article · IEEE Access
A modified crow search optimiser combines an enhanced local search crow search algorithm with a novel bat algorithm to address the economic emission power flow problem. This hybrid approach tackles both single-objective and multi-objective frameworks, incorporating an external archive and dominance comparison alongside a fuzzy-based mechanism to identify the best compromise solutions. Testing took place on standard test systems, including the IEEE 30-bus, the IEEE 118-bus network, and the regional West Delta power grid system. The results indicate superior performance compared to existing algorithms in terms of robustness and solution quality. The optimisation method delivers notable cost reductions while simultaneously maintaining environmental emissions within acceptable thresholds. Assessments using hypervolume indicators confirm the enhanced capability of the modified method over the standard crow search optimiser when scaling to large power systems.
Balancing power generation costs with environmental emissions is a critical challenge for modern electricity networks. As grids grow larger and more complex, operators need effective computational tools to minimise operational expenses without violating environmental limits. This optimisation method provides a scalable way to plan power flows that keep energy production affordable while lowering harmful emissions.
The method could be applied to energy management software for transmission network operators and power utility planners seeking to balance operational costs with emission targets. Because the technique was validated on standard benchmark models and a regional grid simulation rather than deployed in live control rooms, it represents applied research that requires further integration into operational grid dispatch systems before practical commercial use.
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This paper proposes a modified crow search optimizer (MCSO) for solving the combined economic emission power flow (EEPF) problem. In the proposed approach, the local search ability is enhanced into the crow search optimizer (CSO) and aggregated with a novel bat algorithm (NBA). Close accord between CSO, NBA, and MCSO is employed for solving the single and multi-objective frameworks. Moreover, the proposed MCSO incorporates external archive and dominance comparison to handle multi-objective frameworks while the best compromise solution is extracted by using a fuzzy based mechanism. The proposed MCSO, CSO, and NBA are developed and tested to on IEEE 30 bus and West Delta power grid (WDPG) systems. Added to the that, the proposed methodology is tested on a large-scale power system, IEEE 118-bus test system, for measure the scalability of the proposed method. Their output results are compared with the reported algorithms in the literature to demonstrate the MCSO outperformance in terms of solution quality and robustness. Significant economical solutions of the EEPF problem are achieved with respecting the environment concerns at acceptable emission levels. Added to that, the multi objective framework is assessed with hypervolume indictor that show the high capability of the proposed MCSO compared with CSO.
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DOI: 10.1109/access.2021.3060710
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