Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems
Abstract This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery en...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-02-01
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| Series: | IET Generation, Transmission & Distribution |
| Online Access: | https://doi.org/10.1049/gtd2.12316 |
| _version_ | 1798017132051562496 |
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| author | Camilo Alberto Sepúlveda Rangel Luciane Neves Canha Mauricio Sperandio Vladimiro Miranda |
| author_facet | Camilo Alberto Sepúlveda Rangel Luciane Neves Canha Mauricio Sperandio Vladimiro Miranda |
| author_sort | Camilo Alberto Sepúlveda Rangel |
| collection | DOAJ |
| description | Abstract This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed‐integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling—GMDH method, with strong computing speeding‐up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33‐bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process. |
| first_indexed | 2024-04-11T16:01:53Z |
| format | Article |
| id | doaj.art-c0791765c5a64586b6e4becea6999a79 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-11T16:01:53Z |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-c0791765c5a64586b6e4becea6999a792022-12-22T04:14:57ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952022-02-0116464165510.1049/gtd2.12316Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systemsCamilo Alberto Sepúlveda Rangel0Luciane Neves Canha1Mauricio Sperandio2Vladimiro Miranda3Department of Electrical Engineer Federal University of Santa Maria Rodovia BR 287 1000 Santa Maria Rio Grande do Sul BrazilDepartment of Electrical Engineer Federal University of Santa Maria Rodovia BR 287 1000 Santa Maria Rio Grande do Sul BrazilDepartment of Electrical Engineer Federal University of Santa Maria Rodovia BR 287 1000 Santa Maria Rio Grande do Sul BrazilFEUP – Faculty of Engineering of the University of Porto and INESC TEC ‐ Institute for Systems and Computer Engineering, Technology and Science Portugal, R. Dr. Roberto Frias Porto PortugalAbstract This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed‐integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling—GMDH method, with strong computing speeding‐up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33‐bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.https://doi.org/10.1049/gtd2.12316 |
| spellingShingle | Camilo Alberto Sepúlveda Rangel Luciane Neves Canha Mauricio Sperandio Vladimiro Miranda Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems IET Generation, Transmission & Distribution |
| title | Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| title_full | Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| title_fullStr | Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| title_full_unstemmed | Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| title_short | Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| title_sort | mixed integer stochastic evaluation of battery energy storage system integration strategies in distribution systems |
| url | https://doi.org/10.1049/gtd2.12316 |
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