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 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.

The industrial‐scale microgrids are revolutionizing the way commercial and industrial organizations traditionally anticipated to feed their production facilities for meeting up the energy demands. Due to the large volume and a diverse range of heterogeneous electrical components combining together in these microgrids, setting up a proper communication system becomes challenging. Along many others, the authors have also found it challenging to properly set up a communication system during the deployment of industrial‐scale experimental microgrid at PrInCE Lab. This paper is aimed at providing the guidelines for the research and industrial communities outlining the set of communication challenges along the way and highlighting their possible remedies with the intention to make it possible even for the nontechnical industrial staff handling these issues with ease while setting up their own industrial microgrids.

The aim of this paper is to develop a self-adaptive control methodology capable of optimizing in real-time the operation of PV-powered microgrids by dynamically managing both the output powers of battery energy storage systems (BESSs) and power exchanges with the utility grid. Control actions are evaluated by solving a constrained multi-objective optimization problem that integrates the optimal state-of-charge (SoC) management of BESSs within a broader economic dispatch framework. In this way, the SoC is continuously optimized alongside other economic objectives, such as minimizing operating costs and maximizing revenues from energy sales to the grid, while maintaining the microgrid’s energy balance. This ensures that BESSs operate efficiently within their optimal ranges, preventing premature depletion or overload and thereby safeguarding overall microgrid performance. To enable real-time adaptability, the methodology employs a Lyapunov-based optimization algorithm combined with a sensitivity analysis, ensuring rapid convergence to optimal solutions, even under rapidly changing conditions. Computer simulations performed on a low-voltage PV-BESS-based microgrid under different operating conditions confirm the effectiveness of the proposed methodology in enhancing real-time economic performance, operational efficiency, and microgrid reliability.

The aim of this article is to develop a simplified equivalent dynamic model capable to capture the transient behavior of a gas-fueled co-generator. To comply with this exigency, an online identification methodology based on the sensitivity theory in conjunction with the Lyapunov function is adopted. By this methodology, stable solutions can be achieved in the online environment, renewing the parameters of the model at every change occurring on the physical system. In order to investigate the effectiveness of the proposed methodology in deriving a simplified dynamic model, several simulations and experimental tests were performed on the Prince Lab microgrid located at the Polytechnic University of Bari.