Optimal Scheduling of Distributed Generation Units in a Hybrid Micro-grid Based on a Combined Attractiveness Index and Pollutants Emission

By developing the distributed generation units, the hybrid micro-grids usage besides the energy storage systems has changed the future of the electricity industry. In addition to the many benefits of the micro-grids, they can undermine security, reliability, stability, and other network indices if not properly scheduled. In this paper, a new attractiveness index is defined in order to optimal schedule the DGs generation and charging/discharging of the energy storage system (ESS) in a hybrid micro-grid. Also, the pollutant emission of the units is considered as the second objective along with the proposed attractiveness index, which constitutes a two-objective optimization problem. To solve this nonlinear and non-convex optimization problem, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been used. The main advantages of this algorithm are the ability to escape the local optimal traps and fast convergence. For further comparison, the Quantum Particle Swarm Optimization (QPSO) algorithm has been implemented. The performance of both algorithms in solving the proposed optimization problem is evaluated on a standard hybrid micro-grid. The results show faster convergence and better performance of the NSGA-II algorithm in terms of the final optimization solution.