article · IEEE Systems Journal
A multiobjective formulation addresses the combined economic and environmental operation of hybrid alternating current and multi-terminal high-voltage direct current power grids. In these networks, voltage source converters support enhanced active and reactive power control. The primary targets are simultaneously minimising generator fuel costs, pollutant emissions, and electrical transmission losses. To solve this complex, nonlinear problem, an improved crow search algorithm combines standard search mechanics with local searching around the best solutions, supported by Pareto dominance to retain a diverse pool of operating alternatives. Validated through simulations on standard test systems and the Egyptian West Delta Power Network, the technique demonstrates robust performance and delivers well-distributed operating points compared with earlier computational methods.
Modern electrical grids increasingly blend alternating and direct current links to manage complex power flows. Finding the best operating settings requires balancing commercial fuel expenses against environmental pollution and energy losses. Computational tools that reliably solve these conflicting demands can help grid planners and power utilities achieve cleaner, more cost-effective electricity delivery across large-scale networks.
The tool could be used by transmission system operators and electrical utility engineers seeking to optimize power dispatch in hybrid grid architectures. Having been tested in simulations on standard test buses and the Egyptian West Delta Power Network, the technology is at an applied, simulation-tested stage and would need adaptation into commercial energy management software before live deployment.
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This article develops a nonlinear, multimodal, and multiobjective formulation of the combined economic environmental operation (CEEO) problem in hybrid AC-multiterminal (AC-MT) high-voltage direct current grids. In these grids, the technologies of voltage source converters support more active and reactive power control in AC grids. The aim of the CEEO issue is to minimize the overall cost of fuel and the pollutant emissions of generators. Also, transmission loss minimization is another target. An improved crow search algorithm (ICSA) is proposed for obtaining the solution of the formulated problem. The proposed ICSA combines the merits of CSA by randomly switching into local search around the best crows’ position. Pareto dominance is activated to improve the crow's memory and the external repository for multiobjective models. The ICSA is tested on modified IEEE 30-bus, the Egyptian West Delta Power Network, and the large-scale 118-bus system to solve the CEEO problem in AC-MTDC grids. The simulation results illustrate the proposed ICSA capability for finding diversified Pareto solutions with several possible operating points. Furthermore, the effectiveness of the proposed ICSA is demonstrated in terms of its solution robustness compared with previous techniques.
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DOI: 10.1109/jsyst.2021.3076515
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