Author : Owana Marzia Moushi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Book Synopsis Optimizing Behavior of Energy Storage Systems to Improve the Performance of Power Systems by : Owana Marzia Moushi
Download or read book Optimizing Behavior of Energy Storage Systems to Improve the Performance of Power Systems written by Owana Marzia Moushi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis utilizes nonlinear optimization (also known as Optimal Power Flow in the context of power systems) to determine a best behavior of Energy Storage Systems to improve performance of power systems with inherent variability including that from sources of Renewable Energy. The outcome of the optimization for chosen objectives is not only the response of the Energy Storage Systems, but also an ability to determine preferred locations and capacities for the Energy Storage Systems. Models of two power systems are considered; one power system is at the transmission-level with wind generation and loads that assume values from recorded data and the other power system is at the distribution-level with photovoltaic generation and loads that assume values from recorded data. Equality constraints for the nonlinear optimization are the average and reactive power balance equations at each bus in the power systems and the difference equations that capture the relationship between power and energy for the Energy Storage Systems.The transmission-level power system has five buses, conventional generation, wind generation and loads, and Energy Storage Systems that were added to achieve each one of three objectives considered. The three objectives were a reduction in the conventional generator’s output power, smoothing of the conventional generator’s output power to lessen ramping, and a reduction of losses in the transmission lines. Time series of recorded data for wind generation and loads were taken as known inputs at every thirty minutes and the nonlinear optimization generated time series for the decision variables. The daily energy demand of the loads was 160 p.u.hr and the peak load was 7 p.u. The daily energy supplied from wind generation was 100.45 p.u.hr and the maximum power output was 6 p.u. The capacity of all Energy Storage Systems was taken to be 5% of daily energy demand, which is a total combined capacity of 8 p.u.hr.