The transition of power grids from systems based on synchronous generators to those integrating increasing shares of renewable energy sources (RES) reduces system inertia and challenges frequency stability, particularly in weak grids. Grid-forming (GFM) control has emerged as a key solution, enabling inverter-based resources to provide synthetic inertia and autonomous grid support. This paper proposes a method for assigning virtual inertia and damping coefficients to multiple GFM inverters in a microgrid or distribution feeder. The objective is to ensure that the aggregated dynamic response at the point of common coupling aligns with target values specified by the grid operator. The proposed method is validated through simulations on an IEEE test network.

Voltage Source Converters (VSCs) are essential for grid-connected systems, enabling efficient active and reactive power control. However, classic fixed-gain controllers struggle with nonlinear dynamics, parameter uncertainties, and harmonic distortions, leading to performance degradation under non-ideal conditions. This paper introduces an unsupervised learning-based inner control loop (UL-ICL) that dynamically adjusts controller gains and decoupling parameters using fuzzy c-means clustering. By addressing these challenges, the UL-ICL ensures accurate current tracking and robust decoupling, even under dynamic conditions. Simulation results validate its effectiveness, outperforming fixed-gain controllers in precision and resilience. The proposed method offers a promising solution for real-time control of grid-connected VSC systems.

The recent advances regarding the decentralization of renewable energy production, the new technologies involved in the management of smart grids, and the opening of national energy markets, enriched with the use of demand-response strategies, have led to a notable diffusion of local energy markets. A local energy market is defined as an aggregation of energy producers, consumers, and prosumers that are located in a restricted area and see an interest in joining together to form a so-called “energy district.” In this paper, we present a two-stage approach that enables sharing renewable energy within a district and minimizes the costs and/or maximizes the revenues deriving from the provision and the sale of energy, both for single prosumers and for the district as a whole. The main novelty with respect to the state-of-the-art is the introduction in the optimization process of a second stage that, starting from the energy exchanges determined in the first stage, redistributes to the prosumers the surplus energy, i.e., the energy produced locally that exceeds the demand of the prosumers. The two-stage approach benefits have been assessed in a real-life testbed deployed on an Italian university campus.