Pub Date : 2023-07-12DOI: 10.13052/dgaej2156-3306.3856
B. N. Kar, P. Samuel, A. Mallick, J. K. Pradhan
This article proposes a unidirectional power flow of a grid-connected brushless DC motor powered water pumping system fed by a photovoltaic array using a bridgeless power factor corrected (PFC) boost converter. The system consists of a bridgeless PFC converter, a boost converter, and a voltage source inverter to drive a brushless DC motor coupled with a pump. As a backup source, the grid is used. This system allows the water pump to run at maximum capacity regardless of the weather conditions. The grid will provide power if the photovoltaic array is unable to fulfil the required power demand. The unidirectional power flow through a conventional power factor corrected (PFC) boost converter causes conduction loss in the input bridge rectifier, thereby hurting efficiency, power factor, and THD. This paper presents a bridgeless PFC boost converter topology to reduce the conduction losses, thereby increasing the efficiency and obtaining a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. The system is simulated using MATLAB /Simulink. The proposed system’s real-time validation is realized through the OPAL-RT simulator OP5700. The THD results obtained are well within the specified standard of IEC 61000-3-2 and IEEE 519-1992.
{"title":"Grid-Connected Solar PV Fed BLDC Motor Drive for Water Pumping System","authors":"B. N. Kar, P. Samuel, A. Mallick, J. K. Pradhan","doi":"10.13052/dgaej2156-3306.3856","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3856","url":null,"abstract":"This article proposes a unidirectional power flow of a grid-connected brushless DC motor powered water pumping system fed by a photovoltaic array using a bridgeless power factor corrected (PFC) boost converter. The system consists of a bridgeless PFC converter, a boost converter, and a voltage source inverter to drive a brushless DC motor coupled with a pump. As a backup source, the grid is used. This system allows the water pump to run at maximum capacity regardless of the weather conditions. The grid will provide power if the photovoltaic array is unable to fulfil the required power demand. The unidirectional power flow through a conventional power factor corrected (PFC) boost converter causes conduction loss in the input bridge rectifier, thereby hurting efficiency, power factor, and THD. This paper presents a bridgeless PFC boost converter topology to reduce the conduction losses, thereby increasing the efficiency and obtaining a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. The system is simulated using MATLAB /Simulink. The proposed system’s real-time validation is realized through the OPAL-RT simulator OP5700. The THD results obtained are well within the specified standard of IEC 61000-3-2 and IEEE 519-1992.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85984524","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.3851
Xifeng Xie, Deng Luo, Jiangwei Wang, Xuesong Wu, Tao Chen
Aiming at deficiencies of output voltage distortion and circulating current generation caused by dead-time effect in the modulation process, a simple and feasible dead time compensation strategy is presented. Firstly, the influence of dead-time effect on the output voltage of bridge is analysed, and a dead time compensation strategy is added between the modulation signal and dead time procession. According to the current direction of the bridge, the rising edge or falling edge of the driving signal is selectively delayed to compensate for dead-time effect. Secondly, an Optimized Triple Phase-shift (OTPS) modulation strategy is adopted with minimizing leakage inductor current Root-Mean-Square (RMS) control, which minimizes current stress, achieves soft-switching operation, avoids phase-shift errors caused by Dead-Time Effect, and optimizes control performance of DC-DC converters. Finally, simulated and experimental results are added to verify the correctness and effectiveness of the proposed method.
{"title":"Dead-Time Effect and Compensation Technology for an Isolated Dual Active Bridge Converter","authors":"Xifeng Xie, Deng Luo, Jiangwei Wang, Xuesong Wu, Tao Chen","doi":"10.13052/dgaej2156-3306.3851","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3851","url":null,"abstract":"Aiming at deficiencies of output voltage distortion and circulating current generation caused by dead-time effect in the modulation process, a simple and feasible dead time compensation strategy is presented. Firstly, the influence of dead-time effect on the output voltage of bridge is analysed, and a dead time compensation strategy is added between the modulation signal and dead time procession. According to the current direction of the bridge, the rising edge or falling edge of the driving signal is selectively delayed to compensate for dead-time effect. Secondly, an Optimized Triple Phase-shift (OTPS) modulation strategy is adopted with minimizing leakage inductor current Root-Mean-Square (RMS) control, which minimizes current stress, achieves soft-switching operation, avoids phase-shift errors caused by Dead-Time Effect, and optimizes control performance of DC-DC converters. Finally, simulated and experimental results are added to verify the correctness and effectiveness of the proposed method.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83334628","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.38513
Zexi Chen, Pu Wang, Bin Li, E. Zhao, Zhigang Hao, Dongqiang Jia
With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.
{"title":"Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network","authors":"Zexi Chen, Pu Wang, Bin Li, E. Zhao, Zhigang Hao, Dongqiang Jia","doi":"10.13052/dgaej2156-3306.38513","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38513","url":null,"abstract":"With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74560950","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.38514
Han Jinglin, Hu Ping, Zhao Hui, He Chunguang, Hou Ruosong, Li Bo
As the continuous development of integrated energy system and distributed power supply, operation economy and regional energy Internet reliability, especially micro-energy system, are increasing. Therefore, it is necessary to build multi-energy complementary micro-energy system, innovate energy supply mode, realize collaborative and efficient utilization among multi-energy systems, improve energy utilization efficiency and absorb renewable energy. In this paper, the decision model of distribution network planning scheme including distributed generator supply is established from four aspects: investment and operation cost, extra reserve capacity, energy conservation, reliability cost. The decision model involves a lot of parameter calculation and selection judgment, so after the decision goal is determined, an decision framework based on DS-MAS is established, that is, parameters are automatically calculated and selected based on different scenarios. Model validity is proved via a practical decision project.
{"title":"Multi-objective Collaborative Planning Method for Micro-energy Systems Considering Thermoelectric Coupling Clusters","authors":"Han Jinglin, Hu Ping, Zhao Hui, He Chunguang, Hou Ruosong, Li Bo","doi":"10.13052/dgaej2156-3306.38514","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38514","url":null,"abstract":"As the continuous development of integrated energy system and distributed power supply, operation economy and regional energy Internet reliability, especially micro-energy system, are increasing. Therefore, it is necessary to build multi-energy complementary micro-energy system, innovate energy supply mode, realize collaborative and efficient utilization among multi-energy systems, improve energy utilization efficiency and absorb renewable energy. In this paper, the decision model of distribution network planning scheme including distributed generator supply is established from four aspects: investment and operation cost, extra reserve capacity, energy conservation, reliability cost. The decision model involves a lot of parameter calculation and selection judgment, so after the decision goal is determined, an decision framework based on DS-MAS is established, that is, parameters are automatically calculated and selected based on different scenarios. Model validity is proved via a practical decision project.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84979084","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.3858
Ankeshwarapu Sunil, C. Venkaiah, D. Kumar
In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.
{"title":"Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits","authors":"Ankeshwarapu Sunil, C. Venkaiah, D. Kumar","doi":"10.13052/dgaej2156-3306.3858","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3858","url":null,"abstract":"In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75426918","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.3852
Deng Hao, Yilihamu Yaermaimaiti
Aiming at the problems of serious overfitting and poor training results caused by too small a data set of solar cell defect images in the process of deep learning training, an improved DCGAN generation countermeasure network model is proposed. Firstly, CLAHE preprocessing is used to enhance the defect image features, which can improve the defect contrast and avoid excessive noise enhancement at the same time; Secondly, the NAM attention module is introduced into DCGAN to improve the quality of the defect image; Finally, S-RELU is used to replace Leaky Relu in DCGAN discriminator to avoid the influence of too much negative information with gradient on the decision of discriminator. The experimental results of classification and detection show that the data enhancement effect of the improved model is better. Compared with the original model, its accuracy is improved by 2.51%, and the mapped value is improved by 1.92%, which proves the effectiveness of the proposed algorithm.
{"title":"Improved DCGAN for Solar Cell Defect Enhancement","authors":"Deng Hao, Yilihamu Yaermaimaiti","doi":"10.13052/dgaej2156-3306.3852","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3852","url":null,"abstract":"Aiming at the problems of serious overfitting and poor training results caused by too small a data set of solar cell defect images in the process of deep learning training, an improved DCGAN generation countermeasure network model is proposed. Firstly, CLAHE preprocessing is used to enhance the defect image features, which can improve the defect contrast and avoid excessive noise enhancement at the same time; Secondly, the NAM attention module is introduced into DCGAN to improve the quality of the defect image; Finally, S-RELU is used to replace Leaky Relu in DCGAN discriminator to avoid the influence of too much negative information with gradient on the decision of discriminator. The experimental results of classification and detection show that the data enhancement effect of the improved model is better. Compared with the original model, its accuracy is improved by 2.51%, and the mapped value is improved by 1.92%, which proves the effectiveness of the proposed algorithm.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86573933","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.38512
Zexi Chen, Li Yang, J. Tian, Zeng Chen, Xiaoye Xu, E. Zhao
In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.
{"title":"Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing","authors":"Zexi Chen, Li Yang, J. Tian, Zeng Chen, Xiaoye Xu, E. Zhao","doi":"10.13052/dgaej2156-3306.38512","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38512","url":null,"abstract":"In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75861242","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 : 2023-07-12DOI: 10.13052/dgaej2156-3306.3854
Avinash Vujji, R. Dahiya
Permanent magnet synchronous motor (PMSM) with model predictive torque control (MPTC) is popular for its simplified control structure and adaptable in incorporating control parameters into the control algorithm. However, in control technique the primary concern for objective function (OF) depends on the selection of appropriate weighting coefficient (WC). Basically, for weighting coefficient selection, empirical methods are used but it takes additional time and heuristic process. In this paper, Grey Relational Analysis (GRA) technique is introduced in optimization of objective function for selection of appropriate weighting coefficient. In this methodology, stator flux and torque having individual OF are modified from single-OF. This ensures that in each sampling period, selection of grey relational optimal control action is dependent on the preference given to the control parameters in OF. For each sampling, a Grey Relational Grade (GRG) is employed to determine the appropriate control action. The models for two-level inverter fed PMSM are developed in MATLAB/Simulink to test the various operations of PMSM drive and the results are validated on the experimental test bench using dSPACE-1104 R&D controller. In order to highlight the effectiveness of the proposed technique, the results are compared with DTFC and MPTC approach.
{"title":"Enhancement of Weighting Coefficient Selection using Grey Relational Analysis for Model Predictive Torque Control of PMSM Drive: Analysis and Experiments","authors":"Avinash Vujji, R. Dahiya","doi":"10.13052/dgaej2156-3306.3854","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3854","url":null,"abstract":"Permanent magnet synchronous motor (PMSM) with model predictive torque control (MPTC) is popular for its simplified control structure and adaptable in incorporating control parameters into the control algorithm. However, in control technique the primary concern for objective function (OF) depends on the selection of appropriate weighting coefficient (WC). Basically, for weighting coefficient selection, empirical methods are used but it takes additional time and heuristic process. In this paper, Grey Relational Analysis (GRA) technique is introduced in optimization of objective function for selection of appropriate weighting coefficient. In this methodology, stator flux and torque having individual OF are modified from single-OF. This ensures that in each sampling period, selection of grey relational optimal control action is dependent on the preference given to the control parameters in OF. For each sampling, a Grey Relational Grade (GRG) is employed to determine the appropriate control action. The models for two-level inverter fed PMSM are developed in MATLAB/Simulink to test the various operations of PMSM drive and the results are validated on the experimental test bench using dSPACE-1104 R&D controller. In order to highlight the effectiveness of the proposed technique, the results are compared with DTFC and MPTC approach.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360042","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 : 2023-05-18DOI: 10.13052/dgaej2156-3306.38411
P. Kumar, V. Das, Ashutosh Kumar Singh, P. Karuppanan
Energy storage system (ESS) plays a critical role in maintaining the reliability of microgrids. ESS selection for microgrids depends on energy density, specific power, specific energy, and economics. This paper analyses the economic benefits of various combinations of short-, medium-, and long-term ESS, i.e., multi-energy storage systems (MESS) in a microgrid. The economic feasibility of the system is analyzed using Homer software. The net present cost (NPC), the Levelized cost of energy (LCOE), and pollutant gas emission are chosen as parameters for analyzing the economic feasibility of the microgrids. The results show that amongst all the scenarios, the system with Hydrogen Storage System (HSS) with Proton exchange membrane fuel cell (PEMFC) and electrolyzer is the most feasible solution with the lowest LCOE and pollutant emission.
{"title":"Levelized Cost of Energy-Based Economic Analysis of Microgrid Equipped with Multi Energy Storage System","authors":"P. Kumar, V. Das, Ashutosh Kumar Singh, P. Karuppanan","doi":"10.13052/dgaej2156-3306.38411","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38411","url":null,"abstract":"Energy storage system (ESS) plays a critical role in maintaining the reliability of microgrids. ESS selection for microgrids depends on energy density, specific power, specific energy, and economics. This paper analyses the economic benefits of various combinations of short-, medium-, and long-term ESS, i.e., multi-energy storage systems (MESS) in a microgrid. The economic feasibility of the system is analyzed using Homer software. The net present cost (NPC), the Levelized cost of energy (LCOE), and pollutant gas emission are chosen as parameters for analyzing the economic feasibility of the microgrids. The results show that amongst all the scenarios, the system with Hydrogen Storage System (HSS) with Proton exchange membrane fuel cell (PEMFC) and electrolyzer is the most feasible solution with the lowest LCOE and pollutant emission.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84034038","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 : 2023-05-18DOI: 10.13052/dgaej2156-3306.3846
Xiuming Niu, Xu-feng Luo
The technique of directly converting solar energy into electricity using PV modules is distributed photovoltaic (PV) power generation. It is frequently used in a system and is referred to as a distributed PV power system. The system generates power in the surrounding areas and connects to the neighbouring utility grid. A distributed energy storage (DES) system is a bundled solution that stores energy for future use. In the short term, one of the most significant problems with solar power storage is that the batteries utilized for the application are still costly and giant. The more power requires the bigger battery must be. Further research revealed that maximizing solar and wind energies minimizes greenhouse gas emissions and lower the total cost of energy. The ability to store energy is crucial in balancing because it makes the grid more adaptable and stable. The mission of energy conservation and energy storage (ECES) aims to help integrate energy-storage technology research, production, deployment, and integration to improve the energy efficiency of all energy systems and enable the increased use of renewable energy in place of fossil fuels. Storage benefits are examined in terms of distribution transformer loads and storage support during energy fluctuations from renewable energy. However, the results show that the methodology’s recommended framework is successful and obtained with enhanced performance with a reliability of 95.6%. The proposed technique improves the Reliability analysis ratio of 95.4%, Performance analysis comparison ratio of 98.6%, accuracy analysis ratio of 91.3%, ECES model’s efficiency is estimated at 95.6%.
{"title":"Policies and Economic Efficiency for Distributed Photovoltaic and Energy Storage Industry","authors":"Xiuming Niu, Xu-feng Luo","doi":"10.13052/dgaej2156-3306.3846","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3846","url":null,"abstract":"The technique of directly converting solar energy into electricity using PV modules is distributed photovoltaic (PV) power generation. It is frequently used in a system and is referred to as a distributed PV power system. The system generates power in the surrounding areas and connects to the neighbouring utility grid. A distributed energy storage (DES) system is a bundled solution that stores energy for future use. In the short term, one of the most significant problems with solar power storage is that the batteries utilized for the application are still costly and giant. The more power requires the bigger battery must be. Further research revealed that maximizing solar and wind energies minimizes greenhouse gas emissions and lower the total cost of energy. The ability to store energy is crucial in balancing because it makes the grid more adaptable and stable. The mission of energy conservation and energy storage (ECES) aims to help integrate energy-storage technology research, production, deployment, and integration to improve the energy efficiency of all energy systems and enable the increased use of renewable energy in place of fossil fuels. Storage benefits are examined in terms of distribution transformer loads and storage support during energy fluctuations from renewable energy. However, the results show that the methodology’s recommended framework is successful and obtained with enhanced performance with a reliability of 95.6%. The proposed technique improves the Reliability analysis ratio of 95.4%, Performance analysis comparison ratio of 98.6%, accuracy analysis ratio of 91.3%, ECES model’s efficiency is estimated at 95.6%.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83678440","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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