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Optimized coordination of mixed-energy power sources in the electricity market
Project (M.S., Electrical and Electronic Engineering)--California State University, Sacramento, 2019.
Statement of Problem: Integration of renewable energy sources (RES) with the power system has been of great interest over the past few decades. However, there are technical concerns about reliability and stability of the grid, as penetration of the RES increases. One solution is to manage a combination of conventional synchronous generators, RES and storage units to minimize schedule deviation between predicted and real-time operation, hence enhancing the reliability of the grid. In order to exploit RES to its full extent, optimization methods may be applied to minimize fossil fuel consumption and to reduce the cost of generating electricity, while taking into account physical limitations on the generation units. This project investigates optimized operation of a mixed-energy power plant consisting of renewable resources of energy, gas turbine generators, and battery storage units. Assuming that the total demand and RES generation are both forecasted and available within a known period of time, the difference between the two needs to be shared between gas turbine generators and battery storage units. To ease the computational burden, several gas turbine generators are aggregated to form a bulk synchronous generation unit. Then a series of inequality and equality constraints for the units are written, and an objective function is defined to minimize gas turbine generation. After share of the bulk unit and each battery energy storage units is found, a unit commitment study for individual gas turbine generators is performed. Sources of Data: To solve the optimization problem, SCADA data of CAISO in July 2014 has been adopted. Furthermore, for the sake of running the MATLAB code, an example based on characteristics of the power plant described in (1) has been used. Conclusions Reached: The results of the study show that optimized operation of the mixed-energy power plant results in significantly less schedule deviation compared to when non-optimized, reactive methods are used.