2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)最新文献

Integrating Machine Learning Techniques into the Decision-making Process for Hydro Scheduling 将机器学习技术整合到水电调度决策过程中

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072120

J. Kong, H. Skjelbred, Piri Babayev, Zhirong Yang

{"title":"Integrating Machine Learning Techniques into the Decision-making Process for Hydro Scheduling","authors":"J. Kong, H. Skjelbred, Piri Babayev, Zhirong Yang","doi":"10.1109/APPEEC53445.2022.10072120","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072120","url":null,"abstract":"During the past half-century, numerous optimization models have been developed to help hydropower producers to determine the optimal power generation schedules. Nevertheless, the producers must manually set up the executive commands before running the optimization models. Limited by human analytic competence, the producers usually use the default setting. The value of the optimization tools could be further carried forward if the commands are dynamically determined according to the specific operating and market conditions. In this paper, we propose a framework and methodologies to facilitate the decision-making process for hydropower producers by realizing the automatic setup of executive commands. This automation is achieved by integrating machine learning (ML) techniques with a comprehensive understanding of the hydro systems and the hydro scheduling tools. It is demonstrated that nonphysical spills from reservoirs can be 100% avoided using the command setting predicted by ML compared to the result obtained by the default setting. The calculation time can reduce by 45% compared to the robust setting.","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124882007","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

A Frequency Stability Analysis for BESS Placement Considering the Loads and Wind farms Locations 考虑负荷和风电场位置的BESS布置频率稳定性分析

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072092

Hassan Alsharif, M. Jalili, K. Hasan

{"title":"A Frequency Stability Analysis for BESS Placement Considering the Loads and Wind farms Locations","authors":"Hassan Alsharif, M. Jalili, K. Hasan","doi":"10.1109/APPEEC53445.2022.10072092","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072092","url":null,"abstract":"Power systems operating with a high level of inverter-based renewable generation resources, e.g. photovoltaic systems and wind turbines, may impose significant challenges on the power system dynamics, including the system frequency stability. The Battery Energy Storage Systems (BESS) has been recognised as a viable solution for system stability issues as it can rapidly respond to system disturbances, and also it provides high flexibility in terms of the storage capacity and placement locations. In the literature, the focus of BESS placement in frequency stability studies is mostly limited to that, the BESS placement is being identified based on the location of the highest frequency deviation in the power system using different optimisation techniques. The main issue of this technique is that it partly or even completely ignores some of the important factors in power systems, i.e. the overall system dynamics, the uncertainty of load demand and intermittent generation sources output power and the locations of the loads and the intermittent generation sources. This research, therefore, aims to fill this gap by considering the aforementioned factors in the BESS placement under two different analysis approaches, i.e. deterministic and probabilistic (long-term investigation). The simulation has been carried out in DIgSILENT PowerFactory software with Python interface and has been demonstrated using the IEEE 39-bus power system. The obtained results revealed that the BESS placement closer to the load's side (consumers' side) provides better frequency stability than when the BESS was placed near the wind farms' side (generation side).","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123564061","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

Robust Energy Management of Multi-microgrids System Considering Incentive-Based Demand Response Using Price Elasticity 基于价格弹性的基于激励的需求响应多微电网系统鲁棒能量管理

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072220

Juhi Datta, D. Das

{"title":"Robust Energy Management of Multi-microgrids System Considering Incentive-Based Demand Response Using Price Elasticity","authors":"Juhi Datta, D. Das","doi":"10.1109/APPEEC53445.2022.10072220","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072220","url":null,"abstract":"The worldwide exponential augmentation of energy consumption has prompted the emergence of microgrids (MGs) integrated with various distributed generations, storage systems, renewable energy resources (RERs), and plug-in hybrid electric vehicles (PHEVs). This paper investigates the energy scheduling and trading of the interconnected MGs to minimize the total operational cost of the multi-MGs system. In this regard, a robust optimization technique is employed for attributing the intrinsic intermittencies of RERs, load demands, and PHEVs charging demands. The proposed scheme also considers an incentive-based demand response model and assesses the impacts of the energy pricing along with incentives in response to the pricing elasticity for effective consumer participation. Within this framework, each MG schedules its operation and responds to energy trading with the adjacent MGs and the utility grid via peer-to-peer communication. The formulated EM problem is solved using a hybrid grey-wolf and whale optimization algorithm. Comprehensive studies are conducted on a 5-MG system comprising residential and industrial MGs and several simulation outcomes are reported to validate the proposed scheme.","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125277228","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

Optimizing Power System Observability Using GAMS 利用GAMS优化电力系统可观测性

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072202

C. Mohini, G. Shubham, J. Dhanashree, Swannil. J

引用次数: 0

Combined Optimization of Steady-state Operation and Small-signal Dynamics in Hybrid AC/DC Power Systems 交直流混合电源系统稳态运行与小信号动力学组合优化

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072101

D. Mende, Lothar Löwer, Yannic Harms, David Sebastian Stock, Salvatore D'Arco, R. Barrera-Cardenas, J. Suul

{"title":"Combined Optimization of Steady-state Operation and Small-signal Dynamics in Hybrid AC/DC Power Systems","authors":"D. Mende, Lothar Löwer, Yannic Harms, David Sebastian Stock, Salvatore D'Arco, R. Barrera-Cardenas, J. Suul","doi":"10.1109/APPEEC53445.2022.10072101","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072101","url":null,"abstract":"This paper presents an approach for calculating optimized operating conditions in hybrid AC/DC systems while considering constraints on the small-signal dynamics. The implementation is based on a closed form power flow optimization considering the steady-state operating conditions and the damping of the eigenvalues calculated from a linearized system model. The optimization utilizes the eigenvalue parametric sensitivities to calculate the next time steps' operating points. The small-signal modelling for the calculation of the eigenvalues and their sensitivities are based on a modular approach that facilitates the scalability to larger systems. An implementation in AMPL and Matlab is presented to assess convergence and computational performance. Finally, the methodology and the software implementation are validated numerically on an example hybrid AC/DC system with a multi-terminal HVDC grid. Simulation results show that the combined optimization allows to improve the steady-state operating point according to dynamic system behaviour.","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000819","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

Analysis and Prediction of PV Impacts on Medium Voltage Australian Distribution Networks 光伏发电对澳大利亚中压配电网的影响分析与预测

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072136

E. Sarker, S. Islam, A. Gargoom, Md. Apel Mahmud, Amanullah Maung Than Oo, J. Yaghoobi, David Dart

{"title":"Analysis and Prediction of PV Impacts on Medium Voltage Australian Distribution Networks","authors":"E. Sarker, S. Islam, A. Gargoom, Md. Apel Mahmud, Amanullah Maung Than Oo, J. Yaghoobi, David Dart","doi":"10.1109/APPEEC53445.2022.10072136","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072136","url":null,"abstract":"This paper studies the impact of solar generation on the distribution network voltage and analyses the impact using a number of voltage indices. These voltage indices take into account any deviation or rise from nominal voltage, voltage ramps, and voltage variances. In addition, a machine learning (ML) based model to predict the voltage indices as a function of solar generation has been implemented. A real-life case study considering 18 medium voltage distribution feeders in Australia including residential, industrial, hospital and laboratory feeders with three phase voltage data from phasor measurement units and postcode level solar generation data has been presented. Statistical analysis of the voltage indices shows that most of the feeders experience voltage rise during maximum solar generation periods. The analysis also considers the impact of a generation trip event and finds that the voltage indices violate significantly before a power outage. It can be observed that the ML models can predict the voltage indices more accurately when these indices are discretized.","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"2 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114109106","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

Parameter Identification of Chaotic System in Finite-Time Without Persistence of Excitation 无激励持续的有限时间混沌系统参数辨识

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072265

S. J, C. Chaitali, P. P

引用次数: 0

Data-Driven Control of Distributed Energy Resources Using Smart Meters Data 使用智能电表数据的分布式能源数据驱动控制

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072051

Yasin Zabihinia Gerdroodbari, Abu Bakr Pengwah, R. Razzaghi

引用次数: 0

Error Spectrum Analysis of Solar Power Prediction for Deakin Microgrid Digital Twin 迪肯微电网数字孪生体太阳能发电预测误差谱分析

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072286

Iynkaran Natgunanathan, A. Anwar, S. Rajasegarar, Vicky H. Mak-Hau

{"title":"Error Spectrum Analysis of Solar Power Prediction for Deakin Microgrid Digital Twin","authors":"Iynkaran Natgunanathan, A. Anwar, S. Rajasegarar, Vicky H. Mak-Hau","doi":"10.1109/APPEEC53445.2022.10072286","DOIUrl":"https://doi.org/10.1109/APPEEC53445.2022.10072286","url":null,"abstract":"In 2021, the AUD 23 million Deakin University Renewable Energy Microgrid commenced operation. To date, it has the largest solar farm at an Australian University- one that is capable of producing 7.25-megawatt of power. In the year following that, a web-based digital twin was developed with the aims of providing operators with intelligence and insights through AI-driven capabilities. A key component of these AI-driven capabilities is a fast algorithm that predicts power generation from weather data for rapid decision-making and frequent health assessment. In particular, the accuracy of the prediction algorithm needs to be high and consistent, regardless of the magnitude of the power generated. To this end, we tested a number of commonly-used ML algorithms. From our experiments, Extreme Gradient Boost (XGBoost) and Random Forest (RF) are capable of training 7500 samples well within a few seconds with reasonable accuracy. With this observation, we further tuned the two methods and compared them for accuracy and consistency using data from different sections of the solar farm, mainly the three main blocks (each with over 6000 solar panels) and the four research arrays (each with 144 solar panels). Given a similar (or much less) computation time, RF outperformed XGBoost in terms of prediction accuracy using the new accuracy profile metrics we have introduced: namely the x-percentile Closeness Scores and the x-percentile Absolute Errors. These new metrics measure the performance by reporting the whole error spectrum instead of just a single value.","PeriodicalId":341247,"journal":{"name":"2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131682533","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

Integration of Silicon-Carbide Schottky Diodes with High-Frequency Transformer 碳化硅肖特基二极管与高频变压器的集成

2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Pub Date : 2022-11-20 DOI: 10.1109/APPEEC53445.2022.10072147

Weichong Yao, T. Parveen, Junwei Lu, A. Seagar, F. Bai, Foad Taghizadeh

引用次数: 0