Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033004
Dian Chen, Runzhao Lu, Xi Wang, Yongcan Wang
For power system calculation and analysis, the accuracy and rationality of operation mode selection is the key to determine the calculation quality. With the access of a high proportion of renewable energy, the traditional manual selection method is not applicable. How to automatically extract the typical operation mode form the data set obtained from production simulation is an urgent scientific and technical problem to be solved. This paper firstly carries out the demand analysis of operation mode extraction of high proportion renewable energy power system. Secondly, an automatic mode extraction algorithm based on K-means++ algorithm and improved cluster validity index is proposed. Then this paper designed a mode extraction approach with joint manual processing and automatic algorithm. Finally, based on the practical data of a region power grid in China, the numerical experiments demonstrate the effectiveness and rationality of the proposed algorithm based on the comparison with the manually selected operation mode from two aspects of mode characteristics and security check. The contribution of the algorithm in improving the level of power system planning was proved.
{"title":"Power System Operation Mode Identification Method Based on Improved Clustering Algorithm","authors":"Dian Chen, Runzhao Lu, Xi Wang, Yongcan Wang","doi":"10.1109/iSPEC54162.2022.10033004","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033004","url":null,"abstract":"For power system calculation and analysis, the accuracy and rationality of operation mode selection is the key to determine the calculation quality. With the access of a high proportion of renewable energy, the traditional manual selection method is not applicable. How to automatically extract the typical operation mode form the data set obtained from production simulation is an urgent scientific and technical problem to be solved. This paper firstly carries out the demand analysis of operation mode extraction of high proportion renewable energy power system. Secondly, an automatic mode extraction algorithm based on K-means++ algorithm and improved cluster validity index is proposed. Then this paper designed a mode extraction approach with joint manual processing and automatic algorithm. Finally, based on the practical data of a region power grid in China, the numerical experiments demonstrate the effectiveness and rationality of the proposed algorithm based on the comparison with the manually selected operation mode from two aspects of mode characteristics and security check. The contribution of the algorithm in improving the level of power system planning was proved.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132227427","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033074
M. Zulfiqar, M. B. Rasheed
In the routine operation of a smart grid (SG), accurate short-term load forecasting (STLF) is paramount. To predict short-term load more effectively, this paper proposes an integrated evolutionary deep learning strategy based on navel feature engineering (FE), long short-term memory (LSTM) network, and Genetic algorithm (GA). First, FE eradicates repetitious and irrelevant attributes to guarantee high computational efficiency. The GA is then used to optimize the parameters (ReLU, MAPE, RMSprop batch size, Number of neurons, and Epoch) of LSTM. The optimized LSTM is used to get the actual STLF results. Furthermore, most literature studies focus on accuracy improvement. At the same time, the importance and productivity of the devised model are confined equally by its convergence rate. Historical load data from the independent system operator (ISO) New England (ISO-NE) energy sector is analyzed to validate the developed hybrid model. The MAPE of the proposed model has a small error value of 0.6710 and the shortest processing time of 159 seconds. The devised model outperforms benchmark models such as the LSTM, LSTM-PSO, LSTM-NSGA-II, and LSTM-GA in aspects of convergence rate and accuracy. In other words, the LSTM errors are effectively decreased by the GA hyperparameter optimization. These results may be helpful as a procedure to shorten the time-consuming process of hyperparameter setting.
{"title":"Short-Term Load Forecasting using Long Short Term Memory Optimized by Genetic Algorithm","authors":"M. Zulfiqar, M. B. Rasheed","doi":"10.1109/iSPEC54162.2022.10033074","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033074","url":null,"abstract":"In the routine operation of a smart grid (SG), accurate short-term load forecasting (STLF) is paramount. To predict short-term load more effectively, this paper proposes an integrated evolutionary deep learning strategy based on navel feature engineering (FE), long short-term memory (LSTM) network, and Genetic algorithm (GA). First, FE eradicates repetitious and irrelevant attributes to guarantee high computational efficiency. The GA is then used to optimize the parameters (ReLU, MAPE, RMSprop batch size, Number of neurons, and Epoch) of LSTM. The optimized LSTM is used to get the actual STLF results. Furthermore, most literature studies focus on accuracy improvement. At the same time, the importance and productivity of the devised model are confined equally by its convergence rate. Historical load data from the independent system operator (ISO) New England (ISO-NE) energy sector is analyzed to validate the developed hybrid model. The MAPE of the proposed model has a small error value of 0.6710 and the shortest processing time of 159 seconds. The devised model outperforms benchmark models such as the LSTM, LSTM-PSO, LSTM-NSGA-II, and LSTM-GA in aspects of convergence rate and accuracy. In other words, the LSTM errors are effectively decreased by the GA hyperparameter optimization. These results may be helpful as a procedure to shorten the time-consuming process of hyperparameter setting.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134070239","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033049
Song Yue, W. Ji, Jiangbo Xu, Junjun Zhang, Shenglong Wang
The use of photovoltaic generation as black-start power supply is of great significance for the black-start in areas with more photovoltaic and less water. However, photovoltaic generation’s ability of black start is limited due to the extreme weather and unstable generation. For the high-proportion renewable energy system based on the solar-storage operation, this paper proposes a black-start method using grid-forming energy storage as the black-start power supply. The grid-forming energy storage can establish the reliable bus voltage for the photovoltaic generation access. Then the solar-storage system can realize the smooth start of the auxiliary equipment for the traditional power plant to restore the supply of significant loads. This process has been verified in a simulation system established in MATLAB/Simulink. During the whole restart process of the auxiliary equipment, frequency varies within 0.2Hz and can be restored to a stable state. It shows that the grid-forming energy storage is a suitable black-start supply for the high-proportion renewable energy system, capable of starting non-self-starting units in the system and providing reliable power for loads.
{"title":"Research on Black Start of High-Proportion Renewable Energy System Based on Solar-Storage Generation System","authors":"Song Yue, W. Ji, Jiangbo Xu, Junjun Zhang, Shenglong Wang","doi":"10.1109/iSPEC54162.2022.10033049","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033049","url":null,"abstract":"The use of photovoltaic generation as black-start power supply is of great significance for the black-start in areas with more photovoltaic and less water. However, photovoltaic generation’s ability of black start is limited due to the extreme weather and unstable generation. For the high-proportion renewable energy system based on the solar-storage operation, this paper proposes a black-start method using grid-forming energy storage as the black-start power supply. The grid-forming energy storage can establish the reliable bus voltage for the photovoltaic generation access. Then the solar-storage system can realize the smooth start of the auxiliary equipment for the traditional power plant to restore the supply of significant loads. This process has been verified in a simulation system established in MATLAB/Simulink. During the whole restart process of the auxiliary equipment, frequency varies within 0.2Hz and can be restored to a stable state. It shows that the grid-forming energy storage is a suitable black-start supply for the high-proportion renewable energy system, capable of starting non-self-starting units in the system and providing reliable power for loads.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115931763","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033060
Xiangxu Wang, Qili Ding, Zhengwen Li, Hui Zeng, Nan Zou, Zhongbo Wang, Weidong Li
The frequency nadir is both an indicator for situational awareness and a basis for emergency control, which consists of the maximum frequency deviation and the frequency nadir time, thus fast and accurate frequency nadir prediction is important for frequency stability of the modern power system. Considering the strengths and defects of physical-driven and data-driven methods, a physical-data integrated-driven method is proposed. As the physical-driven part, Frequency Nadir Prediction (FNP) model can solve the analytical solution of the frequency nadir and obtain the initial prediction results at high speed. As the data-driven part, Back Propagation Neural Network (BPNN) can correct the errors of the initial prediction results online to improve the accuracy. The serial integration approach is applied to integrate both models and obtain the final prediction results at both high accuracy and speed. Compared with the existing integrated-driven methods, FNP model can preserve more key influences, which greatly reduces the dependence of BPNN on sample data and feature dimensions. The case studies over the New England 39-bus system verify that the proposed FNP-BPNN integrated model can provide a more reliable indicator and basis for power system frequency stability analysis and control.
{"title":"FNP-BPNN Integrated Model for Power System Frequency Nadir Prediction","authors":"Xiangxu Wang, Qili Ding, Zhengwen Li, Hui Zeng, Nan Zou, Zhongbo Wang, Weidong Li","doi":"10.1109/iSPEC54162.2022.10033060","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033060","url":null,"abstract":"The frequency nadir is both an indicator for situational awareness and a basis for emergency control, which consists of the maximum frequency deviation and the frequency nadir time, thus fast and accurate frequency nadir prediction is important for frequency stability of the modern power system. Considering the strengths and defects of physical-driven and data-driven methods, a physical-data integrated-driven method is proposed. As the physical-driven part, Frequency Nadir Prediction (FNP) model can solve the analytical solution of the frequency nadir and obtain the initial prediction results at high speed. As the data-driven part, Back Propagation Neural Network (BPNN) can correct the errors of the initial prediction results online to improve the accuracy. The serial integration approach is applied to integrate both models and obtain the final prediction results at both high accuracy and speed. Compared with the existing integrated-driven methods, FNP model can preserve more key influences, which greatly reduces the dependence of BPNN on sample data and feature dimensions. The case studies over the New England 39-bus system verify that the proposed FNP-BPNN integrated model can provide a more reliable indicator and basis for power system frequency stability analysis and control.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134344001","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033050
Yuichi Shimura, Motoshi Maekawa, Kazuaki Iwamura, Y. Nakanishi, Ryota Minami, Noboru Hattori, J. Fukushima
In line with the global trend, Japan has been planning to transmit wind power from remote areas to urban areas, especially in Honshu, the main island. In this study, we analyzed the impact of wind power uncertainty on bulk transmission systems, where a combined analysis was performed using the generation distribution shift factor method and arbitrary polynomial chaos (APC) method. Furthermore, using the APC method, the power probability distribution was calculated while considering uncertain injected wind power. Afterward, to verify the efficiency of the proposed methods, they were applied to a real transmission network, and the effective power distribution of the network was obtained.
{"title":"Analysis of the Impact of Variable Renewable Energy on Power Flow in Bulk Grid Power System","authors":"Yuichi Shimura, Motoshi Maekawa, Kazuaki Iwamura, Y. Nakanishi, Ryota Minami, Noboru Hattori, J. Fukushima","doi":"10.1109/iSPEC54162.2022.10033050","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033050","url":null,"abstract":"In line with the global trend, Japan has been planning to transmit wind power from remote areas to urban areas, especially in Honshu, the main island. In this study, we analyzed the impact of wind power uncertainty on bulk transmission systems, where a combined analysis was performed using the generation distribution shift factor method and arbitrary polynomial chaos (APC) method. Furthermore, using the APC method, the power probability distribution was calculated while considering uncertain injected wind power. Afterward, to verify the efficiency of the proposed methods, they were applied to a real transmission network, and the effective power distribution of the network was obtained.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"2023 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549541","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033026
Samrat Chakraborty, Rajen Pudur
This paper presents a new topology that focuses on feeding single-phase (l-ph) remote load by delta-connected three-phase (3-ph) SEIG (self-excited induction generator) which is excited through capacitor bank of star connection. In this topology, keeping the value of an excitation capacitor fixed, the value of other two balancing capacitors varies with varying l-ph load and rotational speed of the machine. For analyzing the total circuit under steady-state scenario, symmetrical component technique is used which helps to get three highly non-linear equations that are solved using ‘fsolve’ to obtain three unknowns i.e. values of: (i) per unit (p.u.) frequency and (ii) other two capacitors for performing an ideal balanced operation. Hardware investigation of the proposed topology with different l-ph loading indicates: (i) perfect balancing in phase-to-phase voltages and stator currents; (ii) improvement in frequency with the increase of load and (iii) voltage and current unbalance factor (i.e. VUF and CUF) being almost 0%.
{"title":"Hardware Investigation of a New Phase Balancing Topology for Supplying Single-Phase Loads using Three-Phase SEIG","authors":"Samrat Chakraborty, Rajen Pudur","doi":"10.1109/iSPEC54162.2022.10033026","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033026","url":null,"abstract":"This paper presents a new topology that focuses on feeding single-phase (l-ph) remote load by delta-connected three-phase (3-ph) SEIG (self-excited induction generator) which is excited through capacitor bank of star connection. In this topology, keeping the value of an excitation capacitor fixed, the value of other two balancing capacitors varies with varying l-ph load and rotational speed of the machine. For analyzing the total circuit under steady-state scenario, symmetrical component technique is used which helps to get three highly non-linear equations that are solved using ‘fsolve’ to obtain three unknowns i.e. values of: (i) per unit (p.u.) frequency and (ii) other two capacitors for performing an ideal balanced operation. Hardware investigation of the proposed topology with different l-ph loading indicates: (i) perfect balancing in phase-to-phase voltages and stator currents; (ii) improvement in frequency with the increase of load and (iii) voltage and current unbalance factor (i.e. VUF and CUF) being almost 0%.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132043043","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10032987
Yu He, F. Luo, G. Ranzi
The deployment of the advanced metering infrastructure provides the opportunity for performing Short-Term Load Forecasting (STLF) for a single energy user. In the last few decades, Artificial neural networks (ANNs) have been widely implemented in STLF. Conventionally, a user trains an ANN only based on her/his own historical load data. In recent years, collaborative learning techniques have been applied to facilitate multiple users to train the ANNs to enhance the STLF performance that can hardly be achieved by the users individually. This paper presents a comparison study evaluating the performances of three state-of-the-art collaborative learning STLF methods on an Australian “Smart Grid, Smart City” residential power load dataset. The work is expected to reference researchers and engineers the practical implementation of STLF systems in the residential sector.
{"title":"Comparison Study of Collaborative Learning Techniques on Residential Short-Term Load Forecasting","authors":"Yu He, F. Luo, G. Ranzi","doi":"10.1109/iSPEC54162.2022.10032987","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10032987","url":null,"abstract":"The deployment of the advanced metering infrastructure provides the opportunity for performing Short-Term Load Forecasting (STLF) for a single energy user. In the last few decades, Artificial neural networks (ANNs) have been widely implemented in STLF. Conventionally, a user trains an ANN only based on her/his own historical load data. In recent years, collaborative learning techniques have been applied to facilitate multiple users to train the ANNs to enhance the STLF performance that can hardly be achieved by the users individually. This paper presents a comparison study evaluating the performances of three state-of-the-art collaborative learning STLF methods on an Australian “Smart Grid, Smart City” residential power load dataset. The work is expected to reference researchers and engineers the practical implementation of STLF systems in the residential sector.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132281298","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033067
Haishan Ke, Huawei He, Yue Zhou, Zhaotuo Li
China’s strategic goal of “carbon peak, carbon neutrality” has a huge impact on the new power system. This paper analyzes China’s primary energy consumption, renewable energy proportion, electricity consumption and targets for capacity of photovoltaics and wind turbines. The key development path suitable for China’s new power system are significantly discussed. Results show that it is vital to control the installed capacity of coal power plant strictly, keep the wind power and photovoltaic as main increment, accelerate construction of energy storage and pumped water storage, support nuclear power as base-load supply, strengthen construction of ultra-high voltage (UHV) grid, establish electricity spot market. All those approaches are designated to increase penetration of clean energy and promote reform of the new power system.
{"title":"Analysis of Renewable Energy and Development Path of New Power System under China’s “Dual-Carbon” Situation","authors":"Haishan Ke, Huawei He, Yue Zhou, Zhaotuo Li","doi":"10.1109/iSPEC54162.2022.10033067","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033067","url":null,"abstract":"China’s strategic goal of “carbon peak, carbon neutrality” has a huge impact on the new power system. This paper analyzes China’s primary energy consumption, renewable energy proportion, electricity consumption and targets for capacity of photovoltaics and wind turbines. The key development path suitable for China’s new power system are significantly discussed. Results show that it is vital to control the installed capacity of coal power plant strictly, keep the wind power and photovoltaic as main increment, accelerate construction of energy storage and pumped water storage, support nuclear power as base-load supply, strengthen construction of ultra-high voltage (UHV) grid, establish electricity spot market. All those approaches are designated to increase penetration of clean energy and promote reform of the new power system.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455028","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033044
Zehua Zhao, F. Luo, Jiajia Yang, G. Ranzi
Widespread deployment of distributed renewable energy sources drives the emergence of the Peer-to-Peer (P2P) energy trading paradigm, which refers to the scenario that energy entities in low-voltage distribution networks trade energy with each other. This paper presents a new system that facilitates P2P energy trading based on both the economic profit/cost and non-economic considerations of the participants. The latter includes the social relationships among the participants and their multi-class energy trading preferences, which are represented by a social network model. Based on this, bidding strategies and market operation mechanisms are developed to support the formation of P2P energy trading transactions. Simulation based on the real-world social media data is conducted to validate the effectiveness of the proposed system.
{"title":"A Peer-to-Peer Energy Trading System Considering Participants’ Social Relationships and Multi-class Preferences","authors":"Zehua Zhao, F. Luo, Jiajia Yang, G. Ranzi","doi":"10.1109/iSPEC54162.2022.10033044","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033044","url":null,"abstract":"Widespread deployment of distributed renewable energy sources drives the emergence of the Peer-to-Peer (P2P) energy trading paradigm, which refers to the scenario that energy entities in low-voltage distribution networks trade energy with each other. This paper presents a new system that facilitates P2P energy trading based on both the economic profit/cost and non-economic considerations of the participants. The latter includes the social relationships among the participants and their multi-class energy trading preferences, which are represented by a social network model. Based on this, bidding strategies and market operation mechanisms are developed to support the formation of P2P energy trading transactions. Simulation based on the real-world social media data is conducted to validate the effectiveness of the proposed system.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116228501","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}
Pub Date : 2022-12-04DOI: 10.1109/iSPEC54162.2022.10033047
Liu Haitao, Ma Bingtai, Hao Sipeng, Zhang Kuangyi, Huang Cheng, Lu Heng
In the photovoltaic hybrid energy storage microgrid system, in order to reduce the unreasonable value of decomposition mode number (K) and secondary penalty factor (a) in VMD affect the accuracy of system reconstruction power. A new intelligent algorithm called sooty tern optimization algorithm(STOA) is proposed for the K and a optimization analysis. The parameters of VMD are optimized by STOA to obtain the [K, a] optimal combination quickly and stably, and then the result is applied in VMD to decompose the residual power of microgrid system. So as to improve the coincidence degree between the reconstructed power and the original residual power signal and can allocate the residual power to hybrid energy storage system reasonably, which will be beneficial to optimize the initial power allocation and capacity allocation of hybrid energy storage. This paper analyzes the algorithm and compares it with the results of particle swarm optimization and gray wolf algorithm to verify the effectiveness and superiority of the method.
{"title":"Research on Residual Power Reconfiguration of Hybrid Energy Storage System Based on Microgrid","authors":"Liu Haitao, Ma Bingtai, Hao Sipeng, Zhang Kuangyi, Huang Cheng, Lu Heng","doi":"10.1109/iSPEC54162.2022.10033047","DOIUrl":"https://doi.org/10.1109/iSPEC54162.2022.10033047","url":null,"abstract":"In the photovoltaic hybrid energy storage microgrid system, in order to reduce the unreasonable value of decomposition mode number (K) and secondary penalty factor (a) in VMD affect the accuracy of system reconstruction power. A new intelligent algorithm called sooty tern optimization algorithm(STOA) is proposed for the K and a optimization analysis. The parameters of VMD are optimized by STOA to obtain the [K, a] optimal combination quickly and stably, and then the result is applied in VMD to decompose the residual power of microgrid system. So as to improve the coincidence degree between the reconstructed power and the original residual power signal and can allocate the residual power to hybrid energy storage system reasonably, which will be beneficial to optimize the initial power allocation and capacity allocation of hybrid energy storage. This paper analyzes the algorithm and compares it with the results of particle swarm optimization and gray wolf algorithm to verify the effectiveness and superiority of the method.","PeriodicalId":129707,"journal":{"name":"2022 IEEE Sustainable Power and Energy Conference (iSPEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127433","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}
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