longer limited to the trading of traditional energy, but the trading of new energy derivatives such as Green Certificates (GC), Service Power (SP), and CO2 will be more involved in the energy allocation of the cooperative model. This study was conducted for the multi- entity RIES cooperative model considering the trading of electronics, GC, SP, and CO2. First, a cooperative framework including wind-photovoltaic generation system (WG), combined heat and power system (CHP), and power-carbon-hydrogen load (PCH) is proposed, and the mechanism of energy derivatives trading is also analyzed. Then, the sub-models of each agent in the cooperative model are established separately so that WG has the capability of GC generation, CHP has the capability of GC and CO2 absorption, and PCH can realize the effective utilization of CO2. Then, the WG–CHP–PCH cooperative model is established and equated into two sub-problems of cooperative benefit maximization and transaction payment negotiation, which are solved in a distributed manner by the alternating directed multiplier method (ADMM). Finally, the effectiveness of the proposed cooperative model and distributed solution is verified by simulation. The simulation results show that the WG–CHP–PCH cooperative model can substantially improve the operational efficiency of each agent and realize the efficient redistribution of energy and its derivatives. In addition, the dynamic parameter adjustment algorithm (DP) is further applied in the solving process to improve its convergence speed. By updating the step size during each iteration, the computational cost, the number of iterations, and the apparent oscillations are reduced, and the convergence performance of the algorithm is improved.

多主体区域综合能源系统(RIES)的合作模式不再局限于传统能源的交易,绿色证书(GC)、服务电力(SP)、二氧化碳等新能源衍生品的交易将更多地参与到合作模式的能源配置中。本研究以考虑电子、GC、SP、CO2交易的多实体RIES合作模型为研究对象。首先,提出了包括风电光伏发电系统(WG)、热电联产系统(CHP)和电力-碳氢负荷(PCH)在内的合作框架,并对能源衍生品交易机制进行了分析。然后,分别建立协作模型中各agent的子模型,使WG具有GC生成能力,CHP具有GC和CO2吸收能力,PCH实现CO2的有效利用。然后,建立了WG-CHP-PCH合作模型,并将其等效为合作利益最大化和交易支付协商两个子问题,采用交替定向乘数法(ADMM)进行分布式求解。最后,通过仿真验证了所提出的协作模型和分布式解决方案的有效性。仿真结果表明,WG-CHP-PCH协同模型能够大幅提高各agent的运行效率,实现能量及其衍生物的高效再分配。在求解过程中进一步采用了动态参数调整算法(DP),提高了算法的收敛速度。通过更新每次迭代的步长,减少了算法的计算量、迭代次数和表观振荡,提高了算法的收敛性能。
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引用次数: 0
Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game 基于Stackelberg主从博弈的集成能源系统双层低碳优化策略研究
Q4 ENERGY & FUELS Pub Date : 2023-08-01 DOI: 10.1016/j.gloei.2023.08.002
Lizhen Wu , Cuicui Wang , Wei Chen , Tingting Pei

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

随着综合能源系统改革的不断深入,各个能源子系统作为基于有限理性的参与者,通过相互学习和模仿,对整个综合能源系统的最优调度产生重要影响。合理的多智能体联合运行策略有助于实现该系统的低碳目标。本文提出了一种基于Stackelberg主从博弈和多智能体联合操作的双层低碳IES最优运行策略。所研究的IES包括热电联产、电制气和碳捕获系统。基于Stackelberg主从博弈理论,在上层以卖方作为领导者来设定电力和热能的价格,在下层以能源生产商、储能供应商和负荷聚合商作为追随者来调整系统的运行策略。建立了基于Stackelberg主从博弈的IES双层优化模型。最后,利用Karush-Kuhn-Tucker (KKT)条件和线性松弛技术将双层博弈模型转化为单层博弈模型。CPLEX是一种数学程序求解器,用于求解各主体收益达到最大时系统的均衡问题和碳排放交易成本,并分析不同的碳排放交易价格和增长率对系统运行策略的影响。作为实验演示,我们模拟了一个与IEEE 39节点电网系统、六节点热网系统和六节点燃气网络系统耦合的IES。仿真结果验证了该模型的有效性和可行性。
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引用次数: 0
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Global Energy Interconnection
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