optimal demand response-based ac opf over smart grid platform considering solar and wind power plants and esss with short-term load forecasts using lstm

By adding renewable energy sources, such as solar and wind, advanced metering infrastructure, and energy storage systems, the traditional power grid is becoming a smart grid. to prevent the uneconomic operation of a smart grid and increase the penetration of renewable resources, the demand response (dr) method is crucial for reducing the peak load and passing critical conditions. in this context, this study presents a multi-objective optimization of the ac optimal power flow (ac-opf) problem with respect to dr. the novelty of the proposed demand-response-based opf approach consists of decreasing the system cost through the simultaneous participation of active and reactive power in dr, considering the physical constraints of the ac network and various renewable energy sources in the smart grid, and increasing the calculation accuracy by demand prediction based on previous data using deep learning methods. finally, using the topsis method, the best dr value was determined according to multi-objective optimization. the effectiveness and resiliency of the proposed method were validated using a modified ieee 24-bus testing system. the results illustrate that the optimal demand response (20%) achieved not only peak reduction and valley filling in active and reactive power but also minimized the total voltage deviation and system cost.