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Abstract
The ever expanding of greenhouse gas emission and limitation of fossil energy sources are driving demand for the green energies. Among the variety of the renewable energy sources, the wind power in large scale is known as the best replacement for the conventional source of energies. However, due to the intermittency of wind power, this source without any control is not efficient neither technically nor economically. Technically, fluctuation of wind power should be smoothed while it is dispatched to the grid power to avoid system faults. On the other hand, in competitive deregulated electricity market, the financial issue concerns the economic revenue of wind power plant. It is well understood that applying storage system with proper control mechanism is required to address to technical and economical shortcomings. In this thesis, a new control strategy is presented to manage the amount of energy that is generated by wind farm plant and sold to the electricity market. Contributions of this thesis are three-fold: 1. As the battery plays a fundamental role in our control system, we addressed a novel generic battery model which reflects the effects of chemical reactions as the battery is charging, discharging and storing energy; 2. Our control method reduces the fluctuation of supplied wind power while it empowers the operator to make a balance between energy supply and demand in a profitable way using battery energy storage; 3. Lastly, we employ monotonic charging and discharging strategy in our control system to maximize the profit of WPP operator by significantly reducing the capital cost. Optimization of the overall system behavior given physical constraints using the Model Predictive Control (MPC) is one of the advantages of the proposed system. Moreover, adaptive updating of a reference signal based on system states, predicted price and wind power data helps improve the controllability of the wind farm power generation with the Battery Energy Storage System BESS into the electricity market while keeping the ramp rate of the power signal within a predefined range. Furthermore, it is depicted that battery capacity has highly affected on profit of the WPP. Therefore, choosing a suitable amount of battery for a speci_c WPP can improve the efficiency accordingly. The controller managing the Wind and battery energy storage is based on MPC theory and dynamic programming. Wind and price predictions are attached to the system for enhancing its improvement.