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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Hu, Binjiang Huang, Libin Hu, Yufeng Zhang, Jie Guo, Haiping Lu, Yuanhong Hong, Zeqi Zhang, Jingyue Luo, Hui Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China Electric Power Research Institute China Southern Power Grid Csg Key Laboratory for Power System Simulation Guangzhou China

ISBN: (纸本)9798331518066

With the increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using DQN (Deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime. © 2024 IEEE.

关键词： Deep reinforcement learning

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Design and Implementation of power system Simulation Platform Based on Cloud Computing 4

Design and Implementation of Power System Simulation Platfor...

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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue National Energy Power Grid Technology R&d Centre Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control. © 2024 IEEE.

关键词： Cloud platforms

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power system Load Restoration Based on Data-Driven Constraints Linearization Method

Power System Load Restoration Based on Data-Driven Constrain...

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International Conference on power and Energy Technology (ICPET)

作者： Xiaoting Lv Hongtao Wang Xuemei Fan Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

In recent years, power outages have occurred frequently around the world, and rapid load recovery after power outages can reduce economic losses caused by power outages. In the restoration model, the power flow calculation is a nonlinear equality constraint. The idea based on mathematical optimization is to linearize the power flow equation based on certain assumptions or perform convex relaxation. Such processing may affect the solution accuracy or speed. Therefore, a data-driven constraints linearization method is proposed, using a large amount of simulation data to linearly fit the power flow through the partial least square method. Then, based on the linear constraints, the load restoration problem is established as a mixed integer linear programming (MILP) problem to reduce the difficulty of solving, improve calculation accuracy and provide dispatchers with optimal recovery decisions. Finally, the validity of the method proposed in this paper is verified in IEEE39 node system.

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DC Line Traveling Wave Protection Method Based on Improved Hausdorff Distance Comparison

DC Line Traveling Wave Protection Method Based on Improved H...

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China International Electrical and Energy Conference (CIEEC)

作者： Chengyan Diao Wei Cong Xiang Lin Xiaofei Shao Yudun Li Congcong Li Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

To improve the reliability of conventional protection using fault travelling wave, this paper proposes a line traveling wave protection method based on improved Hausdorff distance comparison. The traveling waves at the two sides of the line have different propagation laws and change characteristics during external fault and internal fault. First, the traveling waveform is processed by time matching and segmentation calculation. Second, splitting the long waveform into short waveforms and averaging the waveforms at both sides of the block processing, thus calculating the average Hausdorff distance. Last, using PSCAD building a bipolar DC transmission system for simulation verification, the results show that the improved method has high reliability.

关键词： Fault diagnosis power transmission lines Simulation Voltage Capacitance Reliability engineering Reflection

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Distributed power system Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL 4

Distributed Power System Simulation and Monitoring Supported...

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4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

This study explores the optimization of Distributed Generation systems (DGS) within a Multi-Agent Deep Reinforcement Learning (MADRL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources. © 2024 IEEE.

关键词： Cloud platforms

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Online Cascading Failure Searching Based on Gradient Boosting Decision Tree

Online Cascading Failure Searching Based on Gradient Boostin...

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power Tech Conference

作者： Tianhao Liu Yutian Liu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

With the penetration of renewable energy sources (RES) increasing rapidly, cascading failures become more complex due to the uncertainty and vulnerability of RES. This paper presents an online cascading failure searching method based on gradient boosting decision tree (GBDT) for the hybrid AC/DC system with high penetration of wind power. First, the cascading failure risk index is established, which considers the impact, probability, and loss of failures. Then, a cascading failure searching based on Monte Carlo tree search and dynamic simulation is performed to acquire the high-risk cascading failures, which are utilized as the training samples. Finally, GBDT is deployed to construct the relationship between operating conditions and failure propagation directions. Online cascading failure searching is realized by combining GBDT failure estimation and dynamic failure simulation. Simulation results of the modified New England 39-bus test system demonstrate that the proposed method can fast and accurately online screen the high-risk cascading failures considering the uncertainty of wind power.

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Proof-of- Reliability Consensus Mechanism of Blockchain for Remote Operation of Relay Protection

Proof-of- Reliability Consensus Mechanism of Blockchain for ...

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China International Electrical and Energy Conference (CIEEC)

作者： Yongjie Zhang Wei Cong Yi Tang Yudun Li Congcong Li Fangfang Shi Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Remote operation of protective setting value can enhance the adaptability of protection to real-time operating conditions. As centralized solutions, existing models face information security risks. The decentralized nature of blockchain is advantageous for addressing reliability issues in remote operations. This paper treats the control center and protection relay system sub-stations as nodes in blockchain, proposing a Proof-of- Reliability (PoRel) consensus mechanism based on node reliability. The paper first defines sub-station node types and classification criteria, achieving optimal sub-station node grouping based on 0–1 integer programming. Next, it provides a method for evaluating sub-station node reliability and outlines the consensus mechanism process. Finally, using the IEEE 39-bus system as an example, a blockchain system with 27 sub-station nodes is built using Python software, and the proposed consensus mechanism is simulated and verified. The results indicate that the proposed consensus mechanism improves efficiency, achieves decentralization, and helps ensure reliable relay setting remote operations.

关键词： Integer programming Protective relaying Information security Software Real-time systems Consensus protocol Software reliability

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable power system Based on Vertical Federated Learning 3

Cooperative Prediction Approach for the Small-Signal Stabili...

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3rd International Conference on power systems and Electrical Technology, PSET 2024

作者： Lai, Xiang Chen, Zhihao Liu, Yun Dai, Tianchen Cai, Haiqing Gu, Haohan Chen, Wei South China University of Technology School of Electric Power Engineering Guangzhou China CSG Electric Power Research Institute State Key Laboratory of HVDC Guangzhou China Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Provincial Key Guangzhou China

ISBN: (纸本)9798350353204

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios. © 2024 IEEE.

关键词： Federated learning

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Embedding P2p Transaction into Demand Response Exchange: A Cooperative Demand Response Management Framework for Ies

SSRN

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SSRN 2024年

作者： Wang, Kang Wang, Chengfu Dong, Xiaoming Yao, Wenliang Zhang, Zhenwei Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Shandong Province Jinan250061 China Shandong University China

The efficient utilization of integrated demand response (IDR) is substantially hindered by the centralized pattern of IDR management, which constrains the potential of IDR to enhance system flexibility and maintain the balance between power supply and demand. In this pattern, the integrated energy operator (IEO) acquires IDR resources from integrated load aggregators (ILAs), while the demand response transactions among ILAs, treated as a unique commodity, lack adequate attention. Therefore, it is imperative to utilize IDR resources in decentralized manner and facilitate the synergy between IEO and ILAs. To this end, a cooperative demand response management (DRM) framework for integrated energy system (IES) is proposed. Firstly, peer-to-peer (P2P) transaction is integrated with demand response exchange (DRX) mechanism to design decentralized market for DR transaction. Meanwhile, Nash bargaining theory and alternating direction method of multipliers (ADMM) algorithm are applied to achieve the optimal allocation of interests and preservation of privacy, respectively. Secondly, the price elasticity matrix of gas-electricity, which depicts cross-price elasticity of natural gas demand with respect to electricity in retail market and vice versa, is introduced to furtherly exploit the flexibility of the demand side resource. Thirdly, the cooperative DRM framework is established, where the P2P market synergizes with the traditional retail market to foster the efficient utilization of IDR resources. Case study demonstrate that the proposed DRM framework effectively facilitates a win-win situation between the ILAs and IEO to harness the potential of IDR. © 2024, The Authors. All rights reserved.

关键词： Electric loads

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Design and Application of Wideband Monitoring Master Station in power systems

Design and Application of Wideband Monitoring Master Station...

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Energy, Electrical and power Engineering (CEEPE), International Conference on

作者： Yiming Zeng Fang Shi Hengxu Zhang Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

The use of power electronics to integrate increasing renewable energy resources into power grids has changed the wideband characteristic of the power system signals. The traditional wide-area measurement system (WAMS) based on synchronized phasor measurement unit (PMU) can only measure the fundamental frequency component, making it unable to meet the measurement requirements of harmonics and interharmonic components. To address this problem, the wideband synchronized measurement unit (WPMU) has been developed, which supports real-time measurement of harmonic and interharmonic components, in addition to the functionalities of PMU. As a result, upgrading the WAMS master station is necessary to make the integration of wideband measurement available. This paper analyzes the characteristics of wideband measurement data, explores its practical applications and discusses the idea of upgrading the WAMS master station for wideband measurement, including designing its data transmission architecture, selecting a suitable database, and outlining advanced application methods. Finally, the paper presents the analysis of one actual engineering application, where the wideband measurement devices are installed, and the data is sent to the master station in real time. This work provides a reference for the design and development of a wideband WAMS master station, and contributes to the safety and stability of the power grid operation by providing wideband measurement information.

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