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Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop. 

Low voltage microgrid systems are characterized by high sensitivity to both active and reactive power for voltage support. Also, the operational conditions of microgrids connected to active distribution systems are time-varying. Thus, the ideal controller to provide voltage support must be flexible enough to handle technical and operational constraints. This paper proposes a model predictive control (MPC) approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system along with operational constraints to solve an online finite-horizon optimization problem. Control signals are then computed such that the defined cost function is minimized. By proper selection of MPC weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in Matlab/Simulink validates the proposed approach for a simplified version of a 100 kVA, 208 V microgrid using typical parameters. Results show that performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller. 

Fast-frequency control strategies have been proposed in the literature to maintain inertial response of electric generation and help with the frequency regulation of the system. However, it is challenging to deploy such strategies when the inertia constant of the system is unknown and time-varying. In this paper, we present a data-driven system identification approach for an energy storage system (ESS) operator to identify the inertial response of the system (and consequently the inertia constant). The method is first tested and validated with a simulated genset model using small changes in the system load as the excitation signal and measuring the corresponding change in frequency. The validated method is then used to experimentally identify the inertia constant of a genset. The inertia constant of the simulated genset model was estimated with an error of less than 5% which provides a reasonable estimate for the ESS operator to properly tune the parameters of a fast-frequency controller. 

In isolated power systems with low rotational inertia, fast-frequency control strategies are required to maintain frequency stability. Furthermore, with limited resources in such isolated systems, the deployed control strategies have to provide the flexibility to handle operational constraints so the controller is optimal from a technical as well as an economical point-ofview. In this paper, a model predictive control (MPC) approach is proposed to maintain the frequency stability of these low inertia power systems, such as microgrids. Given a predictive model of the system, MPC computes control actions by recursively solving a finite-horizon, online optimization problem that satisfies peak power output and ramp-rate constraints. MATLAB/Simulink based simulations show the effectiveness of the controller to reduce frequency deviations and the rate-of-change-of-frequency (ROCOF) of the system. By proper selection of controller parameters, desired performance can be achieved while respecting the physical constraints on inverter peak power and/or ramp-rates. 

Overvoltage is one of the major issues on distribution grids with high penetration of photovoltaic (PV) generation. Overvoltage could be prevented through the control of active/reactive power of PVs. However, given the high R/X ratio of low voltage feeders, voltage control by using reactive power would not be as effective as using active power. Therefore, active power curtailment (APC) of PVs, though not desirable, becomes necessary at times to prevent the overvoltage issues. Existing literature is rich in centralized and droop-based methods for APC and/or reactive power control of PVs to prevent overvoltage issues. In this context, this paper revisits the most popular existing methods, and evaluates the performance of droop-based and centralized methods using a typical North American 240 V low voltage feeder with 24 residential homes. In this work, our key findings are: a) droop-based methods provided conservative solutions or did not eliminate the overvoltages completely, b) power flow sensitivity based droop approach led to 13% more curtailment than the centralized approaches, c) centralized approach had 40% less energy curtailed compared with standard droop while no overvoltages were observed, and d) operating PVs at non-unity power factor in centralized approach led to 5% less energy curtailment. 

The rapid transition towards an inverter-dominated power system has reduced the inertial response capability of modern power systems. As a solution, inverters are equipped with control strategies, which can emulate inertia by exchanging power with the grid based on frequency changes. This paper discusses the various current control techniques for application in these systems, known as virtual inertia systems. Some classic control techniques like the proportional-integral, the proportional-resonant, and the hysteresis control are presented first, followed by the design and discussion of two more advanced control techniques based on model prediction and machine learning, respectively. MATLAB/Simulink-based simulations are performed, and results are presented to compare these control techniques in terms of harmonic performance, switching frequency, and transient response.