network model (both topology and parameter values), that is unavailable or corrupted in practice as a result of communication failures, bad data in state estimation etc. In addition, the impact of disturbances on the inter‐area oscillations cannot be well captured by these methods.
1.3 Data‐Driven Wide‐Area Power System Monitoring and Control
Power grids modeling and control has become a more challenging issue due to the increasing penetration of RESs, changing system structure and the integration of new storage systems, controllable loads and power electronics technologies, and reduction of system inertia. Conventional modeling and control designs may not be any more effective to satisfy all specified objectives in various operation modes of modern power grids. These challenging issues set new demand for the development of more flexible, rapid, effective, precise, and adaptive approaches for power system dynamic monitoring, stability/security analysis, and control problems. Thanks to recent advances in control, communication, and computing technologies, it is possible to tackle mentioned challenges by implementing a data‐driven‐based modeling and control framework as shown in Figure 1.2.
The system data are collected from the distributed PMUs in the grid through a secure communication network. The development of information and communication technology (ICT) enables more flexibility in wide‐area monitoring of power system with fast and large data transmission. Especially, the wide‐area measurement system (WAMS) with PMUs is a promising technique as one of the smart grid technologies in the bulk power grid.
Figure 1.2 An overall data‐driven control framework for renewable integrated power systems.
The measured data are locally saved and then collected by phasor data concentrators (PDCs) for the post analysis or sent to a remote location via a standard data format. These data with the time stamp of the synchronized GPS in real time may applied for parameter and state estimations and finally used for the system protection and/or real‐time control. Figure 1.3 shows how a PMU‐based WAMS can provide data for the power system control center to generate continuous (in normal states) and discontinuous control (in off‐normal states) commands.
Before any application, the collected PMU data need to be cleaned and de‐noised and employed by the data processors for estimation, modeling, and control purposes. The proposed de‐noising method may use a rolling‐averaging window with pre‐specified length to remove noise from the recorded data. The block of parameters estimation algorithms contains high fast and precise algorithms for estimation of some important parameters and transient characteristics that are required to use in control tuning algorithm or to detect a contingency and triggering the emergency control and protection schemes. In case of crossing the assigned thresholds showing an off‐normal and emergency condition, the recorded data and some estimated parameters are used to detect the amount of mismatch (size of disturbance) for the emergency control and protection schemes such as load shedding algorithms. Otherwise, the estimated parameters such as scheduling parameters are employed by the continuous control systems.
Figure 1.3 PMU‐based wide‐area measurement system and control.
As mentioned, using significant number of distributed micro‐sources into power systems adds new technical challenges. As the electric industry seeks to reliably integrate large amounts of DGs/RESs into the power system in regulated environment, considerable effort is needed to accommodate and effectively manage the installed micro‐sources. A key aspect is how to handle changes in topology and dynamics caused by penetration of numerous DGs/RESs in the network and how to make the power grid robust and able to take advantage of the potential flexibility of distributed micro‐sources. In a modern control framework, a part of power produced by available DGs/RESs in the grid are used as a primary energy source of inertia emulator to provide virtual inertia as a supporting control for abovementioned controllers (like a fine tuner) to improve power grid stability.
1.4 Dynamics Modeling and Parameters Estimation
From a system dynamic point of view, the bulk generating units, due to their high inertia, provide a long time constant; such that the rotor speed and thus the grid frequency cannot alter suddenly, while the load changes. Hence, the total rotating mass enhances the dynamic stability. In future, a significant share of DGs/RESs/MGs in the electric power grids is expected. This increases the total system generation power, while does not contribute to the system rotational inertia. System dynamics are faster in power systems with low rotational inertia, making control and power system operation more challenging [32].
A complete understanding of reliability considerations via effective modeling/aggregation techniques is vital to identify a variety of ways that power grids can accommodate the large‐scale integration of the distributed micro‐sources in future. An accurate dynamic model is needed for the stability analysis and control synthesis in a grid with a high degree of DGs/RESs penetration. A proper dynamic modeling and aggregation of the DGs/RESs and MGs, for performance and stability studies, is a key issue to understand the dynamic impact of distributed microsources and simulate their functions in new environment.
The power system is a nonlinear multivariable time‐varying system. It is represented by a nonlinear set of equations for the generators (swing equations), for the transmission lines and for the loads, which for a typical power system has a few hundreds of states. For the control design purpose, usually a reduced‐order linearized model around an operating point is used and it is assumed that all system parameters are known and time‐invariant. These assumptions, however, are not valid in a real power system with dominated DGs/RESs/MGs. The main dynamic modes of the system are varying stochastically during a day because of the variation of load and aggregated inertia. The dynamic modes will change more significantly by integration of new RESs into the power system (e.g. because of long‐term variation of the mean value of the aggregated inertia). Therefore, a fixed linearized time‐invariant model will not represent correctly the behavior of the power system.
The frequency response of the system can be identified offline/online using the data for different load and generation configurations (when the share of DGs/RESs is increased) and saved in a database for the models. The small variation of the system (originated from measurement noise, load variation, and system nonlinearity) will be modeled by frequency domain uncertainty. The long‐term effect of change in system inertia can be considered by identifying several frequency‐domain models for different levels of RES penetration. One can represent this model's database by an LPV model [116]. It should be mentioned that the model of the power system for the frequency, voltage, and rotor angle is different because they have different inputs and outputs and scheduling parameters.
1.4.1 Modeling of Frequency, Voltage, and Angle Controls
The participant bulk SGs with different participation factors are the main actuators for the frequency control system. Following a disturbance, the variation of frequency and tie‐line power is applied to the LFC system via the ACE signal. Then, depending on the accessible amount of regulation power, the LFC system will be activated to compensate the power grid frequency and return it to the nominal value. The LFC system can attenuate the frequency and active power changes from tenth of seconds to few minutes. Therefore, the ACE signal may provide the output of system model for frequency control. Considering
