In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of Ch...In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.展开更多
The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service.To fulfill the automatic generatio...The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service.To fulfill the automatic generation control(AGC)and compensate the influence of wind power fluctuations simultaneously,ramping capacity should be considered in the dispatch model of thermals.Meanwhile,conventional methods in this area do not take the impact of transmission loss into the dispatch model,or rely on offline network model and parameters,failing to reflect the real relationships between the wind farms and thermal generators.This paper proposes an online approach for AGC dispatch units considering the above issues.Firstly,the power loss sensitivity is online identified using recursive least square method based on the real-time data of phasor measurement units.It sets up power balance constraint and results in a more accurate dispatch model.Then,an improved multi-objective optimization model of dispatch is proposed and a connection is established between the thermal units with fast ramping capacity and the wind farms with rapid fluctuations.Genetic algorithm is used to solve the dispatch model.The proposed method is compared with conventional methods in simulation case in the IEEE 30-bus system.Finally,simulation results verify the validity and the feasibility of identification method and optimization model.展开更多
基金supported by the National Key Research and Development Program of China under the sub-theme“Research on the Path of Enhancing the Sustainable Development Capacity of Cities and Towns under the Carbon Neutral Goal”[Grant No.2022YFC3802902-04].
文摘In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.
基金This work is supported in part by Major State Basic Research Development Program of China(No.2012CB215206)National Natural Science Foundation of China(No.51107061).
文摘The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service.To fulfill the automatic generation control(AGC)and compensate the influence of wind power fluctuations simultaneously,ramping capacity should be considered in the dispatch model of thermals.Meanwhile,conventional methods in this area do not take the impact of transmission loss into the dispatch model,or rely on offline network model and parameters,failing to reflect the real relationships between the wind farms and thermal generators.This paper proposes an online approach for AGC dispatch units considering the above issues.Firstly,the power loss sensitivity is online identified using recursive least square method based on the real-time data of phasor measurement units.It sets up power balance constraint and results in a more accurate dispatch model.Then,an improved multi-objective optimization model of dispatch is proposed and a connection is established between the thermal units with fast ramping capacity and the wind farms with rapid fluctuations.Genetic algorithm is used to solve the dispatch model.The proposed method is compared with conventional methods in simulation case in the IEEE 30-bus system.Finally,simulation results verify the validity and the feasibility of identification method and optimization model.
