A DE Modified Quantum PSO based Demand Response program for Social Welfare Optimization with Scalable Renewable Energy penetration in Smart Grid

Abstract

Scalable penetration of Renewable Energy sources (RES) is quite challenging for their ability to disorient the operating conditions of the grid and associated higher investment cost of production of electricity. Incorporation of consumer consumption characteristics through demand response planning can be useful for sustaining the grid operation in presence of RES. This paper presents a flexible demand response algorithm form management of RES within the Smart Grid infrastructure for sustainable and scalable RES integration, maintaining optimal benefit for Generation, Transmission and Distribution Companies causing social welfare. The proposed algorithm also demonstrated to ensure maximum utilization of the available resources such as generator characteristics, demand response, Regional Transmission Unit (RTU) data for optimizing load shedding, cost of electricity generation, adhering standard operating condition in power system. To support the algorithm, a methodology has also been proposed in this paper to obtain the incremental cost characteristics of solar and wind plants from practical data for their effective incorporation in the optimal power flow program (OPF) based demand response program. The algorithm and methodology produced dependable results during testing with worst possible contingencies of the system. The highly nonlinear objective function forced the choice of an artificially intelligent modified Differential Evolution based Quantum Particle swarm optimization (DEQPSO) technique, for obtaining global solution in a highly loaded and altered IEEE 30 bus system.