Electrical and Computer Engineering ETDs

Author

Feng Cheng

Publication Date

7-2-2012

Abstract

Renewable resources are becoming more and more obtainable and affordable due to the development of technology and enactment of government policies. The output from solar power aligns reasonably well with daytime consumption on the electricity grid, reducing the need for new fossil power stations. Industry experts estimate about 5,000 MW of photovoltaic (PV) will connect to the power grid in only 10 years. However, the technical issue about PV is its variability due to weather conditions especially cloud cover. If the PV power is injected into a power system directly on a large scale, it may produce issues in power quality, reliability and stability. It is desirable to select a smoothing algorithm that would filter out the highest frequency intermittency, but would still be fast enough to avoid significant lag with respect to current power production. Usually a moving average algorithm was used. For the alternative methods, the author has tested two other algorithms: moving median algorithm and double moving average algorithm. The key parameters for these three algorithms are analysed. The results are compared, and show that the two alternate algorithms have merits in saving capacity and improving smoothness. Meanwhile, in a power system, the production of electricity should match its consumption. Since the load is not constant for the whole day, many backup power plants only work at peak load times. If utility companies could store power for peak load times, they could eliminate a considerable investment for the backup and peaking plants. Using batteries can save investments through shifting stored energy from a time when the load is low to a peak load time. The shifting algorithm is introduced and the economic cost saving analysis is given.

Keywords

Photovoltaic power systems--Management, Storage batteries--Management, Distributed generation of electric power--Management, Electric power system stability--Management.

Document Type

Thesis

Language

English

Degree Name

Electrical Engineering

Level of Degree

Masters

Department Name

Electrical and Computer Engineering

First Advisor

Lavrova, Olga

Second Advisor

Mammoli, Andrea

First Committee Member (Chair)

Lavrova, Olga

Second Committee Member

Mammoli, Andrea

Third Committee Member

Graham, Edward D. Jr.

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