Study On Energy Storage System Optimization For Smoothing Up The Output Uncertainty Of New Energy Resources

Renewable energy (e.g. wind, solar, water etc.) has begun to get widespread attention because it is environmentally friendly and its reserve is abundant. However, it is also greatly influenced by natural conditions and has some serious drawbacks, such as random fluctuation and intermittence. So it is necessary to use energy storage system (ESS) to reduce the impact of fluctuations on the power grid.This thesis puts forward the optimal allocation of energy storage systems in different scenarios.Firstly, it is studied how to determine the energy capacity for an isolated microgrid and its influencing factors are analyzed. A new method is proposed in this paper to avoid the fluctuating storage level drifting in time by discarding excess power when the power generation is sufficient. The impact on the energy storage capacity of excess generation and mixing ratio of new energy sources is observed. The minimum ESS capacity is obtained through choosing a suitable mixing ratio of new energy sources by genetic algorithm (GA). Then, the energy storage capacity is selected for a grid-connected microgrid. Energy storage system is applied to smooth the power fluctuation of renewable energy generation system. The minimum power and capacity of ESS can be determined based on the spectrum analysis results, taking into account of the charge-discharge efficiency, limitations of state of charge (SOC) and the upper fluctuation rate limit of the power target output. The proposed method is verified on a small hydropower in Suichuan, Jiangxi province, based on the local typical data of water, wind, solar and load. Different sizes of the ESS are given in order to achieve the output smoothing target in scenarios of new energy output smoothing, tie line power smoothing of dry season and wet season. Additionally, the software implementation of the method mentioned above is given in the end.