In recent years, a large number of renewable energy sources (RESs) have been added into modern distribution systems because of their clean and renewable property. Nevertheless, the high penetration of RESs and intermittent nature of some resources such as wind power and photovoltaic (PV) cause the variable generation and uncertainty of power system. In this condition, one idea to solve problems due to the variable output of these resources is to aggregate them together. A collection of distributed generations (DGs) such as wind turbine (WT), PV panel, fuel cell (FC), and any other sources of power, energy storage systems, and controllable loads that are aggregated together and are managed by an energy management system (EMS) are called a virtual power plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost for a 24-h period. To solve the problem, a metaheuristic optimization algorithm, teaching–learning based optimization (TLBO), is proposed to determine optimal management of RESs, storage battery, and load control in a real case study.
Optimal Operational Scheduling of Renewable Energy Sources Using Teaching–Learning Based Optimization Algorithm by Virtual Power Plant
Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 30, 2017; final manuscript received June 9, 2017; published online August 16, 2017. Assoc. Editor: Esmail M. A. Mokheimer.
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Javad Kasaei, M., Gandomkar, M., and Nikoukar, J. (August 16, 2017). "Optimal Operational Scheduling of Renewable Energy Sources Using Teaching–Learning Based Optimization Algorithm by Virtual Power Plant." ASME. J. Energy Resour. Technol. November 2017; 139(6): 062003. https://doi.org/10.1115/1.4037371
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