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Optimal Dispatching of Power System by Introducing Concentrating Solar Power Station to Promote Large-scale Wind Power and Photovoltaic Accommodation

[ Vol. 14 , Issue. 4 ]

Author(s):

Xia Zhou*, Yichen Li, Ping Chang, Jianfeng Dai and Yi Tang   Pages 484 - 492 ( 9 )

Abstract:


Background: With respect to the problem of wind power and photovoltaic (PV) gridconnected accommodation, a power system optimization dispatching method is proposed that introduces a concentrating solar power (CSP) station to promote wind power and PV accommodation, considering the dispatch-ability of the thermal storage system (TSS) and the accommodation capacity of the electric heating device (EHD) in the CSP station.

Methods: The method analyzes an internal simplified model of a CSP station and a short-timescale identification method of wind and PV power ramping events based on a CSP-Wind-PV system structure. Furthermore, the operating characteristics and constraints of various units are considered with the goal of the lowest comprehensive system cost to establish a CSP-Wind-PV optimization model for dispatching power system.

Results: The method is simulated and verified on the IEEE-RTS24 node system by using the MATLAB optimization toolbox.

Conclusion: The results demonstrate that the participation of a CSP station can decrease the number of power ramping events and reduce the curtailment of wind and PV power while ensuring the economical operation of the power system.

Keywords:

Concentrating solar power (CSP) station, wind power and photovoltaic (PV) accommodation, power ramping event, CSP-Wind-PV optimization model.

Affiliation:

Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, School of Electrical Engineering, Southeast University, Nanjing, School of Electrical Engineering, Southeast University, Nanjing, School of Electrical Engineering, Southeast University, Nanjing



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