Optimizing Energy Management in Microgrid Systems with Demand Response using Leaf-Wind Optimization

• Published in: International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (Online) pp. 387 - 393
• Main Authors: Sangeetha, P, Premalatha, M., Balaji, M, Mahapatra, Ranjan Kumar, Sethuraman, R., Patro, B Shivalal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.10.2024
• Subjects: Battery; Costs; Demand response; Energy management; MATLAB; Microgrid; Microgrids; Optimization; Particle swarm optimization; Photovoltaic; Reliability; Renewable energy sources; Supply and demand; Wind Turbine
• ISSN: 2768-0673
• DOI: 10.1109/I-SMAC61858.2024.10714662

An energy management (EM) with demand response (DR) in a microgrid (MG) involves dynamically adjusting energy usage in response to supply conditions to optimize overall energy consumption and reduce costs. However, it can lead to higher operational costs and may impact system efficiency due to the need for sophisticated control mechanisms and potential variability in DR effectiveness. To overcome these drawbacks, this paper proposes an efficient energy management with demand response in MG. The proposed approach is Leaf in Wind Optimization (LiWO). The main goal of the planned scheme is to reduce the operational cost of the MG scheme and maximize the efficiency of the MG system. The proposed LiWO is used to optimize the balance amongst energy supply and request while adhering to operational constraints. By then, the planned method is executed in MATLAB and compared with different existing approaches. The proposed method outperforms all existing approaches such as Crow Search Algorithm- JAYA optimization (CSA-JAYA), Multi-Objective Artificial Hummingbird Optimizer (MOAHA), and Genetic Algorithm-Adaptive Weight Particle Swarm Optimization (GA-AWPSO). The planned technique shows the operating cost of 590 and the efficiency of 99.2%. From the result, the proposed approach cost is low and the efficiency is high when compared to the existing approaches.