article · Alexandria Engineering Journal
An Equilibrium Optimizer Algorithm addresses the optimal power flow problem within hybrid alternating and direct current power grids. The optimisation framework accommodates several economic, technical, and environmental operational criteria, either independently or concurrently to reflect operator priorities. These targets include lowering overall generation costs, cutting environmental emissions, curtailing power losses, and limiting bus voltage deviations. The algorithm relies on dynamic control parameters derived from mass balance models, continually updating search agents to locate optimal system configurations. Evaluated across twelve case studies using a second-order cone programming model, the technique was examined on modified standard test networks and the West Delta power system in Egypt. The evaluation also examined the operational effects of incorporating wind generation units, demonstrating that the algorithm successfully resolves power flow challenges in hybrid systems more effectively than alternative optimisers.
Modern electrical grids increasingly mix alternating and direct current technologies alongside renewable resources like wind. Managing these complex systems requires computational tools that balance commercial costs, network stability, and carbon footprints. Enhancing power flow calculations ensures that grid operators can integrate green energy securely while minimising transmission losses and operating expenses for consumers.
This work is relevant to transmission system operators, utility planning departments, and energy management software vendors managing hybrid electrical networks. The algorithm could be implemented within grid control and management software to optimise dispatch schedules. Because the evidence is based on numerical simulations across standard test cases and a regional network model, the method is at an applied research stage and requires real-world control-room validation prior to commercialisation.
AI-generated from the published abstract. Always read the original work before citing.
This paper develops a recent population-based of Equilibrium Optimizer Algorithm (EOA) for solving the optimal power flow (OPF) problem in hybrid AC/DC power grids. The proposed OPF problem handles several objective functions that reflect multi dimensions economic-technical and environmental operation requirements of modern power systems. The considered objectives incorporate minimizing the total generation costs, generation environmental emissions, total power losses, and the deviations of the bus voltages. These objectives are handled separately and simultaneously to provide the preference capability to the operator objectives. EOA has adaptive dynamic control parameters. It mimics the dynamic and equilibrium states related to the mass balance models where, each concentration of search agent is randomly updated in the sake of reaching the final optimal fitness. Finally, 12 case studies are carried out via the developed EOA, particle swarm optimizer and differential evolution for the modified IEEE 14-, 30-bus test systems and West Delta power system (WDPS) in Egypt. The second-order cone OPF model is considered. Also, adding wind units and their impacts on the OPF solution is assessed for the hybrid AC/DC grid. The simulation results demonstrate the great effectiveness of the developed EOA in solving the OPF in hybrid power systems.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.aej.2020.08.043
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
