Variability analysis of wind and solar energy for optimal power system integration

T Technology (General)

Yuan, Qiheng ; Yang, Jin

University:University of Glasgow

Department:School of Engineering

关键词: Renewable energy integration, Standalone wind and solar power systems, Grid-connected wind and solar power systems, Optimal sizing, Optimal penetration interval, Variability analysis, Variability costs, Big data.;   

Others  :  http://theses.gla.ac.uk/81387/13/2020YuanQihengPhD.pdf

来源: University of Glasgow

【 摘 要 】

The development of renewable energy has made a significant contribution to the mitigation of global climate change and environmental pollution. In particular, the installed capacity of intermittent wind and solar power in the world has increased significantly in the past decade, and this growth is expected to be maintained in the future. Due to the intermittence and uncontrollability of wind and solar energy, the integration of wind and solar energy into power systems brings significant impacts on the operation and profit of power systems. This thesis focuses on exploring the wind and solar power variability and its impacts on power system integration. Chapter 2 proposes a new measure to assess the variability of wind power, solar power and mixed wind-solar at one site, and the variability of interconnected wind and solar power from different sites in both the time domain and frequency domain. In the time domain, the measure mainly includes inter-annual variation, smoothness coefficient and correlation coefficient; while in the frequency domain, it mainly includes frequency spectrum analysis, fluctuation rate, and cumulative energy distribution index. The implications of the proposed measure are explored to facilitate power system integration. Without loss of generality, enormous wind and solar data collected at various locations and spanning a long period are employed to assess the variability of wind and solar power, which are taken from National Renewable Energy Laboratory (NREL) databases. The measurement results indicate that the variability of solar power highly depends on the latitude of its geographic location; the interconnection of wind power can effectively reduce the variability of wind power in the high-frequency range; the intermittent wind/solar power in the time domain can be treated as a Quasi-Time-Invariant (QTI) source of power harmonics in the frequency domain. Based on the proposed variability measure, Chapter 3 investigates the impacts of the wind and solar power variability on the sizing of the standalone wind/solar power systems. Taking the impacts of wind and solar power variability into consideration, big data simulations of the six Satandalone Wind Power (SAWP) and six Standalone Photovoltaic power (SAPVP) systems with the same residential load demand at the six sites were carried out to reveal the dependency between the sizing of the system components (i.e., the battery and the wind turbines/PV panels) and the power supply reliability. Case studies of optimal sizing of the SAWP system at Chicago and optimal sizing of the SAPVP system at Houston were carried out to demonstrate the feasibility of the proposed methods, which aims is to minimize the system cost while satisfying the requirement of power supply reliability.The chapter 4 attempts to employ the cumulative energy distribution index to evaluate the variability costs for the integration of high penetration level wind/solar power into power grids. Big data simulations of the Electric Reliability Council of Texas power system (ERCOT) in 2018 reveal the impacts of grid flexibility on wind/solar energy curtailment rate and capacity factor at different penetrations. The maximum wind/solar energy penetration can be roughly determined according to the requirements of the wind/solar power capacity factor and energy curtailment of the power systems with specific flexibility. A case study of 70% grid flexibility with 20 wind farms and 10 solar plants interconnected ERCOT power system shows that the developed large time scale variability costs index can be used to estimate the variability cost when wind and solar energy penetration is between 30% to the maximum penetration.