Energy Storage System Event-Driven Frequency Control Using Neural Networks to Comply with Frequency Grid Code

Abstract

As the penetration of renewable energy sources (RESs) increases, the rate of conventional generators and the power system inertia are reduced accordingly, resulting in frequency-stability concerns. As one of the solutions, the battery-type energy storage system (ESS), which can rapidly charge and discharge energy, is utilized for frequency regulation. Typically, it is based on response-driven frequency control (RDFC), which adjusts its output according to the measured frequency. In contrast, event-driven frequency control (EDFC) involves a determined frequency support scheme corresponding to a particular event. EDFC has the advantage that control action is promptly performed compared to RDFC. This study proposes an ESS EDFC strategy that involves estimating the required operating point of the ESS according to a specific disturbance through neural-network training. When a disturbance occurs, the neural networks can estimate the proper magnitude and duration of the ESS output to comply with the frequency grid code. A simulation to validate the proposed control method was performed for an IEEE 39 bus system. The simulation results indicate that a neural-network estimation offers sufficient accuracy for practical use, and frequency response can be adjusted as intended by the system operator.

Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments

“…A comparison of several methods for frequency control with auxiliary devices is summarized in Table 2. Due to the nonlinearity of the system frequency characteristics, it is hard to procure reliable information [91,93] BSS i. Battery storage is utilized to provide inertia and primary frequency regulation ii.…”

The Influence of Power Network Disturbances on Short Delayed Estimation of Fundamental Frequency Based on IpDFT Method with GMSD Windows

“…The return to very low estimation errors after a low-frequency pulse is much faster for the IpDFT method than for the ZC method (Figure 17d,e). The pulse modeled by (5) results in larger estimation errors than that modeled by (4), even though the condition of Shannon's theorem on sampling frequency is satisfied, because the energy contributed by pulse ( 5) is much larger than that by pulse (4). Similar to the study in Section 5.3, it is clear from the performance of the ZC method that it is not possible to apply this method to low-frequency pulse disturbances without low-pass filtering at the input of the method.…”

“…Monitoring the frequency of voltage waveforms in power supply networks is one of the most important parameters from the point of view of, e.g., power quality measurements [1,2] and power network protection automation devices, especially those occurring in inverters used in the photovoltaic industry [3][4][5]. In the first industry, incorrect estimation of the fundamental frequency of the waveform results in a so-called spectrum leakage, and consequently, in an increased uncertainty of measurement of spectral parameters such as harmonics and interharmonics.…”

Novel Coordinated Control Strategy of BESS and PMSG-WTG for Fast Frequency Response

“…VSWTGs and energy storage systems (ESSs) are representative IBRs that can be used as FFR resources [2]. Research has been conducted on the ability of the IBR control of VSWTGs to contribute to system inertia.…”

“…The electrostatic inertia method uses the capacitor potential energy as inertia by regulating the voltage on the DC-link capacitor in the converter [6]. Suboptimal power point tracking (SPPT) is also a method of mitigation, which generates additional output by changing the operating point from the operating point on the maximum power point tracking (MPPT) curve to a suboptimal point [2,7,8]. Torque limit control, one type of SPPT control, is a control method that instantly maximizes the electrical torque through converter control [9].…”