Intelligent energy management in microgrid using prediction errors from uncertain renewable power generation

This study proposes an efficient local energy management system (LEMS) based on the generalised power prediction model for the uncertain operation of renewable distributed generations (DGs)-based microgrid. Photovoltaic with battery energy storage, and wind power generation are considered as primary DGs to compensate intermittency. Conventional direct power prediction models are limited to specific DG applications, where the plant data acquisition system is a necessity. Solar irradiance and wind speed are considered here as prediction targets to cope with such additional expenditure for a microgrid. To ensure a robust reduction in prediction error (e(p)), a short-term prediction model is developed by virtue of the proposed robust regularised random vector functional link network. A maximum-likelihood estimator using Huber's cost function is employed to attain the robustness of this model. Further, a direct renewable energy source-power calculation is opted to address model accuracy under local uncertainties. The LEMS operation is completed by compensating e(p) with distributed adaptive droop-based primary controllers for multi-DG based microgrid. To ensure the performance of the prediction model, solar irradiance, wind speed and power at different atmospheric conditions (seasonal volatility) and time span (i.e. 5, 10 and 60 min) have been implemented in MATLAB and real time.