EVALUATING THE RELIABILITY CONTRIBUTION OF PHOTOVOLTAICS IN ELECTRIC POWER SYSTEMS
The utilization of renewable energy sources in electric power systems has increased considerably in recent years due to global environmental concerns and the acceleration in energy costs associated with the use of conventional energy sources. Solar power is widely acknowledged as a cost-effective source of energy with financial support from government and private organizations. Due to the intermittent nature of the solar irradiation at system locations, solar power has a varied impact on generating system reliability when compared to conventional power sources. It is therefore, important to assess the impact of adding photovoltaic (PV) sources to an electric power system in terms of their reliability contribution to meeting energy demands. Two test systems consisting of conventional and PV generation, and representative load model, are utilized in this thesis to examine the adequacy of the overall generation system and to determine the capacity value of PV generation. A probabilistic technique using analytical methods was employed and different studies were conducted taking into consideration peak load variations, installed PV capacity, geography and weather factors. PV generation produces most of its power during summertime, less in spring and fall, little in winter, and zero at night. Power output ranges from high to low as the geographic location moves from 0º to 50º latitude with lower power levels in cloudy areas. It is therefore important to develop methods that can incorporate the impacts of location and weather factors in evaluating the system adequacy and the capacity value of PV generation. The results presented in this thesis illustrate the ability to perform quantitative analyses on integrated system reliability and the capacity contribution of solar power located at different latitudes around the world.
Reliability-Loss of load expatiation-Loss of energy expectation-Photovoltaic-Capacity Credit
Master of Science (M.Sc.)
Electrical and Computer Engineering