Науковий вісник НГУ

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №1 2024

Last Updated on 29 February 2024

Published on 30 November -0001

Hits: 2040

Authors:

O.M.Sinchuk, orcid.org/0000-0002-9078-7315, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.V.Rogoza, orcid.org/0000-0002-2395-227X, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Yu.Mykhailenko*, orcid.org/0000-0003-2898-6652, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.V.Kobeliatskyi, orcid.org/0009-0006-1308-7426, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.O.Fedotov, orcid.org/0000-0002-6536-5591, Kryvyi Rih National University, Kryvyi Rih, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (1): 084 - 091

https://doi.org/10.33271/nvngu/2024-1/084

Abstract:

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead.

Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality.

Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers.

Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product.

Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

Keywords: power, up to 1000 V electrical receivers, heuristic algorithm, genetic algorithm, underground mine

References.

1. Verkhovna Rada of Ukraine. Legislation of Ukraine (n.d.). On Electricity Market. Retrieved from https://zakon.rada.gov.ua/go/2019-19.

2. Sinchuk, O., Strzelecki, R., Sinchuk, I., Веridzе, Т., Fedotov, V., Baranovskyi, V., & Budnikov, K. (2022). Mathematical model to assess energy consumption using water inflow-drainage system of iron-ore mines in terms of a stochastic process. Mining of Mineral Deposits, 16(4), 19-28. https://doi.org/10.33271/mining16.04.019.

3. Sinchuk, I., Mykhailenko, O., Kupin, A., Ilchenko, O., Budni­kov, K., & Baranovskyi, V. (2022). Developing the algorithm for the smart control system of distributed power generation of water drainage complexes at iron ore underground mines. 2022 IEEE 8 ^th International Conference on Energy Smart Systems (ESS), 116-122. https://doi.org/10.1109/ESS57819.2022.9969263.

4. Mykhailenko, O., & Budnikov, K. (2022). Economic aspects of introducing pumped-storage hydroelectric power plants into the mine dewatering system for distributed power generation. IOP Conference Series: Earth and Environmental Science (EES), 1049, 012055. https://doi.org/10.1088/1755-1315/1049/1/012055.

5. Denysiuk, S., Opryshko, V., & Danilin, O. (2020). Assessment of electricity consumption level influence at system loses. 2020 IEEE 7 ^th International Conference on Energy Smart Systems (ESS), 182-185. Kyiv, Ukraine. https://doi.org/10.1109/ESS50319.2020.9160106.

6. Assad, U., Hassan, M. A. S., Farooq, U., Kabir, A., Khan, M. Z., Bukhari, S. S. H., …, & Popp, J. (2022). Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods. Energies, 15(6), Article 6. https://doi.org/10.3390/en15062003.

7. Usman, R., Mirzania, P., Alnaser, S. W., Hart, P., & Long, C. (2022). Systematic Review of Demand-Side Management Strategies in Power Systems of Developed and Developing Countries. Energies, 15(21), Article 21. https://doi.org/10.3390/en15217858.

8. Bastani, M., Damgacioglu, H., & Celik, N. (2018). A -constraint multi-objective optimization framework for operation planning of smart grids. Sustainable Cities and Society, 38, 21-30. https://doi.org/10.1016/j.scs.2017.12.006.

9. Güler, E., & Filik, Ü. B. (2020). Optimal Residential Load Control Comparison Using Linear Programming and Simulated Annealing For Energy Scheduling. Eskişehir Technical University Journal of Science and Technology A – Applied Sciences and Engineering, 21(1), Article 1. https://doi.org/10.18038/estubtda.648767.

10. Logenthiran, T., Srinivasan, D., & Shun, T. Z. (2012). Demand Side Management in Smart Grid Using Heuristic Optimization. IEEE Transactions on Smart Grid, 3(3), 1244-1252. https://doi.org/10.1109/TSG.2012.2195686.

11. Ullah, K., Khan, T. A., Hafeez, G., Khan, I., Murawwat, S., Alamri, B., …, & Khan, S. (2022). Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid. Energies, 15(19), Article 19. https://doi.org/10.3390/en15196900.

12. Menos-Aikateriniadis, C., Lamprinos, I., & Georgilakis, P. S. (2022). Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision. Energies, 15(6), Article 6. https://doi.org/10.3390/en15062211.

13. Nayak, S. K., Sahoo, N. C., & Panda, G. (2015). Demand side management of residential loads in a smart grid using 2D particle swarm optimization technique. 2015 IEEE Power, Communication and Information Technology Conference (PCITC), 201-206. https://doi.org/10.1109/PCITC.2015.7438160.

14. Albogamy, F. R., Ashfaq, Y., Hafeez, G., Murawwat, S., Khan, S., Ali, F., Aslam Khan, F., & Rehman, K. (2022). Optimal Demand-Side Management Using Flat Pricing Scheme in Smart Grid. Processes, 10(6), Article 6. https://doi.org/10.3390/pr10061214.

15. Ramezani, M., Bahmanyar, D., & Razmjooy, N. (2020). A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: Applications in smart home. SN Applied Sciences, 2(12), 2075. https://doi.org/10.1007/s42452-020-03885-7.

16. Khan, Z. A., Khalid, A., Javaid, N., Haseeb, A., Saba, T., & Shafiq, M. (2019). Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid. IEEE Access, 7, 140102-140125. https://doi.org/10.1109/ACCESS.2019.2942813.

17. Tamilarasu, K., Sathiasamuel, C. R., Joseph, J. D. N., Madurai Elavarasan, R., & Mihet-Popa, L. (2021). Reinforced Demand Side Management for Educational Institution with Incorporation of User’s Comfort. Energies, 14(10), Article 10. https://doi.org/10.3390/en14102855.

18. Niharika, & Mukherjee, V. (2018). Day-ahead demand side management using symbiotic organisms search algorithm. IET Generation, Transmission & Distribution, 12(14), 3487-3494. https://doi.org/10.1049/iet-gtd.2018.0106.

19. Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., & Niaz, I. A. (2017). A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. Energies, 10(3), Article 3. https://doi.org/10.3390/en10030319.

20. Nethravathi, S., & Murali, V. (2022). A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic. Distributed Generation & Alternative Energy Journal, 557-586. https://doi.org/10.13052/dgaej2156-3306.3739.

21. Jasim, A. M., Jasim, B. H., Neagu, B.-C., & Alhasnawi, B. N. (2023). Efficient Optimization Algorithm-Based Demand-Side Management Program for Smart Grid Residential Load. Axioms, 12(1), Article 1. https://doi.org/10.3390/axioms12010033.

22. Logenthiran, T., Srinivasan, D., & Shun, T. Z. (2011). Multi-Agent System for Demand Side Management in smart grid. 2011 IEEE Ninth International Conference on Power Electronics and Drive Systems, 424-429. https://doi.org/10.1109/PEDS.2011.6147283.

23. Hasanpor Divshali, P., Choi, B. J., Liang, H., & Söder, L. (2017). Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs. Energies, 10(10), Article 10. https://doi.org/10.3390/en10101640.

24. Matallanas, E., Castillo-Cagigal, M., Gutiérrez, A., Monasterio-Huelin, F., Caamaño-Martín, E., Masa, D., & Jiménez-Leube, J. (2012). Neural network controller for Active Demand-Side Management with PV energy in the residential sector. Applied Energy, 91(1), 90-97. https://doi.org/10.1016/j.apenergy.2011.09.004.

25. Philipo, G. H., Kakande, J. N., & Krauter, S. (2022). Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping. Energies, 15(14), Article 14. https://doi.org/10.3390/en15145215.

26. Keshtkar, A., & Arzanpour, S. (2014). A fuzzy logic system for demand-side load management in residential buildings. 2014 IEEE 27 ^th Canadian Conference on Electrical and Computer Engineering (CCECE), 1-5. https://doi.org/10.1109/CCECE.2014.6900956.

27. Shah, Z. A., Sindi, H. F., Ul-Haq, A., & Ali, M. A. (2020). Fuzzy Logic-Based Direct Load Control Scheme for Air Conditioning Load to Reduce Energy Consumption. IEEE Access, 8, 117413-117427. https://doi.org/10.1109/ACCESS.2020.3005054.

28. Foster, J. D., Berry, A. M., Boland, N., & Waterer, H. (2014). Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning. IEEE Transactions on Power Systems, 29(2), 833-843. https://doi.org/10.1109/TPWRS.2013.2287880.

29. Kolahan, F., & Doughabadi, M. H. (2012). The Effects of Parameter Settings on the Performance of Genetic Algorithm through Experimental Design and Statistical Analysis. Advanced Materials Research, 433-440. https://doi.org/10.4028/www.scientific.net/AMR.433-440.5994.

30. Kavoosi, M., Dulebenets, M. A., Abioye, O. F., Pasha, J., Wang, H., & Chi, H. (2019). An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem. Advanced Engineering Informatics, 42, 100972. https://doi.org/10.1016/j.aei.2019.100972.

31. Market operator (n.d.). Day ahead market. Retrieved from https://www.oree.com.ua/index.php/IDM_graphs.

• < Prev
• Next >