Logical simulation of power system operation

UDK: 621.311 : 622.276.012
DOI: 10.24887/0028-2448-2019-2-94-98
Key words: power system, controlled object, power system reliability, system operation, supply of consumers, normalization of reliability indicators
Authors: I.Yu. Lisin (Caspian Pipeline Consortium-R JSC, RF, Moscow), S.V. Ganaga (The Pipeline Transport Institute LLC, RF, Moscow), A.M. Korolenok (Gubkin University, RF, Moscow), Yu.V. Kolotilov (Gubkin University, RF, Moscow)

The paper describes the approaches to ensuring the supply reliability of power supply from the power system, presents approaches to the normalization of reliability indicators. The approach to continuous planning and correction of the developing solutions is outlined. The decision maker should be able quickly evaluate the optimality of decisions on changes in the structure of traffic flows in the system, the operational characteristics of facilities, the use of reserves, and the effectiveness of management under various hypotheses regarding the dynamics of supply and demand. In modern conditions, the time reserves for making decisions are limited, and the amount of available information and requirements for detailing decisions have increased significantly, which tightens the requirements for automation and computerization of planning and operational management. One of the approaches to the development of a control system for power system is the use of logical simulation of a power system implemented on the principles of hierarchical modelling.

The main advantage of logical simulation is the ability to solve complex problems of energy system management, which are often impossible to solve within the framework of other approaches: ensuring reliable power supply of consumers; optimal use of all available system reserves; minimizing the consequences of the implementation of abnormal and emergency situations caused by natural, man-made, social and political risks; compensation for seasonal irregular power consumption; ensuring the stability of the power system during periods of cold snaps and abnormally cold winters.

Improvement of existing and development of new models for managing the reliability of power systems will allow creating effective tools of intellectual and data computing support for decision making in managing the development and operation of power systems.

References

1. Nogin V.D., Suzhenie mnozhestva Pareto. Aksiomaticheskiy podkhod (The narrowing of the Pareto set. Axiomatic approach), Moscow: Fizmatlit Publ., 2016, 272 p.

2. Yazenin A.V., Osnovnye ponyatiya teorii vozmozhnostey. Matematicheskiy apparat dlya prinyatiya resheniy v usloviyakh gibridnoy neopredelennosti (Basic concepts of the theory of possibilities. Mathematical decision-making apparatus in a hybrid uncertainty), Moscow: Fizmatlit Publ., 2016, 144 p.

3. Kuznetsov V.A., Cherepakhin A.A., Sistemnyy analiz, optimizatsiya i prinyatie resheniy (System analysis, optimization and decision making), Moscow: Infra-M Publ., 2017, 256 p.

4. Rodionova S.G., Revel'-Muroz P.A., Lisin Yu.V. et al., Scientific-technical, socio-economic and legal aspects of oil and oil products transport reliability (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2016, no. 5 (25), pp. 20–31.

5. Radionova C.G., Lisin Yu.V., Polovkov S.A. et al., Methodical basis of ensuring of the fuel and energy complex’s industrial safety on the example of the oil and petroleum products pipeline transportation (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov, 2016, no. 5 (25), pp. 72–77.

6. Boev V.D., Imitatsionnoe modelirovanie sistem (Simulation modeling systems), Moscow: Yurayt Publ., 2017, 253 p.

7. Strogalev V.P., Tolkacheva I.O., Imitatsionnoe modelirovanie (Simulation), Moscow: Publ. of BMSTU, 2018, 296 p.

8. Reshmin B.I., Imitatsionnoe modelirovanie i sistemy upravleniya (Simulation and control systems), Moscow: Infra-Inzheneriya Publ., 2016, 74 p.

9. Novitskiy N.N., Sukharev M.G., Sardanashvili S.A. et al., Truboprovodnye sistemy ehnergetiki: matematicheskoe i kompʹyuternoe modelirovanie (Energy pipeline systems: mathematical and computer modeling), Novosibirsk: Nauka Publ., 2014, 274 p.

10. Novitskiy N.N., Sukharev M.G., Tevyashev A.D. et al., Truboprovodnye sistemy ehnergetiki: metodicheskie i prikladnye problemy matematicheskogo modelirovaniya (Energy pipeline systems: methodological and applied problems of mathematical modeling), Novosibirsk: Nauka Publ., 2015, 476 p.

11. Atavin A.A., Novitskiy N.N., Shalaginova Z.I. et al., Truboprovodnye sistemy ehnergetiki: matematicheskie i kompʹyuternye tekhnologii intellektualizatsii (Energy Pipeline systems: Mathematical and computerized intellectualization technologies), Novosibirsk: Nauka Publ., 2017, 384 p.

12. Slepnev V.N., Maksimenko A.F., The basic principles of building a quality management system for prevention, localization and liquidation of effects of accidents at pipeline transport facilities (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2018, V. 8, no. 4, pp. 456–468, DOI: 10.28999/2541-9595-2018-8-4-456-467.

13. Stepin Yu.P., Kompʹyuternaya podderzhka formirovaniya, mnogokriterialʹnogo ranzhirovaniya i optimizatsii upravlencheskikh resheniy v neftegazovoy otrasli (Computer support for the formation of multi-criteria ranking and optimization of management decisions in the oil and gas industry), Moscow: Nedra Publ., 2016, 421 p.

14. Gvozdeva T.V., Belov A.A., Informatsionnaya tekhnologiya organizatsionnogo razvitiya predpriyatiya (Information technology of organizational development of the enterprise), Ivanovo: Publ. of ISPU, 2013, 192 p.

15. Volovich K.I., Denisov S.A., Methodology of creating web-service interactions in the system of distributed situational centers (In Russ.), Sistemy i sredstva informatiki, 2016, V. 26, no. 4, pp. 51–59.

The paper describes the approaches to ensuring the supply reliability of power supply from the power system, presents approaches to the normalization of reliability indicators. The approach to continuous planning and correction of the developing solutions is outlined. The decision maker should be able quickly evaluate the optimality of decisions on changes in the structure of traffic flows in the system, the operational characteristics of facilities, the use of reserves, and the effectiveness of management under various hypotheses regarding the dynamics of supply and demand. In modern conditions, the time reserves for making decisions are limited, and the amount of available information and requirements for detailing decisions have increased significantly, which tightens the requirements for automation and computerization of planning and operational management. One of the approaches to the development of a control system for power system is the use of logical simulation of a power system implemented on the principles of hierarchical modelling.

The main advantage of logical simulation is the ability to solve complex problems of energy system management, which are often impossible to solve within the framework of other approaches: ensuring reliable power supply of consumers; optimal use of all available system reserves; minimizing the consequences of the implementation of abnormal and emergency situations caused by natural, man-made, social and political risks; compensation for seasonal irregular power consumption; ensuring the stability of the power system during periods of cold snaps and abnormally cold winters.

Improvement of existing and development of new models for managing the reliability of power systems will allow creating effective tools of intellectual and data computing support for decision making in managing the development and operation of power systems.

References

1. Nogin V.D., Suzhenie mnozhestva Pareto. Aksiomaticheskiy podkhod (The narrowing of the Pareto set. Axiomatic approach), Moscow: Fizmatlit Publ., 2016, 272 p.

2. Yazenin A.V., Osnovnye ponyatiya teorii vozmozhnostey. Matematicheskiy apparat dlya prinyatiya resheniy v usloviyakh gibridnoy neopredelennosti (Basic concepts of the theory of possibilities. Mathematical decision-making apparatus in a hybrid uncertainty), Moscow: Fizmatlit Publ., 2016, 144 p.

3. Kuznetsov V.A., Cherepakhin A.A., Sistemnyy analiz, optimizatsiya i prinyatie resheniy (System analysis, optimization and decision making), Moscow: Infra-M Publ., 2017, 256 p.

4. Rodionova S.G., Revel'-Muroz P.A., Lisin Yu.V. et al., Scientific-technical, socio-economic and legal aspects of oil and oil products transport reliability (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2016, no. 5 (25), pp. 20–31.

5. Radionova C.G., Lisin Yu.V., Polovkov S.A. et al., Methodical basis of ensuring of the fuel and energy complex’s industrial safety on the example of the oil and petroleum products pipeline transportation (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov, 2016, no. 5 (25), pp. 72–77.

6. Boev V.D., Imitatsionnoe modelirovanie sistem (Simulation modeling systems), Moscow: Yurayt Publ., 2017, 253 p.

7. Strogalev V.P., Tolkacheva I.O., Imitatsionnoe modelirovanie (Simulation), Moscow: Publ. of BMSTU, 2018, 296 p.

8. Reshmin B.I., Imitatsionnoe modelirovanie i sistemy upravleniya (Simulation and control systems), Moscow: Infra-Inzheneriya Publ., 2016, 74 p.

9. Novitskiy N.N., Sukharev M.G., Sardanashvili S.A. et al., Truboprovodnye sistemy ehnergetiki: matematicheskoe i kompʹyuternoe modelirovanie (Energy pipeline systems: mathematical and computer modeling), Novosibirsk: Nauka Publ., 2014, 274 p.

10. Novitskiy N.N., Sukharev M.G., Tevyashev A.D. et al., Truboprovodnye sistemy ehnergetiki: metodicheskie i prikladnye problemy matematicheskogo modelirovaniya (Energy pipeline systems: methodological and applied problems of mathematical modeling), Novosibirsk: Nauka Publ., 2015, 476 p.

11. Atavin A.A., Novitskiy N.N., Shalaginova Z.I. et al., Truboprovodnye sistemy ehnergetiki: matematicheskie i kompʹyuternye tekhnologii intellektualizatsii (Energy Pipeline systems: Mathematical and computerized intellectualization technologies), Novosibirsk: Nauka Publ., 2017, 384 p.

12. Slepnev V.N., Maksimenko A.F., The basic principles of building a quality management system for prevention, localization and liquidation of effects of accidents at pipeline transport facilities (In Russ.), Nauka i tehnologii truboprovodnogo transporta nefti i nefteproduktov = Science & Technologies: Oil and Oil Products Pipeline Transportation, 2018, V. 8, no. 4, pp. 456–468, DOI: 10.28999/2541-9595-2018-8-4-456-467.

13. Stepin Yu.P., Kompʹyuternaya podderzhka formirovaniya, mnogokriterialʹnogo ranzhirovaniya i optimizatsii upravlencheskikh resheniy v neftegazovoy otrasli (Computer support for the formation of multi-criteria ranking and optimization of management decisions in the oil and gas industry), Moscow: Nedra Publ., 2016, 421 p.

14. Gvozdeva T.V., Belov A.A., Informatsionnaya tekhnologiya organizatsionnogo razvitiya predpriyatiya (Information technology of organizational development of the enterprise), Ivanovo: Publ. of ISPU, 2013, 192 p.

15. Volovich K.I., Denisov S.A., Methodology of creating web-service interactions in the system of distributed situational centers (In Russ.), Sistemy i sredstva informatiki, 2016, V. 26, no. 4, pp. 51–59.


Attention!
To buy the complete text of article (a format - PDF) or to read the material which is in open access only the authorized visitors of the website can. .

Mobile applications

Read our magazine on mobile devices

Загрузить в Google play

Press Releases

11.10.2021
07.10.2021
29.09.2021
Конкурс на соискание молодежной премии имени академика И.М. Губкина