Optimal Day Ahead Active and Reactive Power Management in Residential Buildings using Particle Swarm Optimization

Abstract

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.