Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

A smart energy management controller can improve efficiency, save costs, and reduce carbon emissions consumption while accurately catering to consumer habits. Having integrated various renewable systems (RESs) a battery storage system (BSS), we proposed an optimization-based demand-side (DSM) scheduler (SEMC) for home. The suggested SEMC creates DSM-based operational plan regarding user-centered comfort-aware preferences. Using the generated appliances operation plan, consumers emissions, peak-to-average ratio (PAR), their comfort in terms of thermal, illumination, usage schedule residential is using ant colony optimization (ACO), teaching learning-based (TLBO), Jaya algorithm, rainfall firefly our hybrid ACO TLBO (ACTLBO) algorithm. Five existing algorithms-based frameworks validate DSM framework that relies on ACTLBO. results integration RESs BSS, adapting algorithm under demand response program real-time price reduced bill PAR CO2 Case I: only external grid (EG) by 42.14%, 22.05%, 28.33%, II: EG with 21.79%, 11.27%, 17.02%, III: BSS 28.76%, 41.53%, 21.86%, respectively as compared without employing SEMC. Moreover, user improvement index-ratio scheduling ACTLBO 7.77%, 24.73%, 5.00%, 3.43% average delay, indoor air quality, visual, respectively. Simulation show outperforms mitigate peak loads, comfort.