Comprehensive optimization of active and reactive power scheduling in smart microgrids by accounting for line transmission losses using genetic algorithm

Unlike most studies that neglect reactive power and line losses, this paper addresses both factors to ensure more accurate power dispatch decisions, reduce voltage deviations, and minimize transmission losses. Smart microgrids face increasingly complex challenges in providing optimal power flow due to the integration of intermittent renewable energy sources, reactive power demand, and transmission line losses. These factors can lead to voltage fluctuations, increased operational costs, and accelerated aging of battery storage systems (BSS). This paper presents an innovative approach to smart microgrid (SMG) power control that integrates active and reactive power balancing while accounting for line losses. The goal is to minimize energy costs, promote Distributed Energy Resources (DER) support for reactive power, reduce voltage drop, lower CO₂ emissions, and limit BSS aging. The grid-connected SMG consists of photovoltaic (PV) cells, BSS, and residential loads. A Genetic Algorithm (GA)-based method is proposed to optimize power management in three scenarios: (i) active power only (AOM), (ii) active and reactive power without line losses (ARM), and (iii) active and reactive power considering line losses (ARLM). Real-world data is utilized to validate these scenarios. Simulations show that the ARLM strategy reduces energy costs by 26 % and 13 % compared to AOM and ARM, respectively. It achieves over 60 % reduction in RMSE voltage drop and significantly improves the power factor to an average of 0.92. The proposed strategy aligns with the ARLM framework and highlights the importance of integrating line losses into active and reactive power management for more accurate and efficient microgrid operations.