Maximum power-point tracking (MPPT) is applied to enable effective operation of photovoltaic (PV) systems under different external conditions. MPPT is based on a control system that aims at maintaining the PV system operation in the most effective conditions of maximum power output. This paper demonstrates the effective application of a novel adaptive control approach developed to be used in the field of power electronics. The application to MPPT is developed by using a non-inverted Buck-Boost converter applied to the PV system. The novel control methodology is based on the application of the Lyapunov stability concepts. The strength of this novel control technique is confirmed by the accurate comparison among the results obtained by using the proposed solution and some controllers proposed in the literature.

This paper presents an innovative control strategy for three-phase inverters. The proposed model reference adaptive control utilizes the Torelli Control Box (TCB) methodology, previously applied successfully to DC-DC converters, to regulate a three-phase inverter without load current sensors. This direct control approach overcomes the limitations of using traditional dual-loop control methodologies based on linear control systems and offers a robust solution against non-linearities and system disturbances. This paper formulates the overall framework for using the TCB method to control three-phase inverters. The performance of the TCB method is compared with linear and non-linear control strategies proposed in the literature, demonstrating the relative advantages in managing three-phase inverter complexities. The results have been verified in the laboratory on an experimental three-phase inverter installation that supplies a resistive load, with the proposed controller implemented in a microcontroller.

Cold ironing, also known as electrification of onshore power systems, is an important strategy for reducing emissions from vessels while berthed at port. However, effective planning of cold ironing infrastructure requires reliable estimation of the electrical power demand of different vessel categories, especially in ports where measured ship consumption data are limited or unavailable. This paper presents a data-driven approach to synthetic ship power demand modelling for cold ironing decision support, with a case study focused on Cyprus Port of Limassol. Using data collected from various sources, a Random Forest Regression model is developed which creates a relationship between vessel characteristics and power demand. This relationship is expressed as a power density coefficient and is employed to generate a synthetic power consumption profile of each ship visiting the port. The individual ship demand values are then aggregated to determine the total daily power consumption required for cold ironing operation at the port. The results provide insights into vessel-level and port-level electrical demand, supporting cold ironing feasibility studies, shore-side infrastructure sizing, port electrification planning, and energy management decisions.

Islanded and weakly interconnected power systems face increasing operational challenges as the penetration of renewable energy grows, particularly in environments with limited flexibility and reserve support. This study investigates a residential energy community in Cyprus using real time prosumer data and proposes a hierarchical decentralized Energy Management System (EMS). The EMS was initially implemented as a rule-based Minimum Viable Prototype (MVP), enabling practical validation of system operation and data integrity under realistic conditions. Building on this foundation, the final EMS adopts a three-layer control architecture in which rule-based household storage operation and peer-to-peer energy trading are complemented by a community-level Model Predictive Control (MPC) strategy for shared battery management. The MPC leverages short-term net-energy forecasts to proactively schedule charging and discharging of the community battery with the objective of reducing grid energy imports and improving local renewable energy utilization, while respecting battery operational limits and regulatory constraints. Simulation results based on measured data demonstrate consistent improvements over the rule-based baseline, achieving up to 23.4% reduction in electricity cost, approximately 6–7% reduction in grid energy imports, and a 1–2% increase in community self-sufficiency. Although the reduction in imported energy is moderate, its cumulative impact over long-term operation leads to significant economic benefits under time-varying tariffs. These results demonstrate that even when MPC is applied exclusively at the community battery layer, coordinated system-level energy management can deliver stable and economically meaningful improvements under realistic operating conditions.