<![CDATA[This study examines the integration of wind power plants and the application of Emergency Demand Response (EDRP) in the Unit Commitment (UC) problem in the electric power system, by utilizing the Binary Particle Swarm Optimization (BPSO) method. The UC problem focuses on optimal scheduling of generating units to meet load requirements with minimum operating costs. However, the intermittent characteristics of wind energy cause significant uncertainty in the scheduling process. Therefore, EDRP is applied as an adaptive strategy to change the load demand pattern dynamically, so as to improve system reliability and reduce dependence on conventional generators. This study aims to develop and implement an optimization method usingBinary Particle Swarm Optimization (BPSO) Algorithm in determining unit commitment scenarioin the electric power system involvingDistributed Energy Resources (DERs), in order to improve operational efficiency and reliability of the power system. This type of research issimulation-based experimental quantitative research. Population: Electric power systems with DERs that have various load profiles and generating unit characteristics.Sample: A standard distribution system or test case system (e.g. IEEE 30-bus or hypothetical system) modified to include DERs such as PV (photovoltaic), microturbines, and energy storage (batteries). The data collection method is done by software simulation (e.g. MATLAB or Python) to generate load data, DER capacity, and system parameters.The Data Analysis Method Used isComputational Analysis. The simulation results show that the implementation of the fourth scenario results in lower generation costs and improves the operational efficiency of the system, indicating the potential for integrating demand strategies and renewable energy sources in modern power systems. The implications of this study can be used as a reference forshort term operational planning in spower system with high DER penetration.]]>
