The main aim of this paper is to assess and control impacts that a grid-tied microgrid may have on a distribution network. For this purpose, the tie-line power flow has been considered in a performance index to be minimized. In particular, three statistical indices have been compared on the microgrid operated under a classical day-ahead dispatching policy and an on-line optimal control strategy. The on-line optimization process starts from the same day-ahead dispatching policy and try to compensate the tie-line power flow deviations from the scheduled value. For this purpose, a real-time economic dispatching algorithm based on a PID controller has been developed and implemented on the PrInCE Lab microgrid, located at the Polytechnic University of Bari, Italy.

The contribution of power generation from converter-dominated renewable energy sources (RES) has increased enormously. Consequently, the system inertia is decreasing, and it impacts the frequency of the system. With large-scale integration of power electronic inverter-based power generation from RES, inertia from energy storage devices would be unavoidable in future power grids. In this paper, the inertia emulator is formed with a supercapacitor (IE–SC) to improve inertia in a low inertia grid. To emulate the inertia in a low inertia grid, this paper proposes a fuzzy logic controller-based emulated inertia controller (FL-EIC) for an inverter attached to IE–SC. The proposed fuzzy logic controller estimates the inertial power required based on the frequency deviation and rate of change of frequency (ROCOF). The output of the fuzzy controller adds to the conventional emulated inertia control (EIC) technique to alter the load angle for the power electronic inverter of IE–SC. Specifically, the proposed FL-EIC achieves inertia emulation by proportionally linking the time derivative of the grid frequency and frequency deviation to active power references of IE–SC. A comparison of the conventional EIC and FL-EIC is carried out to prove the effectiveness of the proposed FL-EIC. Furthermore, real-time simulations with the help of the OPAL-RT real-time simulator (OP 5700) are presented to validate the advantage of the FL-EIC.

The aim of this paper was to develop a multi-area decentralized controller for improving the voltage profile of large active distribution networks. Voltages at control buses of each area are kept as close as possible to their reference values by managing the reactive power of distributed generators within the same areas. Moreover, in order to avoid exchanging a considerable amount of data on more or less large portions of the network, the proposed methodology adopted an equivalent reduced network for each area. This equivalent network model is seen at control buses and nodes where distributed generation units are connected. With this simplification, each area controller will have to evaluate simultaneously, the unknown parameters of the reduced network and the optimal control laws for the voltage profile optimization of its control area. To comply with this exigency, a multi-objective optimization problem was formulated. The solution of this problem formulation was found by adopting an algorithm operating in the continuous time domain. Test results are provided on a 49-bus distribution network, demonstrating the effectiveness of the developed methodology.

Sliding Mode Controllers (SMCs) are commonly used in Permanent Magnet Synchronous Machines (PMSMs) for current control due to their robustness and simplicity. However, high gains used in traditional discontinuous SMC implementations can induce chattering. To address this, disturbance observers are employed to maintain robustness without resorting to high gains. This paper introduces a novel continuous asymptotic SMC method for PMSM currents that avoids the need for disturbance observers, resulting in reduced complexity and tuning efforts. The control laws of the two $dq$ -axes currents are obtained through the sensitivity of the tracking errors with respect to the controller outputs. The robustness and convergence properties of the proposed control laws are theoretically studied using the Lyapunov approach. Numerical simulations are used to evaluate the performance and robustness of the proposed controller, followed by experiments to compare it to a discontinuous terminal SMC with and without a disturbance observer. The results clearly demonstrate the superiority of the proposed controller which ensures fast convergence, low chattering and high robustness to parameter variations without requiring the design of additional disturbance observers.

This paper presents an innovative method for practical Static Security Assessment (SSA) of a Smart MicroGrid (MG). The proposed methodology defines a feasibility region where the MG exhibits stable behavior according to voltage-related relay settings. This offline approach facilitates the proactive feasibility assessment of any assumed operating condition expressed in terms of active and reactive injected powers. The feasibility region is defined through an ellipsoid that, with a minimum volume, encloses feasible points randomly generated and tested by load flow analyses. If an infeasible operating point is detected, the method is able to reschedule generators, storages and loads by proposing a new condition having a minimal Euclidean distance from the infeasible one. The method's simplicity, its ability to rapidly assess the feasibility of any assumed operating point and the possibility to implement fast preventive control strategies, give rise to a practical and useful tool in small enterprises owning MGs where sophisticated algorithms are not usually available, even if it can be adopeted also in more large and complex systems. The proposed strategy was subsequently applied to the static model of a subset of the experimental MG in the PrInCE laboratory, located at the Polytechnic University of Bari.

This paper presents a novel adaptive moving window forgetting factor method specifically designed for the decoupled recursive least squares approach applied to the second-order equivalent circuit model of batteries, The proposed technique adjusts weights using historical and current modeling errors, reducing complexity and improving parameter accuracy. To further improve its efficiency, the jellyfish search algorithm was employed to fine-tune the hyperparameters. Three distinct load profiles—the federal urban driving schedule, the dynamic stress test, and the supplemental federal test procedure driving schedule for United States —were selected to validate the effectiveness of the proposed algorithms. Experimental results show that the proposed method reduces the mean absolute error of terminal voltage by up to 74.3%, with an average improvement of 34.6% over the fixed forgetting factor method. Furthermore, it outperforms the sliding window difference forgetting factor method by up to 52.6%, with an average gain of 27.8%. Lastly, the method demonstrates up to 42.6% improvement and an average enhancement of 26.0% 20 over the original adaptive moving window forgetting factor method. In comparison to the aforementioned three methods, the proposed approach shows the capability to reduce mean absolute error of state of charge by up to 40.5%, 23.0%, and 7.6%, respectively