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| Hybrid Energy Storage Control with SOC Self-Recovery to Smooth Out Wind Power Fluctuations |
| Lin Li1, Lin Yulu1, Tan Huidan1, Jia Yuanqi1,2, Kong Xianyu1, Cao Yapei1 |
1. Xuefeng Mountain Energy Equipment Safety National Observation and Research Station Chongqing University Chongqing 400044 China;
2. State Grid Chongqing Electric Power Company Shinan Power Supply Branch Chongqing 400060 China |
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Abstract The increase in penetration of new energy sources such as wind power poses a huge threat to the security and stability of the power grid operation. This paper used a hybrid energy storage system with a battery and supercapacitor to cope with the complex fluctuations of wind power. To achieve the smoothing of grid-connected power fluctuations while reducing the lifetime losses of the storage system, this paper proposed a hybrid energy storage control method taking into account the state of charge (SOC) self-recovery to smooth out wind power fluctuations, including model predictive control (MPC) to predict the target power of hybrid storages and weighted moving average (WMA) method with fuzzy control to allocate target power.
This paper first established the MPC optimization target function combining SOC recovery for energy storage systems and grid-connected power fluctuation smoothing capability. To characterize the relationship between the SOC and the charge and discharge margin of the energy storage system, a charge and discharge saturation capacity function was proposed. This function was also introduced as a weighting factor into the MPC optimization target function, achieving rapid adjustment of the SOC during power smoothing and improving the long-term stable operation capability of energy storage. An improved WMA method was then proposed to distribute the MPC-predicted target power between supercapacitor and battery power. Considering the small capacity of supercapacitor which is easy to overcharge and over-discharge, the SOC of the supercapacitor at the previous moment and the change of SOC of future hybrid energy storage were taken as fuzzy control inputs. According to the different operating conditions, the fuzzy control rules were designed to dynamically adjust the WMA sliding window length d and the α weighted of past and future reference values, thus improving the adaptability of the battery and supercapacitor to different target power and different SOC.
A wind-storage joint model was developed in Matlab, and the simulation compared the control effects of three methods of optimization targets. Method 1 is SOC closed to 0.5 at real-time. Method 2 is minimizing the fluctuation rates of grid-connected power. Method 3 is the optimization method for this paper. The simulation results show that the fluctuation rates of grid-connected power are less than 2% by method 3, in which 43.4% of the fluctuation rates are below 0.2%, and the average fluctuation rate is 0.61%, which is between the other two methods. The total charge and discharge energy of the hybrid energy storage is 28.93, which is much lower than the 47.67 of method 2. The average charge and discharge margin of 0.948 6 is close to 0.978 7 for method 1, but much higher than 0.591 4 for method 2. For different initial SOC, the SOC can gradually recover to around 0.5 and eventually follow the change of the control group with the initial value of SOC=0.5. Simulations were then carried out to verify the power allocation strategy with improved WMA-fuzzy control. The results show that the power variation of the battery is relatively gentle compared to that of the supercapacitor, and only varies greatly in the time of 240 min to 480 min and 720 min to 960 min when the power demand is high.
The simulation analysis leads to the following conclusions. Firstly, the proposed model predictive control for the target power of hybrid energy storage can effectively smooth out wind power fluctuations, and also effectively optimize the operation interval of SOC and reduce the lifetime loss of energy storage. Secondly, the designed allocation strategy adaptively adjusts the distribution of energy storage power according to the power demand. This strategy reduces the depth of discharge and the charge-to-discharge transition state of the battery at low levels of power output, thus reducing the loss of life. In contrast, increasing the battery output when the power demand is high. It reduces the pressure on the supercapacitor and improves the rationality of the power distribution.
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Received: 18 October 2022
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2024, Vol. 39 
