A Novel Self-Adaptive Polar Stochastic Energy Management Approach For Hybrid Microgrids With High Penetration Of Renewable Energy Sources

This articles develops a probabilistic scheme for the efficient controlling of hybrid microgrids seeing renewable sources and power storages. Owing to the increasing popularity of DC loads (such as laptops, phones, etc) and distributed generation, hybrid AC-DC microgrids can provide more advantages than AC microgrids by direct power supply to AC and DC loads individually. In order to incorporate the randomness of forecast values' impacts, a new load flow approach constructed based on unscented transform is engaged to handle the uncertainties of electrical demand, bidding pricing, tidal turbine output power and photovoltaic output. As the planned problem is a hard non-linear optimization, an influential polar crow search optimization algorithm is created to explore the problem domain. The polar version helps to search the optimal solution in a smoother and symmetrical space. Also, a new self-adaptive three platform correction system is used to reinforce the search aptitude of crow search optimization algorithm when evading early convergence. The total energy losses has improved from the initial condition of 4,065.363 $ to 3,216.129, 3,147.086 and 3,276.394 $ in the 1st, 2nd and 3rd scenarios, respectively. Moreover, the second scenario could get into the best voltage profile with deviation of 0.077428 pu.