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Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage

Chinese Journal of Electrical engineering 2023年第3期9卷 39-49页

作者： Bo Li Chunjie Qin Ruotao Yu Wei Dai Mengjun Shen Ziming Ma Jianxiao Wang School of Electrical Engineering Guangxi UniversityNanning 530004China State Grid Sichuan Electric Power Company Chengdu 610041China National Power Dispatching and Control Center State Grid Corporation of ChinaBeijing 100031China National Engineering Laboratory for Big Data Analysis and Applications(Peking University) Beijing 100871China

Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals(state-of-charge(SOC)constraint of LTS,ramping rate,and minimum up/down time constraints of thermal units),resulting in a significant computational ***,an iterative-based fast solution method is proposed to solve the long-term UC with ***,the UC with coupling constraints is split into several sub problems that can be solved in ***,the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling ***,a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the *** price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub ***,the sub problem with the SOC boundary of the LTS is iteratively solved *** proposed method was verified using a modified IEEE 24-bus *** results showed that the computation time of the unit combination problem can be reduced by 97.8%,with a relative error of 3.62%.

关键词： Constraint splitting long-term storage mix-integer programming unit commitment

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Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

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Chinese Journal of Electrical engineering 2023年第3期9卷 15-25页

作者： Youzhen Wu Jianxiao Wang Yiyang Song Yunyun Xie College of Engineering Peking UniversityBeijing 100871China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate *** has sparked discussions on the resilience of power ***-storage systems(ESSs)are critical for enhancing the resilience of power ***,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and ***,in turn,ensures the power system’s stable ***,there is limited systematic research quantifying the economic value of energy storage in resilience ***,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was *** first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time *** the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of ***,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch *** index helps determine the balance between the energy storage investment cost and resilience ***,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed *** findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage *** optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highligh

关键词： Energy storage dispatch model power system resilience resilience-oriented valuation two-stage optimization model

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An experimental evaluation of large scale GBDT Systems 45th

An experimental evaluation of large scale GBDT Systems

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45th International Conference on Very Large data Bases, VLDB 2019

作者： Fu, Fangcheng Jiang, Jiawei Shao, Yingxia Cuix, Bin Peking University China Center for Data Science and National Engineering Laboratory for Big Data Analysis and Applications Peking University China Beijing University of Posts and Telecommunications China Tencent Inc. China

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload. © 2019, is held by the owner/author(s).

关键词： Decision trees

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Differentiable Feature Aggregation Search for Knowledge Distillation 16th

Differentiable Feature Aggregation Search for Knowledge Dist...

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16th European Conference on Computer Vision, ECCV 2020

作者： Guan, Yushuo Zhao, Pengyu Wang, Bingxuan Zhang, Yuanxing Yao, Cong Bian, Kaigui Tang, Jian Peking University Beijing China Technology Inc Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China DiDi AI Labs Beijing China

ISBN: (纸本)9783030585198

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing distillation methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design. © 2020, Springer Nature Switzerland AG.

关键词： Distillation

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INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEARNING WITH SOFT labELS 10

INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEAR...

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10th International Conference on Learning Representations, ICLR 2022

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Cao, Meng Huang, Ping Shan, Jiulong Yang, Zhi Cui, Bin School of CS Peking University China Apple National Engineering Laboratory for Big Data Analysis and Applications China Institute of Computational Social Science Peking University Qingdao China

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Graph neural networks

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A Person-job Matching Method Based on BM25 and Pre-Trained Language Model 6

A Person-job Matching Method Based on BM25 and Pre-Trained L...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Tang, Jiaqi Chen, Heyun Chen, Zifan Pan, Jiahua He, Yijiang Zhao, Jie Li, Zhen PKU-Changsha Institute for Computing and Digital Economy Changsha China Center for Data Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

ISBN: (纸本)9798400709241

To address the inefficiency of traditional methods for person-job matching and the lack of interpretability of deep learning approaches, a novel approach for person-job matching based on BM25 and pre-Trained language models is proposed. First, the BM25 algorithm is utilized to perform an initial screening of resumes based on job descriptions. By calculating the matching degree of keywords, it rapidly filters out candidate resumes that do not align with the job requirements. Subsequently, the pre-Trained language model BERT is employed to perform fine-grained sorting of the resumes that passed the initial screening, identifying the candidates that best match with the job. By combining the screening capability of the BM25 algorithm with the semantic understanding ability of pre-Trained language models, this method enhances the accuracy and interpretability of person-job matching results while maintaining matching speed. Furthermore, to improve model generalization, large-scale language models are employed for data augmentation. Experimental results indicate the significant potential for the proposed approach in practical applications, offering improved solutions for the challenges of talent recruitment across diverse job demands. © 2023 ACM.

关键词： Semantics

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A Heuristic Method for Route Programming in Puzzle-Based Energy Storage Systems 7

A Heuristic Method for Route Programming in Puzzle-Based Ene...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Zou, Yang Wang, Jianxiao School of Engineering Peking University Beijing China National Engineering Laboratory For Big Data Analysis and Applications Peking University Beijing China Peking University Ordos Research Institute of Energy Ordos China

ISBN: (纸本)9798350345094

Recently, many energy storage-related enterprises have been facing difficulties brought out by the limitation of land and the increase in loan cost. As a promising approach to improving space utilization rate, puzzle-based energy storage systems (PBESSs) are drawing more attention from logistics researchers. In previous research about PBESS, concentration has been paid to single target item problems. However, there are no consensus algorithms to solve load retrievals route programming in PBESSs with multiple target items. In this paper, a heuristic algorithm is proposed to solve load retrieval route programming in PBESSs with multiple target items, multiple escorts, and multiple (Inputs Outputs) IOs. This paper defines new concepts about the proposed algorithm, including target IOs, target position of escorts, number of escorts required, et al. Then, the decision procedures are designed according to these concepts. Based on the Markov decision process, the proposed algorithm makes the decision of the next action by analyzing the current status, until all the target items arrive at the IOs. The case study results have shown the effectiveness and efficiency of the proposed heuristic algorithm. © 2023 IEEE.

关键词： Markov processes

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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Caution About Truncation-By-Death in Clinical Trial Statistical analysis:A Lesson from Remdesivir

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China CDC weekly 2021年第25期3卷 538-540,I0001页

作者： Yuhao Deng Xiao-Hua Zhou School of Mathematical Sciences Peking UniversityBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Lab for Big Data Analysis and Applications Peking UniversityBeijingChina

In an effort to combat coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),clinicians across the globe have been working tirelessly to find effective *** 2020,inspir... 详细信息

关键词： acute respiratory

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A peer-to-peer joint energy and reserve market considering renewable generation uncertainty: A generalized Nash equilibrium approach

Energy Conversion and Economics

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Energy Conversion and Economics 2024年第3期5卷 168-181页

作者： Chen, Xiupeng Wang, Lu Jiang, Yuning Wang, Jianxiao Jan C. Willems Center for Systems and Control University of Groningen Groningen Netherlands Distributed Electrical Systems Laboratory EPFL Lausanne Switzerland Automatic Control Laboratory EPFL Lausanne Switzerland National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

Peer-to-peer (P2P) energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real-time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two-stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game (GNG), considering that prosumers are self-interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy-preserving algorithm to guide prosumers toward the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33-power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium. © 2024 The Author(s). Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Electric power distribution

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