Journal of Modern Power Systems and Clean Energy最新文献_第9页

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks 主动配电网络中分布式能源资源优化调度的强化学习与增强安全性

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-18 DOI: 10.35833/MPCE.2023.000893

Xu Yang;Haotian Liu;Wenchuan Wu;Qi Wang;Peng Yu;Jiawei Xing;Yuejiao Wang

{"title":"Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks","authors":"Xu Yang;Haotian Liu;Wenchuan Wu;Qi Wang;Peng Yu;Jiawei Xing;Yuejiao Wang","doi":"10.35833/MPCE.2023.000893","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000893","url":null,"abstract":"As numerous distributed energy resources (DERs) are integrated into the distribution networks, the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks (ADNs). Since accurate models are usually unavailable in ADNs, an increasing number of reinforcement learning (RL) based methods have been proposed for the optimal dispatch problem. However, these RL based methods are typically formulated without safety guarantees, which hinders their application in real world. In this paper, we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic (S3AC) for the optimal dispatch of DERs in ADNs, which not only minimizes the operational cost but also satisfies safety constraints during online execution. In the proposed S3AC, the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition (SCADA) system, effectively providing enhanced safety for executed actions. Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1484-1494"},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability 考虑可靠性的径向配电网络中电动汽车充电站和电容器的优化同步分配

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-18 DOI: 10.35833/MPCE.2023.000674

B. Vinod Kumar;Aneesa Farhan M A

{"title":"Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability","authors":"B. Vinod Kumar;Aneesa Farhan M A","doi":"10.35833/MPCE.2023.000674","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000674","url":null,"abstract":"The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1584-1595"},"PeriodicalIF":5.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10505134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event Detection Based on Robust Random Cut Forest Algorithm for Non-Intrusive Load Monitoring 基于鲁棒随机砍林算法的非侵入式负荷监测事件检测

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-09 DOI: 10.35833/MPCE.2023.000901

Lingxia Lu;Ju-Song Kang;Miao Yu

{"title":"Event Detection Based on Robust Random Cut Forest Algorithm for Non-Intrusive Load Monitoring","authors":"Lingxia Lu;Ju-Song Kang;Miao Yu","doi":"10.35833/MPCE.2023.000901","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000901","url":null,"abstract":"Non-intrusive load monitoring (NILM) can provide appliance-level power consumption information without deploying submeters for each load, in which load event detection is one of the crucial steps. However, the existing event detection methods do not efficiently detect both the starting time of an event (STE) and the ending time of an event (ETE), and their adaptability to scenarios with different sampling rates is limited. To address these problems, in this paper, an event detection method based on robust random cut forest (RRCF) algorithm, which is an unsupervised learning method for detecting anomalous data points within a dataset, is proposed. First, the mean-pooling preprocessing is applied to the aggregated load power series with a high sampling rate to minimize fluctuations. Then, the power differential series is obtained, and the anomaly score of each data point is calculated using the RRCF algorithm for preliminary detection. If an event has been preliminarily detected, misidentification caused by fluctuation will be further eliminated by using an adaptive power difference threshold approach. Finally, linear fitting is used to finely and accurately adjust the STE and ETE. The proposed method does not require any pretraining of the detection model and has been validated with both the BLUED dataset (with high and low sampling rates) and the REDD dataset (with low sampling rate). The experimental results demonstrate that the proposed method not only meets real-time requirements, but also exhibits strong adaptability across multiple scenarios. The precision is greater than 92% in distinct sampling rate scenarios, and the F1 score of phase B on the BLUED dataset reaches 94% in the scenario with a high sampling rate. These results indicate that the proposed method outperforms other state-of-the-art methods.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"2019-2029"},"PeriodicalIF":5.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495845","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operation Strategy of Rail Transit Green Energy System Considering Uncertainty Risk of Photovoltaic Power Output 考虑光伏发电不确定性风险的轨道交通绿色能源系统运行策略

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-09 DOI: 10.35833/MPCE.2023.000788

Yanbo Chen;Haoxin Tian;Guodong Zheng;Yuxiang Liu;Maja Grbić

{"title":"Operation Strategy of Rail Transit Green Energy System Considering Uncertainty Risk of Photovoltaic Power Output","authors":"Yanbo Chen;Haoxin Tian;Guodong Zheng;Yuxiang Liu;Maja Grbić","doi":"10.35833/MPCE.2023.000788","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000788","url":null,"abstract":"The integration of photovoltaic power generation is a new development into the traction power supply system (TPSS). However, traditional research on the TPSS operation strategy has not fully considered the risk of uncertainty in photovoltaic power output. To this end, we propose an operation strategy for the rail transit green energy system that considers the uncertainty risk of photovoltaic power output. First, we establish a regenerative braking energy utilization model that considers the impact of time-of-use (TOU) electricity price on the utilization efficiency and economic profit of regenerative braking energy and compensates for non-traction load. Then, we propose an operation strategy based on the balance of power supply and demand that uses an improved light robust (ILR) model to minimize the total cost of the rail transit green energy system, considering the risk of uncertainty in photovoltaic power output. The model incorporates the two-step load check on the second-level time scale to correct the operational results, solve the issue of different time resolutions between photovoltaic power and traction load, and achieve the coordinated optimization of risk cost and operation cost after photovoltaic integration. Case studies demonstrate that the proposed model can effectively consider the impact of the uncertainty in photovoltaic power output on the operation strategy, significantly improving the efficiency and economy of the system operation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1859-1868"},"PeriodicalIF":5.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Neural Network-Based State Estimator for Transmission System Considering Practical Implementation Challenges 考虑实际实现挑战的传输系统深度神经网络状态估计器

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-09 DOI: 10.35833/MPCE.2023.000997

Antos Cheeramban Varghese;Hritik Shah;Behrouz Azimian;Anamitra Pal;Evangelos Farantatos

{"title":"Deep Neural Network-Based State Estimator for Transmission System Considering Practical Implementation Challenges","authors":"Antos Cheeramban Varghese;Hritik Shah;Behrouz Azimian;Anamitra Pal;Evangelos Farantatos","doi":"10.35833/MPCE.2023.000997","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000997","url":null,"abstract":"As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1810-1822"},"PeriodicalIF":5.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee 基于并网逆变器的频率调节安全强化学习与稳定性保证

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-09 DOI: 10.35833/MPCE.2023.000882

Hang Shuai;Buxin She;Jinning Wang;Fangxing Li

引用次数: 0

Byzantine-Resilient Economical Operation Strategy Based on Federated Deep Reinforcement Learning for Multiple Electric Vehicle Charging Stations Considering Data Privacy 考虑数据隐私的多电动汽车充电站基于联邦深度强化学习的拜占庭弹性经济运行策略

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-05 DOI: 10.35833/MPCE.2023.000850

Bin Feng;Huating Xu;Gang Huang;Zhuping Liu;Chuangxin Guo;Zhe Chen

{"title":"Byzantine-Resilient Economical Operation Strategy Based on Federated Deep Reinforcement Learning for Multiple Electric Vehicle Charging Stations Considering Data Privacy","authors":"Bin Feng;Huating Xu;Gang Huang;Zhuping Liu;Chuangxin Guo;Zhe Chen","doi":"10.35833/MPCE.2023.000850","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000850","url":null,"abstract":"With the goal of low-carbon energy utilization, electric vehicles (EVs) and EV charging stations (EVCSs) are becoming increasingly popular. The economical operation strategy is always a primary concern for EVCSs, while users' behavior and operating data leakage problems in EVCSs have not been taken seriously. Herein, federated deep reinforcement learning, a privacy-preserving method, is applied to learn the optimal strategy for multiple EVCSs. However, it is prone to Byzantine attacks. It is urgent to achieve an economical operation strategy while preserving data privacy and defending against Byzantine attacks. Therefore, this paper proposes a Byzantine-resilient federated deep reinforcement learning (BR-FDRL) method to address these problems. First, the distributed EVCS data are utilized by the federated deep reinforcement learning to train an economical operation strategy while preserving privacy by only transmitting gradients. The sampling efficiency is enhanced by both federated learning and stochastically controlled stochastic gradient. Then, the Byzantine-resilient gradient filter (BRGF) designs two distance rules to keep malicious gradients out. The case study verifies the effectiveness of the proposed BRGF in resisting Byzantine attacks and the effectiveness of federated deep reinforcement learning in improving convergence speed and reward and preserving privacy. The resluts show that the BR-FDRL method minimizes the operation cost by an average of 35% compared with the rule-based method while meeting the state of charge demand as much as possible.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1957-1967"},"PeriodicalIF":5.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault Diagnosis Based on Interpretable Convolutional Temporal-Spatial Attention Network for Offshore Wind Turbines 基于可解释卷积时空注意力网络的近海风力涡轮机故障诊断

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-04-05 DOI: 10.35833/MPCE.2023.000606

Xiangjing Su;Chao Deng;Yanhao Shan;Farhad Shahnia;Yang Fu;Zhaoyang Dong

{"title":"Fault Diagnosis Based on Interpretable Convolutional Temporal-Spatial Attention Network for Offshore Wind Turbines","authors":"Xiangjing Su;Chao Deng;Yanhao Shan;Farhad Shahnia;Yang Fu;Zhaoyang Dong","doi":"10.35833/MPCE.2023.000606","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000606","url":null,"abstract":"Fault diagnosis (FD) for offshore wind turbines (WTs) are instrumental to their operation and maintenance (O&M). To improve the FD effect in the very early stage, a condition monitoring based sample set mining method from super-visory control and data acquisition (SCADA) time-series data is proposed. Then, based on the convolutional neural network (CNN) and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by: ① a convolution feature extraction module to extract features based on time intervals; ② a spatial attention module to extract spatial features considering the weights of different features; and ③ a temporal attention module to extract temporal features considering the weights of intervals. The proposed CT-SAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of the temporal-spatial attention weights. The effectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1459-1471"},"PeriodicalIF":5.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Full-Model-Free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-Terminal Soft Open Point Voltage Control in Distribution Systems 配电系统多终端软开点电压控制的全无模型自适应图深度确定性策略梯度模型

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-03-31 DOI: 10.35833/MPCE.2024.000177

Huayi Wu;Zhao Xu;Minghao Wang;Youwei Jia

{"title":"Full-Model-Free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-Terminal Soft Open Point Voltage Control in Distribution Systems","authors":"Huayi Wu;Zhao Xu;Minghao Wang;Youwei Jia","doi":"10.35833/MPCE.2024.000177","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000177","url":null,"abstract":"High penetration of renewable energy sources (RESs) induces sharply-fluctuating feeder power, leading to voltage deviation in active distribution systems. To prevent voltage violations, multi-terminal soft open points (M-SOPs) have been integrated into the distribution systems to enhance voltage control flexibility. However, the M-SOP voltage control recalculated in real-time cannot adapt to the rapid fluctuations of photovoltaic (PV) power, fundamentally limiting the voltage controllability of M-SOPs. To address this issue, a full-model-free adaptive graph deep deterministic policy gradient (FAG-DDPG) model is proposed for M-SOP voltage control. Specifically, the attention-based adaptive graph convolutional network (AGCN) is leveraged to extract the complex correlation features of nodal information to improve the policy learning ability. Then, the AGCN-based surrogate model is trained to replace the power flow calculation to achieve model-free control. Furthermore, the deep deterministic policy gradient (DDPG) algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate model. Numerical tests have been performed on modified IEEE 33-node, 123-node, and a real 76-node distribution systems, which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPG model.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1893-1904"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-Carbon Economic Dispatch of Integrated Energy Systems Considering Extended Carbon Emission Flow 考虑扩展碳排放流的综合能源系统低碳经济调度

IF 5.7 1区工程技术

Journal of Modern Power Systems and Clean Energy Pub Date : 2024-03-31 DOI: 10.35833/MPCE.2023.000743

Yumin Zhang;Pengkai Sun;Xingquan Ji;Fushuan Wen;Ming Yang;Pingfeng Ye

{"title":"Low-Carbon Economic Dispatch of Integrated Energy Systems Considering Extended Carbon Emission Flow","authors":"Yumin Zhang;Pengkai Sun;Xingquan Ji;Fushuan Wen;Ming Yang;Pingfeng Ye","doi":"10.35833/MPCE.2023.000743","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000743","url":null,"abstract":"Carbon capture and storage (CCS) systems can provide sufficient carbon raw materials for power-to-gas (P2G) systems to reduce the carbon emission of traditional coal-fired units, which helps to achieve low-carbon dispatch of integrated energy systems (IESs). In this study, an extended carbon-emission flow model that integrates CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G systems is established to realize the low-carbon economic operation of P2G systems. On the energy storage side, the concept of “state of carbon” is introduced to describe the carbon emission characteristics of the ESS to exploit the potential of coordinated low-carbon dispatch in terms of both energy production and storage. In addition, a low-carbon economic dispatch model that considers multiple uncertainties, including wind power output, electricity price, and load demands, is established. To solve the model efficiently, a parallel multidimensional approximate dynamic programming algorithm is adopted, while the solution efficiency is significantly improved over that of stochastic optimization without losing solution accuracy under a multilayer parallel loop nesting framework. The low-carbon economic dispatch method of IESs is composed of the extended carbon emission flow model, low-carbon economic dispatch model, and the parallel multidimensional approximate dynamic programming algorithm. The effectiveness of the proposed method is verified on E14-B6-G6 and E57-B12-G12 systems.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1798-1809"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543262","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0