A differential evolution modified quantum PSO algorithm for social welfare maximisation in smart grids considering demand response and renewable generation

The power grids worldwide are changing both policy and infrastructure and are becoming Smart each day in order to support renewable energy sources (RES). These Smart Grids are offering interesting modern techniques like demand response (DR) for more profitable and sustainable operation of the grids in presence of RES. Traditionally demand response concentrates on electricity price which may invite many other technical challenges such as limit violation of vital system parameters like voltage, line flow, Power factor and security issues. To address this problem this work proposes an optimisation framework which tries to achieve security constrained social welfare optimisation by a novel application of DR technique, taking into account several operational issues such as intermittency of Renewable Generation, lowering of system inertia due to RES, degradation of bus PF, line stability,deviation of voltage etc. for all the Load Dispatch Centres (LDC)/consumers by the virtue of optimised curtailment, reduction of network losses, improvement of operating power factor and mitigation of line congestion. The proposed method uses Differential Evolution modified Quantum Particle Swarm Optimisation (DEQPSO) to achieve the proposed objective. When tested on modified IEEE 30 Bus system the proposed algorithm produced encouraging results.