In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The seagull optimization algorithm (SOA), crow search algorithm (CSA), tunicate swarm algorithm (TSA), and firefly algorithm (FFA), as both nature and biologic phenomena-based algorithms, are investigated to solve DCEED problems. Our proposed algorithms, SOA, TSA, and FFA, were evaluated and applied on the IEEE five-unit test system, and the effectiveness of the proposed CSA approach was applied on two-unit, five-unit, and ten-unit systems by considering VPE. We defined CSA for different objective functions, such as cost of production, emission, and CEED, by considering VPE. The obtained results reveal the efficiency and robustness of the CSA compared to SOA, TSA, FFA, and to other optimization algorithms reported recently in the literature. In addition, Matlab simulation results show the advantages of the proposed approaches for solving DCEED problems.

Photovoltaic (PV) system penetration in modern power grids necessitates highly efficient and adaptive maximum power point tracking (MPPT) techniques to avoid multiple local power peaks under partial shading conditions (PSCs), such as both static and dynamic irradiation conditions. This study addresses global MPPT in grid‐connected PV energy systems under PSC through comparative evaluation of the horse herd optimization algorithm (HOA) and four enhanced variants: Cauchy‐HOA, quasi‐opposition, and fast acceleration HOA (QC‐HOA‐FAST), multicomponent HOA (MCHOA), and hybrid HOA with pattern search (PS‐HOA). Each variant involved distinct mechanisms: stochastic diversification through Cauchy mutation, quasioppositional learning with faster convergence, adaptive weighting at multiple levels, and enhanced accuracy in local exploitation precision through pattern search hybridization. A grid‐connected PV system was tested under eight shading scenarios: four static (PSC1–PSC4) and four dynamic, with temporal variation (PSC5–PSC8). Under static conditions, QC‐HOA‐FAST achieved the fastest convergence [0.025–0.04 s] and the highest efficiency (up to 99.82%), although it significantly degraded under complex multimodal shading (79.74% in PSC4), while MCHOA and HOA‐PS maintained high efficiency (up to 99% and 98.49%, respectively), compared with the original HOA efficiency collapse, which reached 74.89%. Under dynamic shading, MCHOA outperforms HOA in relative efficiency under PSC7, reaching 99.99%, while Cauchy‐HOA ensures the best overall consistency, with an average relative efficiency of 98.7%, an improvement over the classic HOA; MCHOA, Cauchy‐HOA, and QC‐HOA‐FAST have also expanded their voltage tracking range to capture a higher peak power, up to 841 W in the most severe scenario (PSC7). Overall, MCHOA (95.55% ± 2.20) and HOA‐PS (95.55% ± 2.26) achieved the highest and most consistent overall tracking efficiency across all scenarios that was reported as significantly higher than the standard HOA by the Wilcoxon test. Throughout all scenarios, grid‐injected current recorded a total harmonic distortion (THD) between 2.42% and 2.46% below the limit of 5% (IEEE 519–2022). These results establish MCHOA and HOA‐PS as the most robust and generalizable MPPT strategies for maintaining high‐quality, high‐efficiency power extraction from a PV source under both static and highly dynamic partial shade.