Research On Load Frequency Control Of Hybrid Energy System With Energy Storage System

In recent years,for the sake of solving the increasingly prominent environmental problems,hybrid energy systems such as wind-fire complementary power generation systems and light-water complementary power generation systems have been rapidly developed.However,due to the strong volatility of renewable energy,with the large-scale renewable energy access to the power grid,its penetration rate in the grid continues to increase,resulting in the weakening of the frequency regulation ability of traditional energy.In order to solve such adverse effects,it is necessary to introduce energy storage equipment and new frequency regulation strategies to ensure the effective application of new energy.Firstly,this thesis studies the application of wind turbines on the load frequency control（LFC）of the grid.The primary frequency modulation and secondary frequency modulation of the traditional energy power generation system are discussed,and the model of the two-area reheat steam turbine connected to the wind turbine is built based on the mathematical transfer function.At the same time,the regional frequency control before and after the introduction of the wind turbine is simulated and compared.The results show that the participation of wind turbines in grid frequency regulation speeds up the regulation speed of the system and improves the frequency control performance to a certain extent.Secondly,based on the LFC model of the hybrid energy system,this thesis proposes a data-driven LFC method based on the model.A reward function including control performance standard（CPS）and dynamic performance index is constructed,the LFC problem is transformed into a maximum reward function problem,and deep deterministic policy gradient algorithm is introduced to solve it.Finally,the medium and long-term control performance is analyzed by adding continuous stepped disturbance or actual wind speed disturbance.The results show that when the system is disturbed,the proposed deep deterministic policy gradient algorithm can not only suppress the fluctuation more effectively,but also can greatly shorten the adjustment time required to complete the LFC.Finally,aiming at the limitation of the LFC strategy,electrochemical energy storage is introduced as an auxiliary frequency modulation method in this thesis.The mathematical model of energy storage power supply in the form of transfer function is built,and an LFC method based on the deep reinforcement learning algorithm is proposed for the interconnected power grid of wind,fire and storage systems.At the same time,a multi-objective optimal scheduling model is proposed with the objectives of minimum of CPS index and waste air volume and the maximum of adjustable energy storage margin.The weighted sum method and the improved entropy-weighted technique for order preference by similarity to ideal solution（TOPSIS）are used to carry out the multi-objective optimization problems.The simulation results show that the introduction of electrochemical energy storage can significantly improve the LFC effects of the power grid.At the same time,the comprehensive optimal solution that obtained has a higher overall benefit by introducing multi-objective optimization,which can not only meet the operating conditions of the power grid,but also take into account the subjective wishes of the staff.