Multiobjective Sizing of an Autonomous Hybrid Microgrid Using a Multimodal Delayed PSO Algorithm: A Case Study of a Fishing Village

Abstract EN

Renewable energy (RE) systems play a key role in producing electricity worldwide.

The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system.

The A-HMG concept provides a series of technological solutions that must be managed optimally.

As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization algorithm named a multimodal delayed particle swarm optimization (MDPSO).

The proposed algorithm is applied to an A-HMG to find the minimum levelized cost of energy (LCOE), the lowest loss of power supply probability (LPSP), and the maximum renewable factor (REF).

Firstly, a smart energy management scheme (SEMS) is proposed to coordinate the power flow among the various system components that formed the A-HMG.

Then, the MDPSO is integrated with the SEMS to perform the optimal sizing for the A-HMG of a fishing village that is located in the coastal city of Essaouira, Morocco.

The proposed A-HMG comprises photovoltaic panels (PV), wind turbines (WTs), battery storage systems, and diesel generators (DGs).

The results of the optimization in this location show that A-HMG system can be applied for this location with a high renewable factor that is equal to 90%.

Moreover, the solution is very promising in terms of the LCOE and the LPSP indexes that are equal to 0.17$/kWh and 0.12%, respectively.

Therefore, using renewable energy can be considered as a good alternative to enhance energy access in remote areas as the fishing village in the city of Essaouira, Morocco.

Furthermore, a sensitivity analysis is applied to highlight the impact of varying each energy source in terms of the LCOE index.