Another important factor affecting the aging of lithium-ion batteries is the state of charge of solar cells, which refers to the operating range of their state of charge during operation and the level of charge they are at when they are idle. When a renewable energy grid connected system containing renewable energy is connected to the grid, scheduling strategies adjust the operation of the entire renewable energy grid connected system to deal with the volatility and uncertainty of renewable energy, improve the controllability of renewable energy, and thus help to increase the penetration rate of renewable energy in the energy system and promote energy transformation. The high cost of lithium-ion batteries has always been one of the important factors hindering grid operators from promoting this renewable energy utilization method. In fact, grid connected scheduling strategies can read and regulate the state of charge of solar energy storage systems. A reasonable scheduling strategy can keep lithium-ion batteries in a favorable and healthy state of charge for a long time, effectively slowing down the aging of lithium-ion batteries, extending the lifespan of lithium-ion battery energy storage systems, and improving the overall economy of the system.
In recent years, in response to the call for energy transformation, domestic and foreign researchers have conducted extensive research on grid connection scheduling methods and optimization. At present, common grid connection scheduling methods include low-pass filtering, spectral analysis, average power method, and minimum to maximum method.
(1) Low pass filtering method
As early as 2006, Japanese researchers proposed a control system called “charging state feedback control” to reduce the impact of intermittent power output from wind power generation on its national grid. The system generates a dispatch power signal through a first order lag filter, Using solar cell energy storage systems to smooth the power output of wind farms. Later, Teleke et al. proposed a control strategy applicable to wind and photovoltaic renewable energy grid connected systems, which essentially processes wind and photovoltaic signals using the same principles as filters.
Around 2013, Jiang et al. proposed a two-layer control strategy and Li et al. proposed a three-layer control strategy, one of which used a first-order low-pass filter. Nguyen et al. also studied the low-pass filter control method, A zero phase low-pass filter was also proposed to reduce signal hysteresis. Deng et al. optimized the control method based on low-pass filters and proposed a finite time convergence robust control algorithm for solar cell energy storage systems, To deal with fluctuations in wind power generation.
Taking photovoltaic power generation as an example, the structure of a photovoltaic renewable energy grid connected system controlled by a low-pass filter method is shown in Figure 1.
Among them, the photovoltaic power generation and the power of the solar energy storage system regulated by the power conversion system are connected to the power grid through a common coupling point. When the photovoltaic power generation system is in operation, the low-pass filter receives its power signal and processes it to obtain a dispatch power signal that meets the grid connection requirements. After this signal is transmitted to the power conversion system, the power conversion system regulates the power of the solar cell energy storage system. Finally, The final power sent to the grid by the entire system is the value of the grid connected dispatch power signal emitted by the low-pass filter.
(2) Spectral analysis method
Spectral analysis is mainly used to decompose the power that needs to be smoothed into different frequency bands, and then determine the maximum charging and discharging power of the solar cell energy storage system based on the compensation frequency band. This method has been widely used to predict power generation, power load, and current, as well as to match the charging needs of renewable energy and electric vehicles. In recent years, this method has been used to handle the output fluctuations of renewable energy, and there are three main types of spectral analysis methods: Fourier transform (FT) Wavelet transform (WT) and Hilbert Huang transform (HHT), Figure 2 shows the structure of a solar cell energy storage renewable energy grid connected system controlled by a wavelet transform method in spectral analysis.
Similar to the low-pass filter method, the online wavelet collaborative controller receives the photovoltaic power signal and decomposes it into high-frequency and low-frequency components through several iterations according to the grid connection requirements. Then, these two signals are respectively sent to the power conversion system that controls the supercapacitor energy storage system and the solar cell energy storage system, The power of the supercapacitor energy storage system regulated by the power conversion system, the power of the solar cell energy storage system, and the power of the photovoltaic power generation system are sent to the grid through a common coupling point. This renewable energy grid connected system uses two types of solar cell energy storage systems: supercapacitors (with ultra-high energy density, especially suitable for handling ultra short term power fluctuations with a time scale of a few seconds to one minute, high price) and batteries (relatively low price, can be used to handle power fluctuations with longer time scales).
Overall, the low-pass filter method and spectral analysis method are both based on grid connection requirements. By real-time processing of renewable energy generation power signals, the scheduling power is obtained. The advantage of these two methods is that they do not need to predict the amount of electricity generated from renewable sources, making them easy to apply. However, according to modern electricity market rules, all generator units must submit their output power plan to the grid operator several hours in advance, and roll over the ultra short term power generation plan for the next scheduling interval at each scheduling interval, and proceed with production according to the established production plan. Therefore, the disadvantage of low-pass filter method and spectral analysis method is that they do not consider cooperation with grid operators, making it difficult for grid operators to achieve efficient scheduling of renewable energy power that has already been connected to the grid. Additionally, low-pass filter method and spectral analysis method also have the disadvantages of phase delay and calculation delay, respectively.
(3) Average power method
The average power method is based on the prediction of renewable energy generation power, setting the planned generation power for each scheduling interval as the average of the predicted generation power for that interval, thereby achieving efficient cooperation with grid operators. For example, The scheduling strategy proposed by Wee et al. for the wind power supercapacitor battery renewable energy grid connected system, Nguyen et al. for the wind power dual battery renewable energy grid connected system, and Gholami et al. for a certain wind power pool renewable energy grid connected system with multiple backup batteries The value of planned power generation for a certain period of time is set as the average predicted power generation during that time interval.
This method has two main drawbacks:
One is the need to predict power. Although some researchers claim that with the help of machine learning, the accuracy of short-term photovoltaic power prediction based on numerical weather forecast information has exceeded 97%, and the accuracy of wind power generation power prediction has exceeded 93%. However, unavoidable prediction errors often lead to solar cell energy storage systems being unable to operate as planned, Not conducive to the lifespan of solar energy storage systems;
Secondly, when using the average power method to determine the scheduling power, it is often necessary to use a solar cell energy storage system with multiple independent energy storage units, which increases the energy storage cost. If a single energy storage unit is used, taking the battery as an example, the charging and discharging states of the battery need to be frequently switched during each scheduling interval, which can damage the health of the battery.
(4) Minimum to maximum method
In order to ensure the charging and discharging depth of the battery and fully unleash its energy storage potential, Li et al. first proposed the minimum maximum method in 2011. This method is based on the prediction of power generation and the capacity of the solar cell energy storage system, with scheduling power set at intervals of the charging and discharging duration of the solar cell energy storage system. When the solar energy storage system is in a discharge state, the scheduling power is set to the maximum predicted value of the power generation throughout the entire discharge state duration. When the solar energy storage system is in a charging state, the scheduling power is set to the minimum predicted value of the power generation throughout the entire charging state duration. The advantage of this method is that it ensures the charging and discharging depth of the solar cell energy storage system, which can maximize the battery life. However, its disadvantage is also obvious, that the charging and discharging interval of the battery will change according to the power prediction curve, often inconsistent with the scheduling interval. Therefore, its cooperation with grid operators is not as efficient as the average power method.
To ensure efficient cooperation with grid operators, in 2013, Nguyen et al. improved the minimum to maximum method proposed by Li et al., using the scheduling interval specified by the grid operator as a benchmark, and specifying that the solar cell energy storage system only charges or discharges one type of energy in each scheduling interval, consistent with the original method, When the solar cell energy storage system is discharged, the scheduling power is set as the maximum predicted value of the power generation in the scheduling interval. When the solar cell energy storage system is charged, the scheduling power is set as the economic efficiency of the solar cell energy storage system in the scheduling interval, which is slightly lower than the minimum maximum method improved by Nguyen et al. In 2015, Fathima et al. also improved the minimum maximum method and applied it to the photovoltaic wind power battery renewable energy grid connection system, achieving good scheduling and economic benefits.