Weighted Particle Swarm Optimization Algorithms and Power Management Strategies for Grid Hybrid Energy Systems
In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially risen...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/123 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially risen, with solar and wind being the two primary sources in RESs. In modern power systems, small-scale distributed networks are growing at a rapid pace and distributed generation (DG) plays an important role. Micro grids are very recent additions to electrical infrastructures. Power management is primarily required for smooth operation, maintaining consistency, and robustness, as well as controlling the actual and reactive power of independent DG. However, the batteries are expensive; moreover, during the charging and discharging process, huge amounts of power are lost, characterizing important problems which have to be averted. This paper introduces the weighted particle swarm optimization (WPSO) method for controlling energy systems and grid hybrid energy systems that comprise photovoltaic (PV), wind turbine, batteries, and diesel generators. By maximizing the power derived from RES and reducing battery power usage, energy is preserved, and the cost of energy consumption (energy of diesel) is reduced. Meteorological data from Spain were used in this study’s simulations. The method depends on the data forecast of renewable energy one day in advance and the everyday load power consumption profile. The results of the simulation show that WPSO outperforms existing algorithms in terms of energies, costs, and battery lives. |
|---|---|
| ISSN: | 2673-4591 |