Pub Date : 2025-02-01DOI: 10.1016/j.gloei.2024.11.013
Yiqiao Shen , Jing Meng , FuLong Song , Chunyang Liu , Xiaozhong Chen , Hanrun Wang
During the transitional period of electricity market reforms in China, scheduling simulations of technical virtual power plants (TVPPs) are crucial owing to the lack of operational experience. This study proposes a model for TVPPs participating in the current multi-market; that is, TVPP coordinate bidding in the day-ahead energy and ramping ancillary market while purchasing unbalanced power and providing frequency regulation service in the real-time market. A multi-scenario optimization approach was employed in the day-ahead stage to manage uncertainty, and an improved Shapley value was utilized for revenue allocation. By employing linearization techniques, the model is transformed into a mixed-integer second-order cone-programming problem that can be efficiently solved using linear solvers. Numerical simulations based on actual provincial electricity market rules were conducted to validate the effectiveness of a TVPP coalition profitability.
{"title":"A multi-market scheduling model for a technical virtual power plant coalition","authors":"Yiqiao Shen , Jing Meng , FuLong Song , Chunyang Liu , Xiaozhong Chen , Hanrun Wang","doi":"10.1016/j.gloei.2024.11.013","DOIUrl":"10.1016/j.gloei.2024.11.013","url":null,"abstract":"<div><div>During the transitional period of electricity market reforms in China, scheduling simulations of technical virtual power plants (TVPPs) are crucial owing to the lack of operational experience. This study proposes a model for TVPPs participating in the current multi-market; that is, TVPP coordinate bidding in the day-ahead energy and ramping ancillary market while purchasing unbalanced power and providing frequency regulation service in the real-time market. A multi-scenario optimization approach was employed in the day-ahead stage to manage uncertainty, and an improved Shapley value was utilized for revenue allocation. By employing linearization techniques, the model is transformed into a mixed-integer second-order cone-programming problem that can be efficiently solved using linear solvers. Numerical simulations based on actual provincial electricity market rules were conducted to validate the effectiveness of a TVPP coalition profitability.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 1","pages":"Pages 13-27"},"PeriodicalIF":1.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.gloei.2024.11.014
Yuzhe Yang , Weiye Song , Shuang Han , Jie Yan , Han Wang , Qiangsheng Dai , Xuesong Huo , Yongqian Liu
The development of wind power clusters has scaled in terms of both scale and coverage, and the impact of weather fluctuations on cluster output changes has become increasingly complex. Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting. To this end, this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm. From the perspective of causality, key meteorological forecasting factors under different cluster power fluctuation processes were screened, and refined training modeling was performed for different fluctuation processes. First, a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes. A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is proposed to screen meteorological forecasting factors under multiple types of typical fluctuation processes. Finally, a refined modeling method for a variety of different typical fluctuation processes is proposed, and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power. An example analysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55 %, which is 1.57–7.32 % higher than that of traditional methods.
{"title":"Power forecasting method of ultra-short-term wind power cluster based on the convergence cross mapping algorithm","authors":"Yuzhe Yang , Weiye Song , Shuang Han , Jie Yan , Han Wang , Qiangsheng Dai , Xuesong Huo , Yongqian Liu","doi":"10.1016/j.gloei.2024.11.014","DOIUrl":"10.1016/j.gloei.2024.11.014","url":null,"abstract":"<div><div>The development of wind power clusters has scaled in terms of both scale and coverage, and the impact of weather fluctuations on cluster output changes has become increasingly complex. Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting. To this end, this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm. From the perspective of causality, key meteorological forecasting factors under different cluster power fluctuation processes were screened, and refined training modeling was performed for different fluctuation processes. First, a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes. A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is proposed to screen meteorological forecasting factors under multiple types of typical fluctuation processes. Finally, a refined modeling method for a variety of different typical fluctuation processes is proposed, and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power. An example analysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55 %, which is 1.57–7.32 % higher than that of traditional methods.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 1","pages":"Pages 28-42"},"PeriodicalIF":1.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.010
Tiandong Ma , Feng Li , Renlong Gao , Siyu Hu , Wenwen Ma
The accurate prediction of photovoltaic (PV) power generation is an important basis for hybrid grid scheduling. With the expansion of the scale of PV power plants and the popularization of distributed PV, this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction. The proposed model, called DRAM, concatenates a dilated convolutional neural network (DCNN) module with a bidirectional long short-term memory (BiLSTM) module, and integrates an attention mechanism. First, the processed data are input into the DCNN layer, and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data. Subsequently, the temporal characteristics between the features are extracted in the BiLSTM layer. Finally, an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables. In addition, the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model. In this study, the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.
{"title":"Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy","authors":"Tiandong Ma , Feng Li , Renlong Gao , Siyu Hu , Wenwen Ma","doi":"10.1016/j.gloei.2024.11.010","DOIUrl":"10.1016/j.gloei.2024.11.010","url":null,"abstract":"<div><div>The accurate prediction of photovoltaic (PV) power generation is an important basis for hybrid grid scheduling. With the expansion of the scale of PV power plants and the popularization of distributed PV, this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction. The proposed model, called DRAM, concatenates a dilated convolutional neural network (DCNN) module with a bidirectional long short-term memory (BiLSTM) module, and integrates an attention mechanism. First, the processed data are input into the DCNN layer, and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data. Subsequently, the temporal characteristics between the features are extracted in the BiLSTM layer. Finally, an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables. In addition, the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model. In this study, the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 825-835"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.006
Yufei Wang , Jia-Wei Zhang , Kaiji Qiang , Runze Han , Xing Zhou , Chen Song , Bin Zhang , Chatchai Putson , Fouad Belhora , Hajjaji Abdelowahed
To realize carbon neutrality, there is an urgent need to develop sustainable, green energy systems (especially solar energy systems) owing to the environmental friendliness of solar energy, given the substantial greenhouse gas emissions from fossil fuel-based power sources. When it comes to the evolution of intelligent green energy systems, Internet of Things (IoT)-based green-smart photovoltaic (PV) systems have been brought into the spotlight owing to their cutting- edge sensing and data-processing technologies. This review is focused on three critical segments of IoT-based green-smart PV systems. First, the climatic parameters and sensing technologies for IoT-based PV systems under extreme weather conditions are presented. Second, the methods for processing data from smart sensors are discussed, in order to realize health monitoring of PV systems under extreme environmental conditions. Third, the smart materials applied to sensors and the insulation materials used in PV backsheets are susceptible to aging, and these materials and their aging phenomena are highlighted in this review. This review also offers new perspectives for optimizing the current international standards for green energy systems using big data from IoT-based smart sensors.
{"title":"IoT-based green-smart photovoltaic system under extreme climatic conditions for sustainable energy development","authors":"Yufei Wang , Jia-Wei Zhang , Kaiji Qiang , Runze Han , Xing Zhou , Chen Song , Bin Zhang , Chatchai Putson , Fouad Belhora , Hajjaji Abdelowahed","doi":"10.1016/j.gloei.2024.11.006","DOIUrl":"10.1016/j.gloei.2024.11.006","url":null,"abstract":"<div><div>To realize carbon neutrality, there is an urgent need to develop sustainable, green energy systems (especially solar energy systems) owing to the environmental friendliness of solar energy, given the substantial greenhouse gas emissions from fossil fuel-based power sources. When it comes to the evolution of intelligent green energy systems, Internet of Things (IoT)-based green-smart photovoltaic (PV) systems have been brought into the spotlight owing to their cutting- edge sensing and data-processing technologies. This review is focused on three critical segments of IoT-based green-smart PV systems. First, the climatic parameters and sensing technologies for IoT-based PV systems under extreme weather conditions are presented. Second, the methods for processing data from smart sensors are discussed, in order to realize health monitoring of PV systems under extreme environmental conditions. Third, the smart materials applied to sensors and the insulation materials used in PV backsheets are susceptible to aging, and these materials and their aging phenomena are highlighted in this review. This review also offers new perspectives for optimizing the current international standards for green energy systems using big data from IoT-based smart sensors.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 836-856"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.003
Mohamed M. Reda , Mohamed I. Elsayed , M.A. Moustafa Hassan , Hatem M. Seoudy
This study explores the feasibility of implementing a hybrid microgrid system powered by renewable energy sources. Including solar photovoltaics, wind energy, and fuel cells to ensure a reliable and sustainable electricity supply for the SEKEM farm in WAHAT, Egypt. The study utilizes MATLAB/Simulink software to conduct simulations based on sun irradiation and wind speed data. Various control techniques, such as the proportional-integral (PI) controller, Fuzzy Logic Controller for PI tuning (fuzzy-PI), and neuro-fuzzy controllers, were evaluated to improve the performance of the microgrid. The results demonstrate that the Fuzzy-PI control strategy outperforms the alternative control systems, enhancing the overall dependability and long-term viability of energy provision. The hybrid system was integrated with a voltage source control (VSC) and fuzzy PI controller, which effectively addressed power fluctuations and improved the stability and reliability of the energy supply. Furthermore, it provides insightful information on how to design and implement a 100% renewable energy system, with the fuzzy PI controller emerging as a viable method of control that can guarantee the system’s resilience and outperform other approaches, such as the standalone PI controller and the neuro-fuzzy controller.
{"title":"Enhancing microgrid renewable energy integration at SEKEM farm","authors":"Mohamed M. Reda , Mohamed I. Elsayed , M.A. Moustafa Hassan , Hatem M. Seoudy","doi":"10.1016/j.gloei.2024.11.003","DOIUrl":"10.1016/j.gloei.2024.11.003","url":null,"abstract":"<div><div>This study explores the feasibility of implementing a hybrid microgrid system powered by renewable energy sources. Including solar photovoltaics, wind energy, and fuel cells to ensure a reliable and sustainable electricity supply for the SEKEM farm in WAHAT, Egypt. The study utilizes MATLAB/Simulink software to conduct simulations based on sun irradiation and wind speed data. Various control techniques, such as the proportional-integral (PI) controller, Fuzzy Logic Controller for PI tuning (fuzzy-PI), and neuro-fuzzy controllers, were evaluated to improve the performance of the microgrid. The results demonstrate that the Fuzzy-PI control strategy outperforms the alternative control systems, enhancing the overall dependability and long-term viability of energy provision. The hybrid system was integrated with a voltage source control (VSC) and fuzzy PI controller, which effectively addressed power fluctuations and improved the stability and reliability of the energy supply. Furthermore, it provides insightful information on how to design and implement a 100% renewable energy system, with the fuzzy PI controller emerging as a viable method of control that can guarantee the system’s resilience and outperform other approaches, such as the standalone PI controller and the neuro-fuzzy controller.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 761-772"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.005
Weijie Wu , Dongwei Li , Hui Sun , Yixin Li , Yining Zhang
The evaluation of the electricity market is crucial for fostering market construction and development. An accurate assessment of the electricity market reveals developmental trends, identifies operational issues, and contributes to stable and healthy market growth. This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness. The evaluation framework, comprising nine indicators organized into two tiers, was constructed based on three aspects: market design, market efficiency, and developmental coordination. Furthermore, a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies. This model aimed to ensure both accuracy and comprehensiveness in market operation assessment. Subsequently, empirical analyses were conducted on four typical provincial-level electricity markets in China. The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles. Zhejiang and Shandong ranked second and third, respectively, whereas Sichuan exhibited the poorest market performance. Sichuan’s electricity market must be improved in terms of market design, such that market players can obtain a fairly competitive environment. The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study. Finally, policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.
{"title":"Fuzzy multi-criteria decision-making method-based operational assessment of Chinese electricity markets","authors":"Weijie Wu , Dongwei Li , Hui Sun , Yixin Li , Yining Zhang","doi":"10.1016/j.gloei.2024.11.005","DOIUrl":"10.1016/j.gloei.2024.11.005","url":null,"abstract":"<div><div>The evaluation of the electricity market is crucial for fostering market construction and development. An accurate assessment of the electricity market reveals developmental trends, identifies operational issues, and contributes to stable and healthy market growth. This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness. The evaluation framework, comprising nine indicators organized into two tiers, was constructed based on three aspects: market design, market efficiency, and developmental coordination. Furthermore, a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies. This model aimed to ensure both accuracy and comprehensiveness in market operation assessment. Subsequently, empirical analyses were conducted on four typical provincial-level electricity markets in China. The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles. Zhejiang and Shandong ranked second and third, respectively, whereas Sichuan exhibited the poorest market performance. Sichuan’s electricity market must be improved in terms of market design, such that market players can obtain a fairly competitive environment. The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study. Finally, policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 733-748"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.008
Xinghua Xie , Hejun Yang , Bo Wang , Yinghao Ma , Dabo Zhang , Yuming Shen
With the frequent occurrence of global warming and extreme severe weather, the transition of energy to cleaner, and with lower carbon has gradually become a consensus. Microgrids can integrate multiple energy sources and consume renewable energy locally. The amount of pollutants emitted during the operation of the microgrids become an important issue to be considered. This study proposes an optimal day-ahead scheduling strategy of microgrid considering regional pollution and potential load curtailment. First, considering the operating characteristics of microgrids in islanded and grid- connected operation modes, this study proposes a regional pollution index (RPI) to quantify the impact of pollutants emitted from microgrid on the environment, and further proposes a penalty mechanism based on the RPI to reduce the microgrid’s utilization on non-clean power supplies. Second, considering the benefits of microgrid as the operating entity, utilizing a direct load control (DLC) enables microgrid to enhance power transfer capabilities to the grid under the penalty mechanism based on RPI. Finally, an optimal day-ahead scheduling strategy which considers both the load curtailment potential of curtailable loads and RPI is proposed, and the results show that the proposed optimal day-ahead scheduling strategy can effectively inspire the curtailment potential of curtailable loads in the microgrid, reducing pollutant emissions from the microgrid.
{"title":"Optimal day-ahead scheduling strategy of microgrid considering regional pollution and potential load curtailment","authors":"Xinghua Xie , Hejun Yang , Bo Wang , Yinghao Ma , Dabo Zhang , Yuming Shen","doi":"10.1016/j.gloei.2024.11.008","DOIUrl":"10.1016/j.gloei.2024.11.008","url":null,"abstract":"<div><div>With the frequent occurrence of global warming and extreme severe weather, the transition of energy to cleaner, and with lower carbon has gradually become a consensus. Microgrids can integrate multiple energy sources and consume renewable energy locally. The amount of pollutants emitted during the operation of the microgrids become an important issue to be considered. This study proposes an optimal day-ahead scheduling strategy of microgrid considering regional pollution and potential load curtailment. First, considering the operating characteristics of microgrids in islanded and grid- connected operation modes, this study proposes a regional pollution index (RPI) to quantify the impact of pollutants emitted from microgrid on the environment, and further proposes a penalty mechanism based on the RPI to reduce the microgrid’s utilization on non-clean power supplies. Second, considering the benefits of microgrid as the operating entity, utilizing a direct load control (DLC) enables microgrid to enhance power transfer capabilities to the grid under the penalty mechanism based on RPI. Finally, an optimal day-ahead scheduling strategy which considers both the load curtailment potential of curtailable loads and RPI is proposed, and the results show that the proposed optimal day-ahead scheduling strategy can effectively inspire the curtailment potential of curtailable loads in the microgrid, reducing pollutant emissions from the microgrid.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 749-760"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.012
Hong Yang , Zhen Tang , Wei Wang , Zhihang Xue
In response to the need for robust black-start strategies in modern smart grids during blackouts, this paper proposes a two-stage black-start model that integrates wind turbines (WT), photovoltaic generators (PV), and energy storage systems (ESS). The system restoration path model in the first stage utilizes the Dijkstra algorithm to create a skeleton network and formulate a post-outage generator start-up plan. The second stage involved a load pick-up model, structured as a mixed-integer linear programming problem, aimed at restoring the load with the assistance of the ESS. This stage was designed for computational efficiency, allowing solutions to be obtained using standard commercial solvers. The performance and efficacy of the proposed model were demonstrated through its application to modified IEEE 39/118-bus transmission systems, with the results affirming its high efficiency and effectiveness in power system restoration scenarios.
{"title":"Two-stage power system restoration model","authors":"Hong Yang , Zhen Tang , Wei Wang , Zhihang Xue","doi":"10.1016/j.gloei.2024.11.012","DOIUrl":"10.1016/j.gloei.2024.11.012","url":null,"abstract":"<div><div>In response to the need for robust black-start strategies in modern smart grids during blackouts, this paper proposes a two-stage black-start model that integrates wind turbines (WT), photovoltaic generators (PV), and energy storage systems (ESS). The system restoration path model in the first stage utilizes the Dijkstra algorithm to create a skeleton network and formulate a post-outage generator start-up plan. The second stage involved a load pick-up model, structured as a mixed-integer linear programming problem, aimed at restoring the load with the assistance of the ESS. This stage was designed for computational efficiency, allowing solutions to be obtained using standard commercial solvers. The performance and efficacy of the proposed model were demonstrated through its application to modified IEEE 39/118-bus transmission systems, with the results affirming its high efficiency and effectiveness in power system restoration scenarios.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 773-785"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.gloei.2024.11.009
Zhao Luo , Chenming Dong , Xinrui Dai , Hua Wang , Guihong Bi , Xin Shen
Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets. This study considers multiple neighboring countries in the form of alliances, introduces neighboring countries’ exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory. To this end, this study uses three national agents as examples, constructs a tripartite evolutionary game model, and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value, cost of payment, and additional revenue of the alliance. This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.
{"title":"Research on decision-making behavior of multi-agent alliance in cross-border electricity market environment: an evolutionary game","authors":"Zhao Luo , Chenming Dong , Xinrui Dai , Hua Wang , Guihong Bi , Xin Shen","doi":"10.1016/j.gloei.2024.11.009","DOIUrl":"10.1016/j.gloei.2024.11.009","url":null,"abstract":"<div><div>Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets. This study considers multiple neighboring countries in the form of alliances, introduces neighboring countries’ exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory. To this end, this study uses three national agents as examples, constructs a tripartite evolutionary game model, and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value, cost of payment, and additional revenue of the alliance. This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 707-722"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional active power sharing in microgrids, achieved by the distributed average consensus, requires each controller to continuously trigger and communicate with each other, which is a wasteful use of the limited computation and communication resources of the secondary controller. To enhance the efficiency of secondary control, we developed a novel distributed self-triggered active power-sharing control strategy by introducing the signum function and a flexible linear clock. Unlike continuous communication–based controllers, the proposed self-triggered distributed controller prompts distributed generators to perform control actions and share information with their neighbors only at specific time instants monitored by the linear clock. Therefore, this approach results in a significant reduction in both the computation and communication requirements. Moreover, this design naturally avoids Zeno behavior. Furthermore, a modified triggering condition was established to achieve further reductions in computation and communication. The simulation results confirmed that the proposed control scheme achieves distributed active power sharing with very few controller triggers, thereby substantially enhancing the efficacy of secondary control in MGs.
{"title":"Flexible linear clock–based distributed self-triggered active power-sharing secondary control of AC microgrids","authors":"Yulin Chen , Xing Huang , Guangxin Zhi , Shaohua Yang , Hongxun Hui , Donglian Qi , Yunfeng Yan , Fengkai Gao","doi":"10.1016/j.gloei.2024.11.004","DOIUrl":"10.1016/j.gloei.2024.11.004","url":null,"abstract":"<div><div>Traditional active power sharing in microgrids, achieved by the distributed average consensus, requires each controller to continuously trigger and communicate with each other, which is a wasteful use of the limited computation and communication resources of the secondary controller. To enhance the efficiency of secondary control, we developed a novel distributed self-triggered active power-sharing control strategy by introducing the signum function and a flexible linear clock. Unlike continuous communication–based controllers, the proposed self-triggered distributed controller prompts distributed generators to perform control actions and share information with their neighbors only at specific time instants monitored by the linear clock. Therefore, this approach results in a significant reduction in both the computation and communication requirements. Moreover, this design naturally avoids Zeno behavior. Furthermore, a modified triggering condition was established to achieve further reductions in computation and communication. The simulation results confirmed that the proposed control scheme achieves distributed active power sharing with very few controller triggers, thereby substantially enhancing the efficacy of secondary control in MGs.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 6","pages":"Pages 786-797"},"PeriodicalIF":1.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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