Peak shaving applications provided by energy storage systems enhance the utilization of existing grid infrastructure to accommodate the increased penetration of renewable energy sources. This work investigates the provision of peak shaving services from a flywheel energy storage system installed in a transformer substation. A lexicographic optimization scheme is formulated to define the flywheel power set-points by minimizing the transformer power limit violations and the flywheel energy losses. Convex functions that represent the flywheel power losses and its maximum power are derived and integrated in the proposed scheme. A two-level hierarchical control framework is introduced to operate the transformer-flywheel-system in a way that handles prediction errors and modelling inaccuracies. At the higher level, a model predictive controller is developed that solves the lexicographic optimization scheme using linear programming. At the lower-level, a secondary controller corrects the power set-points of the model predictive controller using real-time measurements. A software platform has been developed for integrating the proposed controllers in an experimental setup to test their effectiveness in a realistic testbed setting, and the flywheel system characteristics are experimentally identified. Simulation and experimental results validate and verify the modeling, identification, control and operation of a real flywheel system for peak shaving services.

The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach.

Power electronics converters are utilized for interconnecting distributed energy storage systems into the grid. These converters can support the stability of the power system under abnormal grid conditions. In this work a new coordinated voltage-frequency support strategy is proposed for energy storage systems considering the reactance to resistance ratio of the grid impedance. Unlike conventional support schemes for transmission grids, where voltage and frequency support are decoupled, the proposed strategy considers the coupling between voltage and frequency due to the resistive characteristics of the grid impedance in low voltage distribution grids. An advanced frequency support scheme is also developed considering both droop-based frequency support and virtual inertia control for improving frequency stability. The proposed strategy ensures a fair compensation between voltage and frequency support by utilizing an adaptive gain that is online calculated according to the short-circuit fault characteristics. Simulations and experimental tests are carried out using a laboratory setup to validate the proposed strategy.

This paper presents a wide area control (WAC) method to coordinate the local controllers of generators and renewable energy sources (RESs), for robust power oscillation damping. The WAC signals are synthesized by a two-level hierarchical controller, which utilizes wide area measurements from all the installed synchronous generators and RESs for maximizing the performance of their corresponding local controllers. In the resulting WAC scheme, the generators are “aware” of the RES oscillations and vice versa, the RESs are “aware” of the generator oscillations. Further, the WAC signals intended for the RESs are adjusted in order to require only slight modifications on the RES local controllers. This study illustrates that the correct coordination of generators and RESs, through the WAC, improves the damping performance of the entire system even under high penetration of renewables. The performance of the proposed scheme has been tested and validated in the IEEE 9-bus and 39-bus test systems.

The effective operation of the grid side converter is crucial for the reliability of renewable energy systems. Grid side converters are responsible for converting the energy produced by renewable energy sources into a form that can be injected into the grid. A grid side converter controller manages the conversion using feedback from voltage and current sensor measurements from both the alternative current (ac) and direct current (dc) sides of the converter. However, these sensory devices may fail or diverge from their normal operation, which can affect the effective operation of the converter. This article proposes a model-based fault diagnosis scheme for sensors located in the dc and ac sides of the grid-tied converter, taking into account the effects of fault propagation due to the physical interconnection between the ac and the dc sides. First, a discrete-time, nonlinear model of the grid side converter is formulated that considers the presence of ac current and dc voltage sensor faults. Second, a nonlinear estimation scheme is designed by considering the effects of sensor noise and modeling uncertainty. The estimator design utilizes the estimator gain matrix that is obtained by solving the linear matrix inequalities problem. Finally, a sensor fault diagnosis scheme is designed based on the analytical redundancy relations considering the estimations of the nonlinear estimation scheme. The performance of the proposed fault diagnosis scheme is validated through both simulation and experimental results, demonstrating its effectiveness in timely detecting and isolating sensor faults.