Optimal Scheduling of Microgrids Considering Offshore Wind Power and Carbon Trading

Abstract

Offshore wind energy entering the grid in coastal areas creates issues with the safe and stable operation of power systems. To control the carbon emission of power systems and increase the proportion of offshore wind consumption, a microgrid optimization model considering offshore wind power and carbon trading is proposed in this paper. To avoid the defect of Particle Swarm Optimization (PSO) falling into the local optimum prematurely, the PSO algorithm is improved by dynamically decreasing inertia weights and chaos factors. Combined with the powerful optimization capability of the genetic algorithm (GA), the improved PSO-GA algorithm is used to solve the model. The simulation results show that the improved algorithm iterates 11 times before the parameters reach the optimal value, with high convergence accuracy. The proposed approach can increase the proportion of offshore wind consumption and ensure the optimal economic performance of the system while reducing the carbon emission.