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    Renewable energy utilization in demand-side energy management system based on linear programming optimization algorithm
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2023) Almashhadani, Muna Kamel Harmoosh; Çevik, Mesut
    Demand-side management (DSM) is an effective strategy in smart grid technology to manage energy demand and promoting the efficient use of energy by regulating appliance scheduling and regularly monitoring energy consumption. By integrating DSM into residential energy management, consumers can be empowered to manage their energy consumption and reduce peak demand, which in turn reduces the need for expensive new production and transmission infrastructure. This approach also helps to reduce carbon emissions and enhance grid reliability. To further enhance DSM, optimization techniques can be used to manage large-scale appliances with varying power ratings. By modelling the energy system as a set of linear equations, these algorithms can determine the optimal use of available resources and minimize waste. Linear programming optimization algorithms are a key component of these systems in addition, optimization system based on forecasted cost, power, and energy. By analysing this data, the system can predict when energy prices will be high or low and adjust energy consumption accordingly. This achieved by suggested more than one scenario to test system reliability and used MATLAB/Simulink to simulate the proposed system taking into account various factors such as weather conditions, energy demand, and pricing trends. Compared to other methods using various optimization algorithms, the suggested approach used for lowering electrical energy costs in a microgrid system while maintaining their regular load and operating hours. By minimizing the energy cost of the community, the algorithm can determine the optimal energy consumption pattern that meets the community's energy needs while minimizing costs. Efficient DSM optimization algorithms can significantly enhance the smart grid's efficiency while reducing electricity costsfor consumers. Ultimately, the integration of DSM optimization algorithms can help to achieve a more sustainable and cost-effective energy future.