This paper examines the influence of environmental factors on the efficiency of photovoltaic (PV) systems and the performance of boost converters in a distributed PV architecture. In distributed PV systems, where each panel is equipped with its own micro-converter, the influence of the environment must be thoroughly investigated. The results of this research have important practical implications for the design and operation of these PV systems. Specifically, it addresses the output power degradation due to shading and overall efficiency of high-voltage direct current (HVDC) connected PV systems. A PV module, designed from an electrical circuit model, is connected to a boost converter in order to be duplicated for parallel PV architecture. This integrated configuration is interfaced with a HVDC bus in a parallel topology. To validates the system’s operational performance, the output power is simulated and compared under different irradiation levels within a reduced DC bus voltage. The results provide valuable insights into the effects of shading and suggest strategies to mitigate power losses, thereby improving the reliability the efficiency of HVDC connected PV systems.
The integration of volatile renewable energy sources, such as photovoltaic (PV) systems, alongside battery energy storage systems (BESS) into DC micro-grids presents significant control challenges, primarily in ensuring DC bus voltage stability and optimizing long-term energy management. This paper introduces a novel synergistic dual-loop control architecture engineered to address these challenges. The inner control loop leverages the structural properties of differential flatness, with robust feedforward compensation, to achieve rapid and precise regulation of the DC bus voltage under disturbances. Complementing this, the outer loop employs a Q-learning-based reinforcement learning (RL) algorithm for adaptive energy resource management. The RL agent learns an optimal policy for BESS dispatch by considering real-time dynamics, including PV availability, fluctuating load profiles, battery state-of-charge (SOC), and voltage deviations, without requiring an explicit model. Validation through 24-hour simulations confirms the efficacy of the proposed architecture. The RL agent exhibits successful convergence with stable cumulative rewards. The integrated system demonstrates superior performance, maintaining tight voltage regulation with a standard deviation of 1.615 around the 120 V set-point, effective SOC management (average SOC 36.53 without limit violations), and efficient resource utilization enabling 51.49 % PV penetration. These results highlight the potential of combining nonlinear control with model-free RL to enhance hybrid micro-grid performance, resilience, and autonomy.
Photovoltaic (PV) systems are increasingly being deployed on building rooftops as part of renewable energy strategies, yet their performance remains highly sensitive to local meteorological conditions and is often poorly correlated with manufacturer datasheet values or generic simulation assumptions. This paper proposes a simplified digital twin (SDT) for a rooftop-mounted 11 years old PV system equipped with Altius photovoltaic panels which integrates real-time operational monitoring and on-site meteorological measurements. Experimental current-voltage (I-V) and power-voltage (P-V) characteristic curves were obtained through direct testing with HT IV400 of the Altius panels and compared to the curves of a new PV panel in order to determine the degradation of the PV panels. A dedicated meteorological station, co-located with the PV installation, provides synchronized values of irradiance, ambient temperature, and PV panel temperature, enabling the digital twin to be continuously updated with real operating conditions rather than standard test conditions (STC) assumptions. The real-time monitoring system collects DC and AC parameters directly from the rooftop PV installation over a one-minute period, allowing the digital twin to compare the real output power with the theoretical one computed using the datasheet characteristics, solar irradiation and PV module temperature in order to provide notifications related to the health of the PV system, if the string is connected or if the inverter is connected to the grid.
This paper presents a comprehensive study of a residential energy configuration, which considers both thermal and electrical resilience. The aim of this study is to increase the autonomy of the residential complex and, at the same time, to reduce dependence from the public energy grid. In order to evaluate the photovoltaic system and energy storage system capacity installed that can sustain thermal and electrical energy demand of a house throughout the year, a MATLAB – based numerical model was developed. Using this numerical model, three scenarios were proposed, one that addresses the maximum resilience of the house, the second scenario considered a compromise between energy requirements and photovoltaic energy produced, focusing on payback period and energy cost reduction, and the third scenario of controlled resilience, which focuses on finding the best compromise between investment cost and partial autonomy. The proposed methodology, presented in this paper, allows the comparative evaluation of the three scenarios in terms of photovoltaic system and storage system capacity, energy imported and exported from and to the grid, energy cost reduction and expected investment recovery period. The results highlight that complete annual energy autonomy requires substantial oversizing of both photovoltaic system and energy storage system capacity, especially due to the seasonal mismatch between solar energy production and heating demand. In constrast, optimization scenarios offer a more favorable balance between resilience, energy independence and economic feasibility. The developed method can be used as a practical design tool for sizing residential energy systems while balancing economic performance with the required levels of thermal and electrical resilience.
The advancement of vehicle technology enables it to handle complex tasks such as augmented reality, automatic parking, and driving, which demand significant computational power and are sensitive to delays. However, the computational capabilities of these vehicles are limited. As a result, Vehicle Edge Computing (VEC) has been introduced to offload some computational burdens to neighbouring vehicles and edge servers. The main challenge of this system lies in managing offloading in scenarios with frequent disconnections and high-speed driving. To address this challenge, we have formulated the task offloading problem with the goal of minimizing overall time, considering its NP-hard nature and multiple constraints. Our proposed solution is a meta-heuristic approach that integrates the honey bee Food Foraging process with Genetics Algorithm (FFGA) for vehicular task offloading. In the food foraging process, scouts are sent to locate nearby area, which in our case are edge servers and gather contextual details, and upon their return, they perform a dance resembling the infinity symbol, where the central angle indicates the direction of the food field and the speed of the dance signifies the quality of the found food source. The vehicle client uses this information to select one edge server to be in charge of the offloading process. Subsequently, we apply the genetic algorithm to generate an optimized allocation scheme of tasks by considering contextual details of nearby nodes. These details include computational capacity, availability, location, and driving speed. The optimization operators of the genetic algorithm are used to determine the assignment of tasks to nodes. Extensive simulations have demonstrated that the proposed FFGA system outperforms other existing schemes, including the hybrid vehicular edge cloud (HVC), particle swarm optimization (PSO), and the multi-decision based offloading (MDO).
