Genetic Algorithm-Based Load Balancing for Effective Demand Response with Renewable Energy Integration in Residential Settings

The demand response management programs are being adopted globally, due to their effectiveness in reducing the peak to average ratio (PAR), minimizing electricity costs and lowering power wastage. The Demand side management (DSM) facilitates shifting energy consumption keeping in consideration various parameters, such as peak hours and hourly pricing. This paper presents the Genetic Algorithm (GA) for demand response management, focusing on residential load scheduling with time-of-use (ToU) pricing schemes. The objective of this research is to minimize PAR and electricity costs in residential settings. The study categorizes household appliances as schedu-lable and non-schedulable, aiming to evenly distribute the load throughout the day to achieve optimal PAR and cost efficiency. Through simulations and performance evaluations, the research compares the effectiveness of GA with that of unscheduled consumption. The analysis compares home energy management systems (HEMS) and HEMS integrated with renewable energy sources. The findings reveal the effectiveness of GA in achieving PAR reduction and electricity cost savings. This research high-lights the benefits of residential load scheduling, identifies the advantages of integrating HEMS with renewable energy sources, and underscores the effectiveness of the algorithm in achieving optimal PAR reduction and cost efficiency. The study provides valuable insights about the optimization algorithm for real-world demand response programs, ensuring efficient load management and promoting sustainable energy consumption.