Publisher

UMBUMB

Journal SINERGISINERGI Abstract

An optimised operation of a thermal power plant is required to optimise fuel consumption cost. It is still high and becomes a problem of economic dispatch in the operation of the plant. Load scheduling and generator capacity are needed to get optimal plant operation, especially energy usage. This paper presented a thermal power plant operation analysis to obtain optimum operational energy costs using the Bat Algorithm (BA). The actual data of 6 thermal power plants to serve the peak loads in 2018 is used for the calculation. The problem solution is simulated and calculated using maximum (95%) capacity and the BA method. The Simulation is done by using MATLAB software. The generator unit and generator load characteristics are collected to obtain objective functions and constraint functions. BA completes this function to get the lowest energy. The BA method will be compared with the calculation of real-time energy generation without BA to analyse its accuracy. The total operational cost of the actual power plant without BA is $1,988,410. BA simulation gave the total energy cost is $1,653,374. So, the generated energy savings is 16.85%, or 335,036 MW reduction.

Conclusion

This research demonstrates that the Bat Algorithm (BA) effectively reduces the operational costs of thermal power plants.The BA method yielded a total operating cost of $1,653,374, representing a 16.85% reduction compared to the real-time operation cost of $1,988,410.The study confirms the potential of BA in optimizing load dispatch and enhancing the efficiency of thermal power plant operations.

Future Research

Further research should investigate the application of the Bat Algorithm to power systems with a higher penetration of renewable energy sources, such as solar and wind, to assess its effectiveness in managing the intermittency and variability of these sources. Additionally, exploring hybrid optimization algorithms that combine the strengths of the Bat Algorithm with other metaheuristic techniques, like Particle Swarm Optimization or Genetic Algorithms, could potentially lead to even more significant cost savings and improved system performance. Finally, a comprehensive sensitivity analysis should be conducted to evaluate the robustness of the Bat Algorithm under various operating conditions and system disturbances, including variations in fuel prices, load demand, and generator availability, to ensure its reliable application in real-world power systems. These investigations will contribute to a more sustainable and efficient energy future by optimizing power plant operations and integrating renewable energy sources effectively.

References

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