A hybrid Improved Salp Swarm Algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost

Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved Salp Swarm Algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader Salp Swarm Algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370<euro> in off-grid mode compared to 19.2676<euro> in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248<euro> in off-grid and 404.7475<euro> for the grid-connected one, compared to other optimization techniques that vary from 667.2141<euro> to 959.5747<euro> in off-grid mode, and from 424.5841<euro> to 813.932<euro> in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765<euro> compared to the best (17.024<euro>) and the worst (47.2423<euro>) cases in off-grid mode, while in grid-connected mode, it is 28.8771<euro> compared to the best (21.6316<euro>) and the worst (45.3774<euro>) values.