Algorithm for the Operation of an Energy Storage System in the Structure of a Gas Industry Facility’s Electrical System
Keywords:
algorithm, electrical engineering complex, energy storage system, optimization, objective function
Abstract
Energy storage systems based on various types of storage batteries are becoming increasingly more widely used in the power supply systems of various household and process facilities. The operation of storage systems involves a number of process difficulties associated with the physico-chemical features of storage battery operation, such as a limited number of charge-discharge cycles and accelerated aging. In this regard, there is a need to develop a specialized system for control of the energy storage system operation to secure the rational use of the energy stored and optimize the storage battery performance characteristics. Within the framework of this study, two different algorithms for planning the operation of a gas industry facility’s electrical system have been developed: an algorithm of strict rules and a dynamic planning algorithm. The main methods used in the study were a scenario-oriented analysis of the electrical system operation for constructing automatic control algorithms on logical units, as well as mathematical multi-criteria optimization based on objective functions and a predator-prey type algorithm. For each of the developed algorithms, the operation scenarios have been worked out, and the relationship between the types of control systems and the structures of existing standard electrical systems of gas industry facilities has been determined.
References
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2. Cheng Y. et al. Profitably Scheduling the Energy Hub of Inhabitable Houses Considering Electric Vehicles, Storage Systems, Revival Provenances and Demand Side Management through a Modified Particle Swarm Optimization. – Sustainable Cities and Society, 2023, vol. 92, DOI: 10.1016/J.SCS.2023.104487.
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4. Hassanzadehfard H., Tooryan F., Dargahi V. Standalone Hybrid System Energy Management Optimization for Remote Village Considering Methane Production from Livestock Manure. – International Journal of Hydrogen Energy, 2023, vol. 48, No. 29, pp. 10778–10796, DOI: 10.1016/J.IJHYDENE.2022.12.085.
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9. Premadasa P.N.D. et al. A Multi-Objective Optimization Model for Sizing an off-Grid Hybrid Energy Microgrid with Optimal Dispatching of a Diesel Generator. – Journal of Energy Storage, 2023, vol. 68, DOI: 10.1016/J.EST.2023.107621.
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11. Гашимов А.М., Гулиев Г.Б., Рахманов Н.Р. Улучшенный алгоритм нечеткой логики для управления реактивной мощностью и напряжением в распределительных сетях. – Энергетика. Известия высших учебных заведений и энергетических объединений СНГ, 2014, № 2, с. 29–39.
12. Алехин Р.А. и др. Обзор метаэвристических методов оптимизации, применяемых при решении электроэнергетических задач. – Вестник Самарского государственного технического университета. Серия: Технические науки, 2019, № 3 (63), с. 6–19.
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14. Dehghani M. et al. A New Methodology Called Dice Game Optimizer for Capacitor Placement in Distribution Systems. – Electrical Engineering and Electromechanics, 2020, No. 1, pp. 61–64, DOI: 10.20998/2074-272X.2020.1.10.
15. Nowdeh S.A. et al. Stochastic Optimization – Based Economic Design for a Hybrid Sustainable System of Wind Turbine, Combined Heat, and Power Generation, and Electric and Thermal Storages Considering Uncertainty: A Case Study of Espoo, Finland. – Renewable and Sustainable Energy Reviews, 2023, vol. 183, DOI: 10.1016/J.RSER.2023.113440.
16. Nair S.P., Sundari M.S.S. Optimizing Day-Ahead Energy Management with Demand Response in a PV-Diesel-Battery System Using a Hybrid GOA-SNN Strategy. – Journal of Energy Storage, 2024, vol. 76, DOI: 10.1016/J.EST.2023.109717.
17. Жуковский Ю.Л., Сизякова Е.В. Внедрение системы энергосбережения и энергоэффективности на предприятиях металлургического комплекса. – Записки Горного института, 2013, т. 202, с. 155–160.
18. Behzadi A., Sadrizadeh S. A Rule-Based Energy Management Strategy for a Low-Temperature Solar/Wind-Driven Heating System Optimized by the Machine Learning-Assisted Grey Wolf Approach. – Energy Conversion and Management, 2023, vol. 277, DOI: 10.1016/J.ENCONMAN.2022.116590.
19. Козярук А.Е. и др. Диагностика и оценка остаточного ресурса электромеханического оборудования, работающего в тяжелых условиях, по электрическим параметрам. – Записки Горного института, 2011, т. 192, с. 161.
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21. Hussain S. et al. Multi-Stage Optimization for Energy Management and Trading for Smart Homes Considering Operational Constraints of a Distribution Network. – Energy Build, 2023, vol. 301, DOI: 10.1016/J.ENBUILD.2023.113722.
22. Kim J.K. et al. Optimization Models for the Cost-Effective Design and Operation of Renewable-Integrated Energy Systems. – Renewable and Sustainable Energy Reviews, 2023, vol. 183, DOI: 10.1016/J.RSER.2023.113429.
23. Yazdani H., Baneshi M., Yaghoubi M. Techno-Economic and Environmental Design of Hybrid Energy Systems Using Multi-Objective Optimization and Multi Criteria Decision Making Methods. – Energy Convers Manag, 2023, vol. 282, DOI: 10.1016/J.ENCONMAN.2023.116873.
24. Yadav S., Kumar P., Kumar A. Grey Wolf Optimization Based Optimal Isolated Microgrid with Battery and Pumped Hydro as Double Storage to Limit Excess Energy. – Journal of Energy Storage, 2023, vol. 74, DOI: 10.1016/J.EST.2023.109440.
25. Balavignesh S. et al. Optimization-Based Optimal Energy Management System for Smart Home in Smart Grid. – Energy Reports, 2023, vol. 10, pp. 3733–3756, DOI: 10.1016/J.EGYR.2023.10.037.
26. Ang Y.Q. et al. Multi-Objective Optimization of Hybrid Renewable Energy Systems with Urban Building Energy Modeling for a Prototypical Coastal Community. – Renew Energy, 2022, vol. 201, pp. 72–84, DOI: 10.1016/J.RENENE.2022.09.126.
27. Литвиненко В.С. и др. Оценка роли государства в управлении минеральными ресурсами. – Записки Горного института, 2023, т. 259, с. 95–111.
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2024-02-29
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