Energy Management System for a Hybrid Solar-Wind Power Plant with Digital Twin Diagnostics and SCADA Monitoring

This paper presents the development and numerical verification of a hierarchical rule-based energy management system (EMS) for a hybrid photovoltaic–wind power plant with a battery storage system, a digital twin and a SCADA monitoring subsystem. The simulation model is implemented in the MATLAB/Simulink environment and includes photovoltaic and wind generators, a battery, an electrical load, a grid connection and a hierarchical rule-based energy management system (EMS). The control algorithm is based on calculating the current energy imbalance, estimating the state of charge of the battery, and selecting one of four power distribution modes: charging, discharging, electricity export and electricity import. The power-balance calculation was checked at 51 EMS evaluation points, with a reported maximum absolute discrepancy and RMSE of 0 kW at the numerical precision of the available results. The integral residual across the selected system-level accounting boundary over 60 min was −8.88 × 10−16 kWh. Internal conversion and interconnection losses lie outside this boundary; therefore, these residuals indicate the algebraic consistency of the recorded boundary power flows and do not quantify physical component losses. The digital twin is implemented as a parallel calculation and diagnostic subsystem for the parallel recalculation of power balance and dynamic monitoring of the battery’s state of charge. For SOC estimation, an RMSE of 0.003225, an MAE of 0.002411 and a maximum absolute error of 0.007687 were obtained. The SCADA subsystem provides centralised collection of ten power, control and diagnostic signals. The main contribution of this study is a modular simulation and verification framework integrating a hierarchical rule-based energy management system (EMS), a parallel computational Digital Twin subsystem, and SCADA signal collection. The architecture separates power-balance calculation, operating-mode selection, power dispatch, diagnostic signal generation, and monitoring, allowing individual functional blocks to be assessed separately. The reported numerical results characterize the consistency and operation of the developed architecture under the simulated conditions. The study does not introduce a new optimization-based control algorithm or claim experimental validation on a physical energy facility. The framework provides a basis for future comparative evaluation of alternative control strategies and further experimental validation.