2023 IEEE Texas Power and Energy Conference (TPEC)最新文献_第6页

Clustering of electricity price: an application to the Italian electricity market 电价聚类:在意大利电力市场中的应用

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078632

M. H. Imani, Mansour Taheri Andani, Hamid Taheri Andani

引用次数: 0

Optimal Placement of Electric Vehicle Charging Stations: A Case Study in Jordan 电动汽车充电站的优化布局:以约旦为例

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078486

Ahmad Bashaireh, Duaa Obeidat, Abdullah A. Almehizia, L. Shalalfeh

引用次数: 1

Voltage Balancing Using Continuously Variable Series Reactor 用连续可变串联电抗器进行电压平衡

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078529

Mohammadali Hayerikhiyavi, A. Dimitrovski

引用次数: 1

Automatic Generation Control Under Single Time-Delay Attack 单时延攻击下的自动生成控制

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078699

Ziran Gao, C. Moya, Jiankang Wang, M. Illindala

引用次数: 0

A Peer-to-Peer Reputation-based Mechanism to Enhance Microgrids’ Power Exchange Quality 基于点对点信誉的微电网交换质量提升机制

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078463

A. Loni, Somayeh Asadi, M. Nazari-Heris

{"title":"A Peer-to-Peer Reputation-based Mechanism to Enhance Microgrids’ Power Exchange Quality","authors":"A. Loni, Somayeh Asadi, M. Nazari-Heris","doi":"10.1109/TPEC56611.2023.10078463","DOIUrl":"https://doi.org/10.1109/TPEC56611.2023.10078463","url":null,"abstract":"Nowadays, microgrids’ power exchanges have incurred structural and technological changes by utilization of renewable energy sources (RESs) and moving toward new power generation. The research proposed the collaborative power exchange mechanism/bed among microgrids without intermediaries in the distribution network aiming at reducing the operating costs and optimal schedule of RESs. This paper presents a decentralized and collaborative power exchange approach among microgrids, in which microgrids attempt to increase their individual payoffs by exchanging power in the coalitional groups. In addition to the decision variables of each coalition such as power loss, and amount of exchanged power, to have a logical and beneficial choice, picking partner(s) in each coalition should be based on a criterion such as reputation and reliability of partners/microgrids. To do so, this study takes advantage of cooperative game theory to (i) model the local power exchanges among microgrids and (ii) propose a peer-to-peer (P2P) reputation-based approach to evaluate the performance levels and effectiveness of microgrids’ power exchanges. Considering the microgrid’s reputation or its’ historical performance in previous interactions not only simplifies the cooperation of microgrids but also boosts the stability of the distribution grid. The obtained simulation results on three different IEEE 6-bus, 10-bus, and 14-bus microgrids indicate that considering the microgrids reputation in their exchanges lessened the costs caused by power outages and not full and on-time power delivery.","PeriodicalId":183284,"journal":{"name":"2023 IEEE Texas Power and Energy Conference (TPEC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122328268","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: 0

Short-Term Dynamic Voltage Stability Status Estimation Using Multilayer Neural Networks 基于多层神经网络的短期动态电压稳定状态估计

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078583

M. Massaoudi, S. Refaat, A. Ghrayeb, H. Abu-Rub

{"title":"Short-Term Dynamic Voltage Stability Status Estimation Using Multilayer Neural Networks","authors":"M. Massaoudi, S. Refaat, A. Ghrayeb, H. Abu-Rub","doi":"10.1109/TPEC56611.2023.10078583","DOIUrl":"https://doi.org/10.1109/TPEC56611.2023.10078583","url":null,"abstract":"The power grid stability is significantly impacted by the exponentially growing electrical demand and the complex electrical systems modernization projects. This intensifies the urgent need and yet challenging Dynamic Security Assessment (DSA) to withstand high-probability severe contingencies. This paper proposes an effective machine-learning solution for Short-Term Voltage Stability (STVS) detection and classification. This work also addresses fault detection and classification into line faults or bus faults under different operating conditions as a supplementary warning system to boost power system protection and resiliency with fast remedial actions. The proposed approach combines three necessary steps for high accuracy: feature subset selection, hyperparameter optimization, and critical bus identification. The efficiency of the proposed forecasting model is assessed using the IEEE New England 39-bus test case with the CLOD composite model. The generated N-1 contingency test cases data from dynamic Power System Simulator/Engineering (PSS/E) time domain simulations for fault-induced voltage events include the measured post-disturbance voltage magnitude, angle, frequency, and active and reactive power trajectories of the system buses. Numerical results from the proposed classifier confirm a high classification accuracy of 94.24% in identifying the post-disturbance stability state. The proposed method will be outperforming traditional shallow learning-based approaches. Further, the robustness of classifiers is demonstrated by evaluating the computational time, accuracy, precision, recall, and F-measure.","PeriodicalId":183284,"journal":{"name":"2023 IEEE Texas Power and Energy Conference (TPEC)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130619967","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: 0

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems 弹性输电系统中有意孤岛的强化学习

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078568

Sobhan Badakhshan, R. Jacob, Binghui Li, J. Zhang

{"title":"Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems","authors":"Sobhan Badakhshan, R. Jacob, Binghui Li, J. Zhang","doi":"10.1109/TPEC56611.2023.10078568","DOIUrl":"https://doi.org/10.1109/TPEC56611.2023.10078568","url":null,"abstract":"Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.","PeriodicalId":183284,"journal":{"name":"2023 IEEE Texas Power and Energy Conference (TPEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621708","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: 0

OUC Gardenia Grid Integration Laboratory: Overview and implementation 开放大学栀子网格集成实验室:概述与实现

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078633

Hossein Panamtash, Q. Sun, Rubin York, P. Brooker, J. Kramer

引用次数: 0

Categorical Databases for Mathematical Formalization of AC Optimal Power Flow 交流最优潮流数学形式化的分类数据库

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078526

M. Barati

引用次数: 0

Identifying Factors Contributing to Poor Performance of Near-Real-Time Power Flow 识别导致近实时潮流性能差的因素

2023 IEEE Texas Power and Energy Conference (TPEC) Pub Date : 2023-02-13 DOI: 10.1109/TPEC56611.2023.10078697

G. Çakir, K. Crawford, M. Baran, V. Cecchi, B. Chowdhury, Oluwatimilehin Adeosun, Mariann Thomas, Cara DeCoste Chacko

引用次数: 1