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Demand-side management (DSM) is an effectual approach by coordinating utility management and routinely tracking energy usage, the intelligent grid assists in controlling energy demand and promotes its efficiency. However, the paper aims to utilize Linear programming optimization algorithms as an effective tool for managing energy demand and maximizing the use of renewable energy sources. These algorithms are able to estimate which is the best utilization of what resources are accessible and reduce consumption by describing the energy system as a collection of linear equations. The optimization system makes assumptions about the various energy costs when it will be high or low and modifies energy use accordingly. We applied different scenarios to assess the resiliency of the system. The simulation took into account a number of variables, including the weather, energy usage, and pricing fluctuations. MATLAB R2023a and Simulink provide an integrated platform with data analytics to build the proposed system and optimization model to minimize cost in MATLAB. Compared to other methods using various optimization algorithms as the binary orientation search algorithm (BOSA), cockroach swarm optimization (CSO), and the sparrow search algorithm (SSA) were applied to DSM methodology for a residential community with a primary focus on decreasing peak energy consumption results as in previous study was, BOSA has a lower standard deviation (0.8) compared to the other algorithms (1.7 for SSA and 1.3 for CSOA), making it more robust and superior, in addition to minimizing cost (5438.98 cents of USD (mean value) and 16.3% savings), the suggested approach is used for lowering electrical energy costs in a micro-grid system while maintaining their regular load and operating hours. [ABSTRACT FROM AUTHOR]