Short-term scheduled power reserve: an artificial neural network approach

Renewable energy sources like photovoltaic systems (PV) often result in significant active power imbalances due to their variable nature. Despite their predictability, the uncertainty in their power production increases the complexity of planned power generation and necessitates the activation of costly and polluting power reserves commonly provided by conventional generators. This paper proposes an intelligent control technique to predict and replace diesel-based power reserves by faster, more economical and environmentally friendly reserves provided by a battery energy storage system (BESS). Following a power imbalance, a frequency droop controller ensures the primary reserve support from a BESS within a few seconds. Building upon the assumption that the variations of PV power are persistent within a 10-minute window, a secondary reserve for the next 5 minutes is commanded from an artificial neural network (ANN)-based adaptive module. This module predicts battery power references with a 1-minute time increment based on the PV and diesel generator power measured 5 minutes earlier. The proposed method's efficacy is showcased through its application on an islanded AC microgrid. Real-time simulations are performed using the Opal-RT simulator, revealing enhanced power balancing along with reductions in both operating costs and CO2 emissions.