This paper presents a Digital Twin (DT) model designed to address the process of coordinating distributed maintenance activities of resource-constrained Power Systems with green energy integrated, employing Machine Learning (ML) for model calibration and Heuristic Optimization (HO) to solve the underlying coordination process. The model integrates real-time operational data collected by a Supervisory Control and Data Acquisition (SCADA) system and centralized in a Structured Query Language (SQL) database. The model performs predictive and prescriptive analyses to minimize convergence risks between maintenance planning, historical system degradation, and operating capacity, over a predefined time horizon, by merging all underlying processes into a single and intuitive risk indicator estimated via the Monte Carlo method, and by using a digital representation of all interconnected modeled processes in MATLAB, but also integrating Python packages into the modeling methodology. Here, the paper mainly describes the role of the DT framework in addressing this solution in practical terms and lists all components modeled with the corresponding link to the operating data. The Power System used as a case study includes a wide matrix of green energy sources and the associated resource constraint of each primary energy source considered.