Transient Stability Constrained Social Welfare Maximization with Demand Response in Smart Electrical Grid

Abstract

The existing power grids round the globe are getting converted to Smart Grid for promoting more economic, reliable and sustainable operation and control in presence of Renewable Energy Sources (RES). In operational optimisation of the grid, not only intermittent renewable generation poses difficulty but also an essential resource Demand Response (DR) that is consumer characteristics which is needed to balance generation and demand,may introduce transient stability problems in the grid. This is due to weather dependent variation of generation of RES and subsequent controllable load curtailment within very short duration of time. Though transient stability problems cannot be predicted accurately in time domain but if planned properly the grid may be operated with substantial stability margin even before any disturbance and subsequent perturbation of the operating point, can happen. The pre-perturbation stability margin helps in restoring the operating point of the system to stable zone of operation during disturbance. Traditional OPF algorithms of the grid like generation cost optimisation or active power loss minimisation may not be sufficient to provide a sustainable generation and demand schedule. This work presents a few new OPF algorithmsfor Smart Grid operational optimisation which takes care of rotor angle stability along with the other important controllable like generation cost, transmission line active power loss, voltage profile, transmission line congestion and load curtailment by producing optimal generation and load schedule without conceding any limit violation. The most prominent result was obtained from one of the proposed algorithms which has been referred here as Transient stability constrained Social Welfare optimisation as it causes benefit simultaneously to GENCOs, TRANSCOs and DISCOs adhering to transient stability constraint of the grid. This work also validates the results of the optimisation (Optimal generation and load schedule), demonstrating how the proposed solution provides shorter time for rotor angle restoration and comparatively lesser relative deviation of rotor angles of the generators during a few cases of simulated faults in MATLAB SIMULINK environment. For the complexity of the objective function a differential evolution modified quantum Particle Swarm Optimisation (DEQPSO) algorithm has been proposed in this work.