article · International Journal on Energy Conversion (IRECON)
A modified sine cosine optimisation algorithm addresses the challenge of balancing economic costs and environmental emissions in electrical power dispatch. The method initialises a population of search agents to simultaneously optimise fuel costs and emissions within non-smooth power generation systems. An opposition-based strategy maintains diversity among potential solutions, which are organised using Pareto front principles. The algorithm evolves these solutions through an updated sine cosine mechanism, picking top-performing candidates at random from preserved Pareto sets, while a parameter tuning mechanism balances exploration and exploitation. The approach was tested on standard six-unit and ten-unit benchmark systems featuring non-smooth fuel cost and emission profiles. When compared against existing optimisation techniques from literature, the numerical outcomes confirm the robustness and effectiveness of the proposed method in solving combined dispatch problems.
Balancing minimal fuel costs with reduced environmental emissions is a major operational challenge for electrical power generation. Standard mathematical techniques often struggle with complex, non-smooth operational constraints. Developing more effective optimisation algorithms helps power system operators identify generation schedules that limit environmental impact without imposing excessive financial penalties.
This research is an early-stage algorithmic development tested on simulated six-unit and ten-unit benchmark power systems. Potential users include power grid operators and software developers building dispatch planning tools. The work provides a computational optimisation method, but moving toward operational deployment would require validation on real-world grid data, integration into commercial energy management software, and testing against live system constraints.
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The current study presents a modified sine cosine optimization (MSCO) algorithm for solving the non-smooth environmental/economic power dispatch problem. In the proposed MSCO algorithm, random search agents’ population is initialized in the search domain for simultaneous optimization of both the combined economic and environmental objectives. Added to that, the proposed MSCO proposes an opposition strategy to preserve the diversity of solutions purposefully. Hence, the Pareto optimal solutions are customized according to the Pareto front concepts. These solutions are evolved using a modified version of the sine cosine algorithm (SCA), where the best agent is selected randomly from the stored Pareto solutions. Furthermore the parameter-based tuning mechanism is designed to improve the balance between the exploration and exploitation abilities. The correctness and effectiveness of the proposed MSCO are validated through experiments results and comparisons on EELD problem. Simulations were conducted on two test systems with non-smooth fuel cost and emission issues. The first system constitutes 6-unit benchmarking system, while the second one constitutes 10- units, and their results are compared with the results of other optimization techniques that were reported in the literature. The numerical comparisons reveal the robustness and effectiveness of the proposed MSCO algorithm.
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DOI: 10.15866/irecon.v5i6.14291
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