Abstract
One of the important tasks of the oil and gas industry is to reduce the hydrocarbon production cost. It is known that the share of electricity in operating costs in the field can reach 40%. This means it is one of the important factors affecting the economy of oil and gas production. Cost-cutting efforts could be started at the planning stage of the facility by optimizing the structure of the power supply system and the equipment composition. The paper deals with the methodology of automated power distribution system planning for oil and gas industry and its software implementation. The power distribution system planning includes: selection of the connection points to the existing power network, optimal places for the transformer and distribution substations location, determination of the transmission lines voltage classes, transmission lines route taking into account the geographical features and determination of the required parameters of the equipment. A list of limitations and assumptions when choosing parameters and locations of equipment in accordance with the requirements of the Russian electrical Installations code is briefly presented. The optimization algorithm and the mathematical methods are described in detail. The algorithm provides for an assessment of the company generating capacities, as well as potential points of connection to the external network. The possibility of corridor design of transmission lines along roads is included. The software product was developed as a decision support tool for investment planning and development of new oil and gas fields. The module allows calculating the total cost of ownership of an object, and work schedule in the form of a Gantt chart. The paper also proposes an approach to creating an ontological model of oil and gas production enterprises acting as a set of processes affecting objects and their properties.
Keywords
Optimization, distribution power network, decision support, ontological model, algorithm, system planning.
1. Bosisio A., Berizzi A., Amaldi E., Bovo C., Morotti A., Greco B., Iannarelli G. A GIS-based approach for high-level distribution networks expansion planning in normal and contingency operation considering reliability. Electric Power Systems Research, 2021, no. 190, pp. 1-8. doi: 10.1016/j.epsr.2020.106684
2. Mokryani G., Fun Hu Y., Pillai P., Rajamani H.S. Active distribution networks planning with high penetration of wind power. Renewable Energy, 2017. no. 104, pp. 40-49. doi: 10.1016/j.renene.2016.12.007
3. Dumbrava V., Miclescu T., Lazaroiu G. C. Power distribution networks planning optimization in smart cities. City Networks. Springer, Cham, 2017, no. 128, pp. 213-226. doi: 10.1007/978-3-319-65338-9_12
4. Amjad B., Al-Ja'afreh M. A. A., Mokryani G. Active Distribution Networks Planning Considering Multi-DG Configurations and Contingency Analysis. Energies, 2021, no. 14, pp. 1-16. doi: 10.3390/en14144361
5. Al-Jaafreh M. A. A., Mokryani G. Planning and operation of LV distribution networks: A comprehensive review. IET Energy Systems Integration, 2019, no. 3, pp. 133-146. doi: 10.1049/iet-esi.2019.0013
6. Schafer F., Scheidler A., Braun M. A Hybrid Optimization Method Combining Network Expansion Planning and Switch-ing State Optimization. IEEE Open Access Journal of Power and Energy, 2020, no. 7, pp. 234-242. doi: 10.1109/OAJPE.2020.3006344
7. Scheidler A., Thurner L., Braun M. Heuristic optimization for automated distribution system planning in network integration studies. IET Renewable Power Generation, 2018, no. 12(5), pp. 530-538. doi: 10.1049/iet-rpg.2017.0394
8. Li Z., Wu W., Zhang B., Tai X. Analytical reliability assessment method for complex distribution networks considering post-fault network reconfiguration. IEEE Transactions on Power Systems, 2019, no. 35(2), pp. 1457-1467. doi: 10.1109/TPWRS.2019.2936543
9. Shen F., Huang S., Wu Q., Repo S., Xu Y., Ostergaard J. Comprehensive congestion management for distribution networks based on dynamic tariff, reconfiguration, and re-profiling product. IEEE Transactions on Smart Grid, 2018, no. 10(5), pp. 4795-4805. doi: 10.1109/TSG.2018.2868755
10. Diaaeldin I., Abdel Aleem S., El-Rafei A., Abdelaziz A., Zobaa A.F. Optimal network reconfiguration in active distribution networks with soft open points and distributed generation. Energies, 2019, no. 12(21), pp. 1-31. doi: 10.3390/en12214172
11. Khorshid-Ghazani B., Seyedi H., Mohammadi-Ivatloo B., Zare K., Shargh S. Reconfiguration of distribution networks considering coordination of the protective devices. IET Generation, Transmission & Distribution, 2017, no. 11(1), pp. 82-92. doi: 10.1049/iet-gtd.2016.0539
12. Ogunwolu L., Ero O., Ibidapo-Obe O. Modeling and optimization of an electric power distribution network planning system using mixed binary integer programming. Nigerian Journal of Technology, 2017, no. 36(2), pp. 552-562. doi: 10.4314/njt.v36i2.31
13. Kazmi S. A. A., Shahzad M. K., Shin D. R. Multi-objective planning techniques in distribution networks: A composite review. Energies, 2017, no. 10(2), pp. 1-44. doi: 10.3390/en10020208
14. Sekhavatmanesh H., Cherkaoui R. Optimal infrastructure planning of active distribution networks complying with service restoration requirements. IEEE Transactions on Smart Grid, 2017, no. 9(6), pp. 6566-6577. doi: 10.1109/TSG.2017.2716192
15. Xiao H., Pei W., Dong Z., Kong L., Wang D. Application and comparison of metaheuristic and new metamodel based global optimization methods to the optimal operation of active distribution networks. Energies, 2018, no. 11(1), pp. 1-29. doi: 0.3390/en11010085
16. Wruk J., Cibis K., Zdrallek M., H, Landsverk Automated Planning of Smart Low Voltage Networks Using an Evolutionary Algorithm. CIRED Conference. AIM, 2019, no. 1092, pp. 1-5. doi: 10.34890/517
17. Koutsoukis N. C., Georgilakis P. S., Hatziargyriou N. D. Multistage coordinated planning of active distribution networks. IEEE Transactions on Power Systems, 2017, no. 33(1), pp. 32-44. doi: 10.1109/TPWRS.2017.2699696
18. Xie S., Hu Z., Zhou D., Li Y., Kong S., Lin W., Zheng Y. Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network. Applied Energy, 2018, no. 219, pp. 207-225. doi: 10.1016/j.apenergy.2018.03.023
19. Ehsan A., Yang Q. Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Applied Energy, 2018, no. 210, pp. 44-59. doi: 10.1016/j.apenergy.2017.10.106
20. Kiselev V.F. A system approach to solving optimization problems in power supply. Vestnik evraziyskoy nauki [The Eurasian Scientific Journal], 2014, no. 6(25), pp. 1-10. (In Russian). doi: 10.15862/120TVN614
21. Coates A., Ng A. Y. Learning feature representations with k-means. Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg, 2012, pp. 561-580.
22. Rachmawati D., Gustin L. Analysis of Dijkstra's Algorithm and A* Algorithm in Shortest Path Problem. Journal of Physics: Conference Series. IOP Publishing. 2020, no. 1566(1), pp. 1-8. doi: 10.1088/1742-6596/1566/1/012061
23. Liu B., Liu X., Li D., Shi Y., Fernandez G., Wang Y. A Vector Line Simplification Algorithm Based on the Douglas-Peucker Algorithm, Monotonic Chains and Dichotomy. ISPRS International Journal of Geo-Information, 2020, no. 9(4), pp. 1-14. doi: 10.3390/ijgi9040251