The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm’s performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results.

This study proposes a novel Mixed Integer Linear Programming (MILP) model for optimal management of Battery Energy Storage System (BESS) plants integrated with a real-time cycle counting algorithm exploiting an innovative logarithmic formulation for battery degradation. By incorporating this logarithmic approach, the model effectively includes various stress factors like State of Energy, C-Rate, Depth of Discharge, and Temperature, along with Solid Electrolyte Interphase film formation, into a tractable linear model. Moreover, the limits of the Rainflow Counting Algorithm (RCA) are overcome. The comparison with the results obtained by applying the RCA method to the full physical model are positive: the count faithfully reflects that obtained through RCA, and degradation estimation is close to the real value, with relative error below 0.1%.

Accurate modelling of battery energy storage systems (BESS) plays a crucial role in the optimal scheduling and planning of isolated microgrids with high penetration of renewable energy sources (RES). However, most mixed-integer linear programming (MILP)–based scheduling models neglect the simultaneous representation of efficiency and degradation effects, which leads to inaccurate cost estimation and suboptimal operational decisions. This paper proposes an advanced MILP formulation for isolated microgrid scheduling that integrates a linearised BESS model capturing variable efficiency and combined cycle and calendar ageing. To tune the ageing models, the study exploits open-access data from the literature. The formulation linearises efficiency and calendar ageing characteristics through a tangent–secant interpolation approach, which achieves high modelling accuracy while maintaining computational tractability.The study validates the BESS model through two distinct formulations applied to a real-world isolated microgrid case study supplying Lipari Island in Italy. A deterministic planning simulation demonstrates the model’s low computational burden. The proposed BESS formulation uses only two binary variables per time step and enables optimisation over a full-year horizon (8,760 time steps) in less than 20 seconds. The results show that explicitly modelling efficiency variations and battery degradation strongly affects scheduling decisions and operating costs. In addition, the paper develops a 24-hour chance-constrained spinning reserve formulation that ensures reliable microgrid operation under uncertainty. The BESS model integrates seamlessly with this complex formulation and confirms that BESS deployment improves microgrid reliability without increasing operating costs.