I. Introduction
“The development of algorithms that optimize the operation of transmission and distribution systems is becoming increasingly important as power systems continue to evolve and incorporate a greater mix of generation sources. These algorithms aim to improve the efficiency and reliability of power systems by optimizing the operation of various components, such as generators, transformers, and energy storage systems.
An optimized power system can handle a variety of generation sources, including conventional power plants, renewable energy resources, and combined heat and power systems. Each of these sources has its unique characteristics, and integrating them into a single system requires careful planning and management.
The optimization algorithms can take into account factors such as weather forecasts, demand forecasts, and power system constraints to determine the most efficient and cost-effective way to operate the system. These algorithms can also help utilities to minimize costs and reduce greenhouse gas emissions by reducing the use of fossil fuels.
Overall, the development of optimization algorithms is essential to ensuring that power systems can adapt to changing conditions and support the transition to a more sustainable energy future.”
Topics of Optimal Power System Operation
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Algorithms for optimizing the reliability of power grid
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Dispatch algorithms for optimizing power generation units
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Dispatch algorithms for optimizing combined heat and power systems
II. Related Publications
1.
Insu Kim, Jean-Ann James, and John Crittenden, “The case study of combined cooling heat and power and photovoltaic systems for building customers using HOMER software,” Electric Power Systems Research, Vol. 143, pp. 490-502, February 2017. https://doi.org/10.1016/j.epsr.2016.10.061
2.
Insu Kim, "The optimization of the location and capacity of reactive power generation units using a hybrid genetic algorithm incorporated by the bus impedance power-flow calculation method," Applied Sciences, Vol. 10, No. 3, February 4, 2020. https://doi.org/10.3390/app10031034
3.
Beopsoo Kim, Nikita Rusetskii, Haesung Jo, and Insu Kim, "The optimal allocation of distributed generators considering fault current and levelized cost of energy using the particle swarm optimization method, " Energies, Vol. 14, No. 418, January 13, 2021. https://doi.org/10.3390/en14020418
4.
Donghyeon Lee, Seungwan Son, and Insu Kim, "Optimal allocation of large-capacity distributed generation with the Volt/Var control capability using particle swarm optimization," Energies, Vol. 14, No. 3112, May 26, 2021. https://doi.org/10.3390/en14113112
5.
Haesung Jo, Jaemin Park, and Insu Kim, "Environmentally constrained optimal dispatch method for combined cooling, heating, and power systems using two-stage optimization," Energies, Vol. 14, No. 4135, July 8, 2021. https://doi.org/10.3390/en14144135
6.
Jaemin Park, Haesung Jo, Insu Kim, "The selection of the most cost-efficient distributed generation type for a combined cooling heat and power system used for residential customers," Energies, Vol.14. No. 5606, September 7, 2021. https://doi.org/10.3390/en14185606
7.
Seung-Wan Son, Dong-Hyeon Lee, Insu Kim, Changhyun Chung, Hyoungkwon Kim, Dongjin Yoon, Joohan Lee, "A New Design of the Objective Function for the Optimal Allocation of Distributed Generation with Short-Circuit Currents," Journal of Electrical Engineering & Technology, Vol. 17, No. 3, pp. 1487-1497, May 2022. https://doi.org/10.1007/s42835-021-00978-0
8.
Kwonchul Kim, Youngjae Kim, Beopsoo Kim, and Insu Kim, "A Study on Optimizing Underground Cable Maintenance and Replacement Cycles," Journal of Electrical Engineering & Technology, Vol.18, No. 4, pp. 2015-2023, July 2022. https://doi.org/10.1007/s42835-021-00979-z
9.
Insu Kim, Beopsoo Kim, and Denis Sidorov, "Machine Learning for Energy Systems Optimization," Energies, Vol. 15, No. 4116, June 3, 2022. https://doi.org/10.3390/en15114116
10.
Beopsoo Kim and Insu Kim, "A case study of stand-alone hybrid power systems for a data center using HOMER and DIgSILENT", Energy Reports, Vol.9, Supplement 1, March 1, 2023. https://doi.org/10.1016/j.egyr.2023.02.044
11.
Jiyeon Jang and Insu Kim, "Distinguishing between Faults and Non-Fault Disturbancesin The Power System with an HVDC Link: A Small-Scale Data-Driven Classification Approach", The Transactions of the Korean Institute of Electrical Engineers, Vol. 72, No.9, pp. 1018-1028. http://doi.org/10.5370/KIEE.2023.72.9.1018