Paper
10 April 2013 Power fluctuation reduction methodology for the grid-connected renewable power systems
Author Affiliations +
Abstract
This paper presents a new methodology for eliminating the influence of the power fluctuations of the renewable power systems. The renewable energy, which is to be considered an uncertain and uncontrollable resource, can only provide irregular electrical power to the power grid. This irregularity creates fluctuations of the generated power from the renewable power systems. These fluctuations cause instability to the power system and influence the operation of conventional power plants. Overall, the power system is vulnerable to collapse if necessary actions are not taken to reduce the impact of these fluctuations. This methodology aims at reducing these fluctuations and makes the generated power capability for covering the power consumption. This requires a prediction tool for estimating the generated power in advance to provide the range and the time of occurrence of the fluctuations. Since most of the renewable energies are weather based, as a result a weather forecast technique will be used for predicting the generated power. The reduction of the fluctuation also requires stabilizing facilities to maintain the output power at a desired level. In this study, a wind farm and a photovoltaic array as renewable power systems and a pumped-storage and batteries as stabilizing facilities are used, since they are best suitable for compensating the fluctuations of these types of power suppliers. As an illustrative example, a model of wind and photovoltaic power systems with battery energy and pumped hydro storage facilities for power fluctuation reduction is included, and its power fluctuation reduction is verified through simulation.
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Fadhil T. Aula and Samuel C. Lee "Power fluctuation reduction methodology for the grid-connected renewable power systems", Proc. SPIE 8688, Active and Passive Smart Structures and Integrated Systems 2013, 86881W (10 April 2013); https://doi.org/10.1117/12.2012111
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KEYWORDS
Solar energy

Photovoltaics

Renewable energy

Systems modeling

Data modeling

Computing systems

System on a chip

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