Renewable Integrated Power System Stability and Control. Hassan Bevrani

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СКАЧАТЬ inertia, aggregated generating time constant, droop and damping coefficient. The measurement‐based dynamics identification and system modeling will be for adaptive control and online parameters tuning of the LFC system. The increasing size and diversification of demand/power sources magnify the importance of this issue in the modern power grids.

      Unlike grid frequency, since the voltage is known as a local variable, a higher number of measured points are required. For proposing the data‐driven models, several concepts like Thevenin equivalent system and oscillation model will be applied and the results will be used for the grid voltage analysis, and then optimal tuning of AVRs. The measurements of voltage, current, and phase deviations of existing nodes are considered as the most important inputs of model. The system output can be the terminal voltage change of the SGs. In order to construct an appropriate LPV model for voltage control, relevant scheduling parameters must be selected. There are several choices for the scheduling parameters (low‐frequency resonance mode, reactive power of the system, Thevenin equivalent impedance/admittance, etc.) that must be compared and discussed using effective analytical and simulation‐based studies. In choosing efficient scheduling parameters, a tradeoff between accuracy and simplicity of the resulting LPV model is needed. The measurements are also used to perform some important graphical tools and curves to evaluate the stress conditions and to analyze the voltage stability criteria. For instance, the data are fitted to the active power–voltage curve (PV curve) of the equivalent system by suitable fitting approaches such as least squares method.

      The SGs equipped with a PSS and RESs participating in power oscillation damping are considered as the main actuators of the control system. It is assumed that some of the RESs are not operating at their maximum generated power, so that they can help for power oscillation damping by reducing/increasing a small percentage of their power generation. The system to be controlled is a multivariable system, where the inputs are the reference voltages for the AVR of the SGs as well as the auxiliary signals that will be added to the reference active and reactive powers of RESs. The system outputs can be the active power or the speed (or both, depending on the type of PSSs) of the SGs and the measured active and reactive powers of the RESs. In order to construct an appropriate model for power oscillation damping, we need to choose the scheduling parameters.

      1.4.2 Parameters Estimation

For estimation of all required parameters, the recorded data from the installed PMUs can be used. As shown in Figure 1.2, firstly, a de‐noising methodology, mostly based on rolling averaging windows, can be employed to prepare the received PMU data for further processing. Afterwards, some data‐driven‐based algorithms are used to estimate the most important parameters (e.g., system inertia, droop characteristic, and damping factor) required for building low order power system models such as frequency response model [32] and oscillation model [3]. In real‐time operation, an accurate and fast estimation of parameters is required. The estimated parameters can be used in the auto‐tuning algorithm of the controllers.

      In a modern power system, in addition to scheduling parameters, several algorithms need to be developed for online estimating of other important system parameters such as synchronizing coefficient between various areas, rate of change of frequency/voltage (ROCOF/ROCOV), frequency/voltage nadir, and time occurrence of frequency/voltage nadir. The estimations must be fast enough and should cover the issues related to the existing time delay. These data‐driven‐based estimation algorithms can be analytically developed based on the concept of swing equation, base‐case systems, regression, and curve fitting. These measurement‐based dynamics identification and system modeling can be used for adaptive online parameters tuning of the targeted controllers. The increasing size and diversification of demand/power sources magnify the importance of this issue in the modern power grids. The estimation approaches may be applicable for both on‐line and off‐line methods. However, for on‐line applications, shorter data windows must be used.

      PMU data‐based power‐load imbalance estimation is a key estimation to successfully handle the emergency control strategies, e.g. load shedding, and protection plans. In case of detecting a contingency or an emergency condition, following comparing of frequency, voltage, and their rate of changes with the specified threshold values, an estimation algorithm must estimate the size of disturbance to use in the available emergency control systems and special protection schemes. This online estimation is an important issue to realize a successful load‐shedding scheme with minimum amount of shed load.

      Conventionally, for the estimation of the size of load‐power mismatch, the swing equation is used. The estimated imbalance based on this method may far from real power mismatch as it relies on three worst assumptions: (i) there is no additional active power variation except for that of disturbance, (ii) there is a negligible reactive‐power imbalance in response to sudden active power imbalance, and (iii) the inertia constant assumed to be known. This under/over‐estimation causes inaccurate calculation of total amount of load to be shed.

In [117], for estimating the size of disturbance, some appropriate base‐case features are selected from a set of pre‐defined base cases. Moreover, an efficient yet simple logic is defined to select appropriate base‐case for the received data. This approach benefits from the use of PMU data to precise calculation of the required amount of load to be shed, in one‐step and in a short time in comparison with the actual time. The proposed scheme relies on fast, yet iterative, estimation of frequency nadir, and time of minimum frequency occurrence. Accordingly, the inertia constant as well as the size of power mismatch are estimated which, in turn, compares with the maximum size of imbalance, satisfying the pre‐specified thresholds, to determine the amount of shed load.

1.5 Summary

      Modern power grids face new technical challenges arising from the increasing penetration of power‐electronic‐connected RESs/DGs. Increasing MGs/DGs penetration level may adversely affect frequency response and voltage and system control and lead to degraded performance of traditional control schemes. This, in turn, may result in large deviations and, potentially, system instability.

      This chapter provides the pre‐requirement terminology and general background for the next chapters of this book. The term power system stability and control with an updated brief review on the areas of frequency, voltage, and angle controls, concerning the penetration of RESs/DGs, is discussed. In response to the existing challenges in penetration of more RESs/DGs to the grid, the necessity of using data‐driven modeling, parameters estimation, and control synthesis in wide‐area power systems is emphasized.

References

      1 1. Steinmetz, C.P. (1920). Power control and stability of electric generating stations. Transactions of the American Institute of Electrical Engineers XXXIX (2): 1215–1287.

      2 2. Smith, J.R., Andersson, G., and Taylor, C.W. (1996). Annotated bibliography on power system stability controls: 1986–1994. IEEE Transactions on Power Systems 11 (2): 794–800.

      3 3. Bevrani, H., Watanabe, M., and Mitani, Y. (2014). Power System Monitoring and Control. Wiley.

      4 4. Bevrani, H. and Hiyama, T. (2009). On load‐frequency regulation with time delays: design and real time implementation, IEEE Transactions on Energy Conversion 24 (1): 292–300.

      5 5. Bevrani, H. and Hiyama, T. (2011). Intelligent Automatic Generation Control. CRC Press.

      6 6. Kundur, P. (1994). Power System Stability and Control, vol. 7. New York: McGraw‐Hill.

      7 7. Kundur, P., Paserba, J., Ajjarapu, V. et al. (2004). Definition and classification of power system stability ieee/cigre joint task force on stability terms and definitions. IEEE Transactions on Power Systems 19 (3): 1387–1401.

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