2019 IEEE Milan PowerTech最新文献_第4页

Optimal Operation of Battery Energy Storage System in Smart Grid for Reducing Tap Changer Operation under Photovoltaic Fluctuation Using Cuckoo Search 基于布谷鸟搜索减少光伏波动下分接开关运行的智能电网电池储能系统优化运行

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810774

Keerachat Tantrapon, P. Jirapong, P. Thararak, Kannathat Mansuwan

{"title":"Optimal Operation of Battery Energy Storage System in Smart Grid for Reducing Tap Changer Operation under Photovoltaic Fluctuation Using Cuckoo Search","authors":"Keerachat Tantrapon, P. Jirapong, P. Thararak, Kannathat Mansuwan","doi":"10.1109/PTC.2019.8810774","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810774","url":null,"abstract":"Renewable energy sources, especially photovoltaic (PV), have grown significantly and become important sources for power generation in distribution systems. However, the PV power generation is fluctuated by cloud movement, weather conditions, and wind speed which directly affect the excessive operation of voltage regulation devices such as the on load tap changer (OLTC). This excessive operation will decrease the expected life cycle and increase maintenance requirements. This paper proposed an optional operation of the battery energy storage system (BESS) in microgrid by optimizing BESS active and reactive power with Cuckoo Search optimization (CSo). The main objective aims to minimize the OLTC tap operation under PV fluctuation. The CSO is implemented in MATLAB while DIgSILENT PowerFactory is used for load flow evaluation. Simulation case studies are performed using system data from the Mae Sa Riang microgrid in Thailand. Results show that the optimal operation of BESS using CSO can effectively reduce the number of OLTC tap operations in microgrid when compared to the results with the base case and microgrid controller.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184262","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

PMUs and SCADA Measurements in Power System State Estimation through Bayesian Inference 基于贝叶斯推理的电力系统状态估计中的PMUs和SCADA测量

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810750

J. Massignan, J. London, Carlos Dias Maciel, Michle Bessani, Vladimiro Miranda

引用次数: 9

Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems 基于增量深度学习的能源管理系统连续负荷预测

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810793

Gustavo Aragón, Harsh Puri, Alexander Grass, S. Chala, C. Beecks

{"title":"Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems","authors":"Gustavo Aragón, Harsh Puri, Alexander Grass, S. Chala, C. Beecks","doi":"10.1109/PTC.2019.8810793","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810793","url":null,"abstract":"In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution - hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786196","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: 6

Transactive Energy Trading of Residential Prosumers Using Battery Energy Storage Systems 使用电池储能系统的住宅消费用户的能源交易

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810458

M. Nizami, M. J. Hossain, B. M. R. Amin, M. Kashif, Edstan Fernandez, K. Mahmud

{"title":"Transactive Energy Trading of Residential Prosumers Using Battery Energy Storage Systems","authors":"M. Nizami, M. J. Hossain, B. M. R. Amin, M. Kashif, Edstan Fernandez, K. Mahmud","doi":"10.1109/PTC.2019.8810458","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810458","url":null,"abstract":"In a transactive energy (TE) framework, prosumers can participate in peer-to-peer (P2P) energy trading with neighbors. TE also allows prosumers to participate in grid services by trading their excess energy or energy consumption flexibility with the grid operators, energy suppliers, and third-party energy companies (e.g., Aggregators). This paper presents a novel bidding strategy for small-scale residential prosumers for energy trading in the day-ahead TE market using the flexibilities of residential battery energy storage systems to maximize the profit from energy trading. The bidding model is formulated as a bi-level optimization problem that determines energy trading bids to maximize profits for the prosumer in the upper level, while the lower-level problem schedules the operation of residential storage units with respect to minimum storage degradation and optimum user comfort. A comprehensive storage model is developed that incorporates the operational constraints and the degradation of storage units when they undergo frequent charge-discharge cycles for the energy trading. The proposed bidding model is evaluated via a case study for a typical Australian prosumer and results indicate the efficacy of the proposed model in terms of profit maximization for the prosumer while satisfying user preferences and constraints related to the operation of the storage units.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115713619","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: 12

A Parallel Processing Approach to Stability Analysis Considering Transmission and Distribution Systems 输配电系统稳定性分析的并行处理方法

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810901

Angie D.Vasquez, T. Sousa

引用次数: 4

Transmission line unavailability due to correlated threat exposure 由于相关的威胁暴露，传输线不可用

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810845

Erlend Sandø Kiel, G. Kjølle

引用次数: 7

Modeling of Consumer Preferences and Constraints for the Optimal Schedule of Consumption Shifting 消费者偏好建模及消费转移最优计划约束

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810732

P. Faria, João Spínola, Z. Vale

引用次数: 0

Optimal Scheduling of Home Appliances in Home Energy Management Systems Using Grey Wolf Optimisation (Gwo) Algorithm 基于灰狼优化(Gwo)算法的家庭能源管理系统中家电的最优调度

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810406

A. R. Jordehi

引用次数: 8

Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges 主动配电网中数据驱动控制设计方案:能力与挑战

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810586

Stavros Karagiannopoulos, Roel Dobbe, P. Aristidou, Duncan S. Callaway, G. Hug

{"title":"Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges","authors":"Stavros Karagiannopoulos, Roel Dobbe, P. Aristidou, Duncan S. Callaway, G. Hug","doi":"10.1109/PTC.2019.8810586","DOIUrl":"https://doi.org/10.1109/PTC.2019.8810586","url":null,"abstract":"Today, system operators rely on local control of distributed energy resources (DERs), such as photovoltaic units, wind turbines and batteries, to increase operational flexibility. These schemes offer a communication-free, robust, cheap, but rather sub-optimal solution and do not fully exploit the DER capabilities. The operational flexibility of active distribution networks can be greatly enhanced by the optimal control of DERs. However, it usually requires remote monitoring and communication infrastructure, which current distribution networks lack due to the high cost and complexity. In this paper, we investigate data-driven control algorithms that use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We elaborate on the suitability of various schemes based on different local features, we investigate safety challenges arising from data-driven control schemes, and we show the performance of the optimized local controls on a three-phase, unbalanced, low-voltage, distribution network.","PeriodicalId":187144,"journal":{"name":"2019 IEEE Milan PowerTech","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126284006","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: 12

A REST based co-simulation interface for distributed simulations 用于分布式仿真的基于REST的联合仿真接口

2019 IEEE Milan PowerTech Pub Date : 2019-06-23 DOI: 10.1109/PTC.2019.8810661

Mike Vogt, F. Marten, Juan Montoya, C. Töbermann, M. Braun

引用次数: 2