Pub Date : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621525
Yaobang Chen, Jie Shi, Xingong Cheng, Xiaoyi Ma
The precision and reliability of photovoltaic (PV) power forecasting play a crucial role in commercial PV plants. However, the stochastic and intermittent nature of solar radiation makes prediction difficult. Inspired by this, 4 different deep learning-based hybrid models are proposed to predict short-term PV power generation using long short term memory (LSTM) neural network and convolutional neural network (CNN) based on Bayesian Optimization (BO) in this paper. In addition, this paper explores feature selection using two benchmark models on different feature sets, and finally selects 5 features for prediction. The performances of direct forecasting results for both 1-hour ahead and 24-hour ahead of the above various models are compared on one year of hourly data from a real PV plant in Shandong, China. It is shown that using Bi-directional LSTM (BiLSTM) and CNN-BiLSTM models are more suitable for 1-hour ahead prediction, LSTM-CNN and CNN-BiLSTM models are more suitable for 24-hour ahead prediction. The case study shows that the model with Bayesian optimized optimal weights can reduce the error rate by up to 32.80% compared to the benchmark model and demonstrates the good prediction performance of the proposed approach on commercial PV plants.
{"title":"Hybrid Models Based on LSTM and CNN Architecture with Bayesian Optimization for ShortTerm Photovoltaic Power Forecasting","authors":"Yaobang Chen, Jie Shi, Xingong Cheng, Xiaoyi Ma","doi":"10.1109/ICPSAsia52756.2021.9621525","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621525","url":null,"abstract":"The precision and reliability of photovoltaic (PV) power forecasting play a crucial role in commercial PV plants. However, the stochastic and intermittent nature of solar radiation makes prediction difficult. Inspired by this, 4 different deep learning-based hybrid models are proposed to predict short-term PV power generation using long short term memory (LSTM) neural network and convolutional neural network (CNN) based on Bayesian Optimization (BO) in this paper. In addition, this paper explores feature selection using two benchmark models on different feature sets, and finally selects 5 features for prediction. The performances of direct forecasting results for both 1-hour ahead and 24-hour ahead of the above various models are compared on one year of hourly data from a real PV plant in Shandong, China. It is shown that using Bi-directional LSTM (BiLSTM) and CNN-BiLSTM models are more suitable for 1-hour ahead prediction, LSTM-CNN and CNN-BiLSTM models are more suitable for 24-hour ahead prediction. The case study shows that the model with Bayesian optimized optimal weights can reduce the error rate by up to 32.80% compared to the benchmark model and demonstrates the good prediction performance of the proposed approach on commercial PV plants.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115283934","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621603
Yuqi Xu, Changfei Zhao, C. Wan, Lanxin Shao
Nowadays, the uncertainty and variability of increasing wind power penetration has brought new challenges to electric power system operation, putting forward higher requirements for its operational flexibility and reserve capability. To realize adequate reserve provision for power system, massive flexible resources in integrated electricity and heating system have been aggregated to supply efficient reserve capacity. In this context, we first propose an operating reserve quantification scheme for analyzing both probabilistic reserve requirements and dynamic reserve capability of integrated electricity and heating system; Then, a simplified model is developed through convex relaxation and polyhedral approximation for computational tractability of district heating system reserve capacity. A noval two-layer iterative algorithm based on the second order cone duality is developed to obtain a two-stage distributionally robust energy and reserve scheduling strategy. Finally, numerical simulations are implemented to validate the effectiveness and economic benefits of the proposed approach.
{"title":"Distributionally Robust Operating Reserve Quantification of Integrated Electricity and Heating System","authors":"Yuqi Xu, Changfei Zhao, C. Wan, Lanxin Shao","doi":"10.1109/ICPSAsia52756.2021.9621603","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621603","url":null,"abstract":"Nowadays, the uncertainty and variability of increasing wind power penetration has brought new challenges to electric power system operation, putting forward higher requirements for its operational flexibility and reserve capability. To realize adequate reserve provision for power system, massive flexible resources in integrated electricity and heating system have been aggregated to supply efficient reserve capacity. In this context, we first propose an operating reserve quantification scheme for analyzing both probabilistic reserve requirements and dynamic reserve capability of integrated electricity and heating system; Then, a simplified model is developed through convex relaxation and polyhedral approximation for computational tractability of district heating system reserve capacity. A noval two-layer iterative algorithm based on the second order cone duality is developed to obtain a two-stage distributionally robust energy and reserve scheduling strategy. Finally, numerical simulations are implemented to validate the effectiveness and economic benefits of the proposed approach.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058466","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621417
H. Ni, S. Ai, Feiyue Ma, Yongning Huang, Y. Fan, Lei Chen, Yujie Gong, Zhiyuan Liu
Evaporation of droplets in vacuum circuit breaker has impact on dielectric strength recovery and breaking ability. In this paper, we use simulation to study impact of different parameters on evaporation. We found that the evaporation process can be separated into two parts, fast evaporation and stable evaporation. This difference is caused by the saturated vapor pressure and it restrains the evaporation. By changing initial speed, temperature, angle and number of droplets, evaporation process shows different pictures.
{"title":"Simulation on Evaporation of Copper Droplet in Vacuum","authors":"H. Ni, S. Ai, Feiyue Ma, Yongning Huang, Y. Fan, Lei Chen, Yujie Gong, Zhiyuan Liu","doi":"10.1109/ICPSAsia52756.2021.9621417","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621417","url":null,"abstract":"Evaporation of droplets in vacuum circuit breaker has impact on dielectric strength recovery and breaking ability. In this paper, we use simulation to study impact of different parameters on evaporation. We found that the evaporation process can be separated into two parts, fast evaporation and stable evaporation. This difference is caused by the saturated vapor pressure and it restrains the evaporation. By changing initial speed, temperature, angle and number of droplets, evaporation process shows different pictures.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125234310","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621746
Zhang Zhixia, Yao Yongge, Jiang Mingming, Pan Yingyue, Yang Jing, Song Wehai
with the gradual decline of environmental air quality in China, the traditional way of energy use in rural areas has been gradually outlawed due to low energy consumption and high pollution. It is urgent to find a low-carbon and environmentally friendly energy supply method suitable for rural areas. Based on low carbon environmental protection as the goal, this paper analyzed the natural resources in the rural areas and proposed a comprehensive energy system suitable to rural area, which composed of the photo voltaic, wind power, ground source heat pump, straw curing fuel for rural energy, can make full use of abundant biomass and idle land in rural area and satisfy rural residents’ demand for cold, heat, electricity. With the pollution emission factor model as the main calculation reference, this paper studies the pollution emission of energy system, and initially adopts the full life cycle method to calculate the pollution emission of photo voltaic and wind power equipment. Considering the flexible consumption of users and the initiative of operators, the capacity of energy supply equipment is selected with the goal of low carbon emission reduction and considering economic constraints. Taking full account of regional differences, the low-carbon emission reduction effects of three different energy supply modes are compared. Taking a rural area in shandong province as an example, the proportion of CO2, SO2, NOX, PM2.5 that can reduce emission is 95.41%, 97.51%, 90.31% and 97.40%, respectively. The emission reduction effect is remarkable.
{"title":"Pollution Reduction Effect of Rural Integrated Energy System Oriented to Low-Carbon Transformation","authors":"Zhang Zhixia, Yao Yongge, Jiang Mingming, Pan Yingyue, Yang Jing, Song Wehai","doi":"10.1109/ICPSAsia52756.2021.9621746","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621746","url":null,"abstract":"with the gradual decline of environmental air quality in China, the traditional way of energy use in rural areas has been gradually outlawed due to low energy consumption and high pollution. It is urgent to find a low-carbon and environmentally friendly energy supply method suitable for rural areas. Based on low carbon environmental protection as the goal, this paper analyzed the natural resources in the rural areas and proposed a comprehensive energy system suitable to rural area, which composed of the photo voltaic, wind power, ground source heat pump, straw curing fuel for rural energy, can make full use of abundant biomass and idle land in rural area and satisfy rural residents’ demand for cold, heat, electricity. With the pollution emission factor model as the main calculation reference, this paper studies the pollution emission of energy system, and initially adopts the full life cycle method to calculate the pollution emission of photo voltaic and wind power equipment. Considering the flexible consumption of users and the initiative of operators, the capacity of energy supply equipment is selected with the goal of low carbon emission reduction and considering economic constraints. Taking full account of regional differences, the low-carbon emission reduction effects of three different energy supply modes are compared. Taking a rural area in shandong province as an example, the proportion of CO2, SO2, NOX, PM2.5 that can reduce emission is 95.41%, 97.51%, 90.31% and 97.40%, respectively. The emission reduction effect is remarkable.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915379","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621620
Yating Liu, Ming Yang, Yixiao Yu, T. Ding, Zhiyuan Si, Menglin Li
High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.
{"title":"Short-Term Wind Generation Combined Forecast Considering Meteorological Similarity","authors":"Yating Liu, Ming Yang, Yixiao Yu, T. Ding, Zhiyuan Si, Menglin Li","doi":"10.1109/ICPSAsia52756.2021.9621620","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621620","url":null,"abstract":"High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117215940","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}
The traditional operation and maintenance method of hot spot detection has some problems, such as low efficiency of inspection, difficult to identify the cause of hot spot under the influence of multiple factors. In this paper, based on the Unmanned Aerial Vehicle(UAV) inspection technology, combined with the slope constraint based infrared image and visible image registration method of hot spot location and based on the improved fish swarm gray combination prediction method, the hot spot information discrimination process was designed. On this basis, an on-line hot spot detection operation and maintenance system of photovoltaic power station(PVPS) based on UAV infrared and visible light detection was constructed, and the accuracy of hot spot detection results of the system was verified by experiments. The system has high accuracy of hot spot location, can actively screen out the external influencing factors of photovoltaic module hot spot, and realize automatic alarm and location investigation of complex hot spot.
{"title":"Research and Application of Photovoltaic Power Station On-line Hot Spot Detection Operation and Maintenance System Based on Unmanned Aerial Vehicle Infrared and Visible Light Detection","authors":"Guanglei Li, Yuejiao Wang, Zheng Xu, Wei-Hua Teng, Xingyou Zhang","doi":"10.1109/ICPSAsia52756.2021.9621375","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621375","url":null,"abstract":"The traditional operation and maintenance method of hot spot detection has some problems, such as low efficiency of inspection, difficult to identify the cause of hot spot under the influence of multiple factors. In this paper, based on the Unmanned Aerial Vehicle(UAV) inspection technology, combined with the slope constraint based infrared image and visible image registration method of hot spot location and based on the improved fish swarm gray combination prediction method, the hot spot information discrimination process was designed. On this basis, an on-line hot spot detection operation and maintenance system of photovoltaic power station(PVPS) based on UAV infrared and visible light detection was constructed, and the accuracy of hot spot detection results of the system was verified by experiments. The system has high accuracy of hot spot location, can actively screen out the external influencing factors of photovoltaic module hot spot, and realize automatic alarm and location investigation of complex hot spot.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121256991","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621493
Yun Yang, Siew-Chong Tan, S. Hui
This paper presents a three-layer hierarchical control scheme that can balance the state-of-charge (SoC) of distributed battery systems (DBS) with distinct state-of-health (SoH) in DC microgrids under the conditions of load variations and cyber attacks. The tertiary control is a consensus control that provides SoC references for the secondary control. The secondary control is an adaptive droop control that provides output voltage deviation reference for the primary control. The primary control is a local proportional-integral (PI) control that tracks the output voltage reference of DBS via the regulation of the grid-connected converter. The effectiveness of the proposed control scheme is validated via a 100 kW photovoltaic (PV)-battery system comprising one new battery and two aged batteries installed on a 380 V DC bus.
{"title":"State-of-Charge Balance Control of Distributed Battery Systems with Distinct State-of-Health in DC Microgrids","authors":"Yun Yang, Siew-Chong Tan, S. Hui","doi":"10.1109/ICPSAsia52756.2021.9621493","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621493","url":null,"abstract":"This paper presents a three-layer hierarchical control scheme that can balance the state-of-charge (SoC) of distributed battery systems (DBS) with distinct state-of-health (SoH) in DC microgrids under the conditions of load variations and cyber attacks. The tertiary control is a consensus control that provides SoC references for the secondary control. The secondary control is an adaptive droop control that provides output voltage deviation reference for the primary control. The primary control is a local proportional-integral (PI) control that tracks the output voltage reference of DBS via the regulation of the grid-connected converter. The effectiveness of the proposed control scheme is validated via a 100 kW photovoltaic (PV)-battery system comprising one new battery and two aged batteries installed on a 380 V DC bus.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177755","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621348
Zihan Chen, Zhenyuan Zhang, Peng Wang, Qi Huang
With the fierce competition of electricity market, demand response (DR) amount is also traded in day-ahead market. From the perspective of retailers, just considering its inside customers’ characteristic is not enough, the competitiveness of DR bidding also matters, because it depends on the qualification of participating in DR market. Thus, this paper constructs a complicated DR strategic bidding model. Firstly, based on managed residential customers’ DR feature, optimize bidding considering competitors’ risk preference with deep reinforcement learning approach and guarantee the probability of winning DR bid as much as possible. Secondly, in the actual quotation process, the inaccuracy DR declaration amount or retailers’ personal bidding preference, aggressive or moderate style, leads to DR vacancy punishment or overage waste, so that produce the loss of income. Based on previous bidding model, design backup schemes for different types of retailers in advance to reduce loss. Then utilize real case to verify the effectiveness of proposed DR bidding models.
{"title":"Competition-Oriented Demand Response Strategic Bidding Model for Retailers Considering Backup Scheme","authors":"Zihan Chen, Zhenyuan Zhang, Peng Wang, Qi Huang","doi":"10.1109/ICPSAsia52756.2021.9621348","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621348","url":null,"abstract":"With the fierce competition of electricity market, demand response (DR) amount is also traded in day-ahead market. From the perspective of retailers, just considering its inside customers’ characteristic is not enough, the competitiveness of DR bidding also matters, because it depends on the qualification of participating in DR market. Thus, this paper constructs a complicated DR strategic bidding model. Firstly, based on managed residential customers’ DR feature, optimize bidding considering competitors’ risk preference with deep reinforcement learning approach and guarantee the probability of winning DR bid as much as possible. Secondly, in the actual quotation process, the inaccuracy DR declaration amount or retailers’ personal bidding preference, aggressive or moderate style, leads to DR vacancy punishment or overage waste, so that produce the loss of income. Based on previous bidding model, design backup schemes for different types of retailers in advance to reduce loss. Then utilize real case to verify the effectiveness of proposed DR bidding models.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115358493","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}
Transformerless photovoltaic (PV) inverters are more widely adopted due to their high efficiency, low cost, light weight, etc. H6 transformerless PV inverters can suppress leakage current while do not have the bidirectional capability for a photovoltaic-energy storage system (PV-ES). Therefore, this paper proposes a bidirectional leakage current-less modulation strategy for H6 inverter topology, by improving the modulation strategy in the rectifier stage, only two switches are turned on at high frequency in rectifier mode. The leakage current in inverter and rectifier mode can be suppressed while switching loss can also be reduced. Finally, to validate the proposed modulation strategy, a simulation is also built and tested. The simulation results validate the theoretical analysis.
{"title":"Bidirectional Leakage Current-Less Modulation of H6 Inverter with Reduced Switching Loss","authors":"Zaixun Ling, Jing-Jing Zheng, Yibo Cui, Yu Guo, Zihong Zhang, Yiqun Kang, Mingjie Gao","doi":"10.1109/ICPSAsia52756.2021.9621726","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621726","url":null,"abstract":"Transformerless photovoltaic (PV) inverters are more widely adopted due to their high efficiency, low cost, light weight, etc. H6 transformerless PV inverters can suppress leakage current while do not have the bidirectional capability for a photovoltaic-energy storage system (PV-ES). Therefore, this paper proposes a bidirectional leakage current-less modulation strategy for H6 inverter topology, by improving the modulation strategy in the rectifier stage, only two switches are turned on at high frequency in rectifier mode. The leakage current in inverter and rectifier mode can be suppressed while switching loss can also be reduced. Finally, to validate the proposed modulation strategy, a simulation is also built and tested. The simulation results validate the theoretical analysis.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115552562","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 : 2021-07-18DOI: 10.1109/ICPSAsia52756.2021.9621477
Jing Gou, Gang Wu, Jingrong Guo, Yongtao Guo
This paper proposes a coordinated optimal dispatch model for the electricity-gas integrated energy system considering multiple reserve resources. The model takes into account the reserve resources including generator reserve capacity, energy storage equipment and interruptible load, and considers the transmission capacity of the reserve capacity in the electricity and natural gas network in the face of emergencies to ensure the safety and reliability of system operation. The simulation results confirm that the coordinated optimal scheduling model of the electricity-gas integrated energy system with multiple reserve resources proposed in this paper can reduce the total operating cost of the system and improve the flexibility of system operation.
{"title":"Coordinated Optimization of Electricity-Gas Integrated Energy System","authors":"Jing Gou, Gang Wu, Jingrong Guo, Yongtao Guo","doi":"10.1109/ICPSAsia52756.2021.9621477","DOIUrl":"https://doi.org/10.1109/ICPSAsia52756.2021.9621477","url":null,"abstract":"This paper proposes a coordinated optimal dispatch model for the electricity-gas integrated energy system considering multiple reserve resources. The model takes into account the reserve resources including generator reserve capacity, energy storage equipment and interruptible load, and considers the transmission capacity of the reserve capacity in the electricity and natural gas network in the face of emergencies to ensure the safety and reliability of system operation. The simulation results confirm that the coordinated optimal scheduling model of the electricity-gas integrated energy system with multiple reserve resources proposed in this paper can reduce the total operating cost of the system and improve the flexibility of system operation.","PeriodicalId":296085,"journal":{"name":"2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117003012","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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