This passage discusses a rapid restoration optimization strategy for short-term global coordination and the synergistic autonomous regulation of distributed energy sources based on a model predictive control framework. A short-term rapid recovery global coordination optimization model is established through the prediction of network states under abnormal conditions. This model includes the energy management of distributed energy sources and recovery plans for critical loads. In terms of the autonomous regulation of distributed energy sources, based on the results of global coordination optimization and aiming to minimize load shedding losses and grid losses, an ultra-short-term rolling control strategy is formulated using power output and load switching as control variables. Finally, simulation analysis on the IEEE 33-node distribution network system indicates that the proposed model and method significantly accelerate the recovery speed of the distribution network and effectively enhance its resilience level.
{"title":"Research on a Self-Coordinated Optimization Method for Distributed Energy Resources Targeting Risk Mitigation","authors":"Hongtao Li, Tian Hao, Zijin Li, Ergang Zhao, Chen Wang, Lina Xu","doi":"10.13052/dgaej2156-3306.39312","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.39312","url":null,"abstract":"This passage discusses a rapid restoration optimization strategy for short-term global coordination and the synergistic autonomous regulation of distributed energy sources based on a model predictive control framework. A short-term rapid recovery global coordination optimization model is established through the prediction of network states under abnormal conditions. This model includes the energy management of distributed energy sources and recovery plans for critical loads. In terms of the autonomous regulation of distributed energy sources, based on the results of global coordination optimization and aiming to minimize load shedding losses and grid losses, an ultra-short-term rolling control strategy is formulated using power output and load switching as control variables. Finally, simulation analysis on the IEEE 33-node distribution network system indicates that the proposed model and method significantly accelerate the recovery speed of the distribution network and effectively enhance its resilience level.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832580","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3914
U. V. Anbazhagu, Manjula Sanjay Koti, V. Muthukumaran, V. Geetha, Meram Munrathnam
The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact.
{"title":"Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach","authors":"U. V. Anbazhagu, Manjula Sanjay Koti, V. Muthukumaran, V. Geetha, Meram Munrathnam","doi":"10.13052/dgaej2156-3306.3914","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3914","url":null,"abstract":"The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136018934","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3916
Subramanya Sarma S, K. Sarada, P. Jithendar, Telugu Maddileti, G. Nanda Kishor Kumar
The increasing use of renewable energy systems has led to a rise in the number of grid-connected inverters, which can have a detrimental effect on the superiority and constancy of grid electricity due to the injected current harmonics. In this study, the proportional integral (PI) and proportional resonant (PR) controllers have been investigated for their effectiveness in reducing harmonics in grid-connected inverters. The study also investigates the impact of harmonics compensators (HC) on the control strategies. The results of the study suggest that the implementation of PI and PR controllers in the synchronous frame can effectively reduce the injected current harmonics in grid-connected inverters. The use of harmonics compensators can further enhance the performance of the controllers by reducing the distortion and improving the stability of the grid. The efficiency of the regulator strategies be contingent on the type and level of harmonics in the grid, as well as the design and tuning of the controllers and compensators. The statement that the “PR+HC controller has a superior quality output current” is more specific and suggests that this control method may be more effective than the others in reducing harmonics and enlightening the value of the productivity current. The comparison of the IEEE 1547 standard by three viable inverters from diverse constructors is also noteworthy, as it can provide insights into the compatibility and performance of different types of inverters with the standard. The use of deep learning with the RCNN network for analyzing harmonics and providing information about power is an interesting application of machine learning in power systems research. This approach may have the probable to development the accuracy and competence of harmonics analysis as well as power monitoring in grid-connected inverters. Overall, the study highlights the importance of effective control strategies for managing harmonics in grid-connected inverters, particularly in the context of the increasing usage of renewable energy systems. The findings of the study can inform the development of more efficient and reliable grid-connected inverters, which are essential for the incorporation of renewable energy systems into the power grid.
{"title":"A Deep Learning Based Enhancing the Power by Reducing the Harmonics in Grid Connected Inverters","authors":"Subramanya Sarma S, K. Sarada, P. Jithendar, Telugu Maddileti, G. Nanda Kishor Kumar","doi":"10.13052/dgaej2156-3306.3916","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3916","url":null,"abstract":"The increasing use of renewable energy systems has led to a rise in the number of grid-connected inverters, which can have a detrimental effect on the superiority and constancy of grid electricity due to the injected current harmonics. In this study, the proportional integral (PI) and proportional resonant (PR) controllers have been investigated for their effectiveness in reducing harmonics in grid-connected inverters. The study also investigates the impact of harmonics compensators (HC) on the control strategies. The results of the study suggest that the implementation of PI and PR controllers in the synchronous frame can effectively reduce the injected current harmonics in grid-connected inverters. The use of harmonics compensators can further enhance the performance of the controllers by reducing the distortion and improving the stability of the grid. The efficiency of the regulator strategies be contingent on the type and level of harmonics in the grid, as well as the design and tuning of the controllers and compensators. The statement that the “PR+HC controller has a superior quality output current” is more specific and suggests that this control method may be more effective than the others in reducing harmonics and enlightening the value of the productivity current. The comparison of the IEEE 1547 standard by three viable inverters from diverse constructors is also noteworthy, as it can provide insights into the compatibility and performance of different types of inverters with the standard. The use of deep learning with the RCNN network for analyzing harmonics and providing information about power is an interesting application of machine learning in power systems research. This approach may have the probable to development the accuracy and competence of harmonics analysis as well as power monitoring in grid-connected inverters. Overall, the study highlights the importance of effective control strategies for managing harmonics in grid-connected inverters, particularly in the context of the increasing usage of renewable energy systems. The findings of the study can inform the development of more efficient and reliable grid-connected inverters, which are essential for the incorporation of renewable energy systems into the power grid.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136018933","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3917
P. Pradeep Kumar, M. Rama Prasad Reddy
In the Photovoltaic (PV) system, monitoring, assessing, and detecting the occurred faults is essential. Autonomous diagnostic models are required to examine the solar plants and to detect the anomalies within these PV panels since the prevailing hotspot detection models were unable to detect the faults rapidly and consistently. A novel Log Inverse Bilateral Edge Detector (LIBED) and Gated Bernoulli Logmax Recurrent Unit (GBLRU)-centered Solar Panel (SP) hotspot detection scheme is proposed in this research that analyzed the operating PV module’s thermal images. Images are applied for the image processing steps prior to hotspot detection. By utilizing the Contrast Limited Adaptive Histogram Equalization (CLAHE) model, the image’s contrast has been augmented in the image processing step. The alpha (α) Modified Histogram Blending (αMHB) method is utilized to eliminate the outlier data available in the image. Subsequently, an effective LIBED contour detection method was utilized to detect the SP. Several features are extracted by utilizing the detected panels. Then, optimal features are chosen as of the extracted features by utilizing the Barnacles Mating Optimizer (BMO) algorithm. The GBLRU was utilized to predict the defective panels. The defective panels’ hotspots were isolated by utilizing the Haversine Self-Organizing Map (HSOM) model. The experimental evaluation of the proposed system’s performance is analyzed with the prevailing classifiers. The state-of-art methods were outperformed by the proposed GBLRU-based Hotspot detection system. The efficiency 94.34%, accuracy 97.23%, hot-spot detection rate 91.23% had been attained which were improved outcomes compared to existed models.
{"title":"An Efficient Libed and GBLRU-Based Solar Panel Hotspot Detection System Using Thermal Images","authors":"P. Pradeep Kumar, M. Rama Prasad Reddy","doi":"10.13052/dgaej2156-3306.3917","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3917","url":null,"abstract":"In the Photovoltaic (PV) system, monitoring, assessing, and detecting the occurred faults is essential. Autonomous diagnostic models are required to examine the solar plants and to detect the anomalies within these PV panels since the prevailing hotspot detection models were unable to detect the faults rapidly and consistently. A novel Log Inverse Bilateral Edge Detector (LIBED) and Gated Bernoulli Logmax Recurrent Unit (GBLRU)-centered Solar Panel (SP) hotspot detection scheme is proposed in this research that analyzed the operating PV module’s thermal images. Images are applied for the image processing steps prior to hotspot detection. By utilizing the Contrast Limited Adaptive Histogram Equalization (CLAHE) model, the image’s contrast has been augmented in the image processing step. The alpha (α) Modified Histogram Blending (αMHB) method is utilized to eliminate the outlier data available in the image. Subsequently, an effective LIBED contour detection method was utilized to detect the SP. Several features are extracted by utilizing the detected panels. Then, optimal features are chosen as of the extracted features by utilizing the Barnacles Mating Optimizer (BMO) algorithm. The GBLRU was utilized to predict the defective panels. The defective panels’ hotspots were isolated by utilizing the Haversine Self-Organizing Map (HSOM) model. The experimental evaluation of the proposed system’s performance is analyzed with the prevailing classifiers. The state-of-art methods were outperformed by the proposed GBLRU-based Hotspot detection system. The efficiency 94.34%, accuracy 97.23%, hot-spot detection rate 91.23% had been attained which were improved outcomes compared to existed models.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"36 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104244","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3911
N. Gowtham, V. Prema, Mahmoud F. Elmorshedy, M. S. Bhaskar, Dhafer J. Almakhles
Microgrids are seen as the future of reliable, sustainable and green energy source for myriad applications. The increasing dependence on microgrid also adds challenges on reliable management of power supply to vividly variant consumers, the major chunk being households coupled with an unprecedented rise in the demand for EV charging. This study aims at presenting a deep Long Short-Term Memory with Deep Brief Network model to reliably predict the grouped energy load and solar energy outcome in a community microgrid. A cutting-edge hybrid metaheuristic algorithm will be taken into consideration for optimizing the load dispatch of community microgrids that are connected to the grid. Three different scheduling scenarios are evaluated to establish an ideal dispatching design for a grid-linked community microgrid with solar elements and energy storage systems feeding electricity loads and charging electric vehicles. The prediction outcomes are integrated into the model to accommodate the uncertainties associated with solar energy outcome and residential energy load and EV charging to achieve a supply-demand equilibrium. The objective of the proposed model is to obtain an energy-efficient system capable of balancing the load and power of microgrid system which remains unperturbed by the aforesaid oscillations.
{"title":"A Power Aware Long Short-Term Memory with Deep Brief Network Based Microgrid Framework to Maintain Sustainable Energy Management and Load Balancing","authors":"N. Gowtham, V. Prema, Mahmoud F. Elmorshedy, M. S. Bhaskar, Dhafer J. Almakhles","doi":"10.13052/dgaej2156-3306.3911","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3911","url":null,"abstract":"Microgrids are seen as the future of reliable, sustainable and green energy source for myriad applications. The increasing dependence on microgrid also adds challenges on reliable management of power supply to vividly variant consumers, the major chunk being households coupled with an unprecedented rise in the demand for EV charging. This study aims at presenting a deep Long Short-Term Memory with Deep Brief Network model to reliably predict the grouped energy load and solar energy outcome in a community microgrid. A cutting-edge hybrid metaheuristic algorithm will be taken into consideration for optimizing the load dispatch of community microgrids that are connected to the grid. Three different scheduling scenarios are evaluated to establish an ideal dispatching design for a grid-linked community microgrid with solar elements and energy storage systems feeding electricity loads and charging electric vehicles. The prediction outcomes are integrated into the model to accommodate the uncertainties associated with solar energy outcome and residential energy load and EV charging to achieve a supply-demand equilibrium. The objective of the proposed model is to obtain an energy-efficient system capable of balancing the load and power of microgrid system which remains unperturbed by the aforesaid oscillations.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102349","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3913
Di Wu, Jun Su, Zhengyu Chen, Hanhan Liu
Increasing renewable energy integration in power systems is an important way of decarbonising carbon emissions. Recently, the ever-increasing deployment of distributed generation (DG) is considered effective in reducing carbon emissions and power loss, such as wind, photovoltaic (PV), and combined heat and power generation (CHP) on the demand side. Thus, the evaluation of carbon emission flow (CEF) will be a crucial factor for distribution network planning with the integration of DGs, which may act as a supplemented indicator in addition to traditional power flow study. In the planning stage, it is paramount to ensure that decarbonisation process of the power distribution system is in line with environmental and technical targets. Thus, the paper proposes a modelling strategy to combine the power flow and carbon emission flow. It aims to analyse and calculate the CEF based on the power-flow study. The novel model satisfies the power flow and CEF balance and can be directly used to evaluate the decarbonization of power system. The results of this study can help relevant energy decision-makers to do appropriate renewable energy generation planning and guide the power system to achieve carbon neutrality.
{"title":"Effects of Distributed Generation on Carbon Emission Reduction of Distribution Network","authors":"Di Wu, Jun Su, Zhengyu Chen, Hanhan Liu","doi":"10.13052/dgaej2156-3306.3913","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3913","url":null,"abstract":"Increasing renewable energy integration in power systems is an important way of decarbonising carbon emissions. Recently, the ever-increasing deployment of distributed generation (DG) is considered effective in reducing carbon emissions and power loss, such as wind, photovoltaic (PV), and combined heat and power generation (CHP) on the demand side. Thus, the evaluation of carbon emission flow (CEF) will be a crucial factor for distribution network planning with the integration of DGs, which may act as a supplemented indicator in addition to traditional power flow study. In the planning stage, it is paramount to ensure that decarbonisation process of the power distribution system is in line with environmental and technical targets. Thus, the paper proposes a modelling strategy to combine the power flow and carbon emission flow. It aims to analyse and calculate the CEF based on the power-flow study. The novel model satisfies the power flow and CEF balance and can be directly used to evaluate the decarbonization of power system. The results of this study can help relevant energy decision-makers to do appropriate renewable energy generation planning and guide the power system to achieve carbon neutrality.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104437","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3915
SwornaKokila M L, Venkatarathinam R, Rose Bindu Joseph P, M. A. Manivasagam, Kakarla Hari Kishore
Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time (<0.4 seconds), are used to evaluate the model’s performance. The output of the method excelled all of the other current methods that were taken into account. The system’s rapid response time and its positive environmental impact further highlight its potential in managing smart grid resources effectively.
{"title":"Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques","authors":"SwornaKokila M L, Venkatarathinam R, Rose Bindu Joseph P, M. A. Manivasagam, Kakarla Hari Kishore","doi":"10.13052/dgaej2156-3306.3915","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3915","url":null,"abstract":"Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time (<0.4 seconds), are used to evaluate the model’s performance. The output of the method excelled all of the other current methods that were taken into account. The system’s rapid response time and its positive environmental impact further highlight its potential in managing smart grid resources effectively.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103697","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 : 2023-10-30DOI: 10.13052/dgaej2156-3306.3912
Sandhya Kumari, Sreenu Sreekumar, Sonika Singh, D. P. Kothari
High penetration of wind power plants in power systems resulted in various challenges such as frequent system imbalances due to highly uncertain and variable wind generation. Additional spinning reserves and specific balancing products such as flexible ramp products are used to handle such frequent imbalances. Incorporation of these ancillary services leads to increased total operational costs. Increased operational costs should be transferred to wind power producers as it is caused by wind power plants. This leads to penalizing the wind power producers for the deviation of power generation from forecasts, called deviation charges. These deviation charges can be reduced by improving the forecasting accuracy. Existing forecasting models show performance in terms of error matrices. Such error matrices do not indicate the financial loss associated with it. This can be overcome by expressing forecasting performance in terms of deviation charge and it will directly encourage wind power producers to improve forecasting accuracy or arrange reserves to accommodate the error. This paper proposes a backpropagation-based artificial neural network model for reducing deviation charges in this context. An analysis is conducted on the data collected from the Bonneville Power Administration (BPA) Balancing Area. Seasonal analysis (Spring, Summer, Fall, and Winter) is conducted to show the performance of the proposed model throughout the year. The proposed model performance is compared with linear regression and ARIMA models. The comparison shows that the proposed ANN model gives the least deviation charges in the Spring, Summer, and Winter seasons and deviation charges in the Fall season are higher than the ARIMA model.
{"title":"Wind Power Deviation Charge Reduction using Machine Learning","authors":"Sandhya Kumari, Sreenu Sreekumar, Sonika Singh, D. P. Kothari","doi":"10.13052/dgaej2156-3306.3912","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3912","url":null,"abstract":"High penetration of wind power plants in power systems resulted in various challenges such as frequent system imbalances due to highly uncertain and variable wind generation. Additional spinning reserves and specific balancing products such as flexible ramp products are used to handle such frequent imbalances. Incorporation of these ancillary services leads to increased total operational costs. Increased operational costs should be transferred to wind power producers as it is caused by wind power plants. This leads to penalizing the wind power producers for the deviation of power generation from forecasts, called deviation charges. These deviation charges can be reduced by improving the forecasting accuracy. Existing forecasting models show performance in terms of error matrices. Such error matrices do not indicate the financial loss associated with it. This can be overcome by expressing forecasting performance in terms of deviation charge and it will directly encourage wind power producers to improve forecasting accuracy or arrange reserves to accommodate the error. This paper proposes a backpropagation-based artificial neural network model for reducing deviation charges in this context. An analysis is conducted on the data collected from the Bonneville Power Administration (BPA) Balancing Area. Seasonal analysis (Spring, Summer, Fall, and Winter) is conducted to show the performance of the proposed model throughout the year. The proposed model performance is compared with linear regression and ARIMA models. The comparison shows that the proposed ANN model gives the least deviation charges in the Spring, Summer, and Winter seasons and deviation charges in the Fall season are higher than the ARIMA model.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"47 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103748","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 : 2023-08-29DOI: 10.13052/dgaej2156-3306.3867
Solomon Feleke, Degarege Anteneh, B. Khan, Roberto Marcelo Álvarez
The assessment of wind resource potential and feasibility is critical for generating and forecasting power generation, as wellas resource identification. In Ethiopia, the majority of the country lacks a wind atlas, making it difficult to determine the availability of sources. Seven different areas (Debre Berhan, Alem Ketema, Mehal Meda, Eneware, Gundo Meskel, Majete and Shewa Robit) were investigated. For the work data was collected from various sources and analyzed using MATLAB software. The basic sources of data that were obtained nationally were from the NMA, which is the National Metrological Agency found in Addis Ababa, and were obtained centrally from each local delegate’s registered report from a height of 2 meters and 10 meters in each listed districts above. According to the results analysis, the average wind speed at most sites is reasonable and 4 m/s at a height of 10 meters, and some of the case study sites have an average wind speed of less than 3 m/s. The extrapolation prediction method produces more realistic results at 30 and 50 meters; for example, when 10 meter is extrapolated to 30 and 50 meters, the wind power densities are 75.2 w/m2, 300.9 w/m2, and 680.5 w/m2, respectively. Similarly, the average yearly energy density for 10 meter, 30 meter, and 50 meter is 2110.8, 4122.6, and 8219.9 Kwh/m2/year, respectively. As per the international standard for wind power and wind speed classification, Eneware and Mehal Meda are categorized under class 7, whereas Debre Berhan is categorized under class 3, while the remaining sites such as Shewarobit, Gunde Meskel, Alem Ketema, and Majete are classified under class 1 for the majority of the year.
{"title":"Feasibility and Potential Assessment of Wind Resource a Case Study in North Shewa Zone, Amhara, Ethiopia","authors":"Solomon Feleke, Degarege Anteneh, B. Khan, Roberto Marcelo Álvarez","doi":"10.13052/dgaej2156-3306.3867","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3867","url":null,"abstract":"The assessment of wind resource potential and feasibility is critical for generating and forecasting power generation, as wellas resource identification. In Ethiopia, the majority of the country lacks a wind atlas, making it difficult to determine the availability of sources. Seven different areas (Debre Berhan, Alem Ketema, Mehal Meda, Eneware, Gundo Meskel, Majete and Shewa Robit) were investigated. For the work data was collected from various sources and analyzed using MATLAB software. The basic sources of data that were obtained nationally were from the NMA, which is the National Metrological Agency found in Addis Ababa, and were obtained centrally from each local delegate’s registered report from a height of 2 meters and 10 meters in each listed districts above. According to the results analysis, the average wind speed at most sites is reasonable and 4 m/s at a height of 10 meters, and some of the case study sites have an average wind speed of less than 3 m/s. The extrapolation prediction method produces more realistic results at 30 and 50 meters; for example, when 10 meter is extrapolated to 30 and 50 meters, the wind power densities are 75.2 w/m2, 300.9 w/m2, and 680.5 w/m2, respectively. Similarly, the average yearly energy density for 10 meter, 30 meter, and 50 meter is 2110.8, 4122.6, and 8219.9 Kwh/m2/year, respectively. As per the international standard for wind power and wind speed classification, Eneware and Mehal Meda are categorized under class 7, whereas Debre Berhan is categorized under class 3, while the remaining sites such as Shewarobit, Gunde Meskel, Alem Ketema, and Majete are classified under class 1 for the majority of the year.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79376982","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 : 2023-08-29DOI: 10.13052/dgaej2156-3306.3861
Na Zhang, Taozhu Feng
With the growing proportion of clean energy in integrated energy systems (IES), energy supply uncertainty and spatial-temporal dispersion are becoming increasingly prevalent. System modeling and optimal scheduling are facing greater challenges. In this paper, we improve the non-dominated sorting genetic algorithm (NSGA-II) to address the above problems and propose a two-stage multi-objective benefit-equilibrium optimization coordination of the electric-thermal coupled integrated energy system. Firstly, this paper carries out the thermodynamic characteristics analysis of the equipment components of the electro-thermal coupled energy system, which reflects the structural features of the system, the performance of each equipment under different task conditions, and the mechanism of the system; based on the above characteristic analysis, a two-stage multi-objective optimization of electro-thermal coupled system optimization coordination is proposed to establish the objective function and carry out each objective balance constraint; the NSGA-II algorithm is as well as improved. According to the operation stage, operation generation and the NSGA-II algorithm are improved by dynamically adjusting the operating parameters of evolving individuals of the operation stage, operational generation, and the number of undominated individuals in the current temporary population. By making the algorithm adaptation to improve the adaptive capacity of the evolution operator, we solve the two-step model and obtain the Pareto optimal front for each energy device. In summary, the results of the analysis of the IES under the coupling of power system and thermal system show that the constructed model and the proposed algorithm can effectively improve the accuracy of the renewable energy system and the optimization decision. The results of the research further reflect the benefits of the proposed multi-objective optimization scheme in accounting for economic, renewable energy, and complex operating constraints which ensure the economical and stable operation of the system, as well as the robustness of optimal scheduling.
{"title":"Two-stage Multi-objective Optimization Coordination of Electro-thermal Coupled Integrated Energy System Based on Improved NSGA-II Algorithm","authors":"Na Zhang, Taozhu Feng","doi":"10.13052/dgaej2156-3306.3861","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3861","url":null,"abstract":"With the growing proportion of clean energy in integrated energy systems (IES), energy supply uncertainty and spatial-temporal dispersion are becoming increasingly prevalent. System modeling and optimal scheduling are facing greater challenges. In this paper, we improve the non-dominated sorting genetic algorithm (NSGA-II) to address the above problems and propose a two-stage multi-objective benefit-equilibrium optimization coordination of the electric-thermal coupled integrated energy system. Firstly, this paper carries out the thermodynamic characteristics analysis of the equipment components of the electro-thermal coupled energy system, which reflects the structural features of the system, the performance of each equipment under different task conditions, and the mechanism of the system; based on the above characteristic analysis, a two-stage multi-objective optimization of electro-thermal coupled system optimization coordination is proposed to establish the objective function and carry out each objective balance constraint; the NSGA-II algorithm is as well as improved. According to the operation stage, operation generation and the NSGA-II algorithm are improved by dynamically adjusting the operating parameters of evolving individuals of the operation stage, operational generation, and the number of undominated individuals in the current temporary population. By making the algorithm adaptation to improve the adaptive capacity of the evolution operator, we solve the two-step model and obtain the Pareto optimal front for each energy device. In summary, the results of the analysis of the IES under the coupling of power system and thermal system show that the constructed model and the proposed algorithm can effectively improve the accuracy of the renewable energy system and the optimization decision. The results of the research further reflect the benefits of the proposed multi-objective optimization scheme in accounting for economic, renewable energy, and complex operating constraints which ensure the economical and stable operation of the system, as well as the robustness of optimal scheduling.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80412725","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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