Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid

This work proposes a framework to solve demand-side management (DSM) problem by systematically scheduling energy consumption using flat pricing scheme (FPS) in smart grid (SG). The includes microgrid with renewable sources (solar and wind), storage systems, electric vehicles (EVs), building appliances like time flexible, power base/critical appliances. For the proposed framework, we develop an ant colony optimization (ACO) algorithm, which efficiently schedules appliances, EVs batteries charging/discharging without (W/O) under FPS minimize cost, carbon emission, peak average ratio (PAR). An integrated technique of enhanced differential evolution (EDE) algorithm artificial neural network (ANN) is devised predict solar irradiance wind speed for accurate estimation. To endorse applicability simulations are conducted. Moreover, based on ACO compared mixed-integer linear programming (MILP) W/O frameworks terms PAR. developed reduces PAR, emission 23.69%, 26.20%, 15.35% scenario I, 25.09%, 31.45%, 18.50% II, respectively, as case. results affirm aspects desired objectives.