Research On Real-time Control Strategy And Capacity Determination For Hybrid Energy Storage In Mitigating Wind Power Fluctuations

With the development of wind power generation technology and renewable energy utilization, total installed capacity of wind turbines around the world, as well as the penetration of wind power, is increasing. However, wind power could bring remarkable influence to the power quality of main grid, and challenge its scheduling and planning at the same time, due to the intermittency and uncertainty of wind power. Therefore, many countries have set up standards to guide the connections of wind power to main grid. In this paper, the hybrid energy storage system (HESS) composed of energy storage battery and super capacitor is used as the power compensation device to mitigate the wind power fluctuations and make it meet the fluctuation requirements in any1-min and30-min time window, respectively. Main research work is illustrated as follows:1. With the monthly average wind speed, the synthesis method of generating super-short-term wind speed is proposed. Firstly, the monthly average speed is applied to synthesize10-minute average wind speed based on the Wei-bull distribution and diurnal pattern in a day. Then, with10-minute average wind speed, the wind speed in the time-scale of1second is produced according to the model of turbulence and Vor-karman power spectrum.2. A real-time smoothing strategy based on the self-adaptive filtering algorithm is presented. First, the expected HESS output power to make the wind power connected to the main grid satisfy the fluctuation requirements is determined by the low-pass filtering algorithm where the filtering constant is self-adapting according to the relationship between the two time-scale power fluctuations. Secondly, since the battery has large energy density and long lasting time of charge and discharge, and on the contrary the super capacitor has large power density and rapid transition between charge and discharge, the low-pass filtering algorithm is performed where the filtering constant is self-tuning by considering the state of charging (SOC) and switching frequency between charge and discharge of the battery, to coordinately assign the fluctuations with low frequency to the battery and those with high frequency to the super capacitor.3. An approach for determining the HESS capacity is proposed to make it continuously smooth the wind power fluctuations, in which the real-time smoothing strategy is integrated. For a typical day, HESS capacity optimization model is established, with the objective of minimizing the weighted sum of the SOC change at the beginning and ending of the day and the HESS capacity, and the constraints of battery and super-capacitor SOC are considered. The optimization is solved by the improved particle swarm optimization (PSO) algorithm with the integrated real-time smoothing strategy.